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arXiv 2022年

作者： Bastami, Hamed Moradikia, Majid Behroozi, Hamid Hanzo, Lajos The Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Data Science Worcester Polytechnic Institute WorcesterMA United States The University of Southampton SO17 1BJ Southampton United Kingdom

The Cooperative Rate-Splitting (CRS) scheme, proposed evolves from conventional Rate Splitting (RS) and relies on forwarding a portion of the RS message by the relaying users. In terms of secrecy enhancement, it has been shown that CRS outperforms its non-cooperative counterpart for a two-user Multiple Input Single Output (MISO) Broadcast Channel (BC). Given the massive connectivity requirement of 6G, we have generalized the existing secure two-user CRS framework to the multi-user framework, where the highest-security users must be selected as the relay nodes. This paper addresses the problem of maximizing the Worst-Case Secrecy Rate (WCSR) in a UAV-aided downlink network where a multi-antenna UAV Base-Station (UAV-BS) serves a group of users in the presence of an external eavesdropper (Eve). We consider a practical scenario in which only imperfect channel state information of Eve is available at the UAV-BS. Accordingly, we conceive a robust and secure resource allocation algorithm, which maximizes the WCSR by jointly optimizing both the Secure Relaying User Selection (SRUS) and the network parameter allocation problem, including the RS transmit precoders, message splitting variables, time slot sharing and power allocation. To circumvent the resultant non-convexity owing to the discrete variables imposed by SRUS, we propose a two-stage algorithm where the SRUS and network parameter allocation are accomplished in two consecutive stages. With regard to the SRUS, we study both centralized and distributed protocols. On the other hand, for jointly optimizing the network parameter allocation we resort to the Sequential Parametric Convex Approximation (SPCA) algorithm. Our numerical results show that the proposed solution significantly outperforms the existing benchmarks for a wide range of network loads in terms of the WCSR. © 2022, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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A Transdisciplinary Socio-Technical Systems Approach: Wireless Solutions for the Digital Divide

A Transdisciplinary Socio-Technical Systems Approach: Wirele...

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Annual IEEE Systems Conference

作者： Shamsnaz Virani Bhada Casey Canfield Alexander Wyglinski Systems Engineering Program Worcester Polytechnic Institute Worcester MA Engineering Management & Systems Engineering Missouri University of Science & Technology Rolla MO USA Worcester Polytechnic Institue Worcester MA USA

The digital divide limits access to education, employment, and healthcare - particularly in rural areas. Unfortunately, most wireless solutions have been developed for urban and suburban environments. However, by using transdisciplinary systems approaches, we can highlight gaps and opportunities to resolve this divide by addressing technology, policy, and community challenges. By creating a digital entity to represent the problem space, we can tailor solutions to specific scenarios.

关键词： Wireless communication Sociotechnical systems Navigation Employment Medical services Tools Digital divide

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Corrosion Image data Set for Automating Scientific Assessment of Materials 32

Corrosion Image Data Set for Automating Scientific Assessmen...

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32nd British Machine Vision Conference, BMVC 2021

作者： Yin, Biao Josselyn, Nicholas Considine, Thomas Kelley, John Rinderspacher, Berend Jensen, Robert Synder, James Zhang, Ziming Rundensteiner, Elke Data Science Program Worcester Polytechnic Institute WorcesterMA United States Weapons and Materials Research Directorate US Army Research Laboratory Aberdeen Proving GroundMD United States Weapons and Materials Research Directorate ARL Northeast Regional Extended Site BurlingtonMA United States Weapons and Materials Research Directorate DEVCOM Army Research Laboratory AdelphiMD United States

The study of material corrosion is an important research area, with corrosion degradation of metallic structures causing expenses up to 4% of the global domestic product annually along with major safety risks worldwide. Unfortunately, large-scale and timely scientific discovery of materials has been hindered by the lack of standardized corrosion experimental data in the public domain for developing machine learning models. Obtaining such data is challenging due to the expert knowledge and time required to conduct these scientific experiments and assess corrosion levels. We curate a novel data set consisting of 600 images annotated with expert corrosion ratings obtained over 10 years of laboratory corrosion testing by material scientists. Based on this data set, we find that non-experts even when rigorously trained with domain guidelines to rate corrosion fail to match expert ratings. Challenges include limited data, image artifacts, and millimeter-precision corrosion. This motivates us to explore the viability of deep learning approaches to tackle this benchmark classification task. We study (i) convolutional neural networks powered with rich domain-specific image augmentation techniques tuned to our data, and (ii) a recent self-supervised representation learning approach either pretrained on ImageNet or trained on our data. We demonstrate that pretrained ResNet-18 and HRNet models with tuned augmentations can reach up to 0.83 accuracy. With this corrosion data set, we open the door for the design of more advanced deep learning models to support this real-world task, while driving innovative new research to bridge computer vision and material innovation. Our data and code are available at: https://***. © 2021. The copyright of this document resides with its authors.

关键词： Corrosion rate

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Text Generation to Aid Depression Detection: A Comparative Study of Conditional Sequence Generative Adversarial Networks

Text Generation to Aid Depression Detection: A Comparative S...

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IEEE International Conference on Big data

作者： ML Tlachac Walter Gerych Kratika Agrawal Benjamin Litterer Nicholas Jurovich Saitheeraj Thatigotla Jidapa Thadajarassiri Elke A. Rundensteiner Department of Information Systems & Analytics Center for Health & Behavioral Sciences Bryant University Smithfield USA Data Science and Computer Science Departments Worcester Polytechnic Institute (WPI) Worcester USA School of Information University of Michigan Ann Arbor USA University of Tennessee Knoxville TN

ISBN: (纸本)9781665480468

Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain.

关键词： Systematics Medical services Machine learning Predictive models Big data Depression Generative adversarial networks

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Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis

Dimension Estimation Using Autoencoders with Applications to...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Nitish Bahadur Randy Paffenroth Data Science Worcester Polytechnic Institute Worcester MA

ISBN: (数字)9781728184708

ISBN: (纸本)9781728184715

Dimension Estimation (DE) and Dimension Reduction (DR) are two closely related topics, but with quite different goals. In DR, one attempts to project a random vector, either linearly or non-linearly, to a lower dimensional space that preserves the information contained in the original higher dimensional space. However, in DE, one attempts to estimate the intrinsic dimensionality or number of latent variables in a set of measurements of a random vector. DE and DR are closely linked because reducing the dimension to a smaller value than suggested by DE will likely lead to information loss. In particular, when considering linear methods, such as Principal Component Analysis (PCA), DE and DR are often accomplished simultaneously. However, in this paper we will focus on a particular class of deep neural networks called autoencoders which are used extensively for DR but are less well studied for DE. We show that several important questions arise when using AEs for DE, above and beyond those that arise for more classic DR/DE techniques such as PCA. We address AE architectural choices and regularization techniques that allow one to transform AE latent layer representations into estimates of intrinsic dimension. We demonstrate the effectiveness of our techniques on synthetic, image processing benchmark problems, and, most importantly, the analysis of financial markets.

关键词： Industries Pandemics Estimation Transforms Particle measurements Resource management Principal component analysis

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Anomaly Detection in Exchange Traded Funds

Anomaly Detection in Exchange Traded Funds

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IEEE International Conference on Big data

作者： Nitish Bahadur Randy Paffenroth Data Science Worcester Polytechnic Institute Worcester MA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Detecting anomalous returns in Exchange Traded Fund(ETF) constituents helps us maintain industry sector exposure while at the same time tuning sector ETF holdings by augmenting portfolio with dollar neutral and sector neutral trades. We demonstrate how Robust Principal Component Analysis (RPCA) can be used to tease out observed ETF constituent returns X into L, a low dimensional manifold, and S, an anomalous matrix containing abnormal returns. Abnormality of returns is determined by checking entries in anomalous matrix against public filings in U.S. Securities and Exchange Commission EDGAR database. Additionally, we show how sparsity in S can be controlled by a tuning parameter that gives us improved f1_scores. Moreover, we build several portfolios with daily and weekly turnover to show how trading in sector ETF anomalies can alter the portfolio return profile.

关键词： Manifolds Biotechnology databases Medical services Resource management Tuning Portfolios

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Stability and electronic properties of edges of SnS2

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Physical Review B 2020年第15期102卷 155306-155306页

作者： Tao Yan Pratap M. Rao N. Aaron Deskins Materials Science and Engineering program Worcester Polytechnic Institute Worcester Massachusetts 01609 USA Department of Mechanical Engineering Worcester Polytechnic Institute Worcester Massachusetts 01609 USA Department of Chemical Engineering Worcester Polytechnic Institute Worcester Massachusetts 01609 USA

Because of its two-dimensional structure and semiconducting properties, tin disulfide (SnS2) is of interest for many applications, such as photocatalysis, photovoltaics, sensing, and electronics. While the atomic and electronic structure of bulk and monolayer SnS2 have been studied, much less is known about the edges of layers. Such edges could have a major influence on the performance of SnS2. This paper reports on density functional theory (DFT) simulations of the atomic and electronic structure of the edges of SnS2. We modeled several different edge terminations at various S coverages and orientations, as well as performed thermodynamic analysis of edge terminations. Our results show that edges with 0% and 50% S atoms are most stable and that higher S coverage are unstable. We directly link edge stability with environmental temperature and pressure, which will guide the experimental synthesis of SnS2 materials. We found all the edges to be semiconducting, unlike other metal chalcogenides, and that the band gap energy decreased with increasing S coverage. Semiconducting edges could lead to lower charge recombination rates and better photocatalytic performance. Our calculations also show that edges may have direct or indirect band gaps, depending on the edge termination. Finally, we examined the reactivity of edges through hydrogen dissociation and found edges to be more reactive than basal planes. Our work provides important details on the nature of SnS2 edges and how these edges influence the electrical and chemical features of SnS2.

关键词： Dichalcogenides Surfaces Density functional theory

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Finding shortest and nearly shortest path nodes in large substantially incomplete networks

arXiv

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arXiv 2022年

作者： Kitsak, Maksim Ganin, Alexander Elmokashfi, Ahmed Cui, Hongzhu Eisenberg, Daniel A. Alderson, David L. Korkin, Dmitry Linkov, Igor Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Network Science Institute Northeastern University BostonMA02115 United States Department of Systems and Information Engineering University of Virginia CharlottesvilleVA22904 United States U.S. Army Engineer Research and Development Center Contractor Concord MA01742 United States Simula Metropolitan Center for Digital Engineering Oslo Norway Bioinformatics and Computational Biology Program Worcester Polytechnic Institute WorcesterMA01609 United States Institute for Genomic Medicine Columbia University Medical Center New YorkNY United States Operations Research Department Naval Postgraduate School MontereyCA93943 United States Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States Computer Science Department Worcester Polytechnic Institute WorcesterMA01609 United States U.S. Army Engineer Research and Development Center Environmental Laboratory Concord MA01742 United States

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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Estimating Effectiveness of Identifying Human Trafficking via data Envelopment Analysis

arXiv

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arXiv 2020年

作者： Dimas, Geri L. Khalkhali, Malak El Bender, Alex Maass, Kayse Lee Konrad, Renata A. Blom, Jeffrey S. Zhu, Joe Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute United States Business School Worcester Polytechnic Institute United States Mechanical and Industrial Engineering Department Northeastern University United States Love Justice International United States Business School Worcester Polytechnic Institute Data Science Program Worcester Polytechnic Institute United States

Transit monitoring is a preventative approach used to identify possible cases of human trafficking prior to exploitation while an individual is in transit or before one crosses a border. Transit monitoring is often conducted by non-governmental organizations (NGOs) who train staff to identify and intercept suspicious activity. Love Justice International (LJI) is a well-established NGO that has been conducting transit monitoring for years along the Nepal-India border at multiple monitoring stations. In partnership with LJI, we developed a system that uses data envelopment analysis (DEA) to help LJI decision-makers evaluate the performance of these stations at intercepting potential human-trafficking victims given the amount of resources (e.g. staff, etc.) available and make specific operational improvement recommendations. Our model consists of 91 decision-making units (DMUs) from 7 stations over 13 quarters and considers three inputs, four outputs, and 3 homogeneity criteria. Using this model we identified efficient stations, compared rankings of station performance, and recommended strategies to improve efficiency. To the best of our knowledge, this is the first application of DEA in the anti-human trafficking domain. Copyright © 2020, The Authors. All rights reserved.

关键词： data envelopment analysis

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Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks

Gaussian Mixture Graphical Lasso with Application to Edge De...

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IEEE International Conference on Big data

作者： Hang Yin Xinyue Liu Xiangnan Kong Data Science Worcester Polytechnic Institute Worcester USA Alexa AI Amazon Boston USA Computer Science Worcester Polytechnic Institute Worcester USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Sparse inverse covariance estimation (i.e., edge detection) is an important research problem in recent years, where the goal is to discover the direct connections between a set of nodes in a networked system based upon the observed node activities. Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities are generated from a single Gaussian distribution (i.e., one graph). However, this assumption is too strong for many real-world applications. In many real-world applications (e.g., brain networks), the node activities usually exhibit much more complex patterns that are difficult to be captured by one single Gaussian distribution. In this work, we are inspired by Latent Dirichlet Allocation (LDA) [4] and consider modeling the edge detection problem as estimating a mixture of multiple Gaussian distributions, where each corresponds to a separate sub-network. To address this problem, we propose a novel model called Gaussian Mixture Graphical Lasso (MGL). It learns the proportions of signals generated by each mixture component and their parameters iteratively via an EM framework. To obtain more interpretable networks, MGL imposes a special regularization, called Mutual Exclusivity Regularization (MER), to minimize the overlap between different sub-networks. MER also addresses the common issues in read-world data sets, i.e., noisy observations and small sample size. Through the extensive experiments on synthetic and real brain data sets, the results demonstrate that MGL can effectively discover multiple connectivity structures from the observed node activities.

关键词： Image edge detection Gaussian distribution Big data Brain modeling data models Robustness Noise measurement

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