2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)最新文献_第2页

An Adaptive Black-box Defense against Trojan Attacks on Text Data 针对文本数据木马攻击的自适应黑盒防御

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732112

Fatima Alsharadgah, Abdallah Khreishah, M. Al-Ayyoub, Y. Jararweh, Guanxiong Liu, Issa M. Khalil, M. Almutiry, Nasir Saeed

{"title":"An Adaptive Black-box Defense against Trojan Attacks on Text Data","authors":"Fatima Alsharadgah, Abdallah Khreishah, M. Al-Ayyoub, Y. Jararweh, Guanxiong Liu, Issa M. Khalil, M. Almutiry, Nasir Saeed","doi":"10.1109/SNAMS53716.2021.9732112","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732112","url":null,"abstract":"Trojan backdoor is a poisoning attack against Neural Network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans into model parameters for backdoor breaches through a poisoned training process. Most of the proposed defenses against Trojan attacks assume a white-box setup, in which the defender either has access to the inner state of NN or can run back-propagation through it. Moreover, most of exiting works that propose white-box and black-box methods to defend Trojan backdoor focus on image data. Due to the the difference in the data structure, these defenses cannot be directly applied for textual data. We propose T-TROJDEF which is a more practical but challenging black-box defense method for text data that only needs to run forward-pass of the NN model. T-TROJDEF tries to identify and filter out Trojan inputs (i.e., inputs augmented with the Trojan trigger) by monitoring the changes in the prediction confidence when the input is repeatedly perturbed. The intuition is that Trojan inputs are more stable as the misclassification only depends on the trigger, while benign inputs will suffer when perturbed due to the perturbation of the classification features.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116270307","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

Towards an OLAP Cubes Recommendation Approach in Cloud Computing Environment 云计算环境下OLAP多维数据集推荐方法研究

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732105

Rahma Djiroun, Meriem Amel Guessoum, K. Boukhalfa, E. Benkhelifa

引用次数: 0

Honorary Chairs 名誉主席

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/snams53716.2021.9732092

Mohammed Essaaidi, M. Belkasmi, Mahdi Ben Ghorbel, Abdeslam Ahmadi

引用次数: 0

BERT-fused Model for Finnish-Swedish Translation 芬兰语-瑞典语翻译的bert融合模型

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9731849

I. Kumpulainen, J. Vankka

引用次数: 0

Reinforcement Learning Method for Autonomous UAVs Monitoring an Uncertain Target 自主无人机监测不确定目标的强化学习方法

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732147

Mohannad Al-Hefnawi, Ala’eddin Masadeh, H. Salameh, A. Musa

{"title":"Reinforcement Learning Method for Autonomous UAVs Monitoring an Uncertain Target","authors":"Mohannad Al-Hefnawi, Ala’eddin Masadeh, H. Salameh, A. Musa","doi":"10.1109/SNAMS53716.2021.9732147","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732147","url":null,"abstract":"Autonomous unmanned aerial vehicles are able to sense their surrounding environments, and fly safely with little or no human intervention. Autonomous unmanned aerial vehicles are characterized by their ability to make decisions based on predicting future possible situations and learning from previous experiences. In this paper, we aim at developing algorithms that enable unmanned aerial vehicles to monitor and detect a dynamic uncertain target autonomously. This work considers a real monitoring system consists of a mission area, an autonomous unmanned aerial vehicle, a charging station, and a dynamic uncertain target. The mission area consists of two main areas, which are the area where the charging station is placed and the area where the target moves. The target area is divided to a number of subareas. We also adopt a time slotted system that has M equal-duration slots. The unmanned aerial vehicle is equipped with a battery of finite energy that can be recharged from the charging station. It can fly from one subarea to another during one time slot. The target moves from one subarea to another according to an unknown Markov process. In this context, we propose to using reinforcement learning algorithms that enables autonomous unmanned aerial vehicles to learn the movement of a dynamic uncertain target autonomously. Simulation results show that reinforcement learning algorithms outperform the performance of random and circular algorithms.11This work was supported by the ASPIRE Award for Research Excellence Program 2020 (Abu Dhabi, UAE) under grant AARE20-161.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132131842","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: 3

Redundancy Avoidance in Entity Resolution Based On Social Networks Paradigm 基于社会网络范式的实体解析中的冗余回避

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9731846

Mohammad Sharif Daoud, Tarik Elamsy, Yazeed Ghadi, Ghina Albrazi, Mariam Shabou

引用次数: 0

Sentimental Analysis for Studying and Analyzing the Spreading of COVID-19 from Twitter Data 从Twitter数据研究和分析COVID-19传播的情感分析

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9731855

Qanita Bani Baker, Ayah Abu Aqouleh, Ola Altiti

{"title":"Sentimental Analysis for Studying and Analyzing the Spreading of COVID-19 from Twitter Data","authors":"Qanita Bani Baker, Ayah Abu Aqouleh, Ola Altiti","doi":"10.1109/SNAMS53716.2021.9731855","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9731855","url":null,"abstract":"Coronavirus, known as COVID-19, rapidly spread on a wide scale in a short time consequently. World Health Organization (WHO) classified it as a global pandemic. Social networks news becomes a valuable resource for massive amounts of data and news about the epidemic in which news is deliberating every day. Twitter is one of these networks which is a popular platform that contains rich information and currently it repre-sents a rich resource of data about COVID-19. In this research, we study and analyze the spreading of the COVID-19 epidemic based on the location and dates using datasets from Twitter. Moreover, the study has done by performing sentiment analysis and making a correlation study between confirmed cases in a set of countries and the sentiment's polarity value including negative and positive as well as a correlation between the number of confirmed cases and number of tweets per country. Also, we have experimented with several machine learning classifiers including Naive base, Support Vector Machine, and Logistic Regression as well as RoBERTa model to predict the sentiment analysis on the dataset. The experimental results show that Logistic Regression outperforms other classifiers with an accuracy of 0.86%, thus, machine learning techniques could be used to study the sentiment of tweets which gives reasonable results.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121693214","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

Collaboration Spotting X - A Visual Network Exploration Tool 协作发现X -一个可视化的网络探索工具

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732139

A. Bobic, J. Goff, Christian Guetl

{"title":"Collaboration Spotting X - A Visual Network Exploration Tool","authors":"A. Bobic, J. Goff, Christian Guetl","doi":"10.1109/SNAMS53716.2021.9732139","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732139","url":null,"abstract":"Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128874132","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

A Real-Time Human Mobility Visualization of Covid-19 Spread from East Asian Countries 东亚国家Covid-19传播的实时人员流动可视化

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732103

Addepalli Lavanya, Panwar Darsha, P. Akhil, Jaime Lloret, Navandar Yogeshwar

引用次数: 0

A Geospatial and ML-based Approach to Health Disparity Identification and Determinant Tracing for Improving Pandemic Health Care 基于地理空间和ml的健康差异识别和决定因素追踪方法，以改善大流行卫生保健

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9731851

Jinwei Liu, R. Aló, Yohn Jairo Parra Bautista, C. Yedjou, Carlos Theran

{"title":"A Geospatial and ML-based Approach to Health Disparity Identification and Determinant Tracing for Improving Pandemic Health Care","authors":"Jinwei Liu, R. Aló, Yohn Jairo Parra Bautista, C. Yedjou, Carlos Theran","doi":"10.1109/SNAMS53716.2021.9731851","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9731851","url":null,"abstract":"The Coronavirus disease 2019 (COVID-19) pan-demic has severely impacted countries around the world with unprecedented mortality and economic devastation and has dis-proportionately and negatively impacted different communities-especially racial and ethnic minorities who are at a particular disadvantage as they are more likely to be the potential target of COVID-19 infection. Black Americans have a long-standing history of disadvantage (e.g., long-standing disparities in health outcomes) and are in a vulnerable position to experience the impact of this pandemic. Some studies indicate high-risk and vulnerability of the elderly and patients with underlying co-morbidities, however, little research paid attention to leveraging geographic information to trace the social and structural health determinants, which can provide a lower level of granularity. In this paper, we propose GMLTrace, a geospatial and ML-based (machine learning based) approach to identify diverse determinants (including the structural, social, and constructural determinants) of health disparities in COVID-19 pandemic, which provides a lower level of granularity. We provide a thorough analysis of health disparities based on multiple COVID-19 datasets and examine the structural, social, and constructural health determinants to assist in ascertaining why disparities (in racial and ethnic minorities who are particularly disadvantaged) occur in infection and death rates due to COVID-19 pandemic. Extensive experimental results show the effectiveness of our approach. The research provides new strategies for health disparity identification and determinant tracing with a goal to improve pandemic health care.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126343026","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