2020 54th Annual Conference on Information Sciences and Systems (CISS)最新文献_第2页

A Bayesian lower bound for parameters with bounded support priors 具有有限支持先验的参数的贝叶斯下界

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570624061

Raksha Ramakrishna, A. Scaglione

引用次数: 1

CISS 2020 Technical Co-sponsorship CISS 2020技术共同赞助

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/ciss48834.2020.9086244

引用次数: 0

Overcoming Challenges for Estimating Virus Spread Dynamics from Data 克服从数据估计病毒传播动态的挑战

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570627764

Damir Vrabac, Philip E. Paré, H. Sandberg, K. Johansson

引用次数: 5

Transfer Learning for Wildfire Identification in UAV Imagery 无人机图像野火识别的迁移学习

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570617429

Haiyu Wu, Huayu Li, Alireza Shamsoshoara, A. Razi, F. Afghah

{"title":"Transfer Learning for Wildfire Identification in UAV Imagery","authors":"Haiyu Wu, Huayu Li, Alireza Shamsoshoara, A. Razi, F. Afghah","doi":"10.1109/CISS48834.2020.1570617429","DOIUrl":"https://doi.org/10.1109/CISS48834.2020.1570617429","url":null,"abstract":"Due to Wildfire’s huge destructive impacts on agriculture and food production, wildlife habitat, climate, human life and ecosystem, timely discovery of fires enable swift response to fires before they go out of control, in order to minimize the resulting damage and impacts. One of the emerging technologies for fire monitoring is deploying Unmanned Aerial Vehicles, due to their high flexibility and maneuverability, less human risk, and on-demand high quality imaging capabilities. In order to realize a real-time system for fire detection and expansion analysis, fast and high-accuracy image-processing algorithms are required. Several studies have shown that deep learning methods can provide the most accurate response, however the training time can be prohibitively long, especially when using online learning for constant refinement of the developed model. Another challenge is the lack of large datasets for training a deep learning algorithm. In this respect, we propose to use a pretrained mobileNetV2 architecture to implement transfer learning, which requires a smaller dataset and reduces the computational complexity while not compromising the accuracy. In addition, we conduct an effective data augmentation pipeline to simulate some extreme scenarios, which could promise the robustness of our approach. The testing results illustrate that our method maintains a high identification accuracy in different situations - original dataset (99.7%), adding Gaussian blurred (95.3%), and additive Gaussian noise (99.3%) 1 2","PeriodicalId":256370,"journal":{"name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131893565","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: 24

Spectrum Sensing in Interference and Noise Using Deep Learning 基于深度学习的干扰和噪声频谱感知

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570617443

Daniel Chew, A. Cooper

{"title":"Spectrum Sensing in Interference and Noise Using Deep Learning","authors":"Daniel Chew, A. Cooper","doi":"10.1109/CISS48834.2020.1570617443","DOIUrl":"https://doi.org/10.1109/CISS48834.2020.1570617443","url":null,"abstract":"Wireless devices are ubiquitous and consequently the spectrum is congested. Dynamic spectrum access is becoming more widespread in unlicensed bands and as a means to allow secondary users white space in licensed bands. Spectrum sensing capabilities are the cornerstone of dynamic spectrum access. In this paper, the spectrum-sensing problem is transformed into an image recognition problem and machine learning is employed to distinguish between noise and the presence of a signal. An existing Convolutional Neural Network (CNN), AlexNet, is repurposed to sense the spectrum for energy using only a small training set of a few hundred samples. The performance of the CNN detector is then compared to the performance of more traditional energy detection as well as other published results of machine learning used for signal detection. The CNN detector presented here surpasses the other machine learning methods for signal detection. The CNN detector does not require a measurement of the noise floor, which offers a significant improvement over the classic energy detector. The CNN detector also surpasses the performance of the energy detector in the presence of narrowband interference.","PeriodicalId":256370,"journal":{"name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134460918","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: 6

Communication Efficient Framework for Decentralized Machine Learning 分布式机器学习的高效通信框架

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570627384

Anis Elgabli, Jihong Park, A. S. Bedi, M. Bennis, V. Aggarwal

引用次数: 7

An Optimal Stopping Approach for Iterative Training in Federated Learning 联邦学习中迭代训练的最优停止方法

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570616094

Pengfei Jiang, Lei Ying

{"title":"An Optimal Stopping Approach for Iterative Training in Federated Learning","authors":"Pengfei Jiang, Lei Ying","doi":"10.1109/CISS48834.2020.1570616094","DOIUrl":"https://doi.org/10.1109/CISS48834.2020.1570616094","url":null,"abstract":"This paper studies the problem of iterative training in Federated Learning. We consider a system with a single parameter server (PS) and M client devices for training a predictive learning model with distributed data sets on the client devices. The clients communicate with the parameter server using a common wireless channel, so each time only one device can transmit. The training is an iterative process consisting of multiple rounds. At beginning of each round (also called an iteration), each client trains the model, broadcast by the parameter server at the beginning of the round, with its own data. After finishing training, the device transmits the update to the parameter server when the wireless channel is available. The server aggregates updates to obtain a new model and broadcasts it to all clients to start a new round. We consider adaptive training where the parameter server decides when to stop/restart a new round, and formulate the problem as an optimal stopping problem. While this optimal stopping problem is difficult to solve, we propose a modified optimal stopping problem. We first develop a low complexity algorithm to solve the modified problem, which also works for the original problem. Experiments on a real data set shows significant improvements compared with policies collecting a fixed number of updates in each round.","PeriodicalId":256370,"journal":{"name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123955242","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

An Adaptive Content Skipping Methodology based on User Behavioral Modeling 基于用户行为建模的自适应内容跳过方法

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570629135

Yuwei Tu, Elizabeth Tenorio, Christopher G. Brinton

{"title":"An Adaptive Content Skipping Methodology based on User Behavioral Modeling","authors":"Yuwei Tu, Elizabeth Tenorio, Christopher G. Brinton","doi":"10.1109/CISS48834.2020.1570629135","DOIUrl":"https://doi.org/10.1109/CISS48834.2020.1570629135","url":null,"abstract":"Adaptive Educational Systems (AES) have demonstrated the potential of improving learning efficacy by individualizing course delivery to particular user needs. Whereas the algorithms driving today’s AES are primarily based on user responses to quiz questions, these systems are now capable of capturing fine-granular behavioral data on users, such as keystroke measurements on lecture videos and social interactions on discussion forums. In this paper, we develop a methodology that leverages behavioral data for the task of content skipping in an AES, i.e., detecting content segments that are unnecessary for a user and passing over them automatically. Our methodology contains three modules: (1) a Behavioral Data Processor, which converts user behaviors and course content into algorithm features including course topics, (2) a User State Tracer, which maintains an estimate of user knowledge state and interest on a per-topic basis, and (3) a Content Skipping Trigger, which determines the segments to be removed from the course for this user. In evaluating our approach on two real-world datasets collected from courses hosted on our existing platform, we find 80-90% accuracy in terms of identify segments that users would themselves eventually skip. In doing so, we also perform some exploratory analysis to show how the prediction results can help instructors to improve the course design quality.","PeriodicalId":256370,"journal":{"name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128826947","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

A Statistical Decision-Theoretic Approach for Measuring Privacy Risk and Utility in Databases 数据库中隐私风险与效用度量的统计决策方法

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570617434

Alisa Miyashita, Akira Kamatsuka, Takahiro Yoshida, T. Matsushima

引用次数: 0

Convolutional Neural Network Based Radio Tomographic Imaging 基于卷积神经网络的无线电层析成像

2020 54th Annual Conference on Information Sciences and Systems (CISS) Pub Date : 2020-03-01 DOI: 10.1109/CISS48834.2020.1570617238

Hongzhuang Wu, Xiaoli Ma, C. Yang, Songyong Liu

{"title":"Convolutional Neural Network Based Radio Tomographic Imaging","authors":"Hongzhuang Wu, Xiaoli Ma, C. Yang, Songyong Liu","doi":"10.1109/CISS48834.2020.1570617238","DOIUrl":"https://doi.org/10.1109/CISS48834.2020.1570617238","url":null,"abstract":"Radio tomographic imaging (RTI) is a promising technology to reconstruct the attenuation caused by physical objects in wireless networks, which has shown potential for various applications (e.g., static structure estimation, through-wall imaging, device-free localization, intrusion detection). Unfortunately, since the inverse problem of radio tomography is often ill-posed, as well as the prior information of the tomographic model and imaging features is limited, traditional RTI methods cannot achieve high accuracy. In addition, most existing methods can only reconstruct the changes in the imaging area, or they require a separate model calibration step which may cause some mismatching error. In this paper, an uncalibrated RF sensor network with mobile nodes is used to gather RSS measurements. We analyze the forward model of radio tomography in detail, and propose a convolutional neural network (CNN) based deep learning method to solve the inverse problem. Two CNNs which have the same structure are designed to reconstruct the static tomographic images and estimate the model parameters, respectively. Besides, we present a CNN based image post-processing method to improve imaging quality further. Simulation results validate the efficacy of the proposed methods.","PeriodicalId":256370,"journal":{"name":"2020 54th Annual Conference on Information Sciences and Systems (CISS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131187707","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