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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Wu, Yanwei Wang, Yun Dejulius, Jack Abboud, Riad Woitel, Melvin Osadchyy, Bohdan Richardson, Kyree University of Wisconsin Computer Science and Software Engineering PlattevilleWI United States Bradley University Computer Science and Information Systems PeoriaIL United States

ISBN: (纸本)9798350331240

The Resource Public Key Infrastructure (RPKI) is an essential technology for securing the Border Gateway Protocol (BGP) routing on the Internet. Although the global RPKI-ROV (Route Origin Validation) rate of unique Prefix-Origin Pairs(IPv4) has been improved from 17% in June 2019 (pre-pandemic) to 41.12% in March 2023 (post-pandemic) [1] - [3], the overall RPKI-ROV rate remains below 50%. This indicates a significant insufficiency in RPKI-ROV deployment for ensuring secure BGP routing and persistent security challenges continue to persist in the Internet. To address this, we first develop software to analyze and visualize the relationships among Autonomous systems (ASes), degree distribution, and their RPKI validation status, given a region in IL. Next, we investigate the RPKI validation rates along real-time routing paths from Peoria, IL to all 50 states in the USA. Based on the analysis, we propose a novel lightweight, decentralized, easy-to-deploy, offline protocol called Comp-RPKI, which aims to complement and address the partial deployment of RPKI. Comp-RPKI aims to ensure every segment of a routing path is validated either through online RPKI or secured by offline Comp-RPKI, thereby achieving full RKPI-ROV coverage and enhancing BGP security. © 2023 IEEE.

关键词： Border Gateway Protocol

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Intelligent Daycare: Enhancing Child Safety with IoT and Machine Learning Innovations 9

Intelligent Daycare: Enhancing Child Safety with IoT and Mac...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Prathyanga, Amisha Shyaminda, Pasindu Chamikara, Pasan Lakshan, Sachin Thelijjagoda, Samantha Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350377972

Child safety and well-being in daycare settings are important, thus it is vital that the persons in charge be attentive to them. This research tries to solve the problem by introducing a smart and detailed monitoring system that utilizes new technology to enhance safety and support caretakers. The system has various innovative features, such as child missing detection, behavior and facial expression analysis, cry analysis and categorization, and fall detection. Using high-resolution cameras and on-device Edge AI capabilities, it ensures real-time surveillance of children's activities while protecting privacy. The approach integrates behavior and facial expression analysis to assess emotional states, cry recognition to discover childcare needs, as well as a useful fall detection system that can tell the difference between falls and other movements. All these parts synergize to hand over the necessary information to the caregivers on time and in a way that they can act upon. The quantitative experiments in real-time childcare centers show high accuracy through all the components, attesting to the system's reliability and durability. This research has important significance in improving the safety and the frequency response in daycare centers because it gives caregivers automated tools to quickly address the needs of children. While promising, the system has drawbacks, such as potential biases in data models and a reliance on ideal environmental circumstances. Future work intends to improve flexibility across varied daycare situations, improve accuracy through larger datasets, and investigate ethical frameworks for wider application. © 2024 IEEE.

关键词： Fall detection

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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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Search and Recommendation systems with Metadata Extensions 26

Search and Recommendation Systems with Metadata Extensions

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26th International Conference on Advanced Communications Technology, ICACT 2024

作者： Kim, Woo-Hyeon Kim, Joo-Chang Kyonggi University Division of Ai Computer Science and Computer Engineering Korea Republic of Contents Convergence Software Research Institute Kyonggi University Korea Republic of

ISBN: (纸本)9791188428120

This paper proposes an AI-based video metadata extension model to overcome the limitations of video search and recommendation systems in the multimedia industry. Current video searches and recommendations utilize pre-added metadata. Metadata includes filenames, keywords, tags, genres, etc. This makes it impossible to make direct predictions about the content of a video without pre-added metadata. These platforms also analyze your previous search history, viewing history, etc. to understand your interests in order to serve you personalized videos. This may not reflect the actual content and may raise privacy concerns. In addition, recommendation systems suffer from a cold start problem, which is the lack of an initial target, as well as a bubble effect. Therefore, this study proposes a search and recommendation system by expanding metadata in videos using techniques such as shot boundary detection, speech recognition, and text mining. The proposed method selects the main objects required by the recommendation system based on the object frequency and extracts the corresponding objects from the video frame by frame. In addition, we extract the speech from the video separately, convert the speech to text to extract the script and apply text mining techniques to the extracted script to quantify it. Then, we synchronize the object frequency and the transcript to create a single contextual data. After that, we group videos and clips based on the contextual data and index them. Finally, we utilize Shot Boundary Detection to segment videos based on their content. To ensure that the generated contextual data is appropriate for the video, the proposed model compares the extracted script with the video's subtitle data to check and calibrate its accuracy. The model can then be fine-tuned by tuning and cross-validating the hyperparameter to improve its performance. These models can be incorporated into a variety of content discovery and recommendation platforms. By using expanded

关键词： Speech recognition

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Keyword extraction method based on complex network

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International Journal of Information and Communication Technology 2024年第4期25卷 323-335页

作者： Yang, Zhen Gong, Huajian Ma, Weiyun Yin, Chunlin Li, Jie Liu, Siyang Zhu, Hua Zhao, Na Electric Power Research Institute of Yunnan Power Grid Kunming650217 China School of Computer Science and Technology Xidian University Xian710000 China Key Laboratory in Software Engineering of Yunnan Province School of Software Yunnan University Kunming650091 China

Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex networks do not require a lot of data training in advance and are simple to implement. This paper analyses and realises the application of complex network theory in the Chinese news keyword extraction task. We collected and analysed 2,061 news texts from Sina website as experimental data, and found that the number of keywords extracted affected the performance of keyword extraction method. We also compared the performance of our algorithm with other keyword extraction algorithms. The experiments verify the accuracy and effectiveness of complex networks in keyword extraction of Chinese news texts. Based on PyQt and TextRank, a news text keyword analysis platform is constructed to realise the visualisation of text network and the extraction of Chinese news keywords Copyright © 2024 Inderscience Enterprises Ltd.

关键词： complex network keyword extraction text network TextRank

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Deep learning-based task scheduling in edge computing

Deep learning-based task scheduling in edge computing

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作者： Nagalakshmi, Bantupalli Subramanian, Sumathy School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

A potential paradigm called edge computing (EC) has recently come to light that supports internet of things (IoT) applications that are resource allocation with low latency services at the network edge. For scheduling the application tasks, the edge server’s constrained processing capabilities present significant difficulties. The IoT-EC scenario is used in this research to study the task scheduling problem, and various jobs are scheduled to virtual machines (VMs) set up the edge server by maximising long-term task satisfaction. The proposed optimal task scheduling considers parameters like makespan, execution time, execution cost, and risk probability. Particularly, the risk probability estimation is done by the deep convolutional neural network (D-CNN). This estimation is based on task security and VM security. The scheduling of tasks is carried out via the new hybrid bald eagle Archimedes optimisation (HBEAO) by considering a multi-objective to minimise the makespan, execution time, execution cost, and risk probability. The proposed model is validated with existing models in terms of execution cost, execution time, fitness, makespan, risk probability, etc. It is observed that the HBEAO model attains less execution cost ($37.27), execution time (0.99 seconds), fitness (3.48%), risk probability (0.19%) and computation time (2,325.87 sec) respectively. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Edge computing

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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