2023 57th Annual Conference on Information Sciences and Systems (CISS)最新文献_第9页

Distributed Detection in EH-Powered Mobile WSNs: Adaptive Transmission over Temporally Correlated MIMO Channels with Limited Feedback eh驱动移动wsn的分布式检测:有限反馈时相关MIMO信道的自适应传输

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089747

Ghazaleh Ardeshiri, A. Vosoughi

{"title":"Distributed Detection in EH-Powered Mobile WSNs: Adaptive Transmission over Temporally Correlated MIMO Channels with Limited Feedback","authors":"Ghazaleh Ardeshiri, A. Vosoughi","doi":"10.1109/CISS56502.2023.10089747","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089747","url":null,"abstract":"We address distributed detection problem in a mobile wireless sensor network, where each deployed sensor stores randomly arriving energy units in a finite-size battery. Sensors transmit their symbols simultaneously to a mobile fusion center (FC) with $M > 1$ antennas, over temporally correlated fading channels. To characterize the time variation of the fading channel, we adopt a Markovian model and assume that the fading channel time-correlation is defined by the Jakes-Clark's correlation function. We consider limited feedback of channel gain, defined as the Frobenius norm of MIMO channel matrix, at a fixed feedback frequency from the FC to sensors. Modeling the randomly arriving energy units during a time slot as a Poisson process, and the quantized channel gain and the battery dynamics as homogeneous finite-state Markov chains, we propose an adaptive transmission scheme such that the $J$-divergence based detection metric is maximized at the FC, subject to an average per-sensor transmit power constraint. The proposed scheme is parameterized in terms of the scale factors (our optimization variables) corresponding to the channel gain quantization intervals. This scheme allows each sensor to adapt its transmit power in each time slot, based on its current battery state and the latest available channel gain feedback.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130313220","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

Advancing Temporal Multimodal Learning with Physics Informed Regularization 用物理信息正则化推进时间多模态学习

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089632

Niharika Deshpande, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

{"title":"Advancing Temporal Multimodal Learning with Physics Informed Regularization","authors":"Niharika Deshpande, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon","doi":"10.1109/CISS56502.2023.10089632","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089632","url":null,"abstract":"Estimating multimodal distributions of travel times from real-world data is critical for understanding and managing congestion. Mixture models can estimate the overall distribution when distinct peaks exist in the probability density function, but no transfer of mixture information under epistemic uncertainty across different spatiotemporal scales has been considered for capturing unobserved heterogeneity. In this paper, a physics-informed and -regularized prediction model is developed that shares observations across similarly distributed network segments across time and space. By grouping similar mixture models, the model uses a particular sample distribution at distant non-contiguous unexplored locations and improves TT prediction. Compared to traditional prediction without those updates, the proposed model's 19% of performance show the benefit of indirect learning. Different from traditional travel time prediction tools, the developed model can be used by traffic and planning agencies in knowing how far back in history and what sample size of historic data would be useful for current prediction.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"09 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127374852","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

Quantifying Phase- Amplitude Modulation in Neural Data 量化神经数据中的相位-幅度调制

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089691

Victoria Subritzky-Katz, Aaron L. Sampson, Erik E. Emeric, W. Lipski, Sophia Moreira-González, Jorge González-Martínez, S. Sarma, V. Stuphorn, E. Niebur

引用次数: 0

Slime Mold Algorithm-Based Performance Improvement of PD-Type Indirect Iterative Learning Fuzzy Control of Tower Crane Systems 基于黏菌算法的pd型间接迭代学习模糊控制塔机系统性能改进

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-03-22 DOI: 10.1109/CISS56502.2023.10089708

R. Precup, Raul-Cristian Roman, Elena-Lorena Hedrea, E. Petriu, C. Dragos, Alexandra-Iulia Szedlak-Stînean

{"title":"Slime Mold Algorithm-Based Performance Improvement of PD-Type Indirect Iterative Learning Fuzzy Control of Tower Crane Systems","authors":"R. Precup, Raul-Cristian Roman, Elena-Lorena Hedrea, E. Petriu, C. Dragos, Alexandra-Iulia Szedlak-Stînean","doi":"10.1109/CISS56502.2023.10089708","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089708","url":null,"abstract":"This current paper proposes to improve the performance of three Single Input-Single Output (SISO) fuzzy control systems of controlling every position of tower crane systems using Proportional-Derivative (PD)-type indirect iterative learning rules at the higher hierarchical levels in each SISO control loop. The lower hierarchical levels in the three SISO control loops are built upon three low-cost Takagi-Sugeno Proportional-Integral (PI)-fuzzy controllers tuned by the initial application of Extended Symmetrical Optimum (ESO) method to the linear PI controllers and next the transfer of the results to the PI-fuzzy controllers in terms of the modal equivalence principle. Set-point filters are included at the lower hierarchical level in the context of the ESO method for overshoot reduction. The design approach is presented in a unified way for all three controllers. The gains of the PD-type learning rules are optimally computed in the iteration domain considering a metaheuristic Slime Mold Algorithm (SMA) in a transparent and simplified version, that settles the optimization problems with objective functions expressed as the sums of squared control errors multiplied by time. The enhanced performance is settled considering ten sets of iterations of SMA.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133197824","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

An Accelerated Asynchronous Distributed Method for Convex Constrained Optimization Problems 凸约束优化问题的加速异步分布方法

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-02-21 DOI: 10.1109/CISS56502.2023.10089633

Nazanin Abolfazli, A. Jalilzadeh, E. Y. Hamedani

引用次数: 0

Federated Learning via Indirect Server-Client Communications 通过间接服务器-客户端通信进行联邦学习

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-02-14 DOI: 10.1109/CISS56502.2023.10089783

Jieming Bian, Cong Shen, Jie Xu

{"title":"Federated Learning via Indirect Server-Client Communications","authors":"Jieming Bian, Cong Shen, Jie Xu","doi":"10.1109/CISS56502.2023.10089783","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089783","url":null,"abstract":"Federated Learning (FL) is a communication-efficient and privacy-preserving distributed machine learning framework that has gained a significant amount of research attention recently. Despite the different forms of FL algorithms (e.g., synchronous FL, asynchronous FL) and the underlying optimization methods, nearly all existing works implicitly assumed the existence of a communication infrastructure that facilitates the direct communication between the server and the clients for the model data exchange. This assumption, however, does not hold in many real-world applications that can benefit from distributed learning but lack a proper communication infrastructure (e.g., smart sensing in remote areas). In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e.g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients. Two algorithms, called FedEx-Sync and FedEx-Async, are developed depending on whether the transporters adopt a synchronized or an asynchronized schedule. Even though the indirect communications introduce heterogeneous delays to clients for both the global model dissemination and the local model collection, we prove the convergence of both versions of FedEx. The convergence analysis subsequently sheds lights on how to assign clients to different transporters and design the routes among the clients. The performance of FedEx is evaluated through experiments in a simulated network on two public datasets.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132380273","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

Piecewise Linear and Stochastic Models for the Analysis of Cyber Resilience 网络弹性分析的分段线性和随机模型

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-02-09 DOI: 10.1109/CISS56502.2023.10089725

Michael J. Weisman, A. Kott, J. Vandekerckhove

引用次数: 1

Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios 面部表情识别场景中的三重无损中心损失采样策略

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-02-08 DOI: 10.1109/CISS56502.2023.10089734

Hossein Rajoli, Fatemeh Lotfi, A. Atyabi, F. Afghah

{"title":"Triplet Loss-less Center Loss Sampling Strategies in Facial Expression Recognition Scenarios","authors":"Hossein Rajoli, Fatemeh Lotfi, A. Atyabi, F. Afghah","doi":"10.1109/CISS56502.2023.10089734","DOIUrl":"https://doi.org/10.1109/CISS56502.2023.10089734","url":null,"abstract":"Facial expressions convey massive information and play a crucial role in emotional expression. Deep neural network (DNN) accompanied by deep metric learning (DML) techniques boost the discriminative ability of the model in facial expression recognition (FER) applications. DNN, equipped with only classification loss functions such as Cross-Entropy cannot compact intra-class feature variation or separate inter-class feature distance as well as when it gets fortified by a DML supporting loss item. The triplet center loss (TCL) function is applied on all dimensions of the sample's embedding in the embedding space. In our work, we developed three strategies: fully-synthesized, semi-synthesized, and prediction-based negative sample selection strategies. To achieve better results, we introduce a selective attention module that provides a combination of pixel-wise and element-wise attention coefficients using high-semantic deep features of input samples. We evaluated the proposed method on the RAF-DB, a highly imbalanced dataset. The experimental results reveal significant improvements in comparison to the baseline for all three negative sample selection strategies.","PeriodicalId":243775,"journal":{"name":"2023 57th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128021829","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

Human-machine Hierarchical Networks for Decision Making under Byzantine Attacks 拜占庭攻击下的人机层次网络决策

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2023-01-25 DOI: 10.1109/CISS56502.2023.10089766

Chen Quan, Baocheng Geng, Y. Han, P. Varshney

引用次数: 0

Model Segmentation for Storage Efficient Private Federated Learning with Top $r$ Sparsification 基于Top $r$稀疏的存储高效私有联邦学习模型分割

2023 57th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2022-12-22 DOI: 10.1109/CISS56502.2023.10089698

Sajani Vithana, S. Ulukus

引用次数: 3