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6th International Conference on Signal Processing and Information Security, ICSPIS 2023

作者： Rawat, Deepak Sharma, M.L. Kumar, Roshan Saxena, Nishant Kumar, Atul Kadir, Abdul Indian Institute of Technology Roorkee Centre of Excellence in Disaster Mitigation and Management Uttarakhand Roorkee India Indian Institute of Technology Roorkee Department of Earthquake Engineering Roorkee Uttarakhand Roorkee India Miami College Henan University Department of Electronics and Information Technology Kaifeng Henan China Indian Institute of Technology Roorkee Department of Architecture and Planning Uttarakhand Roorkee India Indian Institute of Technology Roorkee Computer Science and Engineering Department Uttarakhand Roorkee India

ISBN: (纸本)9798350329599

On February 7, 2021, a rockfall in the Rishiganga Valley killed about 200 people. For the integration of seismic, social media, and remote sensing, a case study on this tragedy has been conducted. This study explores the role of social media in managing Himalayan landslides in conjunction with remote sensing data analytics. We look at how these technologies can enhance damage management, quick mobilization, and early warning systems. A literature review and case studies of the successful use of these technologies in other disaster-prone areas will support these suggestions. The intention is to equip decision-makers and experts in disaster management with the practical knowledge they need to effectively prepare for and respond to Himalayan landslide events, saving lives and minimizing the impact of natural hazards. © 2023 IEEE.

关键词： Remote sensing

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Data Availability Optimization for Cyber-Physical Systems 15

Data Availability Optimization for Cyber-Physical Systems

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 IEEE.

关键词： Iterative methods

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Serverless Computation and Related Analysis Based on Matrix Multiplication 1

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2nd International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2022

作者： Gong, Jiajun Sun, Haotian Wang, Yilin College of Architecture and Urban Planning Shenyang Jianzhu University Shenyang110000 China College of Stomatology ChongQing Medical University Chengdu610066 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (数字)9789819945542

ISBN: (纸本)9789819945535

This paper investigates the effect of the change in matrix dimensionality and the relationship between function communication time and execution time, as well as the relationship between memory parameters and execution time, on the efficiency and economic cost of computation when using a serverless platform for high-dimensional matrix data. AWS Lambda was used to create the functions, AWS S3 to store the data, perform the high-dimensional matrix fork multiplication and matrix convolution operations, respectively, and S3 to design the monitoring and data analysis interface, and finally, analyze the statistical data. According to the analysis, the average communication time consumption ratio decreases gradually as the number of matrix dimensions increases. The computation time decreases by increasing the number of nodes as far as concurrency allows. As the memory parameter increases, the computation time decreases, and the total cost decreases and then increases. The experimental results show that the impact of communication costs on serverless computing decreases as the size of the data increases. When the concurrency limit is not reached, there is a significant increase in computational efficiency, reducing the time consumed by individual nodes and the overall computing process. Increased memory improves computational efficiency but does not necessarily result in total cost savings. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Matrix algebra

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Design of Optimal Service Scheduling based Task Allocation for Improving CRM in Cloud Computing 6

Design of Optimal Service Scheduling based Task Allocation f...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Suvarna Kumar, G. Priyadarshini, R. Parmenas, Naik Henokh Tannady, Hendy Rabbi, Fazle Andiyan, Andiyan Cse Mvgr College of Engg India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kalbis Institute Department of Management Indonesia Australian Computer Society Australia Universitas Faletehan Department of Architecture Indonesia

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

Cloud computing is a service level computing that provide various service to the customers in order to establish an effective customer Relationship management (CRM). This offers various services like virtual machine, self-service provisioning, elasticity computing and storage (pay-as-you-go). Cloud computing provides shared services by enhancing resource management scalability, interoperability and prediction resources as a key to realize the resource utilization with high-performance management metrics. However, when the number of users increase, the process of task scheduling and service allocation using a traditional computing environment will degrade the CRM. In order to address this challenge, this research study proposes an Optimal Service Level Scheduling (OSLS) based task allocation design for improving CRM in cloud computing environment. The Adaptive Service Level Scheduling Algorithm (ASLSA) and the Support Level Load Balancer (SLLB) will reduce the workload in cloud computing environment in order to improve the Quality of Service (QoS) of CRM. This process will optimize the resource utilization in cloud platform based on the service requirement. It provides optimal scheduling features to CRM in order to improve the service optimality based on task and enhance the computational processes such as service load management, heterogeneous service delivery, pricing, resource pools and elasticity. The proposed system leverages high performance when compared to the existing models. © 2022 IEEE.

关键词： Quality of service

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A research infrastructure for generating and sharing diversity-aware data 2

A research infrastructure for generating and sharing diversi...

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Workshops at the 2nd International Conference on Hybrid Human-Artificial Intelligence, HHAI-WS 2023

作者： Busso, Matteo Abente Acosta, Ronald Chenu de Götzen, Amalia Department of Information Engineering and Computer Science University of Trento Via Sommarive 9 Trento30123 Italy Department of Architecture Design and Media Technology Aalborg University A. C. Meyers Vænge 15 København2450 Denmark

The intensive flow of personal data associated with the trend of computerizing aspects of people’s diversity in their daily lives is associated with issues concerning not only people protection and their trust in new technologies, but also bias in the analysis of data and problems in their management and reuse. Faced with a complex problem, the strategies adopted, including technologies and services, often focus on individual aspects, which are difficult to integrate into a broader framework, which can be of effective support for researchers and developers. Therefore, we argue for the development of an end-to-end research infrastructure (RI) that enables trustworthy diversity-aware data within a citizen science community. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Data privacy

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Integrating artificial neural networks and neighborhood analysis for enhanced urban growth prediction depending on planned future development areas: A case study of Ar-Ramtha, Jordan

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Journal of Urban Management 2025年

作者： Gharaibeh, Anne A. Al-Rabee, Mohammad S. Obeidat, Rasha Department of City Planning and Design College of Architecture and Design Jordan University of Science and Technology Irbid Jordan Department of Computer Science Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Artificial intelligence (AI) is revolutionizing urban planning by enhancing the simulation of urban growth and land-use changes. Most AI models often fail to predict how proposed development areas will shape future expansion, leading to misaligned planning and urban sprawl. This study addresses these gaps by integrating master plans and spatial policies into an AI-driven urban growth model for Ar-Ramtha. A hybrid model combining artificial neural networks (ANN) and neighborhood analysis was developed to assess the influence of proposed development areas. Historical data (1990–2005) was used for training and validation, while 2005–2020 data was used for testing. Under-sampling techniques addressed data imbalance, and the PLADJ matrix measured urban growth patterns. Results indicate that incorporating neighborhood analysis and development areas improves model accuracy, achieving a validation accuracy of 92.9 %, an F1-micro of 91.0 %, and a Kappa of 82.1 %. Testing performance remained strong, with an F1-micro of 84.0 % and an accuracy of 91.2 %. Simulations suggest Ar-Ramtha is becoming more compact, with PLADJ values rising from 80.67 % in 2020 to 92.7 % by 2035. However, potential sprawl risks remain due to increased road paving and rising land prices. The study recommends defining development areas based on AI model predictions beyond current planning boundaries and adopting a ring road strategy to promote compact urban growth. These findings contribute to bridging gaps in AI-based urban modeling by improving the integration of planning policies, offering a robust tool for sustainable urban development. © 2025 The Authors

关键词： Artificial neural networks (ANN) Neighborhood analysis PLADJ Urban growth Urban sprawl

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EarLock: Personal Authentication System for Hearables Using Sound Leakage Signals

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IEEE Transactions on Mobile Computing 2025年

作者： Amesaka, Takashi Watanabe, Hiroki Sugiura, Yuta Sugimoto, Masanori Shizuki, Buntarou Keio University Department of Information and Computer Science Kanagawa Japan Future University Hakodate Department of Media Architecture Hokkaido Japan Hokkaido University Graduate School of Information Science and Technology Hokkaido Japan the University of Tsukuba Department of Computer Science Ibaraki Japan

Earphone-type wearable devices, also known as "hearables,"will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock's authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2-93.7%, 83.4-94.7%, and 85.9-90.0%. © 2025 IEEE.

关键词： Information leakage

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Inventory Management System based on IoT and Microservices architecture Design

Inventory Management System based on IoT and Microservices A...

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2023 International Electrical Engineering Congress, iEECON 2023

作者： Sithiyopasakul, Paisan Piyatananugoon, Chavinkorn Chaowalittawin, Vasutorn Krungseanmuang, Woranidtha Sathaporn, Posathip Kanjanasurat, Isoon Purahong, Boonchana Archevapanich, Tunjai Lasakul, Attasit King Mongkut's Institute of Technology Ladkrabang Computer Engineering Department Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand College of Computing Khon Kean University KhonKean Thailand Rajamangala University of Technology Suvarnabhumi Faculty of Engineering and Architecture Nonthaburi Thailand

ISBN: (纸本)9798350332360

This paper presents the inventory management system based on IoT and microservices architecture that synced between IoT (Internet of Things) and web application. There are two main parts consisting of IoT forklift vehicle and data transmitted system by MQTT protocol. Microservice can utilize data to process business logic and provides API. Inventory area includes a zone, a subzone and parking area for forklifts to scan QR code on each subzone. Data of forklifts is published to back-end service. The results of system microservice publish information events and processes business data to the admin client with a delay under a second. © 2023 IEEE.

关键词： Internet of things

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The Mechanism for Synchronizing CNC System with External Devices by Using a Part Program

The Mechanism for Synchronizing CNC System with External Dev...

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International Russian Automation Conference (RusAutoCon)

作者： Georgi Martinov Evgeny Salamatin Department of Computer-Architecture Control Systems Moscow State University of Technology "STANKIN" Moscow Russia

The problem of synchronizing parallel tasks in control systems at the level of the part program is reviewed. A general solution, based on a high-level part programming language extension, is proposed for synchronizing parallel tasks within one control system as well as tasks performed by several different control systems. A scheme for synchronizing the CNC with external autonomous devices through the Common PLC Memory, to which the part program, the PLC program and the inputs and outputs (I/Os) of electric automation have access, has been developed. A set of functions in a high-level part programming language, providing zeroing a certain number of bytes, writing bit, byte, long and float data types to the Common PLC Memory and reading them from it, has been implemented. As a part of practical research, a test stand on the basis of the AxiOMA Control CNC system, that controls actuators via the EtherCAT protocol, was assembled; master and slave part programs were implemented; tests, confirming the operability of the mechanism for synchronizing CNC system with external devices by using a part program, were carried out.

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Collection and Storage of Technological Data in Multi-Channel CNC Processing Using a Non-Relational Database and Node-RED

Collection and Storage of Technological Data in Multi-Channe...

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International Russian Automation Conference (RusAutoCon)

作者： Georgi Martinov Kirill Polyakov Department of Computer-Architecture Control Systems Moscow State University of Technology "STANKIN" Moscow Russia

This article contemplates the problem of collecting and storing technological data during multichannel and multi - coordinate machining on CNC machines obtained using the OPC UA protocol and applying Node-RED open-source instrumentation. A structural model of obtaining, visualizing and storing technological data in multichannel and multi - coordinate machining is proposed in this research. The necessary nodes for Collection and storage of technological data in multi-channel CNC processing are implemented and configured. The researchers illustrated practical aspects of building a system for collecting and storing technological data in multichannel machining using the OPC UA protocol and applying Node-RED open-source tools and a non-relational MongoDB database.

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