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arXiv 2020年

作者： Milojević, Staša Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Classification of bibliographic items into subjects and disciplines in large databases is essential for many quantitative science studies. The Web of Science classification of journals into ~250 subject categories, which has served as a basis for many studies, is known to have some fundamental problems and several practical limitations that may affect the results from such studies. Here we present an easily reproducible method to perform reclassification of the Web of Science into existing subject categories and into 14 broad areas. Our reclassification is at a level of articles, so it preserves disciplinary differences that may exist among individual articles published in the same journal. Reclassification also eliminates ambiguous (multiple) categories that are found for 50% of items, and assigns a discipline/field category to all articles that come from broad-coverage journals such as Nature and Science. The correctness of the assigned subject categories is evaluated manually and is found to be ~95%. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

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Deep Representation Learning for Multimodal Brain Networks

arXiv

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arXiv 2020年

作者： Zhang, Wen Zhan, Liang Thompson, Paul Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Electrical and Computer Engineering University of Pittsburgh PA United States Imaging Genetics Center University of Southern California CA United States

Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology, for an individual brain, mining a discriminative network representation from the multimodal brain networks is non-trivial. The recent success of deep learning techniques on graph-structured data suggests a new way to model the non-linear cross-modality relationship. However, current deep brain network methods either ignore the intrinsic graph topology or require a network basis shared within a group. To address these challenges, we propose a novel end-to-end deep graph representation learning (Deep Multimodal Brain Networks - DMBN) to fuse multimodal brain networks. Specifically, we decipher the cross-modality relationship through a graph encoding and decoding process. The higher-order network mappings from brain structural networks to functional networks are learned in the node domain. The learned network representation is a set of node features that are informative to induce brain saliency maps in a supervised manner. We test our framework in both synthetic and real image data. The experimental results show the superiority of the proposed method over some other state-of-the-art deep brain network models. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

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Introduction

Information Security and Cryptography

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Information Security and Cryptography 2020年 3-13页

作者： Alvim, Mário S. Chatzikokolakis, Konstantinos McIver, Annabelle Morgan, Carroll Palamidessi, Catuscia Smith, Geoffrey Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte Brazil Department of Informatics and Telecommunications University of Athens Athens Greece Department of Computing Macquarie University SydneyNSW Australia School of Computer Science & Engineering University of New South Wales Trustworthy Systems Data61 CSIRO SydneyNSW Australia Inria Saclay and LIX École Polytechnique Institut Polytechnique de Paris Palaiseau France School of Computing & Information Sciences Florida International University MiamiFL United States

Protecting sensitive information from improper disclosure or corruption is a longstanding, fundamental goal of computer security: but it is one that is not currently being achieved well at all, as is evident from cont... 详细信息

关键词： Sensitive data

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Rapid detection of hot-spots via tensor decomposition with applications to crime rate data

arXiv

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arXiv 2020年

作者： Zhao, Yujie Yan, Hao Holte, Sarah Mei, Yajun School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Division of Public Health Sciences Fred Hutchinson Cancer Research Center SeattleWA United States

We propose an efficient statistical method (denoted as SSR-Tensor) to robustly and quickly detect hot-spots that are sparse and temporal-consistent in a spatial-temporal dataset through the tensor decomposition. Our main idea is first to build an SSR model to decompose the tensor data into a Smooth global trend mean, Sparse local hot-spots, and Residuals. Next, tensor decomposition is utilized as follows: bases are introduced to describe within-dimension correlation, and tensor products are used for between-dimension interaction. Then, a combination of LASSO and fused LASSO is used to estimate the model parameters, where an efficient recursive estimation procedure is developed based on the large-scale con-vex optimization, where we first transform the general LASSO optimization into regular LASSO optimization and apply FISTA to solve it with the fastest conver-gence rate. Finally, a CUSUM procedure is applied to detect when and where the hot-spot event occurs. We compare the performance of the proposed method in a numerical simulation study and a real-world case study, which contains a dataset including a collection of three types of crime rates for U.S. mainland states during the year 1965-2014. In both cases, the proposed SSR-Tensor is able to achieve the fast detection and accurate localization of the hot-spots. Copyright © 2020, The Authors. All rights reserved.

关键词： Tensors

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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IEEE International Conference on Big Data

作者： Hechen Yang Jinzhu Yang Tao Jiang Xin Zhao Jinghua Zhang Peng Zhao Ao Chen Qianqing Nie Marcin Grzegorzek Chen Li Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang Liaoning China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network.

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Computer development based embedded systems in precision agriculture: tools and application

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Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 2022年第1期72卷 589-611页

作者： Amine Saddik Rachid Latif Abdelhafid El Ouardi Mohamed Elhoseny Adel Khelifi a Laboratory of Systems Engineering and Information Technology LISTI National School of Applied Sciences Ibn Zohr University Agadir Agadir Morocco b Université Paris-Saclay ENS Paris-Saclay CNRS Systèmes et Applications des Technologies de l'Information et de l'Energie Orsay France c College of Computing and Informatics University of Sharjah Dubai UAEd Faculty of Computers and Information Mansoura University Mansoura Egypt e Software Engineering Abu Dhabi University Abu Dhabi UAE

ABSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** agriculture (PA) research aims to design decision systems based on agricultural site control and management. These systems consist of observing fields and measuring metrics to optimize yields and investments while preserving resources. The corresponding applications can be found on large agricultural areas based on satellites, unmanned aerial vehicles (UAVs), and sol robots. All these applications based on various algorithms that are complex in terms of processing time. If these algorithms are evaluated offline on work-stations or desktops, this is not the case for algorithms that need to be embedded and should operate and help make real-time decisions. We, therefore, need an advanced study using hardware-software co-design approach to design decision systems to embed different algorithms, including sensor data acquisition and processing units. In this work, we propose a review in processing information tools-based embedded systems in PA algorithms with different applications: weed detection, numerical counting, monitoring of plant indexes, and disease detection. This review has been based on more than 100 papers to extract useful information on the different techniques used and the information processing systems. The elaborated study presents the various tools, databases, and systems in order to extract the advantages and disadvantages of system and application.

关键词： : Information development precision agriculture satellite unmanned aerial vehicles sol robots algorithms greenhous H-S co-design embedded system databases

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Subspace Communication Driven Search for High Dimensional Optimization

Subspace Communication Driven Search for High Dimensional Op...

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Simulation Winter Conference

作者： Logan Mathesen Kaushik Keezhnagar Chandrasekar Xinsheng Li Giulia Pedrielli K. Selçuk Candan School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9781728132839

ISBN: (纸本)9781728120522

Global optimization techniques often suffer the curse of dimensionality. In an attempt to face this challenge, high dimensional search techniques try to identify and leverage upon the effective, lower, dimensionality of the problem either in the original or in a transformed space. As a result, algorithms search for and exploit a projection or create a random embedding. Our approach avoids modeling of high dimensional spaces, and the assumption of low effective dimensionality. We argue that effectively high dimensional functions can be recursively optimized over sets of complementary lower dimensional subspaces. In this light, we propose the novel Subspace COmmunication for OPtimization (SCOOP) algorithm, which enables intelligent information sharing among subspaces such that each subspace guides the other towards improved locations. The experiments show that the accuracy of SCOOP rivals the state-of-the-art global optimization techniques, while being several orders of magnitude faster and having better scalability against the problem dimensionality.

关键词： Optimization Additives Information management Linear programming Search problems Bayes methods Gaussian processes

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Data Partitioning Scheme for Efficient Distributed RDF Querying Using Apache Spark

Data Partitioning Scheme for Efficient Distributed RDF Query...

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International Conference on Semantic computing

作者： Mahmudul Hassan Srividya K. Bansal School of Computing Informatics Decision Systems Engineering Arizona State University Tempe AZ USA

The rapid growth of semantic data in the form of Resource Description Framework (RDF) triples demands an efficient, scalable, and distributed storage and parallel processing strategies along with high availability and fault tolerance for its management and reuse. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second, solutions are optimized for certain types of query patterns and don't necessarily work well for all types of query patterns, and the third is concerned with reducing pre-processing and data loading times. To address these issues, we propose a relational partitioning scheme called Subset Property Table (SPT) for RDF data that further partitions the existing Property Table approach into subsets of tables to minimize query input and join operation. We combine SPT with another existing model Vertical Partitioning (VP) for storing RDF datasets and demonstrate that our proposed combined (SPT + VP) approach outperforms state-of-the-art systems based on in-memory processing engine in a distributed environment.

关键词： Resource description framework Sparks Distributed databases Semantics Loading Engines

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ROBUSTNESS OF RECOVERY IN LOCATING ARRAY-BASED SCREENING EXPERIMENTS

ROBUSTNESS OF RECOVERY IN LOCATING ARRAY-BASED SCREENING EXP...

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Simulation Multi-Conference

作者： Stephen A. Seidel Charles J. Colbourn Violet R. Syrotiuk School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

Locating arrays (LAs) are experimental designs for screening interactions in engineered systems. LAs are often highly unbalanced, requiring advanced techniques to recover the terms that significantly influence system performance. While perfect recovery is achieved in the absence of noise, real systems are noisy. Therefore, in this paper, we study the robustness of recovery in the presence of noise. Using known models to generate synthetic data, we investigate recovery accuracy as a function of noise. Separation is introduced into LAs to allow more coverage for each t-way interaction;when separation is higher, recovery in noisy scenarios should improve. We find that locating arrays are able to recover the influential terms even with high levels of noise and that separation appears to improve recovery. Under the pessimistic assumption that noise depends on the range of responses, it is no surprise that terms with small coefficients become indistinguishable from noise.

关键词： Screening engineered networks Locating arrays Analysis Robustness Recovery

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A Machine Learning Platform for Multirotor Activity Training and Recognition

A Machine Learning Platform for Multirotor Activity Training...

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International Symposium on Autonomous Decentralized systems (ISADAS)

作者： Matthew De La Rosa Yinong Chen School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe Arizona USA

ISBN: (数字)9781728116723

ISBN: (纸本)9781728116730

Machine learning is a new paradigm of problem solving. Teaching machine learning in schools and colleges to prepare the industry's needs becomes imminent, not only in computing majors, but also in all engineering disciplines. This paper develops a new, hands-on approach to teaching machine learning by training a linear classifier and applying that classifier to solve Multirotor Activity Recognition (MAR) problems in an online lab setting. MAR labs leverage cloud computing and data storage technologies to host a versatile environment capable of logging, orchestrating, and visualizing the solution for an MAR problem through a user interface. This work extends Arizona State University's Visual IoT/Robotics Programming Language Environment (VIPLE) as a control platform for multi-rotors used in data collection. VIPLE is a platform developed for teaching computational thinking, visual programming, Internet of Things (IoT) and robotics application development.

关键词： Robots Machine learning Mars Training Activity recognition Tools

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