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IEEE Transactions on Technology and Society 2021年第3期2卷 116-121页

作者： Jacobs, Danielle McDaniel, Troy Varsani, Arvind Halden, Rolf U. Forrest, Stephanie Lee, Heewook School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Polytechnic School Arizona State University MesaAZ United States Biodesign Center for Fundamental and Applied Microbiomics Arizona State University TempeAZ United States Biodesign Center for Environmental Health Engineering Arizona State University TempeAZ United States Biodesign Center for Biocomputing Security and Society Arizona State University TempeAZ United States

Analysis of municipal wastewater, or sewage for public health applications is a rapidly expanding field aimed at understanding emerging epidemiological trends, including human and disease migration. The newly gained ability to extract and analyze genetic material from wastewater poses important societal and ethical questions, including: How to safeguard data? Who owns genetic data recovered from wastewater? and What are the ethical and legal issues surrounding its use? In the U.S., both corporate and legal policies regarding privacy have been historically reactive instead of proactive. In wastewater-based epidemiology (WBE), the pace of innovation has outpaced the ability of social and legal mechanisms to keep up. To address this discrepancy, early and robust discussions of the research, policies, and ethics surrounding WBE analysis and genetics are needed. This article contributes to this discussion by examining ownership issues for human genetic data recovered from wastewater and the uses to which it may be put. We focus particularly on the risks associated with personally identifiable data, highlighting potential risks, relevant privacy-enhancing technologies, and appropriate ethics. This article proposes an approach for people conducting WBE studies to help them systematically consider the ethical and privacy implications of their work. © 2020 IEEE.

关键词： Sewage

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FSNET: COMPRESSION OF DEEP CONVOLUTIONAL NEURAL NETWORKS BY FILTER SUMMARY 8

FSNET: COMPRESSION OF DEEP CONVOLUTIONAL NEURAL NETWORKS BY ...

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8th International Conference on Learning Representations, ICLR 2020

作者： Yang, Yingzhen Yu, Jiahui Jojic, Nebojsa Huan, Jun Huang, Thomas S. School of Computing Informatics and Decision Systems Engineering Arizona State University United States Beckman Institute University of Illinois Urbana-Champaign United States Microsoft Research StylingAI Inc.

We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters. The proposed method reduces the number of parameters of each convolutional layer by learning a 1D vector termed Filter Summary (FS). The convolutional filters are located in FS as overlapping 1D segments, and nearby filters in FS share weights in their overlapping regions in a natural way. The resultant neural network based on such weight sharing scheme, termed Filter Summary CNNs or FSNet, has a FS in each convolution layer instead of a set of independent filters in the conventional convolution layer. FSNet has the same architecture as that of the baseline CNN to be compressed, and each convolution layer of FSNet has the same number of filters from FS as that of the basline CNN in the forward process. With compelling computational acceleration ratio, the parameter space of FSNet is much smaller than that of the baseline CNN. In addition, FSNet is quantization friendly. FSNet with weight quantization leads to even higher compression ratio without noticeable performance loss. We further propose Differentiable FSNet where the way filters share weights is learned in a differentiable and end-to-end manner. Experiments demonstrate the effectiveness of FSNet in compression of CNNs for computer vision tasks including image classification and object detection, and the effectiveness of DFSNet is evidenced by the task of Neural Architecture Search. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Convolution

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A Brief Review of Network Embedding

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Big Data Mining and Analytics 2019年第1期2卷 35-47页

作者： Yaojing Wang Yuan Yao Hanghang Tong Feng Xu Jian Lu the State Key Laboratory for Novel Software Technology Nanjing University the School of Computing Informatics and Decision Systems Engineering Arizona State University

Learning the representations of nodes in a network can benefit various analysis tasks such as node classification, link prediction, clustering, and anomaly detection. Such a representation learning problem is referred to as network embedding, and it has attracted significant attention in recent years. In this article, we briefly review the existing network embedding methods by two taxonomies. The technical taxonomy focuses on the specific techniques used and divides the existing network embedding methods into two stages, i.e., context construction and objective design. The non-technical taxonomy focuses on the problem setting aspect and categorizes existing work based on whether to preserve special network properties, to consider special network types, or to incorporate additional inputs. Finally, we summarize the main findings based on the two taxonomies, analyze their usefulness,and discuss future directions in this area.

关键词： network embedding node representations context construction

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Automatic fault detection baseline construction for building HVAC systems using joint entropy and enthalpy

Automatic fault detection baseline construction for building...

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IISE Annual Conference and Expo 2021

作者： Huang, Jiajing Wu, Teresa Yoon, Hyunsoo Pradhan, Ojas Win, Jin O'Neill, Zheng School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Systems Science and Industrial Engineering Binghamton University Binghamton New York United States Department of Civil Architectural and Environmental Engineering Drexel University PhiladelphiaPA United States Department of Mechanical Engineering Texas A&M University College Station TX United States

ISBN: (纸本)9781713838470

Studies indicate that a large energy saving can be realized by applying automatic fault detection and diagnosis (AFDD) to building systems, which consumes more than 40% of the primary energy in the U.S. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Different from many other systems, a building system behaves differently under different weather conditions and hence needs its baseline model to reflect such weather dependence. Existing research shows baseline constructed using nonlinear mathematical models has performed well. However, determining the sample size, which is necessary to capture the totality of data space needed for accurate baseline construction, relies on trial-and-error experiments conducted offline. There is a lack of easy-to-use method that can provide guidance on whether enough sample size has been collected for baseline construction. In this research, we have developed a data-driven approach for AFDD baseline model construction based on information entropy, in conjunction with enthalpy, a measurement of outdoor air conditions to reflect weather conditions. The developed method is compared with our previously-reported baseline construction method using real building data. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Buildings

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Design Strategies and Approximation Methods for High-Performance computing Variability Management

arXiv

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arXiv 2022年

作者： Wang, Yueyao Xu, Li Hong, Yili Pan, Rong Chang, Tyler Lux, Thomas Bernard, Jon Watson, Layne Cameron, Kirk Department of Statistics Virginia Tech BlacksburgVA24061 United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Department of Computer Science Virginia Tech BlacksburgVA24060 United States

Problem: Performance variability management is an active research area in high-performance computing (HPC). In this paper, we focus on input/output (I/O) variability, which is a complicated function that is affected by many system factors. To study the performance variability, computer scientists often use grid-based designs (GBDs) which are equivalent to full factorial designs to collect I/O variability data, and use mathematical approximation methods to build a prediction model. Mathematical approximation models, as deterministic methods, could be biased particularly if extrapolations are needed. In statistics literature, space-filling designs (SFDs) and surrogate models such as Gaussian process (GP) are popular for data collection and building predictive models. The applicability of SFDs and surrogates in the HPC variability management setting, however, needs investigation. In this case study, we investigate their applicability in the HPC setting in terms of design efficiency, prediction accuracy, and scalability. Approach: We first customize the existing SFDs so that they can be applied in the HPC setting. We conduct a comprehensive investigation of design strategies and the prediction ability of approximation methods. We use both synthetic data simulated from three test functions and the real data from the HPC setting. We then compare different methods in terms of design efficiency, prediction accuracy, and scalability. Results: In our synthetic and real data analysis, GP with SFDs outperforms in most scenarios. With respect to the choice of approximation models, GP is recommended if the data are collected by SFDs. If data are collected using GBDs, both GP and Delaunay can be considered. With the best choice of approximation method, the performance of SFDs and GBD depends on the property of the underlying surface. For the cases in which SFDs perform better, the number of design points needed for SFDs is about half of or less than that of the GBD to achieve the

关键词： Scalability

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Class-Specific Attention (CSA) for Time-Series Classification

arXiv

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arXiv 2022年

作者： Hao, Yifan Cao, Huiping Candan, K. Selçuk Liu, Jiefei Chen, Huiying Ma, Ziwei Department of Computer Science New Mexico State University Las CrucesNM United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Mathematics University of Tennessee at Chattanooga ChattanoogaTN United States

Most neural network-based classifiers extract features using several hidden layers and make predictions at the output layer by utilizing these extracted features. We observe that not all features are equally pronounced in all classes;we call such features class-specific features. Existing models do not fully utilize the class-specific differences in features as they feed all extracted features from the hidden layers equally to the output layers. Recent attention mechanisms allow giving different emphasis (or attention) to different features, but these attention models are themselves class-agnostic. In this paper, we propose a novel class-specific attention (CSA) module to capture significant class-specific features and improve the overall classification performance of time series. The CSA module is designed in a way such that it can be adopted in existing neural network (NN) based models to conduct time series classification. In the experiments, this module is plugged into five start-of-the-art neural network models for time series classification to test its effectiveness by using 40 different real datasets. Extensive experiments show that an NN model embedded with the CSA module can improve the base model in most cases and the accuracy improvement can be up to 42%. Our statistical analysis show that the performance of an NN model embedding the CSA module is better than the base NN model on 67% of MTS and 80% of UTS test cases and is significantly better on 11% of MTS and 13% of UTS test cases. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural network models

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Deep Graph Learning for Anomalous Citation Detection

arXiv

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arXiv 2022年

作者： Liu, Jiaying Xia, Feng Feng, Xu Ren, Jing Liu, Huan School of Economics and Management Dalian University of Technology Dalian116024 China School of Engineering IT and Physical Sciences Federation University Australia BallaratVIC3353 Australia School of Software Dalian University of Technology Dalian116620 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, i.e., anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely GLAD (Graph Learning for Anomaly Detection), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks. It exploits not only the relevance of citation contents but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called CPU (Citation PUrpose) to discover the purpose of citation based on citation texts. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. Copyright © 2022, The Authors. All rights reserved.

关键词： Anomaly detection

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Optimal Cost Network Design for Bounded Delay Data Transfer from PMU to Control Center

Optimal Cost Network Design for Bounded Delay Data Transfer ...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： A. Sen S. Roy K. Basu S. Adeniye S. Choudhuri A. Pal School of Computing Informatics and Decision Systems Engg. Arizona State University Tempe AZ USA School of Electrical Computer and Energy Engg. Arizona State University Tempe AZ USA

ISBN: (纸本)9781728181059

Communication network topology design problem in a smart grid environment has received considerable attention in recent times, because in this environment, power transmission control data, generated by Phasor Measurement Units (PMUs), needs to be exchanged between Substations (SS) and Controls Centers (CC) in real time. In this paper, we formalize this design problem studied in a previous paper as the Rooted Delay Constrained Minimum Spanning Tree (RDCMST) problem. While other researchers have studied the RDCMST problem in a topological setting, we study it in a geometric setting. We provide a modified version of the well-known Prim's algorithm for construction of a Minimum Spanning Tree of a graph to solve the RDCMST problem. We (i) establish the necessary and sufficient condition for the existence of a solution for the RDCMST problem, (ii) demonstrate that our algorithm may fail to find the optimal solution for some problem instances, (iii) characterize conditions on the input data which will ensure that our algorithm will find the optimal solution, and (iv) demonstrate that under some pathological condition, the ratio between our algorithm and the optimal solution can be arbitrarily large. We provide an Integer Linear Programming formulation for the problem for computation of the optimal solution. We evaluate the performance of our algorithm with real substation location data of Arizona. In our experiments, our algorithm always produced either optimal or near optimal solutions.

关键词： Pathology Sufficient conditions Substations Network topology Power transmission Phasor measurement units Real-time systems

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Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction

arXiv

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arXiv 2021年

作者： Zhao, Xinyu Yan, Hao Hu, Zhiyong Du, Dongping School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States Industrial Manufacturing & Systems Engineering in Texas Tech University LubbockTX79409 United States

Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i.e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation. Detecting and identifying of cardiac cells that produce abnormal electrical impulses in such nonlinear dynamic systems are important for efficient treatment and planning. To model the nonlinear dynamics, simulation has been widely used in both cardiac research and clinical study to investigate cardiac disease mechanisms and develop new treatment designs. However, existing cardiac models have a great level of complexity, and the simulation is often time-consuming. We propose a deep spatio-temporal sparse decomposition (DSTSD) approach to bypass the time-consuming cardiac partial differential equations with the deep spatio-temporal model and detect the time and location of the anomaly (i.e., malfunctioning cardiac cells). This approach is validated from the data set generated from the Courtemanche-Ramirez-Nattel (CRN) model, which is widely used to model the propagation of the transmembrane potential across the cross neuron membrane. The proposed DSTSD achieved the best accuracy in terms of spatio-temporal mean trend prediction and anomaly detection. © 2021, CC BY.

关键词： Partial differential equations

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Mold - A Framework for Entity Extraction and Summarization

Mold - A Framework for Entity Extraction and Summarization

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International Conference on Semantic computing

作者： Sarthak Tiwari Bharat Goel Srividya Bansal School of Computing Informatics Decision Systems Engineering Arizona State University Mesa Arizona

ISBN: (数字)9781728163321

ISBN: (纸本)9781728163338

With the dawn of Wikis, the collaboration of endless users and the rise of automated agents scraping thorough the internet in search of actionable information, it has become a necessity to create a framework that could serve as a translator between these two parties. Currently, moderators or developers serve this purpose by manually adding the required structure to natural language input while being assisted by one or more assisting applications. In short, the process still requires human intervention and that is what we are aiming to eliminate using the framework suggested in this paper. The framework is divided into two parts, one natural language processing module that uses state-of-the-art Part-of-Speech (PoS) taggers and our own algorithms to convert a natural language text into triples made up of predefined predicates, which are provided to the machine users (autonomous agents). The generated triples are also complemented with their own schema thus enabling machine-based reasoning on the text. The other part i.e. the summarization takes the triples generated by the previous step and creates a natural language text out of it so that the human users of the system could access the information without any knowledge of underlying triples and schema. The framework is trained on a large corpus of English text to optimally find the subject and the object of a given sentence along with the most probable predicate. The predicates will be stored in a separate place in an XML based syntax giving the users the functionality to add/update the schema and predicates as the need arises enabling easy adaptation of the framework to a specific domain.

关键词： Ontologies Natural languages Data mining Information retrieval Resource description framework Blood Semantics

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