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arXiv 2020年

作者： Zhao, Junhan Liu, Xiang Guo, Chen Qian, Zhenyu Cheryl Chen, Yingjie Victor Department of Computer Graphics Technology Purdue University West LafayetteIN47907 United States Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States School of Media Arts and Design James Madison University HarrisonburgVA22807 United States Department of Art and Design Purdue University West LafayetteIN47907 United States

The multidimensional nature of spatial data poses a challenge for visualization. In this paper, we introduce Phoenixmap, a simple abstract visualization method to address the issue of visualizing multiple spatial distributions at once. The Phoenixmap approach starts by identifying the enclosed outline of the point collection, then assigns different widths to outline segments according to the segments' corresponding inside regions. Thus, one 2D distribution is represented as an outline with varied thicknesses. Phoenixmap is capable of overlaying multiple outlines and comparing them across categories of objects in a 2D space. We chose heatmap as a benchmark spatial visualization method and conducted user studies to compare performances among Phoenixmap, heatmap, and dot distribution map. Based on the analysis and participant feedback, we demonstrate that Phoenixmap 1) allows users to perceive and compare spatial distribution data efficiently;2) frees up graphics space with a concise form that can provide visualization design possibilities like overlapping;and 3) provides a good quantitative perceptual estimating capability given the proper legends. Finally, we discuss several possible applications of Phoenixmap and present one visualization of multiple species of birds' active regions in a nature preserve. Copyright © 2020, The Authors. All rights reserved.

关键词： Data visualization

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Comment to Federal Trade Commission on Disclosure Guidelines and Dark Patterns

SSRN

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SSRN 2025年

作者： Burke, Caitlin Gunawan, Johanna King, Jennifer Kshirsagar, Mihir Acquisti, Alessandro Chetty, Marshini Choffnes, David Egelman, Serge Gray, Colin M. Hartzog, Woodrow Im, Jane Mayer, Jonathan Mhaidli, Abraham Narayanan, Arvind Roesner, Franziska Schaffner, Brennan Schaub, Florian Strahilevitz, Lior Tong, Nicole Wang, Charlie Wilson, Christo Zeng, Eric Department of Communication Stanford University United States Khoury College of Computer Sciences Northeastern University United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States Heinz College Carnegie Mellon University United States Department of Computer Science University of Chicago United States Khoury College of Computer Sciences Northeastern University United States United States Purdue University United States Boston University School of Law United States School of Information Division of Computer Science and Engineering University of Michigan United States Princeton University United States School of Information University of Michigan United States Princeton University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States Department of Computer Science University of Chicago United States School of Information University of Michigan United States University of Chicago United States Stanford Institute for Human-Centered Artificial Intelligence United States Princeton’s Center for Information Technology Policy United States CyLab Carnegie Mellon University United States Stanford University School of Law United States Princeton University United States

As documented in several research studies, consumers may encounter dark patterns in many online contexts, such as when making choices to consent to the disclosure of personal information or to cookies, when interacting with services and applications like games or content feeds that seek to capture and extend consumer attention and time spent, and in e-commerce, including at multiple points along a purchasing journey. Dark patterns may start with the advertising of a product or service, and can be present across the whole customer path, including sign-up, purchase, and cancellation. As a result, the Commission’s guidance should cover the business’s entire interaction with the consumer to ensure that they are allowed to engage in free and informed transactions. © 2025, The Authors. All rights reserved.

关键词： Sales

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Domain conditioned adaptation network

arXiv

引用

arXiv 2020年

作者： Li, Shuang Liu, Chi Harold Lin, Qiuxia Xie, Binhui Ding, Zhengming Huang, Gao Tang, Jian School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Information and Technology Indiana University-Purdue University Indianapolis United States Department of Automation Tsinghua University China AI Labs Didi Chuxing China Department of Electrical Engineering and Computer Science Syracuse University United States

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolution

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Receiving UWB antenna for wireless capsule endoscopy communications

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Progress In Electromagnetics Research C 2020年 101卷 53-69页

作者： Kissi, Chaïmaâ Särestöniemi, Mariella Kumpuniemi, Timo Myllymäki, Sami Sonkki, Marko Mäkelä, Juha-Pekka Srifi, Mohamed Nabil Jantunen, Heli Pomalaza-Raez, Carlos Ibn Tofail University Kenitra Morocco Centre for Wireless Communications Faculty of Information Technology and Electrical Engineering University of Oulu Finland Microelectronics Research Unit Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Electrical and Computer Engineering Purdue University Fort WayneIN46805 United States

Wireless capsule endoscopy systems utilize a combination of hardware and software devices to ensure the healthcare of a human being. In praise of involved antennas in the overall medical system design, UWB (Ultra-Wideband) range occupies the highest ranks in the literature. The low-band of UWB is regarded as the best frequency range, within the approved standards, to realize the better transmission of captured medical images by the capsule inside the SI tract, in terms of high resolution and low-path loss. A variety of passive capsules have been designed and made available in the literature, while the accurate design of the corresponding on-body antenna is lagging. For this purpose, this paper provides an extended study of a recently published on-body antenna operating at 3.75–4.25 GHz band. The measured antenna realizes good directivity of 5.78 dBi and 9.50 dBi towards the body without and with the cavity, respectively. The direction of the proposed on-body antenna beam is targeted to be mounted on the body surface. On-body simulations were run with CST Microwave Studio by involving an abdominal multi-layer model, and followed by navel and back areas of the voxel model to predict the antenna behavior close to different lossy body environments. Later, the antenna structure was measured next to a real human abdomen. Simulation results reveal that the proposed antenna with or without the cavity enables enhanced in-body communication when mounted on the abdomen with less path loss. This is supported by the low power totaling 20 dB at the SI (Small Intestine) tract. Furthermore, on-body measurements confirm the good antenna performance. Consequently, the planar compact antenna is regarded as a good on-body candidate for wireless capsule endoscopy systems. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Microwave antennas

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Reconfigurable Quantum Local Area Network Over Deployed Fiber

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PRX Quantum 2021年第4期2卷 040304-040304页

作者： Muneer Alshowkan Brian P. Williams Philip G. Evans Nageswara S.V. Rao Emma M. Simmerman Hsuan-Hao Lu Navin B. Lingaraju Andrew M. Weiner Claire E. Marvinney Yun-Yi Pai Benjamin J. Lawrie Nicholas A. Peters Joseph M. Lukens Quantum Information Science Group Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Autonomous and Complex Systems Group Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Applied Physics Stanford University Stanford California 94305 USA School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Materials Science and Technology Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA

Practical quantum networking architectures are crucial for scaling the connection of quantum resources. Yet quantum network testbeds have thus far underutilized the full capabilities of modern lightwave communications, such as flexible-grid bandwidth allocation. In this work, we implement flex-grid entanglement distribution in a deployed network for the first time, connecting nodes in three distinct campus buildings time synchronized via the Global Positioning System. We quantify the quality of the distributed polarization entanglement via log-negativity, which offers a generic metric of link performance in entangled bits per second. After demonstrating successful entanglement distribution for two allocations of our eight dynamically reconfigurable channels, we realize the first deployed fiber network demonstration of remote state preparation (RSP), a fundamental quantum communications protocol with utility for performing remote private “blind” quantum computing. We further demonstrate RSP not only at one location but over three nodes in three locations. In general, our results highlight an advanced paradigm for managing entanglement resources in quantum networks of ever-increasing complexity and service demands.

关键词： Nonlinear optics Quantum communication Quantum entanglement Quantum networks Quantum optics Timing & synchronization Bayesian methods

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A Survey on the E-learning platforms used during COVID-19 11

A Survey on the E-learning platforms used during COVID-19

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11th Annual IEEE information technology, Electronics and Mobile Communication Conference, IEMCON 2020

作者： Wang, Xianping Chen, Weiru Qiu, Hao Eldurssi, Awad Xie, Fei Shen, Jiayue Purdue University Northwest Dept of Computer Information Science and Graphics HammondIN46323 United States Old Dominion University Dept of Information Technology and Decision Sciences NorfolkVA23529 United States Fort Valley State University Dept of Engineering Technology Fort ValleyGA31030 United States Fort Valley State University Dept of Engineering Technology Fort ValleyGA31030 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAR86011 United States College of Engineering Suny Polytechnic Institute UticaNY13502 United States

ISBN: (纸本)9781728184166

E-learning is globalized in a short period of time due to the physical distancing requirement for curbing the spread of COVID-19. There are two major challenges imposed by the migration from face-To-face learning to E-learning: live lecturing and lab sessions. In this survey, we studied the E-learning platforms used in the COVID-19 pandemic: learning management systems or course management systems, tools for live-lecturing and tools for lab sessions. We found that this migration is quite smooth with contemporary technologies and the Internet. Prescriptive suggestions are also provided for this migration. For the future trend of education, it is expected that E-learning will become a preferable education and learning style in the future, especially for self-study and life-long learning. © 2020 IEEE.

关键词： COVID-19

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Hierarchical Federated Learning based Anomaly Detection using digital twins for smart healthcare

arXiv

引用

arXiv 2021年

作者： Gupta, Deepti Kayode, Olumide Bhatt, Smriti Gupta, Maanak Tosun, Ali Saman Dept. of Computer Science University of Texas at San Antonio San AntonioTX78249 United States Dept. of Computer Science Frostburg State University FrostburgMD21532 United States Dept. of Computer and Information Technology Purdue University West LafayetteIN47907 United States Dept. of Computer Science Tennessee Technological University CookevilleTN38505 United States Dept. of Math. and Comp. Sci. University of North Carolina at Pembroke PembrokeNC28372 United States

Internet of Medical Things (IoMT) is becoming ubiquitous with a proliferation of smart medical devices and applications used in smart hospitals, smart-home based care, and nursing homes. It utilizes smart medical devices and cloud computing services along with core Internet of Things (IoT) technologies to sense patients' vital body parameters, monitor health conditions and generate multivariate data to support just-in-time health services. Mostly, this large amount of data is analyzed in centralized servers. Anomaly Detection (AD) in a centralized healthcare ecosystem is often plagued by significant delays in response time with high performance overhead. Moreover, there are inherent privacy issues associated with sending patients' personal health data to a centralized server, which may also introduce several security threats to the AD model, such as possibility of data poisoning. To overcome these issues with centralized AD models, here we propose a Federated Learning (FL) based AD model which utilizes edge cloudlets to run AD models locally without sharing patients' data. Since existing FL approaches perform aggregation on a single server which restricts the scope of FL, in this paper, we introduce a hierarchical FL that allows aggregation at different levels enabling multiparty collaboration. We introduce a novel disease-based grouping mechanism where different AD models are grouped based on specific types of diseases. Furthermore, we develop a new Federated Time Distributed (FEDTIMEDIS) Long Short-Term Memory (LSTM) approach to train the AD model. We present a Remote Patient Monitoring (RPM) use case to demonstrate our model, and illustrate a proof-of-concept implementation using Digital Twin (DT) and edge cloudlets. © 2021, CC BY.

关键词： Internet of things

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Probabilistic diagnostic tests for degradation problems in supervised learning

arXiv

引用

arXiv 2020年

作者： Valencia-Zapata, Gustavo A. Gonzalez-Canas, Carolina Zentner, Michael G. Ersoy, Okan Klimeck, Gerhard Network for Computational Nanotechnology Purdue University West LafayetteIN United States School of Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Industrial Engineering Purdue University West LafayetteIN United States HUBzero Research Group Information Technology at Purdue West LafayetteIN United States

Several studies point out different causes of performance degradation in supervised machine learning. Problems such as class imbalance, overlapping, small-disjuncts, noisy labels, and sparseness limit accuracy in classification algorithms. Even though a number of approaches either in the form of a methodology or an algorithm try to minimize performance degradation, they have been isolated efforts with limited scope. Most of these approaches focus on remediation of one among many problems, with experimental results coming from few datasets and classification algorithms, insufficient measures of prediction power, and lack of statistical validation for testing the real benefit of the proposed approach. This paper consists of two main parts: In the first part, a novel probabilistic diagnostic model based on identifying signs and symptoms of each problem is presented. Thereby, early and correct diagnosis of these problems is to be achieved in order to select not only the most convenient remediation treatment but also unbiased performance metrics. Secondly, the behavior and performance of several supervised algorithms are studied when training sets have such problems. Therefore, prediction of success for treatments can be estimated across classifiers. Copyright © 2020, The Authors. All rights reserved.

关键词： Supervised learning

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Authorship Analysis of Online Predatory Conversations using Character Level Convolution Neural Networks

Authorship Analysis of Online Predatory Conversations using ...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Kanishka Misra Hemanth Devarapalli Tatiana R. Ringenberg Julia Taylor Rayz Department of Computer and Information Technology Purdue University USA

Authorship Attribution (AA) of written content presents several advantages within the digital forensics domain. While AA has been traditionally applied to long documents, recent works have shown improved performance of neural AA models on short texts such as tweets and online conversations Concurrently, the rise of social media as well as a plethora of chat messaging platforms have made it easier for teenagers to be vulnerable to online predators. In this work, we present an authorship attribution model that trains on a corpus of online conversations involving predators, and perform subsequent analysis of the message representations. Our results show comparable performance relative to prior work for Authorship Attribution and highlight differences between predatory and non-predatory message styles.

关键词： Convolution Neural networks Analytical models Task analysis Feature extraction Data mining Training

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Not So Cute but Fuzzy: Estimating Risk of Sexual Predation in Online Conversations

Not So Cute but Fuzzy: Estimating Risk of Sexual Predation i...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Tatiana R. Ringenberg Kanishka Misra Julia Taylor Rayz Department of Computer and Information Technology Purdue University USA

ISBN: (纸本)9781728145709

The sexual exploitation of minors is a known and persistent problem for law enforcement. Assistance in prioritizing cases of sexual exploitation of potentially risky conversations is crucial. While attempts to automatically triage conversations for the risk of sexual exploitation of minors have been attempted in the past, most computational models use features which are not representative of the grooming process that is used by investigators. Accurately annotating an offender corpus for use with machine learning algorithms is difficult because the stages of the grooming process feed into one another and are non-linear. In this paper we propose a method for labeling risk, tied to stages and themes of the grooming process, using fuzzy sets. We develop a neural network model that uses these fuzzy membership functions of each line in a chat as input and predict the risk of interaction.

关键词： Risk management Law enforcement Task analysis Fuzzy sets Complexity theory Natural language processing Tools

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