# ent

id: cord-020200-g5hy5ncm
type: WORK_OF_ART
entity:
  Towards a Strategic Model for Safeguarding the Preservation of Business Value During Human Interactions with Information Systems


id: cord-027431-6twmcitu
type: WORK_OF_ART
entity:
  Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms date 2020


id: cord-102738-e5zojanb
type: WORK_OF_ART
entity:
  The Motivational Pull of Video Games A Self Determination Theory Approach Rules of Play Game Design Fundamentals


id: cord-102738-e5zojanb
type: CHEMICAL
entity:
  F 2 4 127 12 p 0 001 This indicates that the 7th level has a significantly lower completion ratio than the first six tutorial levels suggesting that the 7th tutorial level which is the first level that includes the zones of death


id: cord-102542-1mglhh41
type: GGP
entity:
  Human AI interaction 7 Sociability empathy understandable medical vocabulary and emerging dialog styles are among the


id: cord-227156-uy4dykhg
type: PROTEIN
entity:
  OfeFernandez PatoBullrich macri macrismo mauriciomacri pichetto MiguelPichetto JuntosPorElCambio alferdez


id: cord-227156-uy4dykhg
type: WORK_OF_ART
entity:
  Proceedings of the 21st International Conference on World Wide Web Use of Machine Learning to Detect Illegal Wildlife Product Promotion and Sales on Twitter Frontiers in Big Data Analyzing


id: cord-355513-vgs96w3b
type: WORK_OF_ART
entity:
  An Empirical Study on Influencing Factors of Continuous Attention to WeChat Public Accounts An Information Characteristics Perspective


id: cord-355513-vgs96w3b
type: ORG
entity:
  Floyd Collins Impact of Communication Measures Implemented During a School Tuberculosis Outbreak on Risk Perception among Parents and School Staff


id: cord-124191-38i44n0m
type: ORG
entity:
  Sustainable Study Guideline for iOS Platform Real World Product Deployment of Adaptive Push Notification Scheduling on Smartphones InterruptMe Designing Intelligent Prompting Mechanisms for Pervasive Applications Emotional


id: cord-310272-utqyuy0n
type: WORK_OF_ART
entity:
  the Technology Acceptance Model Davis and Warshaw 1989 the Unified Theory of Acceptance and Use of Technology Venkatesh et al 2003


id: cord-310272-utqyuy0n
type: ORG
entity:
  The Improvisation Effect A Case Study of User Improvisation and Its Effects on Information System Evolution Qualitative


id: cord-310272-utqyuy0n
type: ORG
entity:
  Corporate Social Networking Sites Modes of Use and Appropriation through Co Evolution Australasian Conference on Information Systems


id: cord-193856-6vs16mq3
type: DNA
entity:
  Multiarmed Bandit Problem Matrix Factorization Techniques for Context Aware Recommendation Social Cognitive Theory of Self Regulation Health Promotion


id: cord-193856-6vs16mq3
type: ORG
entity:
  Spoiled for Choice Personalized Recommendation for Healthcare Decisions A Multi Armed Bandit Approach date 2020


id: cord-193856-6vs16mq3
type: ORG
entity:
  the Multiarmed Bandit Problem Matrix Factorization Techniques for Context Aware Recommendation Social Cognitive Theory of Self Regulation Health Promotion


id: cord-193856-6vs16mq3
type: ORG
entity:
  the Impact of Recommender Systems on Sales From Virtual Parties to Ordering Food How Americans Are Using the Internet During Covid 19 Tracking Multiple Topics for Finding Interesting Articles User Centric Evaluation Framework for Recommender Systems Contextual Combinatorial Bandit and Its Application on Diversified Online Recommendation


id: cord-350000-eqn3kl5p
type: DNA
entity:
  A1 A8 A9 A10 A13 A16 A18 A21 A23 A25 A30 A33 A37 A40 A41 A44 A56 A59 A62 A66 A67 A70 A82 A86 A92 A98 A105 A106 A111 A121 I1 I2 I6 I10 I11 I13 I16 I18 I23 I25 I29 I30 I32 I34 I40 I42 54 Breathing exercises A3 A6 A20 A21 A23 A25 A29 A30 A34 A37 A41 I29 I33 I7 I9 31 Games A31 A32 A43 A46 A51 A58 A60 A63 A65 A84 A96 A101 A112 A119 A120 I12 I14 I15 I17 I20 I22 I24 I27 I28 I31 I36 I39 I41 I43 I44 33


id: cord-350000-eqn3kl5p
type: PROTEIN
entity:
  A6 A11 A20 A29 A38 A86 A112 I1 I13 I18 10 Self esteem and confidence A18 A20 A21 A83 A88 A118 A121 I1 I2 I5 I18 I26 12


id: cord-350000-eqn3kl5p
type: GGP
entity:
  A12 A33 A34 A48 A66 A69 A70 A72 A78 A79 A88 A91 A103 A113 I19 I7 17 Stories A8 A18 A30 A36 A49 A59 A82 A98 A118 I8 I9 I18 I29 I30 I32 I42 16 Mindfulness practices A10 A12 A20


id: cord-243596-ryyokrdx
type: WORK_OF_ART
entity:
  Haptic Retargeting Dynamic Repurposing of Passive Haptics for Enhanced Virtual Reality Experiences Training Intensity Affects Motor Rehabilitation Efficacy


id: cord-243596-ryyokrdx
type: LAW
entity:
  the COVID 19 Pandemic Effects of Virtual Reality Based Exercise Imagery on Pain in Healthy Individuals Self


id: cord-347144-rj76i40v
type: CHEMICAL
entity:
  Demsetz 1972 Simon 1997 Williamson 1985 platform openness decisions need to integrate both the transaction governance logic and the innovation governance logic Felin


id: cord-031617-l9iacaec
type: ORG
entity:
  Suitable Camera and Rotation Navigation for People with Visual Impairment on Looking for Something Using Object Detection Technique


id: cord-139715-jyfmnnf5
type: DNA
entity:
  COVID 19 outbreak DS GVO 10 Prüfsteine für die Beurteilung von Vorschlag für ein Gesetz zur Einführung und zum Betrieb einer App basierten Nachverfolgung von Infektionsrisiken mit dem SARS CoV 2 Corona Virus Engeler M Warum wir ein Corona Tracing Gesetz


id: cord-139715-jyfmnnf5
type: ORG
entity:
  Sample Mobile Application Covid Notions Towards Formal Definitions and Documented Understanding of Privacy Goals and Claimed Protection in Proximity Tracing Services PEPP PT Data Protection Architechture Security


id: cord-251676-m8f6de33
type: ORG
entity:
  Assessing Disease Exposure Risk with Location Data A Proposal for Cryptographic Preservation of Privacy Contact Tracing Mobile Apps


id: cord-251676-m8f6de33
type: ORG
entity:
  Mobile Contact Tracing in Fighting Pandemic COVID 19 Issues Challenges and Solutions Cryptology ePrint Archive Development Finance Division


id: cord-251676-m8f6de33
type: WORK_OF_ART
entity:
  Experiences Challenges with Server Side WiFi Indoor Localization Using Existing Infrastructure Extracting


id: cord-323372-770sos8m
type: WORK_OF_ART
entity:
  Sitting Less and Moving More Improved Glycaemic Control for Type 2 Diabetes Prevention and Management


id: cord-302724-hu0raqyi
type: GGP
entity:
  Signif Oxf DOI 10 1111 1740 9713 01400 sha 1b2db097382d44f928eb49dba848e4c27653f27f doc_id 302724 cord_uid hu0raqyi


id: cord-318195-38gu0yab
type: ORG
entity:
  The Electronic Health Record in Ophthalmology Usability Evaluation Tools for Health Care Professionals


id: cord-355789-x449xflm
type: DISEASE
entity:
  artistic interests R 44 Is sophisticated in art music or literature R denotes reverse scaled items Malicious


# bib

id: cord-000332-u3f89kvg
author: Broeck, Wouter Van den
date: 2011-02-02
txt: ./txt/cord-000332-u3f89kvg.txt
cache: ./cache/cord-000332-u3f89kvg.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3048541/
doi: 10.1186/1471-2334-11-37
summary:
  The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. GLEaMviz is a client-server software system that can model the world-wide spread of epidemics for human transmissible diseases like influenzalike illnesses (ILI), offering extensive flexibility in the design of the compartmental model and scenario setup, including computationally-optimized numerical simulations based on high-resolution global demographic and mobility data. GLEaMviz makes use of a stochastic and discrete computational scheme to model epidemic spread called "GLEaM" -GLobal Epidemic and Mobility model, presented in previously published work [6, 3, 14] which is based on a geo-referenced metapopulation approach that considers 3,362 subpopulations in 220 countries of the world, as well as air travel flow connections and short-range commuting data.

title:
  The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale

abstract:
  BACKGROUND: Computational models play an increasingly important role in the assessment and control of public health crises, as demonstrated during the 2009 H1N1 influenza pandemic. Much research has been done in recent years in the development of sophisticated data-driven models for realistic computer-based simulations of infectious disease spreading. However, only a few computational tools are presently available for assessing scenarios, predicting epidemic evolutions, and managing health emergencies that can benefit a broad audience of users including policy makers and health institutions. RESULTS: We present "GLEaMviz", a publicly available software system that simulates the spread of emerging human-to-human infectious diseases across the world. The GLEaMviz tool comprises three components: the client application, the proxy middleware, and the simulation engine. The latter two components constitute the GLEaMviz server. The simulation engine leverages on the Global Epidemic and Mobility (GLEaM) framework, a stochastic computational scheme that integrates worldwide high-resolution demographic and mobility data to simulate disease spread on the global scale. The GLEaMviz design aims at maximizing flexibility in defining the disease compartmental model and configuring the simulation scenario; it allows the user to set a variety of parameters including: compartment-specific features, transition values, and environmental effects. The output is a dynamic map and a corresponding set of charts that quantitatively describe the geo-temporal evolution of the disease. The software is designed as a client-server system. The multi-platform client, which can be installed on the user's local machine, is used to set up simulations that will be executed on the server, thus avoiding specific requirements for large computational capabilities on the user side. CONCLUSIONS: The user-friendly graphical interface of the GLEaMviz tool, along with its high level of detail and the realism of its embedded modeling approach, opens up the platform to simulate realistic epidemic scenarios. These features make the GLEaMviz computational tool a convenient teaching/training tool as well as a first step toward the development of a computational tool aimed at facilitating the use and exploitation of computational models for the policy making and scenario analysis of infectious disease outbreaks.


id: cord-000925-91fhb66m
title: Advanced Querying Features for Disease Surveillance Systems
author: Hashemian, Mohammad R.
date: 2010-04-09
txt: ./txt/cord-000925-91fhb66m.txt
cache: ./cache/cord-000925-91fhb66m.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3615752/
doi: 10.5210/ojphi.v2i1.2847
summary:
  The objective of the Advance Querying Tool (AQT) is to build a more flexible query interface for most web-based disease surveillance systems. Our prototype system, the Advanced Querying Tool (AQT), allows the investigators to handle complex cases where one can incorporate any data elements available in a disease surveillance system, then mix and match these data elements in order to define valid queries. Table 1 provides examples of how a dynamic query tool exploits combinations of data elements available to disease surveillance systems. The following objectives summarize the design features of the AQT: The tool''s interface will help generate queries that can process any kind of data regardless of its source (e.g., emergency room visit, office visit, pharmacy, and laboratory). Making the tool adaptable to many web-based systems requires the AQT to contain all the processing dynamically, including validating the query syntax and changing the contents of the list boxes.

abstract:
  Most automated disease surveillance systems notify users of increases in the prevalence of reports in syndrome categories and allow users to view patient level data related to those increases. Occasionally, a more dynamic level of control is required to properly detect an emerging disease in a community. Dynamic querying features are invaluable when using existing surveillance systems to investigate outbreaks of newly emergent diseases or to identify cases of reportable diseases within data being captured for surveillance. The objective of the Advance Querying Tool (AQT) is to build a more flexible query interface for most web-based disease surveillance systems. This interface allows users to define and build their query as if they were writing a logical expression for a mathematical computation. The AQT allows users to develop, investigate, save, and share complex case definitions. It provides a flexible interface that accommodates both advanced and novice users, checks the validity of the expression as it is built, and marks errors for users.


id: cord-020200-g5hy5ncm
author: Grobler, Chris D.
date: 2020-03-06
txt: ./txt/cord-020200-g5hy5ncm.txt
cache: ./cache/cord-020200-g5hy5ncm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7134310/
doi: 10.1007/978-3-030-44999-5_29
summary:
  In adopting a slightly dystopic view, our focus in this paper is seated within the context of the potentially negative impact that end-users have on organisations when discontinuing the use of a particular mandated IS [2] , or making misuse of information within an IS that is intended to drive value realisation [3, 4] . The primary purpose is to build a value framework from which an empirically-endorsed model can be constructed, and through which the unintended business value dissipating effects on institutions, as a direct result of end-user''s misuse of IS, may be investigated and moderated. Three secondary objectives that dictate the structure of this paper are pursued: (1) to review key characteristics from several germane models and theories relating to the business impact of HCI that maps to, and refines, a rudimentary Conceptual Technology Value Framework (CTVF), (2) to apply the CTVF as a basis for a qualitative investigation from which an Adjusted Technology Value Model (ATVM) may be derived and contextualized, and (3) to present the ATVM as a first benchmark to identify, investigate, mitigate and minimise or eliminate unintentional value destroying effects.

title:
  Towards a Strategic Model for Safeguarding the Preservation of Business Value During Human Interactions with Information Systems

abstract:
  This paper considers the dichotomy inherent in Information Systems where its introduction, for the purposes of creating new or sustaining existing business value, subsequently also inadvertently or deliberately dissipates value. We investigate root people-induced causes, delineated within a rudimentary Conceptual Technology Value Framework. To support a qualitative investigation, the framework is forthwith applied as the basis for a series of interviews within a major South African financial institution operating within the disciplines of information technology, business operations and organisational development. The constructs identified are discussed and find gestalt in an Adjusted Technology Value Model which can be used to safeguard business value against destructive HCI behaviors.


id: cord-020820-cbikq0v0
title: Dualism in Topical Relevance
author: Papadakos, Panagiotis
date: 2020-03-24
txt: ./txt/cord-020820-cbikq0v0.txt
cache: ./cache/cord-020820-cbikq0v0.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148031/
doi: 10.1007/978-3-030-45442-5_40
summary:
  To this end, in this paper we elaborate on the idea of leveraging the available antonyms of the original query terms for eventually producing an answer which provides a better overview of the related conceptual and information space. In this paper we elaborate on the idea of leveraging the available antonyms of the original query terms (if they exist), for eventually producing an answer which provides a better overview of the related information and conceptual space. In their comments for these queries, users mention that the selected (i.e., dual) list "provides a more general picture" and "more relevant and interesting results, although contradicting". For the future, we plan to define the appropriate antonyms selection algorithms and relevance metrics, implement the proposed functionality in a meta-search setting, and conduct a large scale evaluation with real users over exploratory tasks, to identify in which queries the dual approach is beneficial and to what types of users.

abstract:
  There are several concepts whose interpretation and meaning is defined through their binary opposition with other opposite concepts. To this end, in this paper we elaborate on the idea of leveraging the available antonyms of the original query terms for eventually producing an answer which provides a better overview of the related conceptual and information space. Specifically, we sketch a method in which antonyms are used for producing dual queries, which can in turn be exploited for defining a multi-dimensional topical relevance based on the antonyms. We motivate this direction by providing examples and by conducting a preliminary evaluation that shows its importance to specific users.


id: cord-020891-lt3m8h41
author: Witschel, Hans Friedrich
date: 2020-03-17
txt: ./txt/cord-020891-lt3m8h41.txt
cache: ./cache/cord-020891-lt3m8h41.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148211/
doi: 10.1007/978-3-030-45439-5_50
summary:
  It supports both the user and the system in keeping track of the context/current focus of the search via a novel interaction concept that combines pointing/clicking and asking questions in natural language, described in Sect. In order to support them in grasping relationships between new concepts in the -often very complex -answers to their fuzzy questions, IR researchers have proposed result set visualisations that provide a better overview than the typical ranked lists of document references [1, 20] . Our contribution consists mainly in proposing a new interaction paradigm which allows users to ask questions in natural language and to receive answers in the form of visualised subgraphs of a knowledge graph. In this work, we have proposed a novel context-aware sequential question answering system, especially suited for exploratory search, based on graph visualisation for result presentation and iterative refinement of information needs.

title:
  KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources

abstract:
  Exploring a knowledge base is often an iterative process: initially vague information needs are refined by interaction. We propose a novel approach for such interaction that supports sequential question answering (SQA) on knowledge graphs. As opposed to previous work, we focus on exploratory settings, which we support with a visual representation of graph structures, helping users to better understand relationships. In addition, our approach keeps track of context – an important challenge in SQA – by allowing users to make their focus explicit via subgraph selection. Our results show that the interaction principle is either understood immediately or picked up very quickly – and that the possibility of exploring the information space iteratively is appreciated.


id: cord-020901-aew8xr6n
title: TransRev: Modeling Reviews as Translations from Users to Items
author: García-Durán, Alberto
date: 2020-03-17
txt: ./txt/cord-020901-aew8xr6n.txt
cache: ./cache/cord-020901-aew8xr6n.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148221/
doi: 10.1007/978-3-030-45439-5_16
summary:
  TransRev learns vector representations for At training time, a function''s parameters are learned to compute the review embedding from the word token embeddings such that the embedding of the user translated by the review embedding is similar to the product embedding. Methods that fall into this category such as [31, 32] learn latent representations of users and items from the text content so as to perform well at rating prediction. Similar to sentiment analysis methods, TransRev trains a regression model that predicts the review rating from the review text. We compare to the following methods: a SVD matrix factorization; HFT, which has not often been benchmarked in previous works; and DeepCoNN [38] , which learns user and item representations from reviews via convolutional neural networks. Representation learning of users and items for review rating prediction using attention-based convolutional neural network

abstract:
  The text of a review expresses the sentiment a customer has towards a particular product. This is exploited in sentiment analysis where machine learning models are used to predict the review score from the text of the review. Furthermore, the products costumers have purchased in the past are indicative of the products they will purchase in the future. This is what recommender systems exploit by learning models from purchase information to predict the items a customer might be interested in. The underlying structure of this problem setting is a bipartite graph, wherein customer nodes are connected to product nodes via ‘review’ links. This is reminiscent of knowledge bases, with ‘review’ links replacing relation types. We propose TransRev, an approach to the product recommendation problem that integrates ideas from recommender systems, sentiment analysis, and multi-relational learning into a joint learning objective. TransRev learns vector representations for users, items, and reviews. The embedding of a review is learned such that (a) it performs well as input feature of a regression model for sentiment prediction; and (b) it always translates the reviewer embedding to the embedding of the reviewed item. This is reminiscent of TransE [5], a popular embedding method for link prediction in knowledge bases. This allows TransRev to approximate a review embedding at test time as the difference of the embedding of each item and the user embedding. The approximated review embedding is then used with the regression model to predict the review score for each item. TransRev outperforms state of the art recommender systems on a large number of benchmark data sets. Moreover, it is able to retrieve, for each user and item, the review text from the training set whose embedding is most similar to the approximated review embedding.


id: cord-020936-k1upc1xu
title: Axiomatic Analysis of Contact Recommendation Methods in Social Networks: An IR Perspective
author: Sanz-Cruzado, Javier
date: 2020-03-17
txt: ./txt/cord-020936-k1upc1xu.txt
cache: ./cache/cord-020936-k1upc1xu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148256/
doi: 10.1007/978-3-030-45439-5_12
summary:
  Recently, it has been shown that classical information retrieval (IR) weighting models – such as BM25 – can be adapted to effectively recommend new social contacts to a given user. In this paper, we analyze the reasons behind the effectiveness of IR approaches for the task of recommending contacts in social networks, through an exploratory analysis of the importance and validity of the fundamental IR axioms [13] . Interestingly, we find that while this is generally true, the axioms related to length normalization negatively impact the contact recommendation performance, since they interfere with a key evolutionary principle in social networks, namely preferential attachment [8] . 3. As the only difference between the original and the version of BM25 defined by Sanz-Cruzado and Castells is just the definition of the candidate length, it is straightforward to prove that all edge weight constraints and NDC are satisfied in the same way as they are for textual IR: NDC is unconditionally true, whereas all EWC axioms depend just on the condition:

abstract:
  Contact recommendation is an important functionality in many social network scenarios including Twitter and Facebook, since they can help grow the social networks of users by suggesting, to a given user, people they might wish to follow. Recently, it has been shown that classical information retrieval (IR) weighting models – such as BM25 – can be adapted to effectively recommend new social contacts to a given user. However, the exact properties that make such adapted contact recommendation models effective at the task are as yet unknown. In this paper, inspired by new advances in the axiomatic theory of IR, we study the existing IR axioms for the contact recommendation task. Our theoretical analysis and empirical findings show that while the classical axioms related to term frequencies and term discrimination seem to have a positive impact on the recommendation effectiveness, those related to length normalization tend to be not desirable for the task.


id: cord-024430-r0gbw5j6
title: Modeling Users’ Multifaceted Interest Correlation for Social Recommendation
author: Wang, Hao
date: 2020-04-17
txt: ./txt/cord-024430-r0gbw5j6.txt
cache: ./cache/cord-024430-r0gbw5j6.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206155/
doi: 10.1007/978-3-030-47426-3_10
summary:
  Many methods have been proposed for social recommendation in recent years, and these methods can be mainly grouped into two categories: (1) memory-based methods [1, 12, 14] use social relation as an indicator that filters relevant users and directly recommend friends'' visited items to a user; (2) model-based methods [4, 5, 9, 10, 22, 27, 29, 31] integrate social relation into factorization methods to constrain that friends share similar interest embeddings. We propose to use a correlation vector, instead of a scalar value, to characterize the interest correlation between each pair of friends, and design a dimension-wise attention mechanism with the social network as input to learn it. To accommodate our problem, we further design a dimension-wise attention mechanism and use it to learn a correlation vector for each pair of friends, building their multi-dimensional interest correlation for social recommendation.

abstract:
  Recommender systems suggest to users the items that are potentially of their interests, by mining users’ feedback data on items. Social relations provide an independent source of information about users and can be exploited for improving recommendation performance. Most of existing recommendation methods exploit social influence by refining social relations into a scalar indicator to either directly recommend friends’ visited items to users or constrain that friends’ embeddings are similar. However, a scalar indicator cannot express the multifaceted interest correlations between users, since each user’s interest is distributed across multiple dimensions. To address this issue, we propose a new embedding-based framework, which exploits users’ multifaceted interest correlation for social recommendation. We design a dimension-wise attention mechanism to learn a correlation vector to characterize the interest correlation between a pair of friends, capturing the high variation of users’ interest correlation on multiple dimensions. Moreover, we use friends’ embeddings to smooth a user’s own embedding with the correlation vector as weights, building the elaborate unstructured social influence between users. Experimental results on two real-world datasets demonstrate that modeling users’ multifaceted interest correlations can significantly improve recommendation performance.


id: cord-024433-b4vw5r0o
title: CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums
author: Morales, Alex
date: 2020-04-17
txt: ./txt/cord-024433-b4vw5r0o.txt
cache: ./cache/cord-024433-b4vw5r0o.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206158/
doi: 10.1007/978-3-030-47426-3_46
summary:
  title: CrowdQM: Learning Aspect-Level User Reliability and Comment Trustworthiness in Discussion Forums CrowdQM addresses these limitations by modeling the fine-grained aspect-level reliability of users and incorporate semantic similarity between words to learn a latent trustworthy comment embedding. CrowdQM addresses both limitations by jointly modeling the aspect-level user reliability and latent trustworthy comment in an optimization framework. The update of the embeddings depend on the submission context v c , latent trustworthy comment embedding, a * m as well as user-post reliability score, R m,n . Note that both approaches do not model aspect-level user reliability but use semantic representations of comments. To the best of our knowledge, there has been no work that models both fine-grained user reliability with semantic representations of the text to discover trustworthy comments from community responses. We proposed an unsupervised model to learn a trustworthy comment embedding from all the given comments for each post in a discussion forum.

abstract:
  Community discussion forums are increasingly used to seek advice; however, they often contain conflicting and unreliable information. Truth discovery models estimate source reliability and infer information trustworthiness simultaneously in a mutual reinforcement manner, and can be used to distinguish trustworthy comments with no supervision. However, they do not capture the diversity of word expressions and learn a single reliability score for the user. CrowdQM addresses these limitations by modeling the fine-grained aspect-level reliability of users and incorporate semantic similarity between words to learn a latent trustworthy comment embedding. We apply our latent trustworthy comment for comment ranking for three diverse communities in Reddit and show consistent improvement over non-aspect based approaches. We also show qualitative results on learned reliability scores and word embeddings by our model.


id: cord-026948-jl3lj7yh
title: Towards Explainability in Using Deep Learning for the Detection of Anorexia in Social Media
author: Amini, Hessam
date: 2020-05-26
txt: ./txt/cord-026948-jl3lj7yh.txt
cache: ./cache/cord-026948-jl3lj7yh.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7298178/
doi: 10.1007/978-3-030-51310-8_21
summary:
  Results show that the weights assigned by the user-level attention strongly correlate with the amount of information that posts provide in showing if their author is at risk of anorexia or not, and hence can be used to explain the decision of the neural classifier. Previous work in NLP for clinical psychology has typically used this type of attention mechanism to create a representation of social media users: a collection of online posts from each user is fed to the model and the inter-document attention (also referred to as user-level attention) creates a representation of the user through a weighted average of the representations of their online posts, with the most informative posts are assigned higher weights. In this paper, we propose a quantitative approach, specifically focused on the user-level (inter-document) attention mechanism in a binary classification task of detection of a specific mental health issue, anorexia. In this work, we proposed a quantitative approach to validate the explainability of the user-level attention mechanism for the task of the detection of anorexia in social media users based on their online posts.

abstract:
  Explainability of deep learning models has become increasingly important as neural-based approaches are now prevalent in natural language processing. Explainability is particularly important when dealing with a sensitive domain application such as clinical psychology. This paper focuses on the quantitative assessment of user-level attention mechanism in the task of detecting signs of anorexia in social media users from their posts. The assessment is done through monitoring the performance measures of a neural classifier, with and without user-level attention, when only a limited number of highly-weighted posts are provided. Results show that the weights assigned by the user-level attention strongly correlate with the amount of information that posts provide in showing if their author is at risk of anorexia or not, and hence can be used to explain the decision of the neural classifier.


id: cord-027078-i3a5jwck
title: Social Recommendation in Heterogeneous Evolving Relation Network
author: Jiang, Bo
date: 2020-05-26
txt: ./txt/cord-027078-i3a5jwck.txt
cache: ./cache/cord-027078-i3a5jwck.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302249/
doi: 10.1007/978-3-030-50371-0_41
summary:
  In this paper, we propose a novel social recommendation model based on evolving relation network, named SoERec. The learned evolving relation network is a heterogeneous information network, where the strength of relation between users is a sum of the influence of all historical events. -We propose a novel social recommendation model by jointly embedding representations of fine-grained relations from historical events based on heterogeneous evolving network. -We conduct several analysis experiments with two real-world social network datasets, the experimental results demonstrate our proposed model outperforms state-of-the art comparison methods. Various methods of social recommendation have been proposed from different perspectives in recent years including user-item rating matrix [15] , network structure [11] , trust relationship [5, 10, 18, 27] , individual and friends'' preferences [6, 12] , social information [25] and combinations of different features [19, 26] . In particular, we leverage the LINE model to learn users'' embedded representations of the evolving relation network the firstorder proximity and the second-order proximity.

abstract:
  The appearance and growth of social networking brings an exponential growth of information. One of the main solutions proposed for this information overload problem are recommender systems, which provide personalized results. Most existing social recommendation approaches consider relation information to improve recommendation performance in the static context. However, relations are likely to evolve over time in the dynamic network. Therefore, temporal information is an essential ingredient to making social recommendation. In this paper, we propose a novel social recommendation model based on evolving relation network, named SoERec. The learned evolving relation network is a heterogeneous information network, where the strength of relation between users is a sum of the influence of all historical events. We incorporate temporally evolving relations into the recommendation algorithm. We empirically evaluate the proposed method on two widely-used datasets. Experimental results show that the proposed model outperforms the state-of-the-art social recommendation methods.


id: cord-027120-w6agcu63
title: Conversational Interface for Managing Non-trivial Internet-of-Things Systems
author: Lago, André Sousa
date: 2020-05-25
txt: ./txt/cord-027120-w6agcu63.txt
cache: ./cache/cord-027120-w6agcu63.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302544/
doi: 10.1007/978-3-030-50426-7_29
summary:
  In this work we present Jarvis, a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. Another common, and sometimes complementary, alternative to visual programming, is the many conversational assistants in the market, such as Google Assistant, Alexa, Siri and Cortana, that are capable of answering natural language questions and which recently gained the ability to interact with IoT devices (see [18] and [15] for a comparison of these tools). In this paper we presented a conversational interface prototype able to carry several different management tasks currently not supported by voice assistants, with capabilities that include: (1) Delayed, periodic and repeating actions, enabling users to perform queries such as "turn on the light in 5 min" and "turn on the light every day at 8 am"; (2) The usage of contextual awareness for more natural conversations, allowing interactions that last for multiple sentences and provide a more intuitive conversation, e.g.

abstract:
  Internet-of-Things has reshaped the way people interact with their surroundings. In a smart home, controlling the lights is as simple as speaking to a conversational assistant since everything is now Internet-connected. But despite their pervasiveness, most of the existent IoT systems provide limited out-of-the-box customization capabilities. Several solutions try to attain this issue leveraging end-user programming features that allow users to define rules to their systems, at the cost of discarding the easiness of voice interaction. However, as the number of devices increases, along with the number of household members, the complexity of managing such systems becomes a problem, including finding out why something has happened. In this work we present Jarvis, a conversational interface to manage IoT systems that attempts to address these issues by allowing users to specify time-based rules, use contextual awareness for more natural interactions, provide event management and support causality queries. A proof-of-concept was used to carry out a quasi-experiment with non-technical participants that provides evidence that such approach is intuitive enough to be used by common end-users.


id: cord-027346-ldfgi0vr
title: GCN-IA: User Profile Based on Graph Convolutional Network with Implicit Association Labels
author: Wen, Jie
date: 2020-05-22
txt: ./txt/cord-027346-ldfgi0vr.txt
cache: ./cache/cord-027346-ldfgi0vr.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304041/
doi: 10.1007/978-3-030-50420-5_26
summary:
  title: GCN-IA: User Profile Based on Graph Convolutional Network with Implicit Association Labels Current researches on multi-label user profile either ignore the implicit associations among labels or do not consider the user and label semantic information in the social networks. In this paper, a graph convolutional network with implicit associations (GCN-IA) method is proposed to obtain user profile. As a result, label propagation user profile methods [4] [5] [6] are widely studied, which mainly use the social network information rather than user''s activities. To take advantage of this insight, a graph convolutional networks with implicit label associations (GCN-IA) is proposed to get user profile. A graph convolutional networks with implicit label associations (GCN-IA) method is proposed to get user profile. We first construct the social network graph with the relationship between users and design a probability matrix to record the implicit label associations, and then combine this probability matrix with the classical GCN method to embed user and label semantic information.

abstract:
  Inferring multi-label user profile plays a significant role in providing individual recommendations and exact-marketing, etc. Current researches on multi-label user profile either ignore the implicit associations among labels or do not consider the user and label semantic information in the social networks. Therefore, the user profile inferred always does not take full advantage of the global information sufficiently. To solve above problem, a new insight is presented to introduce implicit association labels as the prior knowledge enhancement and jointly embed the user and label semantic information. In this paper, a graph convolutional network with implicit associations (GCN-IA) method is proposed to obtain user profile. Specifically, a probability matrix is first designed to capture the implicit associations among labels for user representation. Then, we learn user embedding and label embedding jointly based on user-generated texts, relationships and label information. On four real-world datasets in Weibo, experimental results demonstrate that GCN-IA produces a significant improvement compared with some state-of-the-art methods.


id: cord-027431-6twmcitu
title: Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms
author: Mukhina, Ksenia
date: 2020-05-25
txt: ./txt/cord-027431-6twmcitu.txt
cache: ./cache/cord-027431-6twmcitu.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7304753/
doi: 10.1007/978-3-030-50433-5_7
summary:
  To do that we propose a spatiotemporal data processing pipeline that is general enough to fit most of the problems related to working with LBSNs. The proposed pipeline includes four main stages: an identification of suspicious profiles, a background extraction, a spatial context extraction, and a fake transitions detection. Efficiency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts. Thus, all studies based on social networks as a data source face two significant issues: wrong location information stored in the service (wrong coordinates, incorrect titles, duplicates, etc.) and false information provided by users (to hide an actual position or to promote their content).

abstract:
  One of the areas that gathers momentum is the investigation of location-based social networks (LBSNs) because the understanding of citizens’ behavior on various scales can help to improve quality of living, enhance urban management, and advance the development of smart cities. But it is widely known that the performance of algorithms for data mining and analysis heavily relies on the quality of input data. The main aim of this paper is helping LBSN researchers to perform a preliminary step of data preprocessing and thus increase the efficiency of their algorithms. To do that we propose a spatiotemporal data processing pipeline that is general enough to fit most of the problems related to working with LBSNs. The proposed pipeline includes four main stages: an identification of suspicious profiles, a background extraction, a spatial context extraction, and a fake transitions detection. Efficiency of the pipeline is demonstrated on three practical applications using different LBSN: touristic itinerary generation using Facebook locations, sentiment analysis of an area with the help of Twitter and VK.com, and multiscale events detection from Instagram posts.


id: cord-031614-l5seadro
title: Adaptive User Interfaces for People with Cognitive Disabilities within the Easy Reading Framework
author: Heumader, Peter
date: 2020-08-12
txt: ./txt/cord-031614-l5seadro.txt
cache: ./cache/cord-031614-l5seadro.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479802/
doi: 10.1007/978-3-030-58805-2_7
summary:
  This paper describes how such user interfaces are implemented within the Easy Reading framework, a framework to improve the accessibility of web-pages for people with cognitive disabilities. MyUI on the other hand was an EU funded project that enabled the generation of individualized user interfaces that would adapt to the individual users needs in realtime, based on a user profile and the actual device [10, 12, 13] . Adaptations within the Easy Reading framework can be applied to the user interface, the help that is provided, the user interaction (how help is triggered) and finally how the help is rendered and presented within the web-page. • Input Support: Stores the preferred way to triggering help and to select where on the web-page help is needed • Output Support: Specifies the preferred way of rendering the help provided Based on these categories, once the user logs in with his or her user profile, a dynamically optimized configuration is created for the individual user (see Fig. 2 ).

abstract:
  Adaptive user interfaces are user interfaces that dynamically adapt to the users’ preferences and abilities. These user interfaces have great potential to improve accessibility of user interfaces for people with cognitive disabilities. However automatic changes to user interfaces driven by adaptivity are also in contradiction to accessibility guidelines, as consistence of user interfaces is of utmost importance for people with cognitive disabilities. This paper describes how such user interfaces are implemented within the Easy Reading framework, a framework to improve the accessibility of web-pages for people with cognitive disabilities.


id: cord-031616-dckqb6er
title: Automatic Assistance to Cognitive Disabled Web Users via Reinforcement Learning on the Browser
author: Murillo-Morales, Tomas
date: 2020-08-12
txt: ./txt/cord-031616-dckqb6er.txt
cache: ./cache/cord-031616-dckqb6er.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479804/
doi: 10.1007/978-3-030-58805-2_8
summary:
  It aims to infer the user''s current cognitive state by collecting and analyzing user''s physiological data in real time, such as eye tracking, heart beat rate and variability, and blink rate. These tools embed alternative easy-to-read or clarified content directly into the original Web document being visited when the user requests it, thereby enabling persons with a cognitive disability to independently browse the Web. Access methods may be tailored to the specific users based on personal data, generally created by supporting staff or educators [8] . The main advantage of the Easy Reading framework over existing cognitive support methods is that the personalized support tools are provided at the original websites in an automatic fashion instead of depending on separate user experiences which are commonly provided to users in a static, content-dependent manner and that must be manually authored by experts. The Easy Reading Reasoner is the client-based module in charge of solving the problem of inferring the affective state of the user from the current readings of physiological signals collected by a running AsTeRICS model.

abstract:
  This paper introduces a proof of concept software reasoner that aims to detect whether an individual user is in need of cognitive assistance during a typical Web browsing session. The implemented reasoner is part of the Easy Reading browser extension for Firefox. It aims to infer the user’s current cognitive state by collecting and analyzing user’s physiological data in real time, such as eye tracking, heart beat rate and variability, and blink rate. In addition, when the reasoner determines that the user is in need of help it automatically triggers a support tool appropriate for the individual user and Web content being consumed. By framing the problem as a Markov Decision Process, typical policy control methods found in the Reinforcement Learning literature, such as Q-learning, can be employed to tackle the learning problem.


id: cord-031617-l9iacaec
author: Iwamura, Masakazu
date: 2020-08-10
txt: ./txt/cord-031617-l9iacaec.txt
cache: ./cache/cord-031617-l9iacaec.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7479805/
doi: 10.1007/978-3-030-58796-3_57
summary:
  title: Suitable Camera and Rotation Navigation for People with Visual Impairment on Looking for Something Using Object Detection Technique For people with visual impairment, smartphone apps that use computer vision techniques to provide visual information have played important roles in supporting their daily lives. Then, in looking for something as a representative task in a category, we argue suitable camera systems and rotation navigation methods. Obtaining the visual information on the object that the user photographs Current smartphone apps that use computer vision techniques such as [6, 11, 12] can be used. In this experiment, we asked participants to use five rotation navigation methods one by one through Steps 1 (object detection using the omnidirectional camera) and 2 (rotation navigation) in Sect. A user study comprised of seven people with visual impairment confirmed that (1) a camera with a wide FoV is better in such a task, and (2) users have different preferences in rotation navigation.

title:
  Suitable Camera and Rotation Navigation for People with Visual Impairment on Looking for Something Using Object Detection Technique

abstract:
  For people with visual impairment, smartphone apps that use computer vision techniques to provide visual information have played important roles in supporting their daily lives. However, they can be used under a specific condition only. That is, only when the user knows where the object of interest is. In this paper, we first point out the fact mentioned above by categorizing the tasks that obtain visual information using computer vision techniques. Then, in looking for something as a representative task in a category, we argue suitable camera systems and rotation navigation methods. In the latter, we propose novel voice navigation methods. As a result of a user study comprised of seven people with visual impairment, we found that (1) a camera with a wide field of view such as an omnidirectional camera was preferred, and (2) users have different preferences in navigation methods.


id: cord-032466-1nfp1hcs
title: Interaction design for multi-user virtual reality systems: An automotive case study
author: Gong, Liang
date: 2020-09-22
txt: ./txt/cord-032466-1nfp1hcs.txt
cache: ./cache/cord-032466-1nfp1hcs.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7508012/
doi: 10.1016/j.procir.2020.04.036
summary:
  title: Interaction design for multi-user virtual reality systems: An automotive case study It has shown that VR technologies have great advantages to improve areas like factory layout planning, product design, training, etc., especially for globally distributed manufacturing companies that have different functional team located in different parts of the world [3] , [5] [8] . In this study, we have chosen a globally distributed manufacturing company as the case to study the different possibilities of interaction design approaches for the multi-user VR system used in the design review process. In this study, we have chosen a globally distributed manufacturing company as the case to study the different possibilities of interaction design approaches for the multi-user VR system used in the design review process. In this study, we have chosen a globally distributed manufacturing company as the case to study the different possibilities of interaction design approaches for the multi-user VR system used in the design review process.

abstract:
  Virtual reality (VR) technology have become ever matured today. Various research and practice have demonstrated the potential benefits of using VR in different application area of manufacturing, such as in factory layout planning, product design, training, etc. However, along with the new possibilities brought by VR, comes with the new ways for users to communicate with the computer system. The human computer interaction design for these VR systems becomes pivotal to the smooth integration. In this paper, it reports the study that investigates interaction design strategies for the multi-user VR system used in manufacturing context though an automotive case study.


id: cord-033725-rlzbznav
author: Unnikrishnan, Vishnu
date: 2020-09-19
txt: ./txt/cord-033725-rlzbznav.txt
cache: ./cache/cord-033725-rlzbznav.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556387/
doi: 10.1007/978-3-030-61527-7_43
summary:
  title: Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations Where to Learn from? We propose an approach that learns from users who contribute long sequences of inputs to predict the subjective perception of wellbeing for users who contribute only short sequences of input data, including users that have very recently joined the platform. -RQ2: Can we predict the entire sequence of observations of a user in U short with a model trained only on data from users in U long ? (i.e, does a model learned on data from users with long sequences transfer to those with short ones?) -RQ3: How can we incorporate early recordings of users in U short incrementally into the model to improve predictive performance? However, it is still possible that a model learned on those data points from long users bring a modest predictability to the disease development of users in U short .

title:
  Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

abstract:
  Some mHealth apps record user activity continuously and unobtrusively, while other apps rely by nature on user engagement and self-discipline: users are asked to enter data that cannot be assessed otherwise, e.g., on how they feel and what non-measurable symptoms they have. Over time, this leads to substantial differences in the length of the time series of recordings for the different users. In this study, we propose two algorithms for wellbeing-prediction from such time series, and we compare their performance on the users of a pilot study on diabetic patients - with time series length varying between 8 and 87 recordings. Our first approach learns a model from the few users, on which many recordings are available, and applies this model to predict the 2nd, 3rd, and so forth recording of users newly joining the mHealth platform. Our second approach rather exploits the similarity among the first few recordings of newly arriving users. Our results for the first approach indicate that the target variable for users who use the app for long are not predictive for users who use the app only for a short time. Our results for the second approach indicate that few initial recordings suffice to inform the predictive model and improve performance considerably.


id: cord-035285-dx5bbeqm
title: GoCoronaGo: Privacy Respecting Contact Tracing for COVID-19 Management
author: Simmhan, Yogesh
date: 2020-11-11
txt: ./txt/cord-035285-dx5bbeqm.txt
cache: ./cache/cord-035285-dx5bbeqm.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7656502/
doi: 10.1007/s41745-020-00201-5
summary:
  This proximity data of all app users are used to build a temporal contact graph, where vertices are devices, and edges indicate proximity between devices for a certain time period and with a certain Bluetooth signal strength. The use of the GCG App within an institutional setting, with data collection and usage governed by the organization, may lead to higher adoption of the app and enhance its effectiveness in contact tracing. The use of GCG is strictly voluntary, and there is an additional consent required by a user who is infected with COVID-19 before their data can be used for contact tracing-this, despite their data already being available centrally in the backend. Besides tracking Bluetooth contact data, the GCG App offers several features to inform the users about COVID-19 and engage them in preventing its spread.

abstract:
  The COVID-19 pandemic is imposing enormous global challenges in managing the spread of the virus. A key pillar to mitigation is contact tracing, which complements testing and isolation. Digital apps for contact tracing using Bluetooth technology available in smartphones have gained prevalence globally. In this article, we discuss various capabilities of such digital contact tracing, and its implication on community safety and individual privacy, among others. We further describe the GoCoronaGo institutional contact tracing app that we have developed, and the conscious and sometimes contrarian design choices we have made. We offer a detailed overview of the app, backend platform and analytics, and our early experiences with deploying the app to over 1000 users within the Indian Institute of Science campus in Bangalore. We also highlight research opportunities and open challenges for digital contact tracing and analytics over temporal networks constructed from them.


id: cord-102542-1mglhh41
title: Chatbots as conversational healthcare services
author: Jovanovi''c, Mladjan
date: 2020-11-08
txt: ./txt/cord-102542-1mglhh41.txt
cache: ./cache/cord-102542-1mglhh41.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2011.03969v1.pdf
doi: 10.1109/mic.2020.3037151
summary:
  This article takes a closer look at how these emerging chatbots address design aspects relevant to healthcare service provision, emphasizing the Human-AI interaction aspects and the transparency in AI automation and decision making. This paper: • identifies salient service provision archetypes that characterize the emerging roles and functions the chatbots aim to fulfill; • assesses the design choices concerning domainspecific dimensions associated with health service provision and user experience; • provides implications for theory and practice that highlight existing gaps. The archetype does not perform the diagnosis but instead support a diagnosis by either i) facilitating access to health services, such as the Pathology Lab Chatbot facilitating access to doctors and scheduling visits, ii) supporting online consultations with health professionals, such as the iCliniq that pairs up users with doctors for online consultation, and iii) providing conversational access to information regarding symptoms and diseases, such as the WebMD.

abstract:
  Chatbots are emerging as a promising platform for accessing and delivering healthcare services. The evidence is in the growing number of publicly available chatbots aiming at taking an active role in the provision of prevention, diagnosis, and treatment services. This article takes a closer look at how these emerging chatbots address design aspects relevant to healthcare service provision, emphasizing the Human-AI interaction aspects and the transparency in AI automation and decision making.


id: cord-102738-e5zojanb
author: Lieberoth, Andreas
date: 2015-06-26
txt: ./txt/cord-102738-e5zojanb.txt
cache: ./cache/cord-102738-e5zojanb.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/1506.08761v1.pdf
doi: 10.15346/hc.v1i2.11
summary:
  Among statistical predictors for retention and in-game high scores, the data from our first year suggest that people recruited based on real-world physics interest and via real-world events, but only with an intermediate science education, are more likely to become engaged and skilled contributors. Recruitment activities in-world and online, an engaging in-game core loop, a structural gameplay to frame, structure and motivate the player''s continual progression through the levels, as well as an active community where participants get a sense of continually contributing to science, are all central components of the strategy laid out to hopefully realizing the scientific goals of Quantum Moves. While Quantum Moves is unique compared to other citizen science games in having an engaging and challenging core game loop that by itself lives up to prominent definitions of (casual) games (Juul, 2005; Salen & Zimmerman, 2004) , we also expect that a well-designed structural gameplay (sometimes called metagame) is central to frame, structure and motivate the play experience, both helping and goading players to move from level to level along appropriate learning curves balanced between boredom and anxiety.

title:
  Getting Humans to do Quantum Optimization - User Acquisition, Engagement and Early Results from the Citizen Cyberscience Game Quantum Moves

abstract:
  The game Quantum Moves was designed to pit human players against computer algorithms, combining their solutions into hybrid optimization to control a scalable quantum computer. In this midstream report, we open our design process and describe the series of constitutive building stages going into a quantum physics citizen science game. We present our approach from designing a core gameplay around quantum simulations, to putting extra game elements in place in order to frame, structure, and motivate players' difficult path from curious visitors to competent science contributors. The player base is extremely diverse - for instance, two top players are a 40 year old female accountant and a male taxi driver. Among statistical predictors for retention and in-game high scores, the data from our first year suggest that people recruited based on real-world physics interest and via real-world events, but only with an intermediate science education, are more likely to become engaged and skilled contributors. Interestingly, female players tended to perform better than male players, even though men played more games per day. To understand this relationship, we explore the profiles of our top players in more depth. We discuss in-world and in-game performance factors departing in psychological theories of intrinsic and extrinsic motivation, and the implications for using real live humans to do hybrid optimization via initially simple, but ultimately very cognitively complex games.


id: cord-120017-vsoc9v85
title: Usable Security for ML Systems in Mental Health: A Framework
author: Jiang, Helen
date: 2020-08-18
txt: ./txt/cord-120017-vsoc9v85.txt
cache: ./cache/cord-120017-vsoc9v85.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.07738v1.pdf
doi: nan
summary:
  We aim to weave together threads from different domains, incorporate existing views, and propose new principles and requirements, in an effort to lay out a clear framework where criteria and expectations are established, and are used to make security mechanisms usable for end-users of those ML systems in mental health. In this short paper, we propose that ML systems in mental health use cases, beyond the privacy and security requirements already mandated by legislation''s and regulations -for example, Health Insurance Portability and Accountability Act (HIPPA) [38, 43, 64] in United States, and General Data Protection Regulation (GDPR) in European Union and its member states'' national laws [11, 12] -should consider properties of usable security proposed by this framework''s four pillars, and be evaluated on their (1)context models, (2)functionality criteria, (3)trustworthiness requirements, and (4)recovery principles across their life cycles.

abstract:
  While the applications and demands of Machine learning (ML) systems in mental health are growing, there is little discussion nor consensus regarding a uniquely challenging aspect: building security methods and requirements into these ML systems, and keep the ML system usable for end-users. This question of usable security is very important, because the lack of consideration in either security or usability would hinder large-scale user adoption and active usage of ML systems in mental health applications. In this short paper, we introduce a framework of four pillars, and a set of desired properties which can be used to systematically guide and evaluate security-related designs, implementations, and deployments of ML systems for mental health. We aim to weave together threads from different domains, incorporate existing views, and propose new principles and requirements, in an effort to lay out a clear framework where criteria and expectations are established, and are used to make security mechanisms usable for end-users of those ML systems in mental health. Together with this framework, we present several concrete scenarios where different usable security cases and profiles in ML-systems in mental health applications are examined and evaluated.


id: cord-120498-b1bla3fp
title: SKATE: A Natural Language Interface for Encoding Structured Knowledge
author: McFate, Clifton
date: 2020-10-20
txt: ./txt/cord-120498-b1bla3fp.txt
cache: ./cache/cord-120498-b1bla3fp.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.10597v1.pdf
doi: nan
summary:
  In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We demonstrate how SKATE has been integrated with a natural language rule generation model to interactively acquire structured rules for story understanding, and conclude with a current application that uses SKATE to build COVID-19 policy diagrams. For example, in the second pane of Figure 2 , the template generator has built frame assignment options for the word "take." The resulting micro-dialogue is presented to the user. SKATE''s performance improves with annotated examples, but they are not required, and as discussed in the next subsection, SKATE can generate its own training data as a new frame is selected by the user and elaborated upon in SKATE interactions. Our approach leverages recent advances in language modeling to generate templates from user text and to provide unstructured guidance.

abstract:
  In Natural Language (NL) applications, there is often a mismatch between what the NL interface is capable of interpreting and what a lay user knows how to express. This work describes a novel natural language interface that reduces this mismatch by refining natural language input through successive, automatically generated semi-structured templates. In this paper we describe how our approach, called SKATE, uses a neural semantic parser to parse NL input and suggest semi-structured templates, which are recursively filled to produce fully structured interpretations. We also show how SKATE integrates with a neural rule-generation model to interactively suggest and acquire commonsense knowledge. We provide a preliminary coverage analysis of SKATE for the task of story understanding, and then describe a current business use-case of the tool in a specific domain: COVID-19 policy design.


id: cord-121200-2qys8j4u
title: Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
author: Zogan, Hamad
date: 2020-07-03
txt: ./txt/cord-121200-2qys8j4u.txt
cache: ./cache/cord-121200-2qys8j4u.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.02847v1.pdf
doi: nan
summary:
  While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. To be specific, this work aims to develop a new novel deep learning-based solution for improving depression detection by utilizing multi-modal features from diverse behaviour of the depressed user in social media. To this end, we propose a hybrid model comprising Bidirectional Gated Recurrent Unit (BiGRU) and Conventional Neural network (CNN) model to boost the classification of depressed users using multi-modal features and word embedding features. The most closely related recent work to ours is [23] where the authors propose a CNN-based deep learning model to classify Twitter users based on depression using multi-modal features.

abstract:
  Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts). The CNN model then helps learn useful features. Our experiments show that our model outperforms several popular and strong baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.


id: cord-122159-sp6o6h31
title: COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers
author: Raskar, Ramesh
date: 2020-06-04
txt: ./txt/cord-122159-sp6o6h31.txt
cache: ./cache/cord-122159-sp6o6h31.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.05812v1.pdf
doi: nan
summary:
  By comparing the device users'' location trails or the anonymous ID tokens they have collected with those from people who have COVID-19, one can identify others who have been near the person who is infected; this facilitates contact tracing in a more accurate and timely manner than the traditional manual approach. • An authority (public health official, healthcare provider, government official) collects the location history from the person who is infected and makes it available to users of the app. For this reason, we are building not only a contact-tracing app, but also Safe Places, a web-based tool for public health officials working to contain the COVID-19 pandemic. • Fostering trust • Developing key partnerships, including with community officials who can help drive local support for the solution • Creating solutions that meet the needs of public health officials responding to the pandemic • Focusing on the needs of the users • Providing value to the user during a contact-tracing interview even if they choose not to download the app before they have been diagnosed with COVID-19

abstract:
  A number of groups, from governments to non-profits, have quickly acted to innovate the contact-tracing process: they are designing, building, and launching contact-tracing apps in response to the COVID-19 crisis. A diverse range of approaches exist, creating challenging choices for officials looking to implement contact-tracing technology in their community and raising concerns about these choices among citizens asked to participate in contact tracing. We are frequently asked how to evaluate and differentiate between the options for contact-tracing applications. Here, we share the questions we ask about app features and plans when reviewing the many contact-tracing apps appearing on the global stage.


id: cord-124191-38i44n0m
title: NationalMood: Large-scale Estimation of People''s Mood from Web Search Query and Mobile Sensor Data
author: Okoshi, Tadashi
date: 2020-11-02
txt: ./txt/cord-124191-38i44n0m.txt
cache: ./cache/cord-124191-38i44n0m.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2011.00665v2.pdf
doi: nan
summary:
  Our large-scale data analysis with about 11,000,000 users and 100 recent advertisement log revealed (1) the existence of certain class of advertisement to which mood-status-based delivery would be significantly effective, (2) that our"National Mood Score"shows the ups and downs of people''s moods in COVID-19 pandemic that inversely correlated to the number of patients, as well as the weekly mood rhythm of people. In this paper, as the first contribution, we show that we can estimate the web users'' affective status (concretely, "mood") in such a condition, based on a novel combinational use of their web search queries and mobile sensor data. Then, by combining the web search logs of the 460 participants during the study period and mood status (based on both the users'' original annotation and SMM''s outputs), we create our second model "QMM", which estimates the mood of a user from their search query data.

abstract:
  The ability to estimate current affective statuses of web users has considerable potential towards the realization of user-centric opportune services. However, determining the type of data to be used for such estimation as well as collecting the ground truth of such affective statuses are difficult in the real world situation. We propose a novel way of such estimation based on a combinational use of user's web search queries and mobile sensor data. Our large-scale data analysis with about 11,000,000 users and 100 recent advertisement log revealed (1) the existence of certain class of advertisement to which mood-status-based delivery would be significantly effective, (2) that our"National Mood Score"shows the ups and downs of people's moods in COVID-19 pandemic that inversely correlated to the number of patients, as well as the weekly mood rhythm of people.


id: cord-128041-vmmme94y
title: Bluetooth-based COVID-19 Proximity Tracing Proposals: An Overview
author: Shen, Meng
date: 2020-08-28
txt: ./txt/cord-128041-vmmme94y.txt
cache: ./cache/cord-128041-vmmme94y.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.12469v1.pdf
doi: nan
summary:
  Then, we summarized eight security and privacy design goals for Bluetooth-based COVID-19 proximity tracing proposals and applied them to analyze the five proposals. In the centralized proximity tracing proposals, users broadcast and receive encounter information (anonymous ID, transmission time, etc.) via Bluetooth. In the decentralized proximity tracing proposals, when users are infected with COVID-19, the keys related to the generation of anonymous IDs is uploaded to the server. The two decentralized proposals have roughly similar processes, and the specific difference is reflected in the different algorithms for generating anonymous IDs. In the low-cost design, the seed keys of one user are linkable. But attackers cannot obtain valid information by analyzing these messages due to using the generation algorithm of anonymous IDs. In decentralized proposals, only users who may be at risk of infection can do risk calculation. In the centralized proposals, the server handles user pseudonyms, anonymous IDs generated based on the user pseudonyms and encounter information uploaded.

abstract:
  Large-scale COVID-19 infections have occurred worldwide, which has caused tremendous impact on the economy and people's lives. The traditional method for tracing contagious virus, for example, determining the infection chain according to the memory of infected people, has many drawbacks. With the continuous spread of the pandemic, many countries or organizations have started to study how to use mobile devices to trace COVID-19, aiming to help people automatically record information about incidents with infected people through technologies, reducing the manpower required to determine the infection chain and alerting people at risk of infection. This article gives an overview on various Bluetooth-based COVID-19 proximity tracing proposals including centralized and decentralized proposals. We discussed the basic workflow and the differences between them before providing a survey of five typical proposals with explanations of their design features and benefits. Then, we summarized eight security and privacy design goals for Bluetooth-based COVID-19 proximity tracing proposals and applied them to analyze the five proposals. Finally, open problems and future directions are discussed.


id: cord-130143-cqkpi32z
title: Approach for GDPR Compliant Detection of COVID-19 Infection Chains
author: Tajan, Louis
date: 2020-07-16
txt: ./txt/cord-130143-cqkpi32z.txt
cache: ./cache/cord-130143-cqkpi32z.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.08248v2.pdf
doi: nan
summary:
  While prospect of tracking mobile devices'' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users'' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco''s access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. Even if this regulation does not apply on fields as public health or national security [5] , weaving the proposed Bloom filter based private protocols into infection chains investigation would limit government agencies to solely identify users with high probability of being infected instead of a massive data analysis of all mobile users.

abstract:
  While prospect of tracking mobile devices' users is widely discussed all over European countries to counteract COVID-19 propagation, we propose a Bloom filter based construction providing users' location privacy and preventing mass surveillance. We apply a solution based on Bloom filters data structure that allows a third party, a government agency, to perform some privacy-preserving set relations on a mobile telco's access logfile. By computing set relations, the government agency, given the knowledge of two identified persons, has an instrument that provides a (possible) infection chain from the initial to the final infected user no matter at which location on a worldwide scale they are. The benefit of our approach is that intermediate possible infected users can be identified and subsequently contacted by the agency. With such approach, we state that solely identities of possible infected users will be revealed and location privacy of others will be preserved. To this extent, it meets General Data Protection Regulation (GDPR)requirements in this area.


id: cord-139715-jyfmnnf5
title: Digital Contact Tracing Service: An improved decentralized design for privacy and effectiveness
author: Holzapfel, Kilian
date: 2020-06-29
txt: ./txt/cord-139715-jyfmnnf5.txt
cache: ./cache/cord-139715-jyfmnnf5.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.16960v1.pdf
doi: nan
summary:
  We present a secure solution for a digital contact tracing service (DCTS) that protects the users'' privacy, identity and personal data from attackers. In order to check whether the user has been in contact with an infected person, they download all unchecked TCNs stored on the server and check for matches within their own list of observed TCNs. When encountering matches, the app can perform a risk assessment based on exposure time period and proximity. In order to avoid this attack, we can check for the number of TCNs that are both in our set of encountered TCNs and in the set of infected TCNs. An algorithm determining the private set intersection cardinality with low communication cost (see for example 3 ) is a valuable strategy in order to discover the exposure to infectious contacts without risking the identification of the patient (details are described in Section 3.6).

abstract:
  We propose a decentralized digital contact tracing service that preserves the users' privacy by design while complying to the highest security standards. Our approach is based on Bluetooth and measures actual encounters of people, the contact time period, and estimates the proximity of the contact. We trace the users' contacts and the possible spread of infectious diseases while preventing location tracking of users, protecting their data and identity. We verify and improve the impact of tracking based on epidemiological models. We compare a centralized and decentralized approach on a legal perspective and find a decentralized approach preferable considering proportionality and data minimization.


id: cord-156676-wes5my9e
title: Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter
author: Masud, Sarah
date: 2020-10-09
txt: ./txt/cord-156676-wes5my9e.txt
cache: ./cache/cord-156676-wes5my9e.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.04377v1.pdf
doi: nan
summary:
  For predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich models, with the best performing one achieving a macro F1 score of 0.65. For both detecting and predicting the spread of hate speech over short tweets, the knowledge of context is likely to play a decisive role Present work: Based on the findings of the existing literature and the analysis we presented above, here we attempt to model the dynamics of hate speech spread on Twitter. 1) We formalize the dynamics of hate generation and retweet spread on Twitter subsuming, the activity history of each user and signals propagated by the localized structural properties of the information network of Twit-ter induced by follower connections as well as global endogenous and exogenous signals (events happening inside and outside of Twitter) (See Section III). Features representing hateful behavior encoded within the given tweet as well as the activity history of the users further help RETINA to achieve a macro F1-score of 0.85, significantly outperforming several state-of-the-art retweet prediction models.

abstract:
  Online hate speech, particularly over microblogging platforms like Twitter, has emerged as arguably the most severe issue of the past decade. Several countries have reported a steep rise in hate crimes infuriated by malicious hate campaigns. While the detection of hate speech is one of the emerging research areas, the generation and spread of topic-dependent hate in the information network remain under-explored. In this work, we focus on exploring user behaviour, which triggers the genesis of hate speech on Twitter and how it diffuses via retweets. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower networks, comprising over 161 million tweets from more than $41$ million unique users. We also collect over 600k contemporary news articles published online. We characterize different signals of information that govern these dynamics. Our analyses differentiate the diffusion dynamics in the presence of hate from usual information diffusion. This motivates us to formulate the modelling problem in a topic-aware setting with real-world knowledge. For predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich models, with the best performing one achieving a macro F1 score of 0.65. Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention. RETINA achieves a macro F1-score of 0.85, outperforming multiple state-of-the-art models. Our analysis reveals the superlative power of RETINA to predict the retweet dynamics of hateful content compared to the existing diffusion models.


id: cord-186031-b1f9wtfn
title: Analysis of online misinformation during the peak of the COVID-19 pandemics in Italy
author: Caldarelli, Guido
date: 2020-10-05
txt: ./txt/cord-186031-b1f9wtfn.txt
cache: ./cache/cord-186031-b1f9wtfn.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.01913v1.pdf
doi: nan
summary:
  When analysing the emerging 4 communities, we find that they correspond to 1 Right wing parties and media (in steel blue) 2 Center left wing (dark red) 3 5 Stars Movement (M5S ), in dark orange 4 Institutional accounts (in sky blue) Details about the political situation in Italy during the period of data collection can be found in the Supplementary Material, Section 1.2: ''Italian political situation during the Covid-19 pandemics''. In line with previous results on the validated network of verified users, the table clearly shows how the vast majority of the news coming from sources considered scarce or non reputable are tweeted and retweeted by the center-right and right wing communities; 98% of the domains tagged as NR are shared by them.

abstract:
  During the Covid-19 pandemics, we also experience another dangerous pandemics based on misinformation. Narratives disconnected from fact-checking on the origin and cure of the disease intertwined with pre-existing political fights. We collect a database on Twitter posts and analyse the topology of the networks of retweeters (users broadcasting again the same elementary piece of information, or tweet) and validate its structure with methods of statistical physics of networks. Furthermore, by using commonly available fact checking software, we assess the reputation of the pieces of news exchanged. By using a combination of theoretical and practical weapons, we are able to track down the flow of misinformation in a snapshot of the Twitter ecosystem. Thanks to the presence of verified users, we can also assign a polarization to the network nodes (users) and see the impact of low-quality information producers and spreaders in the Twitter ecosystem.


id: cord-186764-qp4kq139
title: Digital Ariadne: Citizen Empowerment for Epidemic Control
author: Klopfenstein, Lorenz Cuno
date: 2020-04-16
txt: ./txt/cord-186764-qp4kq139.txt
cache: ./cache/cord-186764-qp4kq139.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.07717v1.pdf
doi: nan
summary:
  In this paper, we outline general requirements and design principles of personal applications for epidemic containment running on common smartphones, and we present a tool, called ''diAry'' or ''digital Ariadne'', based on voluntary location and Bluetooth tracking on personal devices, supporting a distributed query system that enables fully anonymous, privacy-preserving contact tracing. The proposed system allows individuals to keep track of movements and contacts on their own private devices and to use local traces to select relevant notifications and alerts from health authorities, thus completely eschewing, by design, any risk of surveillance. The system is composed of: a mobile application, that is voluntarily installed by users on their smartphones, keeping track of their locations through the device''s GPS sensor and interactions with other users through Bluetooth radio beacons, a privacy-aware reward system, which incentivizes app usage while collecting anonymous usage information to feed an open data set, and a distributed query system that allows recognized public authorities to selectively and anonymously notify users about possible contagion sources.

abstract:
  The COVID-19 crisis represents the most dangerous threat to public health since the H1N1 influenza pandemic of 1918. So far, the disease due to the SARS-CoV-2 virus has been countered with extreme measures at national level that attempt to suppress epidemic growth. However, these approaches require quick adoption and enforcement in order to effectively curb virus spread, and may cause unprecedented socio-economic impact. A viable alternative to mass surveillance and rule enforcement is harnessing collective intelligence by means of citizen empowerment. Mobile applications running on personal devices could significantly support this kind of approach by exploiting context/location awareness and data collection capabilities. In particular, technology-assisted location and contact tracing, if broadly adopted, may help limit the spread of infectious diseases by raising end-user awareness and enabling the adoption of selective quarantine measures. In this paper, we outline general requirements and design principles of personal applications for epidemic containment running on common smartphones, and we present a tool, called 'diAry' or 'digital Ariadne', based on voluntary location and Bluetooth tracking on personal devices, supporting a distributed query system that enables fully anonymous, privacy-preserving contact tracing. We look forward to comments, feedback, and further discussion regarding contact tracing solutions for pandemic containment.


id: cord-193856-6vs16mq3
author: Zhou, Tongxin
date: 2020-09-13
txt: ./txt/cord-193856-6vs16mq3.txt
cache: ./cache/cord-193856-6vs16mq3.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2009.06108v1.pdf
doi: nan
summary:
  The first component is a deep-learning-based feature engineering procedure, which is designed to learn crucial recommendation contexts in regard to users'' sequential health histories, health-management experiences, preferences, and intrinsic attributes of healthcare interventions. Our evaluation results suggest that each of our proposed model components is effective and that our recommendation framework significantly outperforms a wide range of benchmark models, including UCB, e -greedy, and state-of-the-art conventional recommendation systems, such as context-aware collaborative filtering (CACF), probabilistic matrix factorization (PMF), and content-based filtering (CB). These research gaps motivate us to propose an online-learning scheme, i.e., multi-armed bandit (MAB), to address the dynamics and diversity in individuals'' health behaviors to improve healthcare recommendations. To better adapt an MAB to the healthcare recommendation setting, we then further enhance our framework by synthesizing two model components, that is, deep-learning-based feature engineering and a diversity constraint. To improve the characterization of individuals'' health-management contexts and enhance recommendation personalization, we design a deep-learning model to construct user embeddings.

title:
  Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach

abstract:
  Online healthcare communities provide users with various healthcare interventions to promote healthy behavior and improve adherence. When faced with too many intervention choices, however, individuals may find it difficult to decide which option to take, especially when they lack the experience or knowledge to evaluate different options. The choice overload issue may negatively affect users' engagement in health management. In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions. Taking into account that users' health behaviors can be highly dynamic and diverse, we propose a multi-armed bandit (MAB)-driven recommendation framework, which enables us to adaptively learn users' preference variations while promoting recommendation diversity in the meantime. To better adapt an MAB to the healthcare context, we synthesize two innovative model components based on prominent health theories. The first component is a deep-learning-based feature engineering procedure, which is designed to learn crucial recommendation contexts in regard to users' sequential health histories, health-management experiences, preferences, and intrinsic attributes of healthcare interventions. The second component is a diversity constraint, which structurally diversifies recommendations in different dimensions to provide users with well-rounded support. We apply our approach to an online weight management context and evaluate it rigorously through a series of experiments. Our results demonstrate that each of the design components is effective and that our recommendation design outperforms a wide range of state-of-the-art recommendation systems. Our study contributes to the research on the application of business intelligence and has implications for multiple stakeholders, including online healthcare platforms, policymakers, and users.


id: cord-201675-3bvshhtn
title: COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing
author: Ng, Pai Chet
date: 2020-05-28
txt: ./txt/cord-201675-3bvshhtn.txt
cache: ./cache/cord-201675-3bvshhtn.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2005.13754v1.pdf
doi: nan
summary:
  The proposed Smart Contact Tracing (SCT) system utilizes the smartphones Bluetooth Low Energy (BLE) signals and machine learning classifier to accurately and quickly determined the contact profile. The proposed Smart Contact Tracing (SCT) system utilizes the smartphones Bluetooth Low Energy (BLE) signals and machine learning classifier to accurately and quickly determined the contact profile. • Privacy-preserving signature protocol: our SCT system provides a secure contact tracing by using the nonconnectable advertising channels and an encrypted packet containing unique signature information based on the ambient environmental features observed by a smartphone. To bridge the gap, this paper studies the proximity sensing with the BLE signals broadcast from the smartphones carried by the user while designing a privacypreserving signature protocol that uses the environmental feature instead of user information for packet broadcasting. Besides signature matching, the application also performs the classification to classify the potential risk of a user according to the time and distance the user spent with the infected individual.

abstract:
  Contact tracing is of paramount importance when it comes to preventing the spreading of infectious diseases. Contact tracing is usually performed manually by authorized personnel. Manual contact tracing is an inefficient, error-prone, time-consuming process of limited utility to the population at large as those in close contact with infected individuals are informed hours, if not days, later. This paper introduces an alternative way to manual contact tracing. The proposed Smart Contact Tracing (SCT) system utilizes the smartphone's Bluetooth Low Energy (BLE) signals and machine learning classifier to accurately and quickly determined the contact profile. SCT's contribution is two-fold: a) classification of the user's contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communications protocol. SCT leverages BLE's non-connectable advertising feature to broadcast a signature packet when the user is in the public space. Both broadcasted and observed signatures are stored in the user's smartphone and they are only uploaded to a secure signature database when a user is confirmed by public health authorities to be infected. Using received signal strength (RSS) each smartphone estimates its distance from other user's phones and issues real-time alerts when social distancing rules are violated. The paper includes extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers. Reported results indicate that a decision tree classifier outperforms other states of the art classification methods in terms of accuracy. Lastly, to facilitate research in this area, and to contribute to the timely development of advanced solutions the entire data set of six experiments with about 123,000 data points is made publicly available.


id: cord-218383-t2lwqrpb
author: Whaiduzzaman, Md
date: 2020-06-23
txt: ./txt/cord-218383-t2lwqrpb.txt
cache: ./cache/cord-218383-t2lwqrpb.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2006.13364v1.pdf
doi: nan
summary:
  To address this problem, we develop an e-government Privacy Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named Automatic Risk Checkers (ARC) and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. However, to the best of our knowledge, there is no integrated fog computing framework alongside contact tracing mobile apps that allows tracing community transmission while preserving users'' data privacy. However, most of these applications and frameworks have failed to ensure user data privacy and suffer from other issues, such as mandatory use of apps, excessive data gathering, questionable transparency of source codes and data flow, unnecessary data usage or processing, and lack of user control in data deletion. Our proposed privacy-preserving e-government framework has four major components: user mobile device and two types of fog nodes (ARC and SUDUN), and a central cloud application that integrates these nodes.

title:
  A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission

abstract:
  To slow down the spread of COVID-19, governments around the world are trying to identify infected people and to contain the virus by enforcing isolation and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission and mass infection. To address this problem, we develop an e-government Privacy Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named Automatic Risk Checkers (ARC) and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. If any confirmed case is found, the ARCs broadcast pre-cautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.


id: cord-223669-hs5pfg4b
title: Blockchain Meets COVID-19: A Framework for Contact Information Sharing and Risk Notification System
author: Song, Jinyue
date: 2020-07-20
txt: ./txt/cord-223669-hs5pfg4b.txt
cache: ./cache/cord-223669-hs5pfg4b.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.10529v1.pdf
doi: nan
summary:
  The proposed system unifies location-based and Bluetooth-based contact tracing services into the Blockchain platform, where the automatically executed smart contracts are deployed so that users can get consistent and non-tamperable virus trails. This system implements the following main functions with smart contracts and Bluetooth embedded: (1) Users can record their visited location information and personal contact history to the blockchain database. • We propose an optimal equation for the operating costs of the system, simulate person-to-person contact and user check-in activities in our system, and evaluate the system performance based on the different quantity of users and smart contracts. In our system, three main technologies guarantee data security and personal privacy: decentralized database in the blockchain, automatic execution of smart contracts, and randomization of Bluetooth mac addresses. In our system, the blockchain database will store all transactions in the network, including users'' Bluetooth contact records, check-in information of the visited locations, and the change of the user''s public health status.

abstract:
  COVID-19 causes a global epidemic infection, which is the most severe infection disaster in human history. In the absence of particular medication and vaccines, tracing and isolating the source of infection is the best option to slow the spread of the virus and reduce infection and death rates among the population. There are three main obstacles in the process of tracing the infection: 1) Patient's electronic health record is stored in a traditional centralized database that could be stolen and tampered with the infection data, 2) The confidential personal identity of the infected user may be revealed to a third party or organization, 3) Existing infection tracing systems do not trace infections from multiple dimensions. Either the system is location-based or individual-based tracing. In this work, we propose a global COVID-19 information sharing system that utilizes the Blockchain, Smart Contract, and Bluetooth technologies. The proposed system unifies location-based and Bluetooth-based contact tracing services into the Blockchain platform, where the automatically executed smart contracts are deployed so that users can get consistent and non-tamperable virus trails. The anonymous functionality provided by the Blockchain and Bluetooth technology protects the user's identity privacy. With our proposed analysis formula for estimating the probability of infection, users can take measures to protect themselves in advance. We also implement a prototype system to demonstrate the feasibility and effectiveness of our approach.


id: cord-227156-uy4dykhg
title: Predicting Shifting Individuals Using Text Mining and Graph Machine Learning on Twitter
author: Albanese, Federico
date: 2020-08-24
txt: ./txt/cord-227156-uy4dykhg.txt
cache: ./cache/cord-227156-uy4dykhg.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2008.10749v1.pdf
doi: nan
summary:
  Moreover, this machine learning framework allows us to identify not only which topics are more persuasive (using low dimensional topic embedding), but also which individuals are more likely to change their affiliation given their topological properties in a Twitter graph. Using graph topological information and detecting topics of discussion of the first network, we built and trained a model that effectively predicts when an individual will change his/her community over time, identifying persuasive topics and relevant features of the shifting users. Given that our objective was to identify shifting individuals and persuasive arguments, we implemented a predictive model whose instances are the Twitter users who were active during both time periods [34] and belonged to one of the biggest communities in both time periods networks. In this paper we presented a machine learning framework approach in order to identify shifting individuals and persuasive topics that, unlike previous works, focused on the persuadable users rather than studying the political polarization on social media as a whole.

abstract:
  The formation of majorities in public discussions often depends on individuals who shift their opinion over time. The detection and characterization of these type of individuals is therefore extremely important for political analysis of social networks. In this paper, we study changes in individual's affiliations on Twitter using natural language processing techniques and graph machine learning algorithms. In particular, we collected 9 million Twitter messages from 1.5 million users and constructed the retweet networks. We identified communities with explicit political orientation and topics of discussion associated to them which provide the topological representation of the political map on Twitter in the analyzed periods. With that data, we present a machine learning framework for social media users classification which efficiently detects"shifting users"(i.e. users that may change their affiliation over time). Moreover, this machine learning framework allows us to identify not only which topics are more persuasive (using low dimensional topic embedding), but also which individuals are more likely to change their affiliation given their topological properties in a Twitter graph.


id: cord-227492-st2ebdah
title: Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic
author: Raskar, Ramesh
date: 2020-03-19
txt: ./txt/cord-227492-st2ebdah.txt
cache: ./cache/cord-227492-st2ebdah.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2003.08567v1.pdf
doi: nan
summary:
  • Users are individuals who have not been diagnosed with an infectious disease who seek to use a contact-tracing tool to better understand their exposure history and risk for disease. • Finally, we broadly speak of the government as the entity which makes location data public and informs those individuals who were likely in close contact with a diagnosed carrier, acknowledging that this responsibility is carried out by a different central actor in every continent, country or local region. The primary challenge for these technologies, as evident from their deployment in the COVID-19 crisis, remains securing the privacy of individuals, diagnosed carriers of a pathogen, and local businesses visited by diagnosed carriers, while still informing users of potential contacts. All containment strategies require analysis of diagnosed carrier location trails in order to identify other individuals at risk for infection.

abstract:
  Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and fraught with privacy concerns. With the recent almost ubiquitous availability of smart phones, many people carry a tool which can be utilized to quickly identify an infected individual's contacts during an epidemic, such as the current 2019 novel Coronavirus crisis. Unfortunately, the very same first-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals. We seek to outline the different technological approaches to mobile-phone based contact-tracing to date and elaborate on the opportunities and the risks that these technologies pose to individuals and societies. We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology. With this paper, our aim is to continue to grow the conversation regarding contact-tracing for epidemic and pandemic containment and discuss opportunities to advance this space. We invite feedback and discussion.


id: cord-236830-0y5yisfk
title: PACT: Privacy Sensitive Protocols and Mechanisms for Mobile Contact Tracing
author: Chan, Justin
date: 2020-04-07
txt: ./txt/cord-236830-0y5yisfk.txt
cache: ./cache/cord-236830-0y5yisfk.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.03544v4.pdf
doi: nan
summary:
  Importantly, these protocols, by default, keep all personal data on a citizens'' phones (aside for pseudonymous identifiers broadcast to other local devices), while enabling these key capabilities; information is shared via voluntary disclosure actions taken, with the understandings relayed via careful disclosure. From a civil liberties standpoint, the privacy guarantees these protocols ensure are designed to be consistent with the disclosures already extant in contract tracing methods done by public health services (where some information from a positive tested citizen is revealed to other at risk citizens). Preventing proximity-based identification of this sort is not possible to avoid in any protocol, even in manual contact tracing as done by public health services, simply because the exposure alert may contain information that is correlated with identifying information. To discuss the consequences of these properties on privacy and integrity, let us refer to users as either "positive" or "negative" depending on whether they decided to report as positive, by uploading their seed to the server, or not.

abstract:
  The global health threat from COVID-19 has been controlled in a number of instances by large-scale testing and contact tracing efforts. We created this document to suggest three functionalities on how we might best harness computing technologies to supporting the goals of public health organizations in minimizing morbidity and mortality associated with the spread of COVID-19, while protecting the civil liberties of individuals. In particular, this work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party. We also explicitly consider the inferential risks involved in any contract tracing system, where any alert to a user could itself give rise to de-anonymizing information. More generally, we hope to participate in bringing together colleagues in industry, academia, and civil society to discuss and converge on ideas around a critical issue rising with attempts to mitigate the COVID-19 pandemic.


id: cord-237721-rhcvsqtk
title: Expressive Interviewing: A Conversational System for Coping with COVID-19
author: Welch, Charles
date: 2020-07-07
txt: ./txt/cord-237721-rhcvsqtk.txt
cache: ./cache/cord-237721-rhcvsqtk.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2007.03819v1.pdf
doi: nan
summary:
  In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues. 1 Research in Expressive Writing (Pennebaker, 1997b) and Motivational Interviewing (Miller and Rollnick, 2012) has shown that even simple interactions where people talk about one particular experience can have significant psychological value. In order to provide reflective feedback, the system automatically detects the topics being discussed (e.g., work, family) or emotions being felt (e.g., anger, anxiety), and responds with a reflective prompt that asks the user to elaborate or to answer a related question to explore that concept more deeply. Nonetheless, we believe that this comparison provides evidence that a dialogue system such as Expressive Interviewing is more effective in helping users cope with COVID-19 issues as compared to a general purpose dialogue system for mental health.

abstract:
  The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system's design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.


id: cord-238444-v9gfh3m1
title: A Smartphone enabled Approach to Manage COVID-19 Lockdown and Economic Crisis
author: Maghdid, Halgurd S.
date: 2020-04-25
txt: ./txt/cord-238444-v9gfh3m1.txt
cache: ./cache/cord-238444-v9gfh3m1.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2004.12240v2.pdf
doi: nan
summary:
  Further, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. From a technical standpoint, we summarise the most important contributions of this paper as follows: 1) We build a tracking model based on positional information of registered users to conduct contact-tracing of confirmed COVID-19 cases. The best thing to do seems to be let people go out for their business, but any body tests positive of COVID-19, we would be able, through proposed framework, to trace everybody in contact with the confirmed case and managing the lockdown and mass quarantine. In this study, K-means as an unsupervised machine learning algorithm is used to cluster the users'' positions information and predict that the area should be locked down or not based on same empirical thresholds.

abstract:
  The emergence of novel COVID-19 causing an overload in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Further, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact tracing is time consuming and labor-intensive task which tremendously overload public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for government officials on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses K-means algorithm as an unsupervised machine learning technique for lockdown management.


id: cord-243596-ryyokrdx
title: When Virtual Therapy and Art Meet: A Case Study of Creative Drawing Game in Virtual Environments
author: Baron, Lauren
date: 2020-10-16
txt: ./txt/cord-243596-ryyokrdx.txt
cache: ./cache/cord-243596-ryyokrdx.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2010.08100v1.pdf
doi: nan
summary:
  In a mixed-design study, healthy participants (N=16, 8 females) completed one of the easy or hard trajectories of the virtual therapy game in standing and seated arrangements using a virtual-reality headset. The results from participants'' movement accuracy, task completion time, and usability questionnaires indicate that participants had significant performance differences on two levels of the game based on its difficulty (between-subjects factor), but no difference in seated and standing configurations (within-subjects factor). In this paper, we introduce a creative drawing game for virtual therapy and investigate user''s comfort, range of motion and movement in multiple scenarios and configurations in a pilot study. The working hypothesis of this study was that our creative drawing VR game would be effective when integrated into therapy by analyzing improved Task Completion Time (TCT), accuracy based on lower number of the mistakes, and user experience (UX).

abstract:
  There have been a resurge lately on virtual therapy and other virtual- and tele-medicine services due to the new normal of practicing 'shelter at home'. In this paper, we propose a creative drawing game for virtual therapy and investigate user's comfort and movement freedom in a pilot study. In a mixed-design study, healthy participants (N=16, 8 females) completed one of the easy or hard trajectories of the virtual therapy game in standing and seated arrangements using a virtual-reality headset. The results from participants' movement accuracy, task completion time, and usability questionnaires indicate that participants had significant performance differences on two levels of the game based on its difficulty (between-subjects factor), but no difference in seated and standing configurations (within-subjects factor). Also, the hard mode was more favorable among participants. This work offers implications on virtual reality and 3D-interactive systems, with specific contributions to virtual therapy, and serious games for healthcare applications.


id: cord-251676-m8f6de33
title: WiFiTrace: Network-based Contact Tracing for Infectious Diseases Using Passive WiFi Sensing
author: Trivedi, Amee
date: 2020-05-25
txt: ./txt/cord-251676-m8f6de33.txt
cache: ./cache/cord-251676-m8f6de33.txt
extension: .txt
mime: text/plain
url: https://arxiv.org/pdf/2005.12045v2.pdf
doi: nan
summary:
  The tool analyses WiFi logs generated by the network, and specifically association and dissociation log messages for this device, at various access points on campus to reconstruct the location(building, room numbers) visited by the user. We note that such a client-centric approach requires a user to first download a mobile app before contact tracing data can be gathered-users who have not downloaded the app (or have opted in) are not visible to other phones that are actively listening for other devices in their proximity. As discussed below, this tier uses time-evolving graphs and efficient graph algorithms to efficiently intersect trajectories of a large number of devices (typically tens of thousands of users that may be present on a university campus) to produce its report. In this section, we describe case studies that evaluate the efficacy of our contact tracing tool and also present results on the efficiency of our graph algorithms and general limitations of our WiFi sensing approach.

abstract:
  Contact tracing is a well-established and effective approach for containment of spread of infectious diseases. While bluetooth-based contact tracing method using phones have become popular recently, these approaches suffer from the need for a critical mass of adoption in order to be effective. In this paper, we present WifiTrace, a network-centric approach for contact tracing that relies on passive WiFi sensing with no client-side involvement. Our approach exploits WiFi network logs gathered by enterprise networks for performance and security monitoring and utilizes it for reconstructing device trajectories for contact tracing. Our approach is specifically designed to enhance the efficacy of traditional methods, rather than to supplant it with a new technology. We design an efficient graph algorithm to scale our approach to large networks with tens of thousands of users. We have implemented a full prototype of our system and deployed it on two large university campuses. We validate our approach and demonstrate its efficacy using case studies and detailed experiments using real-world WiFi datasets.


id: cord-267860-mc0xa5om
author: Lam, Simon C.
date: 2016-05-01
txt: ./txt/cord-267860-mc0xa5om.txt
cache: ./cache/cord-267860-mc0xa5om.txt
extension: .txt
mime: text/plain
url: https://api.elsevier.com/content/article/pii/S0196655315012614
doi: 10.1016/j.ajic.2015.12.013
summary:
  This study, hence, aimed to examine the sensitivity, specificity, predictive values, and likelihood ratios of the user seal check on actual gross leakage detection during normal breath-ing or deep breathing without head and body movement in 3 common respirator models of different designs. The results of the user seal check compared with the gold standard QNFT on actual gross leakage through cross tabulation were used to compute the following diagnostic parameters: sensitivity, specificity, positive and negative predictive values, and likelihood ratios (refer to the "NOTE" in Table 4 for the respective formula). To illustrate the clinical implication of the current results of predictive values and likelihood ratios, by using an example of donning the 3M-A respirator, an interpretative summary of the validity and test performance of the user seal check for identifying actual gross leakage is presented as follows.

title:
  Evaluation of the user seal check on gross leakage detection of 3 different designs of N95 filtering facepiece respirators

abstract:
  BACKGROUND: The use of N95 respirators prevents spread of respiratory infectious agents, but leakage hampers its protection. Manufacturers recommend a user seal check to identify on-site gross leakage. However, no empirical evidence is provided. Therefore, this study aims to examine validity of a user seal check on gross leakage detection in commonly used types of N95 respirators. METHODS: A convenience sample of 638 nursing students was recruited. On the wearing of 3 different designs of N95 respirators, namely 3M-1860s, 3M-1862, and Kimberly-Clark 46827, the standardized user seal check procedure was carried out to identify gross leakage. Repeated testing of leakage was followed by the use of a quantitative fit testing (QNFT) device in performing normal breathing and deep breathing exercises. Sensitivity, specificity, predictive values, and likelihood ratios were calculated accordingly. RESULTS: As indicated by QNFT, prevalence of actual gross leakage was 31.0%-39.2% with the 3M respirators and 65.4%-65.8% with the Kimberly-Clark respirator. Sensitivity and specificity of the user seal check for identifying actual gross leakage were approximately 27.7% and 75.5% for 3M-1860s, 22.1% and 80.5% for 3M-1862, and 26.9% and 80.2% for Kimberly-Clark 46827, respectively. Likelihood ratios were close to 1 (range, 0.89-1.51) for all types of respirators. CONCLUSIONS: The results did not support user seal checks in detecting any actual gross leakage in the donning of N95 respirators. However, such a check might alert health care workers that donning a tight-fitting respirator should be performed carefully.


id: cord-269850-5pidolqb
title: A Smartphone Enabled Approach to Manage COVID-19 Lockdown and Economic Crisis
author: Maghdid, Halgurd S.
date: 2020-08-14
txt: ./txt/cord-269850-5pidolqb.txt
cache: ./cache/cord-269850-5pidolqb.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1007/s42979-020-00290-0
doi: 10.1007/s42979-020-00290-0
summary:
  1. We build a tracking model based on positional information of registered users to conduct contact-tracing of confirmed COVID-19 cases. The best thing to do seems to be let people go out for their business, but any body tests positive of COVID-19, we would be able, through proposed framework, to trace Fig. 3 A framework of contact-tracing using smartphone-based approach everybody in contact with the confirmed case and managing the lockdown and mass quarantine. In this study, k-means as an unsupervised machine learning algorithm is used to cluster the users'' positions information and predict that the area should be locked down or not based on the same empirical thresholds. This Fig. 6 The results of the prediction model for both scenarios is followed by send back notifications from the server to the users to notify them for the crowded area and controlling the spreading the coronavirus COVID-19.

abstract:
  The emergence of novel COVID-19 causes an over-load in health system and high mortality rate. The key priority is to contain the epidemic and prevent the infection rate. In this context, many countries are now in some degree of lockdown to ensure extreme social distancing of entire population and hence slowing down the epidemic spread. Furthermore, authorities use case quarantine strategy and manual second/third contact-tracing to contain the COVID-19 disease. However, manual contact-tracing is time-consuming and labor-intensive task which tremendously over-load public health systems. In this paper, we developed a smartphone-based approach to automatically and widely trace the contacts for confirmed COVID-19 cases. Particularly, contact-tracing approach creates a list of individuals in the vicinity and notifying contacts or officials of confirmed COVID-19 cases. This approach is not only providing awareness to individuals they are in the proximity to the infected area, but also tracks the incidental contacts that the COVID-19 carrier might not recall. Thereafter, we developed a dashboard to provide a plan for policymakers on how lockdown/mass quarantine can be safely lifted, and hence tackling the economic crisis. The dashboard used to predict the level of lockdown area based on collected positions and distance measurements of the registered users in the vicinity. The prediction model uses k-means algorithm as an unsupervised machine learning technique for lockdown management.


id: cord-292065-3p4bf9ik
author: Lai, Lucinda
date: 2020-07-27
txt: ./txt/cord-292065-3p4bf9ik.txt
cache: ./cache/cord-292065-3p4bf9ik.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32730951/
doi: 10.1016/j.jpainsymman.2020.07.016
summary:
  OBJECTIVE: The primary objective of this study was to evaluate usage patterns of PalliCOVID to understand user behavior in relation to this palliative care content platform during the period of the local peak of COVID-19 infection in Massachusetts. • Accurate: Content was reviewed by palliative care experts to reflect the best available scientific evidence • Practical: Recommendations were designed to be useful and implementable by nonpalliative care clinicians in a variety of care settings • Accessible: Content was presented in a format that was optimized for viewing on both mobile devices and desktop computer screens • Applicable: Content was specific to the care of patients with confirmed or suspected COVID-19 infection and took into account the need to limit face-to-face interactions due to enhanced infection control measures and restricted visitor policies The primary objective of this study was to collect and analyze usage data from PalliCOVID as a way to better understand user behavior and gain insights about the population of users accessing this palliative care content platform.

title:
  Usage Patterns of a Web-Based Palliative Care Content Platform (PalliCOVID) during the COVID-19 Pandemic

abstract:
  BACKGROUND: The COVID-19 pandemic has highlighted the essential role of palliative care to support the delivery of compassionate, goal-concordant patient care. We created the web-based application, PalliCOVID (https://pallicovid.app/), in April 2020 to provide all clinicians with convenient access to palliative care resources and support. PalliCOVID features evidence-based clinical guidelines, educational content, and institutional protocols related to palliative care for COVID-19 patients. It is a publicly available resource accessible from any mobile device or desktop computer that provides clinicians with access to palliative care guidance across a variety of care settings, including the emergency department, hospital ward, intensive care unit, and primary care practice. OBJECTIVE: The primary objective of this study was to evaluate usage patterns of PalliCOVID to understand user behavior in relation to this palliative care content platform during the period of the local peak of COVID-19 infection in Massachusetts. DESIGN: We retrospectively analyzed de-identified usage data collected by Google Analytics from the first day of PalliCOVID’s launch on April 7, 2020 until May 1, 2020, the time period that encompassed the local peak of the COVID-19 surge in Massachusetts. MEASURE: ments: User access data was collected and summarized by Google Analytics software that had been integrated into the PalliCOVID web application. RESULTS: 2,042 users accessed PalliCOVID and viewed 4,637 pages from April 7 to May 1, 2020. Users spent an average of 2 minutes and 6 seconds per session. 81% of users were first-time visitors, while the remaining 19% were return visitors. The majority of users accessed PalliCOVID from the United States (87%), with a large proportion of users coming from Boston and the surrounding cities (32% of overall users). CONCLUSIONS: PalliCOVID is one example of a scalable digital health solution that can bring palliative care resources to frontline clinicians. Analysis of PalliCOVID usage patterns has the potential to inform the improvement of the platform to better meet the needs of its user base and guide future dissemination strategies. The quantitative data presented here, although informative about user behavior, should be supplemented with future qualitative research to further define the impact of this tool and extend our ability to deliver clinical care that is compassionate, rational, and well-aligned with patients’ values and goals.


id: cord-302724-hu0raqyi
title: The impact of the Covid‐19 pandemic on Italian mobility
author: Finazzi, Francesco
date: 2020-05-27
txt: ./txt/cord-302724-hu0raqyi.txt
cache: ./cache/cord-302724-hu0raqyi.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1111/1740-9713.01400
doi: 10.1111/1740-9713.01400
summary:
  The impact of the Covid-19 pandemic on Italian mobility Francesco Finazzi and Alessandro Fassò use location data collected by an earthquake-monitoring app to gauge compliance with lockdown measures in Italy make some assessment of the public''s compliance with mobility restrictions during the period of maximum growth of infections and hospitalisations. In order to provide real-time detection and alerts, the app collects phone location FIGURE 1 Mobility in Italy estimated through smartphone data collected by the Earthquake Network project. Cochran explains why a misunderstanding or disregard of exponential growth may have extremely grave consequences D uring his 26 March call into The Sean Hannity Show on Fox News, President Donald Trump questioned whether New York State would actually need the tens of thousands of ventilators its leaders had estimated would be necessary to deal with its expected number of coronavirus cases (bit.ly/3bw0AyZ).

abstract:
  Francesco Finazzi and Alessandro Fassò use location data collected by an earthquake‐monitoring app to gauge compliance with lockdown measures in Italy


id: cord-310272-utqyuy0n
title: Appropriating Information Technology Artefacts through Trial and Error: The Case of the Tablet
author: Zamani, Efpraxia D.
date: 2020-09-18
txt: ./txt/cord-310272-utqyuy0n.txt
cache: ./cache/cord-310272-utqyuy0n.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32982571/
doi: 10.1007/s10796-020-10067-8
summary:
  In this study we examine the use of IT artefacts following negative disconfirmation and use Grounded Theory Method techniques to analyse 136 blogposts, collected between March 2011 – July 2017, to investigate how users appropriate or reject the tablet when technology falls short of users'' expectations. The use of GTM allowed us to identify negative disconfirmation as a fairly relevant conceptual category for our study, and where appropriation and rejection are outcomes of a trial and error process where the user tries out different things in order to identify solutions to this negative disconfirmation. Users reject the tablet because they cannot overcome negative disconfirmation: they continue comparing the new to the old way of completing tasks, and they either deem the tentative solutions as not good enough or the errors as non-tolerable. The following vignettes illustrate trial and error behaviour, where iPad users try out different tentative solutions with the aim to tackle their initial negative disconfirmation.

abstract:
  The concept of appropriation is of paramount importance for the lasting use of an Information Technology (IT) artefact following its initial adoption, and therefore its success. However, quite often, users’ original expectations are negatively disconfirmed, and instead of appropriating the IT artefact, they discontinue its use. In this study we examine the use of IT artefacts following negative disconfirmation and use Grounded Theory Method techniques to analyse 136 blogposts, collected between March 2011 – July 2017, to investigate how users appropriate or reject the tablet when technology falls short of users’ expectations. Our findings show that users overcome negative disconfirmation through a trial and error process. In doing so, we identify that users appropriate the tablet when the attained benefits significantly outweigh the risks or sacrifices stemming out of its use. We discuss our contribution within the context of the appropriation literature, and highlight that the success of IT lies with the user’s success in identifying personal use scenarios within and across diverse contexts of use.


id: cord-318195-38gu0yab
author: Logeswaran, Abison
date: 2020-10-26
txt: ./txt/cord-318195-38gu0yab.txt
cache: ./cache/cord-318195-38gu0yab.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/33105019/
doi: 10.1007/s40123-020-00315-0
summary:
  In this paper, we describe practical qualitative methodologies that can be used by HCPs in the design, implementation and evaluation of ophthalmology EHRs. METHODS: A review of current qualitative usability methodologies was conducted by practising ophthalmologists who are also qualified health informaticians. The impact of COVID-19 has confirmed the necessity and usefulness of structured queries, triage and prioritization; these are elements that can potentially be addressed by well-designed EHRs. This might further drive the usage and adoption of EHRs. EHR vendors in countries such as the USA are obliged to meet certification requirements set by the Office of the National Coordinator for Health Information Technology in efforts to promote user centred design (UCD). UCD processes and usability testing methodology reports provided by vendors can be complex, making it difficult for HCPs who are not trained in usability science to understand the information. A framework for evaluating electronic health record vendor user-centered design and usability testing processes

title:
  The Electronic Health Record in Ophthalmology: Usability Evaluation Tools for Health Care Professionals

abstract:
  INTRODUCTION: The adoption of the electronic health record (EHR) has grown rapidly in ophthalmology. However, despite its potential advantages, its implementation has often led to dissatisfaction amongst health care professionals (HCP). This can be addressed using a user centred design (UCD) which is based on the philosophy that ‘the final product should suit the users, rather than making the users suit the product’. There is often no agreed best practice on the role of HCPs in the UCD process. In this paper, we describe practical qualitative methodologies that can be used by HCPs in the design, implementation and evaluation of ophthalmology EHRs. METHODS: A review of current qualitative usability methodologies was conducted by practising ophthalmologists who are also qualified health informaticians. RESULTS: We identified several qualitative methodologies that could be used for EHR evaluation. These include: 1. Tools for user centred design: shadowing and autoethnography, semi-structured interviews and questionnaires. 2. Tools for summative testing: card sort and reverse card sort, retrospective think aloud protocol, wireframing, screenshot testing and heat maps. CONCLUSION: High-yield, low-fidelity tools can be used to engage HCPs with the process of ophthalmology EHR design, implementation and evaluation. These methods can be used by HCPs without the requirement for prior training in usability science, and by clinical centres without significant technical requirements.


id: cord-323372-770sos8m
author: Glenn, Jeffrey
date: 2020-08-31
txt: ./txt/cord-323372-770sos8m.txt
cache: ./cache/cord-323372-770sos8m.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32878295/
doi: 10.3390/ijerph17176344
summary:
  title: Considering the Potential Health Impacts of Electric Scooters: An Analysis of User Reported Behaviors in Provo, Utah Stand-up electric scooters (e-scooters), two-wheeled vehicles with a small electric motor and a thin deck on which a single rider stands, are a relatively new micro-mobility option for urban areas and have the potential for both positive and negative health impacts [2] [3] [4] . The aim of this study is to explore the health-related behaviors of e-scooter users in Provo, Utah four months after an e-scooter share program was introduced. This finding is particularly relevant for Provo City, a place with problematic winter air pollution and whose primary motivation for introducing e-scooters was to provide a green alternative to motor vehicles; yet, considering disposability issues and emissions due to collecting and placement of e-scooters, important questions remain about the full environmental impact and its implications for health.

title:
  Considering the Potential Health Impacts of Electric Scooters: An Analysis of User Reported Behaviors in Provo, Utah

abstract:
  Electric scooters (e-scooters) are an increasingly popular form of transportation in urban areas. While research on this topic has focused primarily on injuries, there are multiple mechanisms by which e-scooter share programs may impact health. The aim of this study is to explore the health-related behaviors of e-scooter users and to discuss their implications for public health. Data were collected using an online survey emailed to registered e-scooter users. A total of 1070 users completed the survey. Descriptive variable statistics and chi-squared analysis were performed to determine variable dependent relationships and equality of proportions. The most common destinations reported were “just riding around for fun”, home, and dining/shopping. The two most common modes of transportation that would have been used if e-scooters were not available were walking (43.5%) and using a personal vehicle (28.5%). Riding behavior was equally mixed between on the street, on the sidewalk, and equal amounts of both. e-Scooters in Provo are likely having both positive (e.g., air pollution) and negative impacts on health (e.g., injuries, physical inactivity). Future research should further explore patterns of e-scooter use and explicitly examine the linkages between e-scooters and areas of health beyond just injuries.


id: cord-347144-rj76i40v
author: Wang, Jiexiang
date: 2020-09-23
txt: ./txt/cord-347144-rj76i40v.txt
cache: ./cache/cord-347144-rj76i40v.txt
extension: .txt
mime: text/plain
url: https://doi.org/10.1016/j.elerap.2020.101007
doi: 10.1016/j.elerap.2020.101007
summary:
  Through decomposing platform openness into supply-side openness and demand-side openness, as well as introducing demand diversity and knowledge complexity as contextual variables, this study attempts to understand the impact of both types of attributes on performance by considering their configuration. Using fuzzy sets qualitative comparative analysis (fsQCA) method, we find that high demand diversity of platform users and high supply-side openness will lead to better platform performance. To address the causal complexity issue, this study examines the configuration effects of openness dimensions, demand diversity and knowledge complexity on platform performance using fuzzy sets qualitative comparative analysis method (QCA), a widely used method in configuration analysis (Jenson et al, 2016) . The configuration of high knowledge complexity of platform innovation with high levels of supply-side and demand-side openness will lead to high platform performance. In addition, high knowledge complexity required for platform innovation together with high supply-side and demand-side openness will contribute to a high level of platform performance.

title:
  Closed or open platform? The nature of platform and a qualitative comparative analysis of the performance effect of platform openness

abstract:
  Internet platform enterprises have become one of the dominant organizational forms for internet-based businesses. Despite the strategically crucial role that openness decision plays for Internet platform enterprises, the results of existing research on the relationship between platform openness and platform performance are not conclusive. As to the nature of platform, its transaction attribute has been overemphasized while its innovation attribute is mostly neglected. Through decomposing platform openness into supply-side openness and demand-side openness, as well as introducing demand diversity and knowledge complexity as contextual variables, this study attempts to understand the impact of both types of attributes on performance by considering their configuration. Using fuzzy sets qualitative comparative analysis (fsQCA) method, we find that high demand diversity of platform users and high supply-side openness will lead to better platform performance. Moreover, the high knowledge complexity required for platform innovation together with high supply-side and demand-side openness will contribute to a high level of platform performance.


id: cord-350000-eqn3kl5p
author: Drissi, Nidal
date: 2020-07-30
txt: ./txt/cord-350000-eqn3kl5p.txt
cache: ./cache/cord-350000-eqn3kl5p.txt
extension: .txt
mime: text/plain
url: https://www.ncbi.nlm.nih.gov/pubmed/32768994/
doi: 10.1016/j.ijmedinf.2020.104243
summary:
  The objective of this study is to provide an analysis of treatment and management-related functionality and characteristics of high-rated mobile applications (apps) for anxiety, which are available for Android and iOS systems. Results also showed that 51% of the selected apps used various gamification features to motivate users to keep using them, 32% provided social features including chat, communication with others and links to sources of help; 46% offered offline availability; and only 19% reported involvement of mental health professionals in their design. This study aims to analyze the functionality and characteristics of highly 5 J o u r n a l P r e -p r o o f rated anxiety apps to identify users'' preferred features and management methods delivered for anxiety with a smartphone or a tablet.

title:
  An Analysis on Self-Management and Treatment-related Functionality and Characteristics of Highly Rated Anxiety Apps

abstract:
  BACKGROUND AND OBJECTIVE: Anxiety is a common emotion that people often feel in certain situations. But when the feeling of anxiety is persistent and interferes with a person's day to day life then this may likely be an anxiety disorder. Anxiety disorders are a common issue worldwide and can fall under general anxiety, panic attacks, and social anxiety among others. They can be disabling and can impact all aspects of an individual's life, including work, education, and personal relationships. It is important that people with anxiety receive appropriate care, which in some cases may prove difficult due to mental health care delivery barriers such as cost, stigma, or distance from mental health services. A potential solution to this could be mobile mental health applications. These can serve as effective and promising tools to assist in the management of anxiety and to overcome some of the aforementioned barriers. The objective of this study is to provide an analysis of treatment and management-related functionality and characteristics of high-rated mobile applications (apps) for anxiety, which are available for Android and iOS systems. METHOD: A broad search was performed in the Google Play Store and App Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing apps for anxiety. A set of free and highly rated apps for anxiety were identified and the selected apps were then installed and analyzed according to a predefined data extraction strategy. RESULTS: A total of 167 anxiety apps were selected (123 Android apps and 44 iOS apps). Besides anxiety, the selected apps addressed several health issues including stress, depression, sleep issues, and eating disorders. The apps adopted various treatment and management approaches such as meditation, breathing exercises, mindfulness and cognitive behavioral therapy. Results also showed that 51% of the selected apps used various gamification features to motivate users to keep using them, 32% provided social features including chat, communication with others and links to sources of help; 46% offered offline availability; and only 19% reported involvement of mental health professionals in their design. CONCLUSIONS: Anxiety apps incorporate various mental health care management methods and approaches. Apps can serve as promising tools to assist large numbers of people suffering from general anxiety or from anxiety disorders, anytime, anywhere, and particularly in the current COVID-19 pandemic.


id: cord-355513-vgs96w3b
author: Ma, Rongyang
date: 2020-07-14
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url: https://doi.org/10.3390/ijerph17145081
doi: 10.3390/ijerph17145081
summary:
  title: Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis Results: For nonmedical institution accounts in the model, report and story types of articles had positive effects on users'' following behaviors. In this work, we aimed to determine whether and how health information dissemination affected users'' information behavior in terms of following an account and liking a post. We chose the number of different types of articles and the aggregated number of headlines on NCP posted on the selected accounts in a 7-day period as independent variables (a total of seven) to denote the health information source and reflect the dissemination state. We want to explore whether information conveyed in each type of articles posted on WeChat can play the role, impacting users'' following and liking behavior.

title:
  Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis

abstract:
  Background: COVID-19 has greatly attacked China, spreading in the whole world. Articles were posted on many official WeChat accounts to transmit health information about this pandemic. The public also sought related information via social media more frequently. However, little is known about what kinds of information satisfy them better. This study aimed to explore the characteristics of health information dissemination that affected users’ information behavior on WeChat. Methods: Two-wave data were collected from the top 200 WeChat official accounts on the Xigua website. The data included the change in the number of followers and the total number of likes on each account in a 7-day period, as well as the number of each type of article and headlines about coronavirus. It was used to developed regression models and conduct content analysis to figure out information characteristics in quantity and content. Results: For nonmedical institution accounts in the model, report and story types of articles had positive effects on users’ following behaviors. The number of headlines on coronavirus positively impacts liking behaviors. For medical institution accounts, report and science types had a positive effect, too. In the content analysis, several common characteristics were identified. Conclusions: Characteristics in terms of the quantity and content in health information dissemination contribute to users’ information behavior. In terms of the content in the headlines, via coding and word frequency analysis, organizational structure, multimedia applications, and instructions—the common dimension in different articles—composed the common features in information that impacted users’ liking behaviors.


id: cord-355789-x449xflm
title: Susceptibility to phishing on social network sites: A personality information processing model
author: Frauenstein, Edwin Donald
date: 2020-05-01
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url: https://www.sciencedirect.com/science/article/pii/S0167404820301346
doi: 10.1016/j.cose.2020.101862
summary:
  Based on the literature studied, this research presents a theoretical model to address phishing susceptibility on SNSs. Using data collected from 215 respondents, the study examined the mediating role that information processing plays with regard to user susceptibility to social network phishing based on their personality traits, thereby identifying user characteristics that may be more susceptible than others to phishing on SNSs. The results from the structural equation modeling (SEM) analysis revealed that conscientious users were found to have a negative influence on heuristic processing, and are thus less susceptible to phishing on SNSs. The study also confirmed that heuristic processing increases susceptibility to phishing, thus supporting prior studies in this area. As such, this study follows this recommendation by identifying particular users susceptible to social network phishing by their personality traits, as this is one of the factors that have recently been found to influence user behaviour ( Shropshire et al., 2015 ) .

abstract:
  Today, the traditional approach used to conduct phishing attacks through email and spoofed websites has evolved to include social network sites (SNSs). This is because phishers are able to use similar methods to entice social network users to click on malicious links masquerading as fake news, controversial videos and other opportunities thought to be attractive or beneficial to the victim. SNSs are a phisher's “market” as they offer phishers a wide range of targets and take advantage of opportunities that exploit the behavioural vulnerabilities of their users. As such, it is important to further investigate aspects affecting behaviour when users are presented with phishing. Based on the literature studied, this research presents a theoretical model to address phishing susceptibility on SNSs. Using data collected from 215 respondents, the study examined the mediating role that information processing plays with regard to user susceptibility to social network phishing based on their personality traits, thereby identifying user characteristics that may be more susceptible than others to phishing on SNSs. The results from the structural equation modeling (SEM) analysis revealed that conscientious users were found to have a negative influence on heuristic processing, and are thus less susceptible to phishing on SNSs. The study also confirmed that heuristic processing increases susceptibility to phishing, thus supporting prior studies in this area. This research contributes to the information security discipline as it is one of the first to examine the effect of the relationship between the Big Five personality model and the heuristic-systematic model of information processing.


