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arXiv 2021年

作者： Roy, Sohini Sen, Arunabha School of Computing Informatics and Decision System Engineering Arizona State University TempeAZ85281 United States

Smart grid monitoring, automation and control will completely rely on PMU based sensor data soon. Accordingly, a high throughput, low latency Information and Communication Technology (ICT) infrastructure should be opted in this regard. Due to the low cost, low power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSNs) can be a good choice. Yet, the efficiency of a WSN depends a lot on the network design and the routing technique. In this paper a new design of the ICT network for smart grid using WSN is proposed. In order to understand the interactions between different entities, detect their operational levels, design the routing scheme and identify false data injection by particular ICT entities, a new model of interdependency called the Multi State Implicative Interdependency Model (MSIIM) is proposed in this paper, which is an updated version of the Modified Implicative Interdependency Model (MIIM) [1]. MSIIM considers the data dependency and operational accuracy of entities together with structural and functional dependencies between them. A multipath secure routing technique is also proposed in this paper which relies on the MSIIM model for its functioning. Simulation results prove that MSIIM based False Data Injection (FDI) detection and mitigation works better and faster than existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Wireless sensor networks

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A Feasible Compression Scheme for Bangla Natural Text

A Feasible Compression Scheme for Bangla Natural Text

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Md. Ashiq Mahmood Md Ashiqul Amin Pallab Kumar Nandi Bishwajit Kumar Mondal Institute of Information and Communication Technology Khulna University of Engineering & Technology Khulna Bangladesh School of Informatics Computing and Cyber System Northern Arizona University AZ USA

The most significant zone of study for Natural Language Processing (NLP) is text compression. Since the number of characters in Unicode are many, 16 bits are needed to process the entire code. This study suggests a useful method for compressing Bangla natural text called the 5 Bit Compression Scheme for Bangla Text(5BCS), which uses a 5-bit encoding method to change the 16-bit Bangla letters into a 5-bit format. This technique provides an encoding algorithm which representing the 16-bit Bangla characters to 5 bits by utilizing a query table. The query table contains 4 sets which comprises all the Bangla characters as well as transliterations and special symbols. An m-series technique is used for logically determining index of a given characters combination while translating characters into 5 bits. By utilizing the 5BCS, a natural text can be compressed by a maximum of 84%. The algorithm for decompress the compressed data to translate the original data is also detailed in this paper. Additionally, a comparison section is developed to compare our 5BCS technique with the widely used Huffman and LZW technique. The experimental result of the comparison looks very much optimistic.

关键词： Dictionaries Codes Databases Symbols Programming Load management Encoding

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Intelligent Voice Instructor-Assistant system for Collaborative and Interactive Classes

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Journal of Artificial Intelligence and Technology 2021年第2期1卷 121-130页

作者： Matthew Baker Xiaohui Hu Gennaro De Luca and Yinong Chen School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeAZ 85281 USA School of Physics and Telecommunication Engineering South China Normal UniversityGuangzhouGuangdong 510006 China Polytechnic School Arizona State UniversityMesaAZ 85212 USA

College classes are becoming increasingly large.A critical component in scaling class size is the collaboration and interactions among instructors,teaching assistants,and *** develop a prototype of an intelligent voice instructorassistant system for supporting large classes,in which Amazon Web Services,Alexa Voice Services,and self-developed services are *** uses a scraping service for reading the questions and answers from the past and current course discussion boards,organizes the questions in JavaScript object notation format,and stores them in the database,which can be accessed by Amazon web services Alexa *** a voice question from a student comes,Alexa is used for translating the voice sentence into ***,Siamese deep long short-term memory model is introduced to calculate the similarity between the question asked and the questions in the database to find the best-matched *** with no match will be sent to the instructor,and instructor’s answer will be added into the *** show that the implemented model achieves promising results that can lead to a practical *** voice instructor-assistant system starts with a small set of *** can grow through learning and improving when more and more questions are asked and answered.

关键词： natural language processing voice processing machine learning Long short-term memory(LSTM)network questions and answers

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Enhancing Collective Estimates by Aggregating Cardinal and Ordinal Inputs 8th

Enhancing Collective Estimates by Aggregating Cardinal and O...

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8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020

作者： Kemmer, Ryan Yoo, Yeawon Escobedo, Adolfo R. Maciejewski, Ross School of Computing Informatics and Decision Systems Engineering Arizona State University United States

ISBN: (纸本)9781577358480

There are many factors that affect the quality of data received from crowdsourcing, including cognitive biases, varying levels of expertise, and varying subjective scales. This work investigates how the elicitation and integration of multiple modalities of input can enhance the quality of collective estimations. We create a crowd sourced experiment where participants are asked to estimate the number of dots within images in two ways: ordinal (ranking) and cardinal (numerical) estimates. We run our study with 300 participants and test how the efficiency of crowdsourced computation is affected when asking participants to provide ordinal and/or cardinal inputs and how the accuracy of the aggregated outcome is affected when using a variety of aggregation methods. First, we find that more accurate ordinal and cardinal estimations can be achieved by prompting participants to provide both cardinal and ordinal information. Second, we present how accurate collective numerical estimates can be achieved with significantly fewer people when aggregating individual preferences using optimization-based consensus aggregation models. Interestingly, we also find that aggregating cardinal information may yield more accurate ordinal estimates. © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Crowdsourcing

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Learning to Reconnect Interrupted Trajectories for Weakly Supervised Multi-Object Tracking

Learning to Reconnect Interrupted Trajectories for Weakly Su...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yu-Lei Li Yang Lu Jie Li Hanzi Wang Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University China

Recently, some weakly supervised multi-object tracking (MOT) methods learn identity embedding features with pseudo identity labels rather than the high-cost manual ones. However, these pseudo identity labels may contain many false or missing identities, which adversely affect the optimization of tracking networks, resulting in interrupted trajectories of occluded targets. To effectively reconnect the interrupted trajectories caused by noisy pseudo labels, we propose a novel weakly supervised MOT method based on a Trajectory-Reconnecting Transformer (TRTMOT). TRT-MOT performs feature decoupling to extract discriminative embedding features for reconnecting trajectories of occluded targets. Experimental results show that TRTMOT outperforms previous weakly supervised MOT methods by at least +3.6 and +5.6 on MOTA for the MOT17 and MOT20 datasets, respectively.

关键词： Technological innovation Target tracking Manuals Signal processing Feature extraction Transformers Trajectory

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A tool for teaching web dependency analysis 36

A tool for teaching web dependency analysis

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36th Information systems Education Conference, ISECON 2020

作者： Gary, Kevin A. Gupta, Vasu Kalsi, Manit Singh School of Computing Informatics and Decision Systems Engineering Arizona State University United States

Since its inception, web application development has undergone constant evolution, and with it the evolution of the web application software developer. Developers are increasingly specialized in the frameworks and toolchains needed to provide highly functional modern web functionality. This creates a challenge for educators as they seek to teach the underlying fundamentals of web development and produce graduates versed in the latest technologies for their industry constituents. At Arizona State University, an upper-division software engineering course in web development seeks to address this dichotomy, specifically for front-end applications on the HTML5/Javascript/CSS stack. This paper reports on a study of student developers and the nature of syntactic defect injection in web application development. © 2020 Proceedings of the Information systems Education Conference, ISECON. All rights reserved.

关键词： Information use

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Constructing flow graphs from procedural cybersecurity texts

arXiv

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arXiv 2021年

作者： Pal, Kuntal Kumar Kashihara, Kazuaki Banerjee, Pratyay Mishra, Swaroop Wang, Ruoyu Baral, Chitta School of Computing Informatics and Decision Systems Engineering Arizona State University

Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant information or identify the instruction flows to complete a task, which is prone to failures. If such texts are structured, we can readily visualize instruction-flows, reason or infer a particular step, or even build automated systems to help novice agents achieve a goal. However, this structure recovery task is a challenge because of such texts' diverse nature. This paper proposes to identify relevant information from such texts and generate information flows between sentences. We built a large annotated procedural text dataset (CTFW) in the cybersecurity domain (3154 documents). This dataset contains valuable instructions regarding software vulnerability analysis experiences. We performed extensive experiments on CTFW with our LM-GNN model variants in multiple settings. To show the generalizability of both this task and our method, we also experimented with procedural texts from two other domains (Maintenance Manual and Cooking), which are substantially different from cybersecurity. Our experiments show that Graph Convolution Network with BERT sentence embeddings outperforms BERT in all three domains. © 2021, CC BY-NC-ND.

关键词： Automation

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Achieving Multitasking Robots in Multi-Robot Tasks

Achieving Multitasking Robots in Multi-Robot Tasks

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Winston Smith Yu Zhang School of Computing Informatics and Decision Systems Engineering Arizona State University

One simplifying assumption made in the existing and well-performing multi-robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While this assumption is innocent to make in situations with sufficient resources such that robots can work independently, it becomes a restriction when they must share capabilities. In this paper, we consider multitasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap by exploiting task synergies based on the physical constraints required to maintain these coalitions. The key contribution is a general and flexible framework that extends the current multi-robot systems to enable multitasking. The proposed approach is inspired by the information invariant theory, which orients around the equivalence of different information requirements. We map physical constraints to information requirements in our work, thereby allowing task synergies to be identified by reasoning about the relationships between such requirements. We show that our algorithm is sound and complete. Simulation results show its effectiveness under resource-constrained situations and in handling challenging scenarios in a realistic UAV simulator.

关键词： Simulation Conferences Multitasking Cognition Inference algorithms Multi-robot systems Object recognition

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Analyzing gaze behavior for text-embellished narrative visualizations under different task scenarios

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Visual informatics 2020年第3期4卷 41-50页

作者： Chris Bryan Aditi Mishra Hidekazu Shidara Kwan-Liu Ma School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityUnited States Department of Computer Science University of CaliforniaDavisUnited States

We conduct an eye tracking study to investigate perception text-embellished narrative visualizations under different task *** stimuli are data visualizations embellished with text-based elements:annotations,captions,labels,and descriptive *** consider three common viewing tasks that occur when these types of graphics are viewed:(1)simple observation,(2)active search to answer a query,and(3)information memorization for later *** overarching goal is to understand,at a perceptual level,if and how task affects how these visualizations are interacted *** analyzing collected gaze data and conducting advanced semantic scanpath analysis,we find,at a high level,diverse patterns of gaze behavior:simple observation and information memorization lead to similar optical viewing strategies,while active search significantly diverges,both in regards to which areas of the visualization are focused upon and how often embellishments are interacted *** discuss study outcomes in the context of embellishing visualizations with text for various usage scenarios.

关键词： Eye tracking Perception User study

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TWE‐WSD: An effective topical word embedding based word sense disambiguation

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CAAI Transactions on Intelligence Technology 2021年第1期6卷 72-79页

作者： Lianyin Jia Jilin Tang Mengjuan Li Jinguo You Jiaman Ding Yinong Chen Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunmingChina Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunmingChina Library Yunnan Normal UniversityKunmingChina School of Computing Informaticsand Decision SystemsArizona State UniversityTempeArizonaUSA

Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of ***,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are *** order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word *** of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each *** integrating strategies are designed to obtain high quality contextual *** experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.

关键词： embedding Word WSD

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