2021 55th Annual Conference on Information Sciences and Systems (CISS)最新文献_第9页

Malware Subspecies Detection Method by Suffix Arrays and Machine Learning 基于后缀数组和机器学习的恶意软件亚种检测方法

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400219

Kouhei Kita, R. Uda

引用次数: 1

Leveraging Different Types of Predictors for Online Optimization (Invited Paper) 利用不同类型的预测因子进行在线优化(特邀论文)

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400315

Jessica Maghakian, Russell Lee, M. Hajiesmaili, Jian Li, Zhenhua Liu, R. Sitaraman

引用次数: 2

Performance of a Blind Adaptive Digital Beamformer on a Multi-user Rician Channel 盲自适应数字波束形成器在多用户信道上的性能研究

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400226

Peter O. Taiwo, A. Cole-Rhodes

引用次数: 0

Global Citation Recommendation employing Multi-view Heterogeneous Network Embedding 基于多视图异构网络嵌入的全球引文推荐

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400311

Z. Ali, G. Qi, Khan Muhammad, Asim Khalil, Inam Ullah, Amin Khan

{"title":"Global Citation Recommendation employing Multi-view Heterogeneous Network Embedding","authors":"Z. Ali, G. Qi, Khan Muhammad, Asim Khalil, Inam Ullah, Amin Khan","doi":"10.1109/CISS50987.2021.9400311","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400311","url":null,"abstract":"The enormous number of research papers on the Web motivated researchers to propose models that could assist users with personalized citation recommendations. Recently, Citation Recommendation (CR) models applying Network Representation Learning (NRL) techniques have revealed promising outcomes. Still, current NRL-based models are limited in terms of employing salient factors and relations between the objects of Multi-view Heterogeneous Networks (MHNs), hence, they failed to capture researchers' preferences. Besides, these models cannot exploit heterogeneity in the networks and hence suffer from the sparsity problems. To overcome these problems, we propose GCR-MHNE model, which employs a Multi-View Heterogeneous Network Embedding method to generate personalized recommendations. Specifically, it exploits semantic relations between papers based on citations, venue information, topical relevance, authors' information, and relevant labels to learn their vector representations. Moreover, the model captures the most influential features related to each semantic relation employing an attention mechanism. Compared to its counterparts, GCR-MHNE brings 6% and 7% improvements using the openly-available datasets in terms of Mean Average Precision and Normalized Discounted Cumulative Gain metrics, respectively. Furthermore, the proposed model outperforms its counterparts when the networks are sparse.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129632362","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: 5

Clickbait in Social Media: Detection and Analysis of the Bait 标题党在社交媒体:检测和分析的诱饵

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400293

Mini Jain, Peya Mowar, Ruchika Goel, D. Vishwakarma

{"title":"Clickbait in Social Media: Detection and Analysis of the Bait","authors":"Mini Jain, Peya Mowar, Ruchika Goel, D. Vishwakarma","doi":"10.1109/CISS50987.2021.9400293","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400293","url":null,"abstract":"Taking advantage of visual-centric social media's rising popularity, content creators have started using enticing images to lure users into clicking on bothersome clickbaits, in place of previously used text-based baits. In addition, the development of a single model to detect clickbait on multiple image-centric social media platforms is largely an unexplored problem. Therefore, we introduce a novel model that can detect visual clickbaits on both Instagram and Twitter posts. The proposed model consists of a stacking classifier framework composed of six base models (K-Nearest Neighbors, Support Vector Machine, XGBoost, Naive Bayes, Logistic Regression, and Multilayer Perceptron) and a meta-classifier (Random Forest). The developed classifier achieved an accuracy of 88.5% for Instagram posts and 85% for Twitter posts, which is an improvement over previous separate state-of-the-art models for both platforms. Additionally, the stated classifier does not use meta-features (e.g., the number of likes or followers) for classification, which helps to detect potential clickbaits right away, enhancing its applicability in real-time clickbait detection use cases. Furthermore, based on our analysis, we have drawn essential conclusions about the telling characteristics of clickbaits.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124071429","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: 4

Model-free safe policy learning via hard action barrier functions 通过硬动作障碍函数进行无模型安全策略学习

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400210

Agustin Castellano, J. Bazerque, Enrique Mallada

{"title":"Model-free safe policy learning via hard action barrier functions","authors":"Agustin Castellano, J. Bazerque, Enrique Mallada","doi":"10.1109/CISS50987.2021.9400210","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400210","url":null,"abstract":"To enable model-free learning of safe policies in Constrained Markov Decision Processes, we advance the notion of penalties as an information source that complements rewards and is similarly acquired from experience. Without prior knowledge of the safety constraints, the agent must resort to penalties-that signal infeasibility-in order to learn which actions lead to constraint violations. Accordingly, we define the notion of hard action barrier functions, which incorporate penalties as (0,+infinity) binary information, and gradually reveal implicit state-action pair constraints that must be satisfied in order to avoid (possibly future) unsafe states. Using this notion, we characterize a separation principle that decouples safety from reward maximization. Based on this principle we propose an adaptive algorithm that learns the action barrier function independently of the specific reward structure. As a result, our Barrier-Learning algorithm can wrap around standard on-and off-policy algorithms such as Q-Learning and SARSA. Our solution has the added benefit of learning from previous mistakes by avoiding bumping into the same rock twice, i.e., not taking the same unsafe action that led to a constraint violation in the past. This results in a policy that complies with the constraints almost surely. We demonstrate these combined algorithms in a grid-walk with walls that must be avoided on the way to a target.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"62 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132983075","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

Robust Automatic Modulation Classification in the Presence of Adversarial Attacks 存在对抗性攻击的鲁棒自动调制分类

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400326

R. Sahay, D. Love, Christopher G. Brinton

引用次数: 10

Investigating the Statistical Assumptions of Naïve Bayes Classifiers 研究Naïve贝叶斯分类器的统计假设

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400215

A. Kelly, M. Johnson

{"title":"Investigating the Statistical Assumptions of Naïve Bayes Classifiers","authors":"A. Kelly, M. Johnson","doi":"10.1109/CISS50987.2021.9400215","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400215","url":null,"abstract":"This paper investigates the impact of the probability distribution of a Naive Bayes classifier and the statistical distribution of the underlying feature data on the classifier's performance. Typical Naive Bayes performance assumptions lack quantitative and rigorous evidence in the common literature creating risk in rote application of Naive Bayes. This study investigates these performance assumptions to quantify where they are true, and the risk of maintaining those assumptions when utilizing Naive Bayes classifiers. Naive Bayes classifiers' exceptionally fast training times, performance, ease to implement, and minimal required resources often make them candidates for early classification trials, especially in Natural Language Processing tasks such as sentiment analysis. It is frequently assumed that the performance of a Naive Bayes classifier is heavily reliant with the distribution of the underlying data. This assumption is noted both in standard documentation and academic research and has largely been accepted as truth with little verification. This paper outlines an experiment that tests this assumption with real world sentiment analysis data. Naive Bayes classifiers were tested against non-Gaussian data, non-Gaussian feature weighted data, Gaussian-like data, and synthetically generated Gaussian data to observe the relationship between classifier performance and data distribution. Initial findings suggested that while this assumption is partially true, there may be additional factors heavily related with Naive Bayes performance that are not strictly related to a feature's distribution.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130121003","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: 9

A Practical Low Delay Digital Scheme for Wyner-Ziv Coding over Gaussian Broadcast Channel 一种实用的高斯广播信道wner - ziv编码低延迟数字方案

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400282

Zhuobin Lin, Xue-chen Chen, Xuming Lu, Hongzhou Tan

引用次数: 1

A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring 基于深度迁移学习的家庭健康监测边缘计算方法

2021 55th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2021-03-24 DOI: 10.1109/CISS50987.2021.9400321

A. Sufian, Changsheng You, M. Dong

{"title":"A Deep Transfer Learning-based Edge Computing Method for Home Health Monitoring","authors":"A. Sufian, Changsheng You, M. Dong","doi":"10.1109/CISS50987.2021.9400321","DOIUrl":"https://doi.org/10.1109/CISS50987.2021.9400321","url":null,"abstract":"The health-care gets huge stress in a pandemic or epidemic situation. Some diseases such as COVID-19 that causes a pandemic is highly spreadable from an infected person to others. Therefore, providing health services at home for noncritical infected patients with isolation shall assist to mitigate this kind of stress. In addition, this practice is also very useful for monitoring the health-related activities of elders who live at home. The home health monitoring, a continuous monitoring of a patient or elder at home using visual sensors is one such nonintrusive sub-area of health services at home. In this article, we propose a transfer learning-based edge computing method for home health monitoring. Specifically, a pre-trained convolutional neural network-based model can leverage edge devices with a small amount of ground-labeled data and fine-tuning method to train the model. Therefore, on-site computing of visual data captured by RGB, depth, or thermal sensor could be possible in an affordable way. As a result, raw data captured by these types of sensors is not required to be sent outside from home. Therefore, privacy, security, and bandwidth scarcity shall not be issues. Moreover, real-time computing for the above-mentioned purposes shall be possible in an economical way.","PeriodicalId":228112,"journal":{"name":"2021 55th Annual Conference on Information Sciences and Systems (CISS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133682396","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: 9