2022 56th Annual Conference on Information Sciences and Systems (CISS)最新文献_第2页

Sparse Matrix Codes: Rate-Reliability Trade-offs for URLLC 稀疏矩阵码:URLLC的率-可靠性权衡

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751171

Sudarshan Adiga, R. Tandon, T. Bose

引用次数: 0

Traffic Rate Network Tomography via Moment Generating Function Matching 基于矩生成函数匹配的流量速率网络断层扫描

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751201

Y. Ephraim, Joshua Coblenz, B. L. Mark, H. Lev-Ari

{"title":"Traffic Rate Network Tomography via Moment Generating Function Matching","authors":"Y. Ephraim, Joshua Coblenz, B. L. Mark, H. Lev-Ari","doi":"10.1109/CISS53076.2022.9751201","DOIUrl":"https://doi.org/10.1109/CISS53076.2022.9751201","url":null,"abstract":"Network tomography aims at estimating source-destination traffic rates from link traffic measurements. This inverse problem was formulated by Vardi in 1996 for independent Poisson traffic over networks operating under deterministic as well as random routing regimes. Vardi used a second-order moment matching approach to estimate the rates where a solution for the resulting linear matrix equation was obtained using an iterative minimum I-divergence procedure. Vardi's second-order moment matching approach was recently extended to higher order cumulant matching approach with the goal of improving the rank of the system of linear equations. In this paper we go one step further and develop a moment generating function matching approach for rate estimation, and seek a least squares as well as an iterative minimum I-divergence solution of the resulting linear equations. We also specialize this approach to a characteristic function matching approach which exhibits some advantages. These follow from the fact that the characteristic function matching approach results in fewer conflicting equations involving the empirical estimates. We demonstrate that the new approach outperforms the cumulant matching approach while being conceptually simpler.","PeriodicalId":305918,"journal":{"name":"2022 56th Annual Conference on Information Sciences and Systems (CISS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116500614","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

Estimating Test Statistic Distributions for Multiple Hypothesis Testing in Sensor Networks 传感器网络中多重假设检验的检验统计量分布估计

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751186

Martin Gölz, A. Zoubir, V. Koivunen

引用次数: 2

Identifying the Superspreader in Proactive Backward Contact Tracing by Deep Learning 利用深度学习识别主动后向接触追踪中的超级传播者

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751196

Siya Chen, Pei-Duo Yu, C. Tan, H. Poor

引用次数: 2

Deep Learning Based Bacteria Classification from SEM Images Using a Combination of Membrane and Internal Features 基于膜特征和内部特征结合的SEM图像细菌深度学习分类

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751170

Yasuki Kakishita, Hideharu Hattori, Arkadip Ray, A. Hisada, Y. Ominami, J. Baudoin, J. B. Khalil, D. Raoult

{"title":"Deep Learning Based Bacteria Classification from SEM Images Using a Combination of Membrane and Internal Features","authors":"Yasuki Kakishita, Hideharu Hattori, Arkadip Ray, A. Hisada, Y. Ominami, J. Baudoin, J. B. Khalil, D. Raoult","doi":"10.1109/CISS53076.2022.9751170","DOIUrl":"https://doi.org/10.1109/CISS53076.2022.9751170","url":null,"abstract":"Scanning electron microscopes (SEM) take very high-magnification images that allow us to examine the mor-phological features of bacteria samples in great detail. However, there are two major problems associated with this task. First, typical SEM images of bacteria show many different bacteria touching each other. For accurate classification and quantitative analysis, the touching bacteria need to be distinguished correctly into individual bacteria regions. Second, in these images, different types of bacteria might share similar visual features and have only locally distinguishable features. Here, we propose a system to perform multi-class classification of bacteria from SEM images. The system incorporates distance map inference and feature classification of the membrane and internal bacterial region, in order to segment the bacteria regions accurately, distinguish bacteria in contact with one another and identify local bacterial features. Extensive experimentation on an original bacteria dataset that we prepared shows that our system outperforms other object detection and segmentation methods on this problem.","PeriodicalId":305918,"journal":{"name":"2022 56th Annual Conference on Information Sciences and Systems (CISS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123863258","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

Democratizing Language Learning using Machine Learning

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751168

Ahana Gangopadhyay, Indrajit Bardhan, Anirban Das, N. Soman, Santanu Das

{"title":"Democratizing Language Learning using Machine Learning","authors":"Ahana Gangopadhyay, Indrajit Bardhan, Anirban Das, N. Soman, Santanu Das","doi":"10.1109/CISS53076.2022.9751168","DOIUrl":"https://doi.org/10.1109/CISS53076.2022.9751168","url":null,"abstract":"Most apps available in the market for learning a new language are severely limited in terms of the number of languages users can learn on the platform (“target language”), as well as the language in which users can receive instruction (“source language”). Users are also limited by the fixed sets of lessons or the curriculum provided by these apps. In this work, we present an app framework which allows users free choice of source and target languages, as well as the flexibility in learning any input word, phrase or sentence of their choice using machine translation, whose performance and coverage of new languages is continuously improving. The app provides real-time feedback on the correctness of user pronunciation for any input using a text-based similarity metric, and helps learners practice their pronunciation until they perfect it. The app also provides a conversation platform where intermediate and advanced learners can engage in simple, real-time conversations with a chatbot on topics they are most likely to engage in while learning a new language. The conversation platform uses machine translation and speech-to-text tools to convert user query in any source language into an English query, gets the chatbot response and converts it back to the source language. This simple and novel approach allows users to freely converse in any language they want to practice, while having the chatbot trained only on English language-based corpus. The chatbot also integrates a novel intent classification module that classifies user query into one of several available topics, thereby enabling the chatbot to continue conversation with the user in the same topic. Finally, the chatbot is also capable of integrating search capability for specific queries (e.g., weather) with the help of available public domain resources so that it can provide users with real-time updates for such queries, thus making language learning fun and interesting.","PeriodicalId":305918,"journal":{"name":"2022 56th Annual Conference on Information Sciences and Systems (CISS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126238976","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

Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data 数据驱动的时空动态脑连通性分析:应用于感觉运动任务数据

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751190

Khondoker Murad Hossain, Suchita Bhinge, Qunfang Long, V. Calhoun, T. Adalı

{"title":"Data-driven spatio-temporal dynamic brain connectivity analysis using fALFF: Application to sensorimotor task data","authors":"Khondoker Murad Hossain, Suchita Bhinge, Qunfang Long, V. Calhoun, T. Adalı","doi":"10.1109/CISS53076.2022.9751190","DOIUrl":"https://doi.org/10.1109/CISS53076.2022.9751190","url":null,"abstract":"Dynamic functional connectivity (dFC) analysis enables us to capture the time-varying interactions between brain regions and can lead to powerful biomarkers. Most dFC studies are focused on the study of temporal dynamics and require significant post-processing to summarize the results of the dynamics analysis. In this paper, we introduce an effective framework that makes use of independent vector analysis (IVA) with fractional amplitude of low frequency fluctuation (fALFF) features extracted from task functional magnetic resonance imaging (fMRI) data. Our approach, which is based on IVA with fALLF features as input, (IVA-fALLF) produces an effective summary of the dynamics also greatly facilitating the study of both spatial and temporal dynamics in a more concise manner. IVA-fALLF captures the spatial and temporal dynamics of sensorimotor task data and identifies a component with significant difference in dynamic behavior between healthy controls (HC) and patients with schizophrenia (SZ). Finally, our post analysis using behavioral scores finds significant correlation between brain imaging data and the associated behavioral scores, increasing confidence on our results. Our results are consistent with the previous data-driven dFC analysis as we find similar brain networks showing abnormal behavior in patients with SZ. Moreover, our analysis identifies component behavior in task and rest windows separately and provides additional confirmation of results through correlation with behavioral scores.","PeriodicalId":305918,"journal":{"name":"2022 56th Annual Conference on Information Sciences and Systems (CISS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121727803","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

The RBMLE method for Reinforcement Learning 强化学习的RBMLE方法

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751189

A. Mete, Rahul Singh, P. Kumar

引用次数: 2

Convergence Time Minimization for Federated Reinforcement Learning over Wireless Networks 无线网络上联邦强化学习的收敛时间最小化

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751199

Sihua Wang, Mingzhe Chen, Changchuan Yin, H. Poor

{"title":"Convergence Time Minimization for Federated Reinforcement Learning over Wireless Networks","authors":"Sihua Wang, Mingzhe Chen, Changchuan Yin, H. Poor","doi":"10.1109/CISS53076.2022.9751199","DOIUrl":"https://doi.org/10.1109/CISS53076.2022.9751199","url":null,"abstract":"In this paper, the convergence time of federated reinforcement learning (FRL) that is deployed over a realistic wireless network is studied. In the considered model, several devices and the base station (BS) jointly participate in the iterative training of an FRL algorithm. Due to limited wireless resources, the BS must select a subset of devices to exchange FRL training parameters at each iteration, which will significantly affect the training loss and convergence time of the considered FRL algorithm. This joint learning, wireless resource allocation, and device selection problem is formulated as an optimization problem aiming to minimize the FRL convergence time while meeting the FRL temporal difference (TD) error requirement. To solve this problem, a deep Q network based algorithm is designed. The proposed method enables the BS to dynamically select an appropriate subset of devices to join the FRL training. Given the selected devices, a resource block allocation scheme can be derived to further minimize the FRL convergence time. Simulation results with real data show that the proposed approach can reduce the FRL convergence time by up to 44.7% compared to a baseline that randomly determines the subset of participating devices and their occupied resource blocks.","PeriodicalId":305918,"journal":{"name":"2022 56th Annual Conference on Information Sciences and Systems (CISS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125312132","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

CP-ABE encryption over MQTT for an IoT system with Raspberry Pi 基于MQTT的CP-ABE加密的树莓派物联网系统

2022 56th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-03-09 DOI: 10.1109/CISS53076.2022.9751194

Fredy Mendoza-Cardenas, Rai Stiv Leon-Aguilar, Jose L. Quiroz Arroyo

引用次数: 2