Epidemic Estimation over Social Networks using Large Scale Biosensors

2016 ◽  
pp. 287-319
Author(s):  
João Sousa Andrade ◽  
Artur M. Arsénio
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Large Scale ◽  
User Privacy ◽  
Disease Propagation ◽  
Community Data ◽  
The Subject ◽  
Networks Analysis ◽  
Epidemic Prediction ◽  
Personal Devices

Infectious diseases, such as the recent Ebola outbreak, can be especially dangerous for large communities on today's highly connected world. Countermeasures can be put in place if one is able to predict determine which people are more vulnerable to infections or have been in contact with the disease, and where. Contact location, time and relationship with the subject are relevant metrics that affect the probability of disease propagation. Sensors on personal devices that gather information from people, and social networks analysis, allow the integration of community data, while data analysis and modelling may potentially indicate community-level susceptibility to an epidemic. Indeed, there has been interest on social networks for epidemic prediction. But the integration between large-scale sensor networks and these initiatives, required to achieve epidemic prediction, is yet to be achieved. In this context, an opportunistic system is proposed and evaluated for predicting an epidemic outbreak in a community, while guaranteeing user privacy.

Automatic Face Image Tagging in Large Collections

2014 ◽  
pp. 336-358
Author(s):  
Silvio Barra ◽  
Maria De Marsico ◽  
Chiara Galdi
Keyword(s):  
Social Networks ◽  
Face Recognition ◽  
Large Scale ◽  
Face Image ◽  
Image Tagging ◽  
Indexing And Retrieval ◽  
Magazine Publishing ◽  
Face Datasets ◽  
Traditional Approaches ◽  
Personal Devices

In this chapter, the authors present some issues related to automatic face image tagging techniques. Their main purpose in user applications is to support the organization (indexing) and retrieval (or easy browsing) of images or videos in large collections. Their core modules include algorithms and strategies for handling very large face databases, mostly acquired in real conditions. As a background for understanding how automatic face tagging works, an overview about face recognition techniques is given, including both traditional approaches and novel proposed techniques for face recognition in uncontrolled settings. Moreover, some applications and the way they work are summarized, in order to depict the state of the art in this area of face recognition research. Actually, many of them are used to tag faces and to organize photo albums with respect to the person(s) presented in annotated photos. This kind of activity has recently expanded from personal devices to social networks, and can also significantly support more demanding tasks, such as automatic handling of large editorial collections for magazine publishing and archiving. Finally, a number of approaches to large-scale face datasets as well as some automatic face image tagging techniques are presented and compared. The authors show that many approaches, both in commercial and research applications, still provide only a semi-automatic solution for this problem.

SAE: Toward Efficient Cloud Data Analysis Service for Large-Scale Social Networks

2017 ◽  
Vol 5 (3) ◽  
pp. 563-575 ◽  
Author(s):  
Yu Zhang ◽  
Xiaofei Liao ◽  
Hai Jin ◽  
Guang Tan
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Large Scale ◽  
Cloud Data

scholarly journals Privacy-Preserving Multiple Tensor Factorization for Synthesizing Large-Scale Location Traces with Cluster-Specific Features

2021 ◽  
Vol 2021 (2) ◽  
pp. 5-26
Author(s):  
Takao Murakami ◽  
Koki Hamada ◽  
Yusuke Kawamoto ◽  
Takuma Hatano
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Large Scale ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Tensor Factorization ◽  
Statistical Features ◽  
User Privacy ◽  
Posterior Sampling ◽  
Spatial Big Data

Abstract With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting user privacy. In particular, a synthetic trace that preserves a feature specific to a cluster of users (e.g., those who commute by train, those who go shopping) is important for various geo-data analysis tasks and for providing a synthetic location dataset. Although location synthesizers have been widely studied, existing synthesizers do not provide su˚cient utility, privacy, or scalability, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We factorize these two tensors simultaneously via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors, and perform a plausible deniability test for a synthetic trace. We comprehensively evaluate PPMTF using two datasets. Our experimental results show that PPMTF preserves various statistical features including cluster-specific features, protects user privacy, and synthesizes large-scale location traces in practical time. PPMTF also significantly outperforms the state-of-theart methods in terms of utility and scalability at the same level of privacy.

scholarly journals The Role of Online Media in Mobilizing Large-Scale Collective Action

2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110338
Author(s):  
Zhuo Chen ◽  
Poong Oh ◽  
Anfan Chen
Keyword(s):  
Social Networks ◽  
Collective Action ◽  
Large Scale ◽  
Two Dimensions ◽  
Future Research ◽  
Online Media ◽  
Twitter Data ◽  
Networks Analysis ◽  
Organizing Process

This study investigates the role of online media in mobilizing large-scale collective action. Adopting the theoretical framework of collective action space, we formulated the organizing process of collective action into a model with two dimensions—hierarchy and closure—and analyzed how they influence mobilization. The model was tested against Twitter data collected during the 2020 Hong Kong protest, including a total of 54,365 tweets posted by 14,706 distinct users between 1 May and 31 May 2020. Social networks analysis metrics— k-coreness and brokerage of individual users in their following networks—were employed to quantify the organizing process of the protest and estimate their effects on message virality. The results showed that messages generated by users who occupied peripheral positions (i.e., lower k-coreness) and by those connecting others within closed communities (i.e., lower brokerage) were more likely to diffuse than those generated by central users or those who bridged different communities. That is, online media facilitate mobilization in a decentralized yet fragmented fashion. This article concludes with a discussion of the theoretical implications of the current findings and suggests the directions for future research on collective action on online media.

scholarly journals Designing a dictionary of patterns of destructive utterances in the task of identifying destructive information influence

2020 ◽  
Vol 224 ◽  
pp. 03013
Author(s):  
E.P. Okhapkina ◽  
V.P. Okhapkin ◽  
A.O. Iskhakova ◽  
A.Y. Iskhakov
Keyword(s):  
Social Networks ◽  
Hong Kong ◽  
Large Scale ◽  
Modern Society ◽  
Subject Area ◽  
The Political ◽  
Control Actions ◽  
The World ◽  
The Subject ◽  
High Level

Due to the high level of tension in modern society, social networks are widely used for destructive management of the information space. This aspect of the use of social networks has become particularly important in the light of events taking place in the world (Hong Kong, Syria, France and Ukraine). According to statistics, about 50% of politicized active groups of social networks are subjects to targeted control actions aimed at spreading negative moods in the political sphere. The escalation of conflicts in society generates the most dangerous type of destructive information influence (DII) that require rapid, large-scale coordination of participants in order to attract new supporters and their organizations. Massive DII on the participants of social networks groups exacerbated the problem of promptly identifying the facts of influence, and created serious prerequisites for the development and improvement of methods and means of identifying DII in social networks. The relevance of this problem is due to the existence of a number of methodological and technological problems in the subject area under consideration, one of them is the lack of patterns of network messages containing elements of DII. In the study, the authors consider an approach to designing a dictionary of patterns of destructive utterances.

TEACHING READING COMPREHENSION OF NARRATIVE TEXT TO THE TENTH GRADE STUDENTS

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Emi Br Bukit ◽  
Berlin Sibarani ◽  
Rika Rika
Keyword(s):  
Qualitative Research ◽  
Reading Comprehension ◽  
Data Analysis ◽  
Narrative Text ◽  
Teaching Reading ◽  
Qualitative Research Design ◽  
Analysis Technique ◽  
Tenth Grade ◽  
The Subject ◽  
Academic Year

This study aims at describing how the teachers teach reading comprehension of narrative text to the tenth grade students in Sibolangit and revealing the underlying reasons of why do they do that way. This study was conducted by using qualitative research design. The subject of this study were two english teachers who taught at tenth grade students of two SMA in Sibolangit they are : SMA Negeri  1 Sibolangit  and SMA RK Deli Murni Bandar Baru in academic year 2016/ 2017. The data were analyzed by using Miles and Huberman data analysis technique. The  technique of collecting the data was recorded from the classroom process in teaching reading comprehension of narrative text. The findings of the study show that most of teachers’ ways are not yet focusing on teaching reading comprehension but rather focusing teaching the knowledge of genre. The underlying reason of the teachers’ ways in teaching reading comprehension did not facilitate reading comprehension. It was due to the misperception of the concept of teaching reading comprehension.  Keywords : Teaching,Reading Comprehension,Narrative Text.

IMPROVING STUDENT’S VOCABULARY ACHIEVEMENT IN WITING DESCRIPTIVE TEXT THROUGH MAKE A MATCH METHOD

2013 ◽  
Vol 2 (4) ◽  
Author(s):  
Irma Lely Lumban Gaol And Johan Sinulingga
Keyword(s):  
Data Analysis ◽  
Action Research ◽  
Learning Process ◽  
Match Method ◽  
Writing Test ◽  
The Subject ◽  
Classroom Action Research ◽  
Teaching Learning ◽  
Descriptive Writing ◽  
Vocabulary Achievement

This study is concerned with the improving student’s vocabulary achievement in writing descriptive text through Make a Match Method. The objective of this study was to discover whether the use of Make a Match Method could significantly improve students’ vocabulary achievement in writing descriptive text. This study was conducted by applying Classroom Action Research which was carried out in two cycles in six meetings. The subject of this study was students of SMA Negeri 1 Pollung which consisted of 34 students. The instruments for collecting data were descriptive writing test, observation sheet, questionnaire sheet, and diary notes. The techniques for data analysis were quantitative and qualitative. It was found that teaching-learning process ran well. Students were active, enthusiastic and interested on writing descriptive text. The result of this study showed that the use of Make a Match Method significantly improved student’s vocabulary achievement in writing descriptive text.

The Impact of Quantitative Easing on Emerging Markets – Literature Review

e-Finanse ◽  
2018 ◽  
Vol 14 (4) ◽  
pp. 67-76
Author(s):  
Piotr Bartkiewicz
Keyword(s):  
Emerging Markets ◽  
Large Scale ◽  
Empirical Literature ◽  
Quantitative Easing ◽  
Methodological Choices ◽  
The Subject ◽  
Methodological Approaches ◽  
The Impact ◽  
Sufficient Precision

AbstractThe article presents the results of the review of the empirical literature regarding the impact of quantitative easing (QE) on emerging markets (EMs). The subject is of interest to policymakers and researchers due to the increasingly larger role of EMs in the world economy and the large-scale capital flows occurring after 2009. The review is conducted in a systematic manner and takes into consideration different methodological choices, samples and measurement issues. The paper puts the summarized results in the context of transmission channels identified in the literature. There are few distinct methodological approaches present in the literature. While there is a consensus regarding the direction of the impact of QE on EMs, its size and durability have not yet been assessed with sufficient precision. In addition, there are clear gaps in the empirical findings, not least related to relative underrepresentation of the CEE region (in particular, Poland).

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