2020 7th International Conference on Behavioural and Social Computing (BESC)最新文献_第2页

An Efficient Intrusion Detection Model Combined Bidirectional Gated Recurrent Units With Attention Mechanism 结合双向门控循环单元和注意机制的高效入侵检测模型

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348310

Jingyi Wang, Naiyue Chen, Jinhui Yu, Yi Jin, Yidong Li

{"title":"An Efficient Intrusion Detection Model Combined Bidirectional Gated Recurrent Units With Attention Mechanism","authors":"Jingyi Wang, Naiyue Chen, Jinhui Yu, Yi Jin, Yidong Li","doi":"10.1109/BESC51023.2020.9348310","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348310","url":null,"abstract":"In recent years, various types of network attacks emerge in endlessly, the protection of network security has been paid more and more attention by our society. Network Intrusion Detection System (NIDS)is used to protect computer systems from malicious attacks and intrusions, thus has also become a hot research field. Due to the great success of deep learning in industry and academia, there is an increasing interest in the application of deep learning methods for feature representations and classification. In this paper,we propose a intrusion detection model based on time-related deep learning approach with attention mechanism. Firstly, we build a stacked sparse autoencoder(SSAE) to extract high-level feature representations of intrusion information. Then we design a two-layer bidirectional gated recurrent unit(BiGRU) network with attention mechanism to classify traffic data. We perform experiments on a benchmark dataset UNSW-NB15, the results in binary classification indicate that using high-dimensional sparse features extracted by SSAE can significantly accelerate the classification progress. Our model can detect network intrusions effectively and outperform other related methods with reduction of false alarm rate, high accuracy rate, reduced training and testing time.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132896205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Effects of Temporal Factors on Human Mobility Lifestyle Through GPS Information 基于GPS信息的时间因素对人类流动性生活方式的影响

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348333

Tran Thao Phuong, Ryosuke Kobayashi, R. Yamaguchi

引用次数: 0

A Manifold Learning Data Enrichment Methodology for Homicide Prediction 凶杀预测的流形学习数据充实方法

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348295

J. S. M. Pabón, Mateo Dulce Rubio, Yor Castaño, Alvaro J. Riascos, Paula Rodríguez Díaz

{"title":"A Manifold Learning Data Enrichment Methodology for Homicide Prediction","authors":"J. S. M. Pabón, Mateo Dulce Rubio, Yor Castaño, Alvaro J. Riascos, Paula Rodríguez Díaz","doi":"10.1109/BESC51023.2020.9348295","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348295","url":null,"abstract":"Not all types of crime have the same priority in the agendas of policymakers since society tends to be more reluctant to more violent and costly crimes such as homicide. However, relative to other types of crime, homicides are statistically more challenging due to its sparsity and low frequency. For instance, over the last five years the average number of homicides across the city of Bogota has been roughly a thousand events per year, compared to the more than one hundred thousand robberies reported in the same period. Nevertheless, more than 80% of the homicides in the city occur during street fights suggesting a strong spatial and temporal correlation between these two types of crime. With this in mind, we used a manifold learning approach that capitalizes on a rich dataset of street fights to discover a criminal manifold that we use to penalize a KDE model of homicides where sparsity and low frequency is an issue. To implement this we follow a Kernel Warping methodology (Zhou & Matteson, 2015). The methodology reduces the relevant space for homicide prediction to regions of the city where homicides or street fights have occurred, giving more weight to the homicide episodes. We also introduce a temporal decay component to place a larger importance to recent events. The proposed model outperforms a standard KDE trained with homicide data, a KDE trained in both homicide and street fights data for homicide prediction, and a standard self-exciting point process on homicide data: flagging just the 5% of the area of the city with the highest estimated density, the Kernel Warping model correctly identifies between 30% and 35% of the homicides in the test set.11Results of the project “Diseño y validación de modelos de analítica predictiva de fenómenos de seguridad y convivencia para la toma de decisiones en Bogotá” funded by Colciencias with resources from the Sistema General de Regalías, BPIN 2016000100036. The opinions expressed are solely those of the authors.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133414422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Testing tailored weekly feedback messages for behavioral change of people living with diabetes using a mHealth application 使用移动健康应用程序测试定制的每周反馈信息，以改变糖尿病患者的行为

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348279

Noor Al-Ansari, Dena Al-Thani, L. Fernández-Luque, Mohammed El-Rishi

{"title":"Testing tailored weekly feedback messages for behavioral change of people living with diabetes using a mHealth application","authors":"Noor Al-Ansari, Dena Al-Thani, L. Fernández-Luque, Mohammed El-Rishi","doi":"10.1109/BESC51023.2020.9348279","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348279","url":null,"abstract":"This paper extends the previous work in the field of persuasive technology as it aims to examine the solutions of interest that have been developed based on cultural preferences through applying persuasive strategies on tailored persuasive messages. We attempted to examine its effectiveness in changing users' behaviour from a cultural perspective. This was achieved by applying the principles of social influence on the tailored messages of the mobile health (mHealth) application. A between-group design was adopted in this study, where each group received weekly feedback intervention messages related to monitoring diabetes via the mHealth application. The data were collected through monitoring participants with diabetes type II for 16 weeks. The results indicated that the weekly feedback intervention messages were not suitable for the targeted audience as participants in both groups did not read the messages. However, other results that focused on users' engagement allowed us to understand the users' behaviour and their level of engagement with the mHealth application.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122118255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sentiment, Count and Cases: Analysis of Twitter discussions during COVID-19 Pandemic 情绪、数量和案例:COVID-19大流行期间Twitter讨论分析

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348291

Zainab Tariq Soomro, Sardar Haider Waseem Ilyas, Ussama Yaqub

引用次数: 9

An Emerging Security Framework for Connected Autonomous Vehicles 联网自动驾驶汽车的新兴安全框架

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348317

Fatema Rashed Almehrezi, C. Yeun, Paul Yoo, E. Damiani, Y. A. Hammadi, H. Yeun

引用次数: 1

How public emotions change with emergent social events * 公众情绪是如何随着突发社会事件而变化的

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348319

T. Xue, Hongyu Sun, Meng Zhang

{"title":"How public emotions change with emergent social events *","authors":"T. Xue, Hongyu Sun, Meng Zhang","doi":"10.1109/BESC51023.2020.9348319","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348319","url":null,"abstract":"The purpose of this study is to explore the correlation between emergent social events and different types of public emotion. We first extract the indexes of events and emotions from data of Baidu Index. Then the correlation between emergent social events and public emotions and the correlation between different types of public emotions are analyzed by the Granger causality test. Results show that the public's attention to emergent social events involved in this study has gone through a process from explosion, the decline to fading. Moreover, the trajectory of fear in part of the period is consistent with the life cycle model. We also find that fear and anger are the main emotional reactions at the beginning of most emergent social events, and after the attention to the event subsides, sadness and depression increase, and depression has peaked to varying degrees at different stages of the event. Finally, Granger analyses show that most of the event indexes can predict fear in short-term, while they can predict sadness and depression in the middle and long term. Also, fear and anger can more or less predict subsequent sadness and depression. The results could verify the individual response to stress at the group level.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126466520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Attitude Model of Opinion Diffusion: Experiments with Epistemically Motivated Agents 意见扩散的双重态度模型:认知动机主体的实验

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348303

Riyang Phang, Lin Qiu, A. Leung

{"title":"Dual Attitude Model of Opinion Diffusion: Experiments with Epistemically Motivated Agents","authors":"Riyang Phang, Lin Qiu, A. Leung","doi":"10.1109/BESC51023.2020.9348303","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348303","url":null,"abstract":"Opinion diffusion is often simulated in agent-based models to reveal the perpetuation of norms and beliefs. This paper presents a dual attitude model where agents' interaction, information search, and opinion formation are influenced by the need for cognitive closure (NFCC). Two experiments simulated topic advocacy with either high- or low-NFCC agents. Experiment one initiated societies with unbiased distribution of NFCC levels between advocates of two competing topics, while experiment two initiated biased distributions of NFCC levels between the topics. Results in the unbiased condition showed that the popularity of the majority topic increases over time in high NFCC societies while it decreases over time in low NFCC societies. These results are magnified in the biased context where high NFCC agents provided an NFCC-advantage for their advocaed topic. When high NFCC agents' advocated topic is the majority or equal at initiation, the topic's popularity will increase significantly over time. When high NFCC agents' advocated topic is minority at initiation, these agents resist the assimilative pressures of the majority topic to protect their own topic from popularity losses. Tracking simulations over time revealed different dynamics generated between the two experimental conditions, and showed the roles low NFCC agents and edge-of-cluster agents play in enabling the emergence of such patterns. These results may shed light on the impact NFCC individuals have in within-society and between-societies cultural shifts.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133899772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contact Tracing Apps for COVID-19: Access Permission and User Adoption COVID-19接触者追踪应用:访问权限和用户采用

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348327

Amal Awadalla Mohamed Ali, A. ElFadl, M. Abujazar, S. Aziz, Alaa A. Abd-alrazaq, Zubair Shah, S. Belhaouari, M. Househ, T. Alam

引用次数: 5

Your Identity is Yours: Take Back Control of Your Identity Using GDPR Compatible Self-Sovereign Identity 你的身份是你的:使用GDPR兼容的自我主权身份夺回你的身份控制权

2020 7th International Conference on Behavioural and Social Computing (BESC) Pub Date : 2020-11-05 DOI: 10.1109/BESC51023.2020.9348298

N. Naik, Paul Jenkins

{"title":"Your Identity is Yours: Take Back Control of Your Identity Using GDPR Compatible Self-Sovereign Identity","authors":"N. Naik, Paul Jenkins","doi":"10.1109/BESC51023.2020.9348298","DOIUrl":"https://doi.org/10.1109/BESC51023.2020.9348298","url":null,"abstract":"Digital identity has importance in the digital world representing users in a comparable manner to that of the physical identity in the real world. Digital identity comprises certain personal and confidential attributes related to identity owners, managed through an Identity Management (IDM) system. In most IDM systems, identity owners do not control their own identity and its related personal data. However, Self-Sovereign Identity (SSI) is an emerging IDM system which offers users the ownership and full control over their personal data. In the European Union, General Data Protection Regulation (GDPR) is the basic regulatory environment for anyone involved in processing personal data, whilst SSI is concerned with the requirement of managing identity and its associated personal data. If an SSI system could comply with the key GDPR principles then it could become both a desirable and appropriate IDM solution legally and universally. This paper evaluates this aspect of SSI and analyses SSI compliance and alignment with the key principles of GDPR. Furthermore, it investigates two different types of SSI ecosystems public permissionless blockchain based SSI ecosystem uPort and public permissioned blockchain based SSI ecosystem Sovrin, according to the various defined roles and their compatibility with GDPR roles. Finally, this paper performs the comparative analysis of uPort and Sovrin to assess their compliance with the key principles of GDPR.","PeriodicalId":224502,"journal":{"name":"2020 7th International Conference on Behavioural and Social Computing (BESC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131346073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14