trigram

This is a table of type trigram and their frequencies. Use it to search & browse the list to learn more about your study carrel.

trigram	frequency
the number of	234
in order to	158
based on the	135
as well as	120
of the user	104
one of the	99
the use of	92
in terms of	89
can be used	79
due to the	77
on the other	69
the user is	66
trial and error	66
such as the	63
there is a	62
the other hand	59
be used to	59
the user to	59
the spread of	57
that the user	55
as shown in	55
a set of	55
user seal check	55
shown in fig	51
to the server	49
the case of	49
in this paper	49
to the user	49
as a result	48
digital contact tracing	47
for contact tracing	47
of the users	47
a list of	45
at the same	43
the user can	43
the user seal	43
according to the	41
by the user	40
the effectiveness of	40
the impact of	38
the same time	38
we propose a	38
is based on	37
in this study	37
the set of	37
for each user	37
a number of	36
of contact tracing	36
in mental health	35
the user has	35
on social media	35
in addition to	34
focus on the	34
in this section	34
the results of	34
actual gross leakage	34
more likely to	34
in this work	34
that can be	34
the performance of	34
of the app	33
a variety of	33
based contact tracing	33
the importance of	33
part of the	33
the relationship between	32
most of the	32
the role of	32
if the user	31
a series of	31
be able to	31
a total of	31
use of the	31
in the following	30
some of the	30
well as the	30
in the system	30
the easy reading	29
in the case	29
in the same	29
of a user	29
the effect of	28
the user and	28
the fact that	28
of the system	28
we use the	28
in this case	28
it can be	28
large number of	28
users in u	28
to predict the	28
can be found	28
of the most	28
to capture the	28
is used to	27
the probability of	27
the process of	27
when the user	27
analysis of the	27
which can be	27
the smart contract	27
in which the	27
to each other	27
each of the	26
of the virus	26
number of users	26
the gcg app	26
the majority of	26
the rest of	26
for mental health	26
related to the	26
can also be	26
the amount of	26
it is not	26
of the two	25
systems in mental	25
the length of	25
in social media	25
users in the	25
such as a	25
are shown in	25
need to be	25
based on a	25
shown in table	24
the degree of	24
the problem of	24
in contact with	24
a large number	24
contact tracing and	24
is shown in	24
to use the	24
of users and	24
contact tracing is	24
proximity tracing proposals	24
the distance between	24
may not be	24
contact tracing apps	23
bluetooth low energy	23
is important to	23
the ability to	23
in the future	23
based on their	23
in case of	23
to reduce the	23
it is important	23
ml systems in	23
of platform openness	23
along with the	23
of the population	23
is one of	22
spread of the	22
the need for	22
the privacy of	22
of the infected	22
the united states	22
figure shows the	22
to be a	22
the form of	22
of hate speech	22
we use a	22
results of the	22
the dcts app	22
with respect to	22
the target object	22
in the vicinity	22
the risk of	21
in the form	21
used in the	21
different types of	21
period of time	21
rest of the	21
on the server	21
to determine the	21
in the field	21
of the covid	21
state of the	21
to identify the	21
to evaluate the	21
users who have	21
mental health care	21
is able to	21
be found in	21
can be seen	21
positive and negative	20
be used for	20
for the user	20
when a user	20
allows users to	20
results show that	20
is the most	20
access to the	20
used by the	20
there is no	20
of the model	20
the design of	20
the accuracy of	20
in the number	19
as it is	19
the government agency	19
similar to the	19
in the previous	19
the purpose of	19
to ensure that	19
of the time	19
of the world	19
around the world	19
overview of the	19
the system is	19
total number of	19
during the covid	19
the value of	19
shown in figure	19
in u short	19
we focus on	19
to have a	19
of this paper	19
to understand the	19
the context of	19
the definition of	19
we believe that	18
information about the	18
natural language processing	18
and the user	18
are able to	18
be used by	18
an infected person	18
version of the	18
provided by the	18
the most popular	18
the field of	18
that it is	18
it is possible	18
the end of	18
manual contact tracing	18
the influence of	18
the encounter information	18
the users to	18
the analysis of	18
needs to be	18
the development of	18
performance of the	18
difference between the	18
is similar to	18
as part of	18
our recommendation framework	18
more than one	18
length of the	18
number of likes	18
we do not	18
there are two	17
of n respirators	17
that of the	17
it is worth	17
a user is	17
information of the	17
we used the	17
to improve the	17
user vr system	17
in the second	17
the percentage of	17
it does not	17
the selected apps	17
in the past	17
in the next	17
depending on the	17
to do so	17
in the proximity	17
depends on the	17
to interact with	17
world health organization	17
is that the	17
the ground truth	17
it is also	17
the proposed model	17
a result of	17
to this end	17
as long as	17
of this study	17
to the users	17
of verified users	17
the identity of	17
for users to	16
a period of	16
the presence of	16
the contact tracing	16
in the last	16
likely to be	16
systems for mental	16
representation of the	16
that there is	16
compared to the	16
the users in	16
the mobile app	16
majority of the	16
stored in the	16
who is infected	16
size of the	16
of the platform	16
we compare the	16
to contain the	16
private set intersection	16
in the first	16
we can see	16
the infected individual	16
the big five	16
is defined as	16
ml systems for	16
the list of	16
the goal of	16
the size of	16
is designed to	16
to measure the	16
we need to	16
and it is	16
is the set	16
all of the	16
close contact with	16
described in section	16
a lack of	16
the time of	16
for each of	16
the infected user	16
in the data	16
which is a	16
of the target	16
result of the	16
to use a	16
an overview of	16
the user may	16
the total number	16
on the smartphone	16
have been proposed	15
the frequency of	15
data to the	15
a combination of	15
based on user	15
in the literature	15
in the united	15
use of a	15
of the proposed	15
are more likely	15
have been in	15
the quality of	15
of the disease	15
the strength of	15
on the platform	15
table shows the	15
for people with	15
we can find	15
that there are	15
in the present	15
the person who	15
the infected person	15
the effects of	15
a group of	15
contact with an	15
in social networks	15
any of the	15
data from the	15
with regard to	15
of usable security	15
the difference between	15
of the data	15
by using the	15
an example of	14
of actual gross	14
the proposed method	14
when they are	14
two types of	14
it is the	14
with the same	14
with an infected	14
the anonymous ids	14
the app is	14
smart contract group	14
the next step	14
in relation to	14
validity of the	14
public health authorities	14
evolving relation network	14
contact tracing service	14
set of users	14
of being infected	14
user data privacy	14
generated by the	14
case of the	14
received signal strength	14
sent to the	14
data collection and	14
of the same	14
personality traits and	14
of the game	14
public health officials	14
a lot of	14
to be used	14
our proposed model	14
there is an	14
because of the	14
parts of the	14
to share their	14
security and privacy	14
the prevalence of	14
it is a	14
to create a	14
preserving proximity tracing	14
the result of	14
may lead to	14
to be the	14
that they are	14
who have been	14
with the infected	14
risk of infection	14
where the user	14
susceptibility to phishing	14
the type of	14
the characteristics of	14
at risk of	14
accuracy of the	14
for all the	14
we find that	14
of each user	14
features of the	14
to the best	13
there are several	13
we assume that	13
national mood score	13
a subset of	13
interaction with the	13
are used to	13
of trial and	13
would like to	13
in other words	13
it has been	13
this type of	13
goal is to	13
at any time	13
of users in	13
the embedding of	13
refers to the	13
spread of covid	13
consists of a	13
as described in	13
the beginning of	13
the efficacy of	13
easy reading framework	13
the most common	13
we want to	13
and that the	13
who may have	13
whether the user	13
we did not	13
the range of	13
as the number	13
the mobile application	13
sequential question answering	13
referred to as	13
disease surveillance systems	13
the lack of	13
the validity of	13
in the context	13
from the user	13
the period of	13
were more likely	13
person who is	13
to get the	13
we developed a	13
in natural language	13
the most important	13
of our proposed	13
in the network	13
the social network	13
the level of	13
in such a	13
the nature of	13
user is in	13
amount of data	13
this study is	13
interact with the	13
information on the	13
of the application	13
included in the	13
they do not	13
side openness and	13
the validated network	13
nature of the	13
has shown that	13
the label propagation	13
table presents the	13
contact tracing for	13
of the main	13
show that the	13
in u long	13
to provide a	13
applied to the	13
with the user	13
a proximity chain	13
contact with the	13
the details of	13
of social media	12
the following two	12
than that of	12
the users are	12
by a user	12
the previous section	12
found to be	12
platform openness and	12
is an important	12
will not be	12
turn on the	12
the need to	12
organized as follows	12
of data collection	12
is organized as	12
on the one	12
are based on	12
visited by the	12
the dual approach	12
be locked down	12
user u i	12
allows the user	12
with other users	12
has been shown	12
and deep breathing	12
in the background	12
this information is	12
the duration of	12
of the first	12
to the number	12
the best of	12
can be applied	12
the telco company	12
a collection of	12
if a user	12
set intersection cardinality	12
of the pandemic	12
the state of	12
and in the	12
with the system	12
of mental health	12
to be more	12
user u and	12
to be effective	12
and can be	12
as the user	12
they have been	12
we present the	12
infected with covid	12
it would be	12
found that the	12
propose a novel	12
and right wing	12
the basis of	12
with the aim	12
network of verified	12
in ml systems	12
users may be	12
to be in	12
the task of	12
the user in	12
of our knowledge	12
make use of	12
the infection status	12
reverse prox tree	12
adaptive user interfaces	12
should be considered	12
right and right	12
use of it	12
to the system	12
to the same	12
of the number	12
this is the	12
the real world	12
to support the	12
terms of the	12
to learn the	12
qualitative comparative analysis	12
close to the	12
the system to	12
we found that	12
the collected data	12
as can be	11
official accounts of	11
in the user	11
social network phishing	11
as an example	11
fang et al	11
a sense of	11
users of the	11
to address the	11
the one hand	11
all of these	11
our recommendation design	11
the possibility of	11
the detection of	11
the model with	11
on the basis	11
that they can	11
to contribute to	11
of the art	11
should not be	11
the order of	11
structural equation modeling	11
can see that	11
there are a	11
the world health	11
the server is	11
the infection window	11
review of the	11
the mobile device	11
the contact recommendation	11
is less than	11
to access the	11
of users who	11
the success of	11
at the end	11
if there is	11
to explore the	11
in appendix a	11
of our model	11
in the design	11
probabilistic matrix factorization	11
the server and	11
to control the	11
by analyzing the	11
side and demand	11
to develop a	11
looking for something	11
contact tracing in	11
and public health	11
by the users	11
it should be	11
health care workers	11
results in the	11
of infectious diseases	11
of the simulation	11
of the epidemic	11
user has been	11
probability of being	11
of the query	11
of personal data	11
fact that the	11
is difficult to	11
in the current	11
on how to	11
the bloom filter	11
phishing on snss	11
worth noting that	11
is not only	11
description of the	11
the blockchain database	11
from the server	11
that the proposed	11
android and ios	11
trust in the	11
to estimate the	11
the outcome of	11
the system can	11
in line with	11
can only be	11
latent trustworthy comment	11
the help of	11
the output of	11
attention to the	11
one or more	11
the absence of	11
of users with	11
as opposed to	11
to one of	11
allow users to	11
in this area	11
the next section	11
i is the	11
social networking sites	11
is not possible	11
found in the	11
to the public	11
shows that the	11
between any two	11
best of our	11
the review embedding	11
we refer to	11
of all the	11
to generate the	11
our approach is	11
respect to the	11
the proposed approach	11
this can be	11
are in the	10
validated network of	10
social media users	10
the target user	10
of the models	10
to investigate the	10
online social networks	10
privacy of the	10
allows us to	10
to calculate the	10
relationship between the	10
this paper is	10
details of the	10
the best performance	10
of personality traits	10
all the users	10
to obtain a	10
out of the	10
in recent years	10
on the street	10
health care professionals	10
help users to	10
to cope with	10
that could be	10
with the help	10
number of tweets	10
in the social	10
effectiveness of our	10
people with visual	10
information to the	10
time of the	10
focuses on the	10
the platform openness	10
for this task	10
we present a	10
between users and	10
associated with the	10
the sequence of	10
the cloud application	10
users and the	10
does not require	10
illustrated in fig	10
amount of time	10
of the five	10
that has been	10
assume that the	10
this is a	10
does not have	10
the ability of	10
the machine learning	10
to get a	10
wechat official accounts	10
the dcts law	10
a positive user	10
those who have	10
is worth noting	10
in the public	10
between the two	10
to find out	10
evolution of the	10
as a means	10
used to identify	10
on the device	10
in the world	10
means that the	10
to consider the	10
paper is organized	10
from each other	10
available in the	10
there are many	10
have shown that	10
status of the	10
as they are	10
an infection chain	10
the public health	10
the first step	10
contact tracing system	10
relevant to the	10
refer to the	10
which is the	10
our system and	10
number of followers	10
number of the	10
passive wifi sensing	10
we used a	10
and information processing	10
on the phone	10
is required to	10
considered to be	10
the data collection	10
by public health	10
data can be	10
back to the	10
the identification of	10
note that the	10
a user to	10
supported by the	10
to be infected	10
level attention mechanism	10
types of articles	10
the ml system	10
we consider the	10
products and services	10
people with cognitive	10
contribute to the	10
the registered users	10
a case study	10
to assess the	10
may have been	10
we aim to	10
and the other	10
is not available	10
characteristics of the	10
to compute the	10
and improve the	10
into account the	10
the mab without	10
user does not	10
of infected people	10
with visual impairment	10
this means that	10
the right to	10
value of the	10
we can use	10
apple and google	10
of the easy	10
dcts law proposal	10
to examine the	10
a sequence of	10
to a server	10
associations among labels	10
nonmedical institution accounts	10
the current study	10
close to each	10
subset of the	9
and label information	9
impact on the	9
each of these	9
l p r	9
the same location	9
o o f	9
take into account	9
and systematic processing	9
recommendation framework can	9
been exposed to	9
for the first	9
seems to be	9
diagnosed with covid	9
to find the	9
at test time	9
is possible to	9
of the content	9
users whose stress	9
of health information	9
number of posts	9
of the information	9
when users are	9
which have been	9
of user u	9
and stored in	9
the supplementary material	9
we observe that	9
according to their	9
framework can be	9
they are at	9
the introduction of	9
does not know	9
used as a	9
been used in	9
is necessary to	9
to detect the	9
privacy design goals	9
shown in the	9
a wide range	9
if they are	9
which do not	9
people who have	9
on the internet	9
addition to the	9
a summary of	9
users with the	9
the candidate user	9
a range of	9
we set the	9
infected or suspected	9
in the real	9
five personality traits	9
so that the	9
the aim to	9
in front of	9
been shown to	9
the user needs	9
various types of	9
based approach to	9
is not a	9
especially in the	9
an increase in	9
r o o	9
they are in	9
the data from	9
is associated with	9
data will be	9
the results are	9
shows the number	9
to phishing on	9
and ios apps	9
the user does	9
change in the	9
a l p	9
be noted that	9
creative commons licence	9
fraction of the	9
of the tweet	9
to better understand	9
depend on the	9
a review of	9
order to identify	9
can lead to	9
they are not	9
of the device	9
in close contact	9
so as to	9
to see if	9
from the first	9
users need to	9
the availability of	9
in the cloud	9
to note that	9
effect of platform	9
by other users	9
where it is	9
research on the	9
user has a	9
schmitz et al	9
none of the	9
results for the	9
in the model	9
and the system	9
be used in	9
performance of our	9
mobile and fog	9
users based on	9
embedding of the	9
that users are	9
n a l	9
are presented in	9
user in the	9
the end user	9
used in this	9
types of users	9
target user u	9
believe that the	9
identity of the	9
is in the	9
less likely to	9
the concept of	9
a social network	9
hate speech detection	9
over a period	9
of the dcts	9
the application of	9
the proximity tree	9
represented by the	9
a person is	9
for the second	9
well as to	9
the change in	9
tend to be	9
do not have	9
normal and deep	9
openness and demand	9
of ml systems	9
from the app	9
used for the	9
the complexity of	9
user and item	9
to social network	9
are provided in	9
to other users	9
online hate speech	9
ensure that the	9
of the current	9
performance effect of	9
u r n	9
the usefulness of	9
it difficult to	9
locations visited by	9
o u r	9
data should be	9
the scope of	9
in the figure	9
understanding of the	9
have been exposed	9
to generate a	9
regardless of the	9
was used to	9
of thousands of	9
p r e	9
because it is	9
wide range of	9
p r o	9
the it artefact	9
r n a	9
j o u	9
based on this	9
to the infected	9
based on an	9
our results show	9
the received signal	9
that they would	9
contact with a	9
we see that	9
vishwanath et al	9
number of words	9
come in contact	9
corresponding to the	9
results are shown	9
the correlation vector	9
we introduce the	9
right wing parties	9
on the data	9
followed by the	9
heuristic and systematic	9
discussed in the	9
described in the	9
present in the	9
different from the	9
the management of	9
case of a	9
the principle of	9
the app can	9
in close proximity	9
to upload their	9
each user and	9
an infected user	9
a positive influence	8
and fog computing	8
results indicate that	8
be divided into	8
can be divided	8
to the backend	8
community question answering	8
social media is	8
the h n	8
suggests that the	8
seated and standing	8
should be locked	8
information from the	8
extent to which	8
to deal with	8
whether or not	8
of the existing	8
the performance effect	8
as the most	8
that the server	8
that can help	8
when there are	8
the actual gross	8
with cognitive disabilities	8
is used in	8
that our model	8
to participate in	8
in the training	8
be exposed to	8
the fit testing	8
is stored in	8
the steel blue	8
medical institution accounts	8
were able to	8
take advantage of	8
the gcg backend	8
of the ehr	8
have been conducted	8
even if the	8
be seen as	8
number of people	8
in our work	8
which will be	8
acts as a	8
cognitive behavioral therapy	8
some of these	8
the advertising packet	8
the signal strength	8
before and after	8
the text of	8
the reliability of	8
the confusion matrix	8
obtained by the	8
to study the	8
there are three	8
a representation of	8
of recommender systems	8
tcns to the	8
traits and information	8
with a wide	8
time period and	8
our proposed recommendation	8
in contact tracing	8
the surveillance system	8
the extent to	8
that our proposed	8
the efficiency of	8
based feature engineering	8
use is not	8
output of the	8
may be a	8
the aim of	8
purpose of this	8
we observed that	8
a user may	8
the user interface	8
without the need	8
the tool to	8
in the decentralized	8
the data is	8
to learn a	8
be applied to	8
the treatment of	8
structure of the	8
the structure of	8
is provided in	8
to focus on	8
of users that	8
for all users	8
that the system	8
a model that	8
demand diversity of	8
with a new	8
users who are	8
contact trace data	8
time in the	8
range of motion	8
a systematic review	8
that may be	8
security and usability	8
a sample of	8
of confirmed covid	8
this indicates that	8
is related to	8
be at risk	8
deep learning for	8
on the web	8
a way to	8
this could be	8
we have developed	8
who are not	8
is greater than	8
bluetooth signal strength	8
based proximity tracing	8
with the app	8
of the contact	8
on the contrary	8
to characterize the	8
the security of	8
corresponds to the	8
strength of the	8
to guide the	8
allow the user	8
it may be	8
in this way	8
the benchmark models	8
have not been	8
across the world	8
with each other	8
in our model	8
in quantum moves	8
as much as	8
is diagnosed with	8
this paper we	8
user can be	8
the proximity of	8
distance between users	8
and the results	8
the application and	8
u i and	8
which in turn	8
a means to	8
the attention mechanism	8
the embeddings of	8
the raw data	8
beginning of the	8
mobile app will	8
a survey of	8
on the light	8
close proximity to	8
of how the	8
who were in	8
but it is	8
have to be	8
for at least	8
the manufacturing industry	8
end of the	8
of the paper	8
have been used	8
the model is	8
user seal checks	8
with the tablet	8
involved in the	8
of the location	8
for a given	8
which they are	8
smart contract contract	8
sensitivity and specificity	8
appears to be	8
contact recommendation task	8
the higher the	8
of the protocol	8
to the target	8
the location data	8
for the task	8
in this experiment	8
they can be	8
a decentralized approach	8
the official accounts	8
differences in the	8
the time duration	8
a user can	8
the results from	8
focusing on the	8
resulting in a	8
in the third	8
on social networks	8
preserving contact tracing	8
electronic health record	8
based model for	8
a theory of	8
big five personality	8
can find the	8
they were in	8
model and the	8
were asked to	8
to help users	8
quality of the	8
the location l	8
an opportunity to	8
in the blockchain	8
in using the	8
the data of	8
advantage of the	8
is the case	8
of the selected	8
static and dynamic	8
could be used	8
the gleamviz tool	8
effectiveness of the	8
infected and suspected	8
to minimize the	8
a description of	8
of the original	8
in light of	8
the training data	8
in our system	8
is limited to	8
can be easily	8
tens of thousands	8
whether a challenge	8
users a and	8
users on the	8
a and b	8
we plan to	8
for this reason	8
directed validated network	8
in contrast to	8
knowledge of the	8
the th of	8
of the tweets	8
collected from the	8
the health center	8
the perspective of	8
the user was	8
of the message	8
was found to	8
to perform the	8
to an infected	8
of our recommendation	8
of more than	8
infection status of	8
at least one	8
for users who	8
a diagnosed carrier	8
and number of	8
derived from the	8
such an approach	8
devices that are	8
the following three	8
level user reliability	8
be due to	8
is the first	8
we would like	8
to make a	8
the types of	8
the five proposals	8
of the m	8
the public to	8
people who are	8
contact tracing of	8
in two different	8
and the number	8
in real time	8
for social recommendation	8
usable security in	8
proximity of the	8
the virtual environment	8
proportional to the	8
in the healthcare	8
health information dissemination	8
implicit association labels	8
proximity to the	8
user has the	8
the structural gameplay	8
show that our	8
all of them	8
demand diversity and	8
graph convolutional networks	8
during the period	8
on the ipad	8
likely to ride	8
regard to the	8
from the perspective	8
positive or negative	8
such that the	8
to be addressed	7
the limitations of	7
to determine whether	7
seal check is	7
usage of the	7
can be considered	7
be in the	7
processing of personal	7
the focus of	7
that the number	7
the user for	7
that does not	7
able to identify	7
we compute the	7
camera with a	7
that would be	7
the m s	7
of user interfaces	7
model based on	7
in response to	7
distance between the	7
presented in table	7
defined as follows	7
design of the	7
international conference on	7
information can be	7
cannot be used	7
the bipartite network	7
the dynamics of	7
for the two	7
to check the	7
conflict of interest	7
of platform users	7
this may be	7
definition of the	7
the directed validated	7
the data collected	7
social network users	7
of the technology	7
with contact tracing	7
by the server	7
in the validated	7
for public health	7
stored in a	7
the privacy and	7
a smart contract	7
will lead to	7
the positive user	7
apps that use	7
on the user	7
the diversity constraint	7
we examine the	7
the differences between	7
the time period	7
and how the	7
and specificity of	7
attribute of platforms	7
large amount of	7
globally distributed manufacturing	7
the it device	7
not possible to	7
this approach is	7
pointed out that	7
there are also	7
on top of	7
to be considered	7
users who were	7
in that it	7
the gleamviz client	7
in this context	7
by the system	7
we show the	7
in the text	7
health authorities to	7
a hybrid model	7
proceedings of the	7
the healthcare recommendation	7
figure shows a	7
in the recommendation	7
during the lockdown	7
we focused on	7
we provide a	7
google play store	7
and on the	7
creative drawing game	7
would be the	7
of information that	7
from the data	7
stage of the	7
defined as a	7
by means of	7
high knowledge complexity	7
can help users	7
using the tablet	7
serrano and smith	7
models such as	7
relationship between platform	7
information is not	7
none of these	7
summary of the	7
should be taken	7
and future directions	7
computer vision techniques	7
in regard to	7
between platform openness	7
if there are	7
transmission of covid	7
the history of	7
users in a	7
n is the	7
properties of the	7
the user with	7
data of the	7
and does not	7
of the infection	7
they tend to	7
linear regression model	7
is a lack	7
is the only	7
to make the	7
and so on	7
of each of	7
expected to be	7
the existence of	7
a bloom filter	7
worse than the	7
in their daily	7
in the information	7
revealed to the	7
users tend to	7
of the different	7
details about the	7
that they have	7
time interval number	7
in need of	7
health tracing service	7
the most used	7
and mental health	7
unsupervised machine learning	7
health impacts of	7
composed by the	7
a third party	7
and knowledge complexity	7
known as the	7
impacts of e	7
of the study	7
this is not	7
is more than	7
is intended to	7
infected people and	7
to obtain the	7
model on the	7
smart contact tracing	7
an analysis of	7
the representation of	7
achieves the best	7
we describe the	7
are as follows	7
a small number	7
inversely proportional to	7
in doing so	7
of the interaction	7
relationships between the	7
been in contact	7
of the smart	7
or any other	7
a digital contact	7
the existing literature	7
most of these	7
of users u	7
the accounts of	7
which has been	7
the chance of	7
and they are	7
provide users with	7
identified in the	7
for more than	7
security in ml	7
it is difficult	7
used as the	7
albladi and weir	7
the demand side	7
ride on the	7
we examined the	7
of the other	7
when all the	7
instead of the	7
has not been	7
of using the	7
the gleamviz proxy	7
the spreading of	7
i could not	7
each user u	7
is that they	7
in some cases	7
an omnidirectional camera	7
focused on the	7
the interaction between	7
of the italian	7
vast majority of	7
was easy to	7
of the it	7
the emergence of	7
diversity and knowledge	7
the processing of	7
detection of anorexia	7
the individual user	7
number of available	7
the current covid	7
of negative disconfirmation	7
we consider a	7
in our dataset	7
more than million	7
of social networks	7
in a way	7
over all the	7
a camera with	7
is a key	7
does not provide	7
users should be	7
a negative influence	7
a framework for	7
the content of	7
for the number	7
and machine learning	7
development of a	7
is hard to	7
should be noted	7
an infected individual	7
lawson et al	7
for both the	7
has been used	7
this study was	7
being able to	7
location and proximity	7
we consider that	7
the information of	7
is inversely proportional	7
of the registered	7
indicates that the	7
what it is	7
the window size	7
within a community	7
the research on	7
of the actual	7
in the selected	7
the users of	7
a way that	7
front of the	7
and error behaviour	7
machine learning framework	7
contact tracing app	7
to a specific	7
of location data	7
contract contract location	7
centralized and decentralized	7
for usable security	7
in this article	7
can be obtained	7
help the user	7
tracing is an	7
rather than a	7
can then be	7
shown to be	7
have also been	7
for the contact	7
small number of	7
halevi et al	7
the vast majority	7
users from the	7
seen as a	7
widely used in	7
and get the	7
the needs of	7
we applied the	7
use of bluetooth	7
increasing number of	7
this section presents	7
followed by a	7
using the same	7
does not need	7
see that the	7
the idea of	7
with more than	7
of the performance	7
we introduce a	7
that we can	7
outside of the	7
example of a	7
a pilot study	7
the trustworthy comment	7
of our approach	7
at risk to	7
which the user	7
is then used	7
in the game	7
the method of	7
high level of	7
mental health issues	7
through the use	7
are at risk	7
are required to	7
is a common	7
the contact trace	7
when it is	7
text of the	7
for the public	7
is the same	7
negative predictive values	7
stored on the	7
the target and	7
and the second	7
the features of	7
can be further	7
of public health	7
identification of the	7
user is not	7
to be statistically	7
to check if	7
of users to	7
in a single	7
users can be	7
change over time	7
general data protection	7
to the pandemic	7
an empirical study	7
can be done	7
slowing down the	7
the authors have	7
distance between any	7
point cloud data	7
of this work	7
more and more	7
data protection regulation	7
they may not	7
data collected from	7
in our case	7
an unsupervised machine	7
than the other	6
to have the	6
smart contract service	6
the latter two	6
the current results	6
of other users	6
and provide a	6
personality traits on	6
functionality and characteristics	6
the user through	6
on heuristic processing	6
from social media	6
within the easy	6
compared to other	6
less than ten	6
the review score	6
the area should	6
score for each	6
the formation of	6
the increase of	6
none of them	6
a measure of	6
addition to that	6
the diversity of	6
of the review	6
of platform performance	6
the two decentralized	6
to overcome the	6
to define the	6
and of the	6
topics of discussion	6
if a person	6
data and the	6
as a way	6
response to the	6
and the use	6
the alert dialog	6
to be able	6
to phishing susceptibility	6
we propose the	6
have developed a	6
the infected area	6
critical mass of	6
the diagnosed carrier	6
we also show	6
according to a	6
to classify the	6
u i is	6
from the dataset	6
requirements of the	6
at time t	6
of user profile	6
as compared to	6
social media platforms	6
visualization of the	6
higher weights to	6
of the results	6
peak of the	6
of the political	6
to people who	6
at this point	6
was used in	6
on platform performance	6
of a person	6
is subject to	6
perform contact tracing	6
to the original	6
the server to	6
the phone number	6
taken into account	6
the second scenario	6
contact with infected	6
the rss value	6
specific to the	6
isolation of cases	6
for the individual	6
the contact rate	6
on the relationship	6
precise proximity sensing	6
comments on the	6
a user seal	6
can be expressed	6
sensor data and	6
bagayogo et al	6
health use cases	6
on the users	6
within the range	6
the user profile	6
was applied to	6
also be used	6
usage patterns of	6
prevalence of actual	6
in the community	6
to make it	6
data from users	6
to which the	6
will be able	6
a user study	6
they have not	6
contract service layer	6
for platform innovation	6
are similar to	6
taken into consideration	6
to prevent the	6
a significant difference	6
evaluate the performance	6
it to the	6
the proximity chain	6
a state of	6
the anonymous id	6
level of the	6
the social distancing	6
is represented by	6
sum of the	6
directly from the	6
we have also	6
in social network	6
the novel coronavirus	6
fog computing framework	6
implicit associations among	6
the world to	6
the best performing	6
percentage of the	6
number of retweets	6
for the next	6
seal check on	6
to the network	6
by our model	6
at each location	6
graphical user interface	6
bigru and cnn	6
have been infected	6
the second layer	6
machine learning model	6
a balance between	6
and social media	6
number of devices	6
nodes in the	6
and platform performance	6
wu et al	6
the fit of	6
it is still	6
to the application	6
the cost of	6
by the fact	6
it is one	6
be taken into	6
would be able	6
the knowledge of	6
table about here	6
many of these	6
in information retrieval	6
influence on heuristic	6
to keep the	6
a model on	6
task completion time	6
the application will	6
is the number	6
is affected by	6
extension of the	6
right wing community	6
a user u	6
an infectious disease	6
the app and	6
to have an	6
label user profile	6
this does not	6
distribution of the	6
using deep learning	6
is equal to	6
the ratio of	6
hybrid deep learning	6
be considered as	6
the degree sequence	6
to filter out	6
the design and	6
an average of	6
cases where the	6
using natural language	6
of paramount importance	6
in some way	6
for depression detection	6
for the use	6
why and how	6
for which the	6
the distance is	6
of the article	6
it has also	6
to increase the	6
of a mobile	6
of use and	6
anonymous ids and	6
to a high	6
registered users in	6
contact tracing can	6
control and prevention	6
the opportunity to	6
types of information	6
the task is	6
application of the	6
of the dataset	6
for riding e	6
effect on the	6
the design review	6
negative influence on	6
associated to the	6
approach is based	6
to see how	6
johnson et al	6
shows how the	6
left or right	6
to the covid	6
to provide the	6
the experimental results	6
this work is	6
online social media	6
by using a	6
of the patients	6
the participants were	6
social distancing score	6
what is the	6
so that they	6
l s dataset	6
hosted on the	6
all the proposals	6
of an epidemic	6
to the app	6
and error is	6
differences between the	6
in the mobile	6
side openness will	6
the likelihood of	6
only be used	6
who have not	6
places such as	6
of the mobile	6
of the input	6
in an epidemic	6
internet of things	6
be statistically significant	6
in our experiments	6
would need to	6
the superiority of	6
has been exposed	6
out that the	6
positive for covid	6
them to the	6
electronic health records	6
system needs to	6
most of them	6
the infected users	6
function of the	6
wang et al	6
for the same	6
to protect the	6
use of such	6
security design goals	6
for each post	6
to engage in	6
protect the privacy	6
are used for	6
by the framework	6
how many times	6
it is more	6
is followed by	6
in the authors	6
significantly worse than	6
users to be	6
the time interval	6
results from the	6
which means that	6
in the absence	6
the device id	6
shown that the	6
review embedding is	6
at a time	6
that our recommendation	6
system used in	6
an mab to	6
we decided to	6
the training set	6
the differences in	6
the proposed framework	6
as the main	6
user embeddings and	6
centers for disease	6
in figure we	6
be explained by	6
the choice of	6
this study has	6
a target user	6
the smartphone will	6
at the beginning	6
be possible to	6
such as those	6
on their own	6
side openness is	6
mobile apps for	6
and suspected cases	6
developed a smartphone	6
a distance of	6
focus of the	6
for contact recommendation	6
but they are	6
the paper is	6
the mental health	6
who may be	6
step of the	6
dynamics of hate	6
even in the	6
a dashboard to	6
to compare the	6
of the bluetooth	6
on a large	6
of the risk	6
both of these	6
infected by the	6
in information systems	6
be used as	6
the combination of	6
call to actions	6
to the high	6
to complete the	6
affected by the	6
user interfaces for	6
as in the	6
between the user	6
is used by	6
to address this	6
directly or indirectly	6
during contact tracing	6
the personality traits	6
be defined as	6
this is because	6
the centralized proposals	6
be included in	6
that have been	6
social recommendation with	6
gross leakage detection	6
the reason why	6
at the time	6
minimal data collection	6
relationships and label	6
ease of use	6
start and end	6
to enable contact	6
of the tool	6
developed a dashboard	6
the linear regression	6
in the whole	6
this way was	6
by the app	6
to the next	6
of u i	6
by the health	6
mental health problems	6
there may be	6
insert table about	6
if it is	6
because they are	6
could not be	6
is measured by	6
has also been	6
showed that the	6
to understand and	6
our approach to	6
the creative commons	6
number of new	6
signal strength index	6
to limit the	6
on the sidewalk	6
transaction and innovation	6
latent dirichlet allocation	6
our system will	6
post reliability score	6
not have to	6
increase in the	6
that should be	6
to find a	6
the proximity between	6
in cases where	6
are likely to	6
a survey on	6
to ride on	6
number of covid	6
can be a	6
data in the	6
contacts for confirmed	6
methods can be	6
the ir models	6
to ensure the	6
devices in the	6
in the backend	6
user and the	6
to analyze the	6
to collect data	6
location of the	6
belongs to the	6
on the post	6
group of people	6
connected to the	6
mobile contact tracing	6
described in sect	6
specificity of the	6
to the fact	6
to demonstrate the	6
the users and	6
the qca method	6
as a key	6
matrix factorization model	6
it is shown	6
uploaded by the	6
and contact tracing	6
that the tablet	6
majority of users	6
the healthcare system	6
to mental health	6
the proximity chains	6
is essential to	6
top of the	6
the real network	6
attention mechanism has	6
the first two	6
he or she	6
the approximated review	6
that the users	6
google and apple	6
in their proximity	6
the similarity between	6
percentage of users	6
code of the	6
corresponds to a	6
to select the	6
alert the user	6
to use it	6
we can observe	6
is likely to	6
group of users	6
information broadcast by	6
the possibility to	6
are the most	6
have found that	6
have come in	6
contribution to the	6
in a centralized	6
location and contact	6
about the user	6
to build a	6
of a potential	6
where there is	6
and the corresponding	6
of the smartphone	6
limited number of	6
with the increase	6
of data to	6
that use computer	6
to the cloud	6
when a person	6
trustworthy comment embedding	6
the simulation engine	6
a knowledge base	6
in this regard	6
the results in	6
of the above	6
would have been	6
direction of the	6
the regression model	6
the model builder	6
the google play	6
to infer the	6
in the above	6
such as twitter	6
over the past	6
to the central	6
of the various	6
while it is	6
it can also	6
of social recommendation	6
tweets and the	6
mental health use	6
evaluation of the	6
of information on	6
interaction design approaches	6
of the atom	6
is expected to	6
the basic workflow	6
is possible that	6
much as possible	6
has a positive	6
is composed of	6
the sample size	6
of a smartphone	6
means clustering algorithm	6
associated with a	6
portion of the	6
presented in the	6
system based on	6
negative user seal	6
by the use	6
jain and wallace	6
the user will	6
high demand diversity	6
from the european	6
to address these	6
hybrid model that	6
their daily lives	6
to the virus	6
more attention to	6
of an individual	6
we have the	6
if they have	6
the computer system	6
importance of the	6
users do not	6
used to predict	6
consistent with the	6
we seek to	6
with digital contact	6
the magnitude of	6
as the first	6
goal of this	6
in which they	6
role in the	6
ability of the	6
share their location	6
broadcast by a	6
trustworthy comment embeddings	6
enables us to	6
the capability to	6
of the health	6
of vr systems	6
to implement the	6
it was not	6
a comparison of	6
the one of	6
to a user	6
a positive test	6
use computer vision	6
they are more	6
are located in	6
higher than the	6
of the item	6
high number of	6
easy reading reasoner	6
thousands of users	6
recommended by the	6
the system and	6
the goals of	6
the usage of	6
the following sections	6
discussed in section	6
the european union	6
the measurement data	6
was able to	6
are asked to	6
of these methods	6
health status and	6
to information retrieval	6
members of the	6
approach can be	6
a certain period	6
we define the	6
mobile service layer	6
point of view	6
for the purpose	6
be solved by	6
in the end	6
users tested positive	6
data were collected	6
have used the	6
we calculated the	6
information such as	6
from the same	6
stress bef ore	6
users are not	6
in conjunction with	6
other forms of	6
the new interaction	6
of data elements	6
order to provide	6
defined as the	6
of the gcg	6
in the appendix	5
of the aqt	5
goal of the	5
the message is	5
of infected users	5
the local storage	5
the study of	5
to the other	5
out by the	5
of cases and	5
of a specific	5
the sum of	5
in which case	5
and error and	5
can help to	5
our sct system	5
on the specific	5
to protect themselves	5
of the day	5
convolutional neural networks	5
and how they	5
the first to	5
for data collection	5
contributes to the	5
epidemic control with	5
been shown that	5
with regards to	5
of the news	5
in the vr	5
is a long	5
of individuals in	5
of controlling covid	5
next step is	5
and story types	5
it is necessary	5
the goal is	5
number of locations	5
is infected and	5
interacting with the	5
is sent to	5
jointly based on	5
use of an	5
is a well	5
and weak ties	5
belongs to a	5
the personal data	5
machine learning algorithms	5
data cannot be	5
at prediction time	5
privacy and security	5
lead to a	5
provided to users	5
the following section	5
short period of	5
adaptive structuration theory	5
but also to	5
of these models	5
the generic reflections	5
users to the	5
the recommendation performance	5
of smart contracts	5
in contact recommendation	5
of transportation that	5
window size is	5
not allowed to	5
to the prevalence	5
in the existing	5
instead of a	5
a large amount	5
heterogeneous evolving network	5
from the list	5
to a given	5
the innovation attribute	5
contact tracing using	5
have chosen a	5
in such cases	5
summarized in table	5
the original query	5
be interesting to	5
ministry of health	5
the result is	5
contain the covid	5
case study of	5
hate speech on	5
in the manufacturing	5
motivate users to	5
at which the	5
have more than	5
to the presence	5
public health organizations	5
disease control and	5
of the last	5
information from a	5
to palliative care	5
link to the	5
is computed as	5
for the virus	5
different parts of	5
deep learning models	5
the score of	5
for each category	5
is the frequency	5
of the u	5
the most recent	5
to help people	5
the measurement model	5
that a benchmark	5
a personal vehicle	5
fit of n	5
of web content	5
a better understanding	5
appear to be	5
can be safely	5
in south korea	5
seem to be	5
user can also	5
our contact tracing	5
individuals with the	5
other devices in	5
models for the	5
does not consider	5
the decentralized proposals	5
the second stage	5
voice and gesture	5
find the object	5
enough to be	5
which could be	5
in the e	5
on the global	5
not able to	5
high levels of	5
belong to the	5
mab without either	5
we present an	5
the virus in	5
the base stations	5
person is diagnosed	5
the user experience	5
are provided with	5
to the following	5
number of patients	5
living room light	5
health histories and	5
to a large	5
to as the	5
that the results	5
which are not	5
once the user	5
identifiable natural person	5
in hong kong	5
explained by the	5
to the model	5
we have used	5
of the previous	5
users are able	5
have an impact	5
for medical institution	5
not only to	5
be collected from	5
better understanding of	5
openness will lead	5
the mode of	5
is relevant to	5
on the accuracy	5
at the top	5
to the game	5
the system will	5
users and items	5
the social media	5
large numbers of	5
vr system used	5
has been widely	5
performing contact tracing	5
by u i	5
and with a	5
history of the	5
outbreaks by isolation	5
of it devices	5
on the topic	5
the point cloud	5
have a significant	5
we propose to	5
two decentralized proposals	5
conditions of the	5
node in the	5
results on the	5
by the infected	5
the tutorial levels	5
of the four	5
the present study	5
for users whose	5
and tend to	5
decentralized proximity tracing	5
king et al	5
aspects of the	5
the server can	5
were in contact	5
because the user	5
such as turn	5
a natural language	5
the decentralized approach	5
data obtained from	5
the aggregated number	5
of those who	5
of the recommendation	5
recommendations based on	5
to the current	5
to achieve the	5
the bluetooth signal	5
by the authors	5
also have a	5
initial negative disconfirmation	5
a contact tracing	5
while this may	5
are included in	5
influence on systematic	5
physical distancing rule	5
depending on their	5
user engagement and	5
result in a	5
to extract the	5
of all users	5
ensures that the	5
such as an	5
by clicking on	5
scope of this	5
spread of hate	5
the vr environment	5
parameters of the	5
user based on	5
attention mechanism to	5
be attributed to	5
of locations visited	5
can be observed	5
of visual information	5
pseudorandom id generation	5
design of our	5
collected from a	5
of our work	5
the learning of	5
not in the	5
hooper et al	5
susceptible to phishing	5
h n influenza	5
with an infectious	5
have access to	5
has been in	5
of products and	5
this is also	5
the system in	5
conditions for each	5
through trial and	5
of the embeddings	5
research has shown	5
its ability to	5
a correlation vector	5
location l is	5
to optimize the	5
openness and performance	5
scooter share programs	5
the first place	5
if the system	5
proximity chain a	5
we hope that	5
triggered by the	5
t is the	5
the user as	5
personal use scenarios	5
there is still	5
is tested positive	5
infected user has	5
snapshot of the	5