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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Ge, Yiyuan Zhang, Ji Chen, Zhihao Li, Bi Beijing Information Science and Technology University School of Instrument Science and Opto-Electronics Engineering Beijing China Beijing Information Science and Technology University Computer School Beijing China Xidian University School of Mechano- Electronic Engineering Xian China

ISBN: (纸本)9798350331417

Within the domain of computer vision, person search stands out as a crucial and intricate undertaking. With the growth of urban population and the popularity of surveillance cameras, person search demonstrates its versatility in a multitude of applications, particularly within the realm of video surveillance, public safety and intelligent transportation. The person search task is comprised of two subtasks, namely person detection and person re-identification (ReID),the task of person detection concentrates on identifying similarities among diverse identities. In contrast, person ReID task centres on discovering dissimilarities among distinct identities. In order to reduce the influence of variations in tasks during the model training phase, we segment the ResNet50 network according to the subtasks and propose an end-to-end content-aware person search framework. Where the detection branch is built on top of shallow features and the person ReID branch is built on top of deep features. Meanwhile, we introduce gated deformable convolution module(GDCM)to sense the location of the person and further distinguish the background from the person. Finally, the effectiveness of the method is demonstrated on two authoritative datasets(CUHK-SYSUPRW). © 2023 IEEE.

关键词： Content-aware Gated Deformable Convolution Person search

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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computer Systems science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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A Meta Distribution-Based Fine-Grained Analysis for Contention-based WiFi Backscatter Networks 21

A Meta Distribution-Based Fine-Grained Analysis for Contenti...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Yulei Zhao, Qinglin Feng, Li Zhou, MengChu Shen, Meng Luo, Yu Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology 999078 China College of Electronics and Information Engineering South-Central Minzu University Wuhan430074 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science Guangdong University of Technology Guangzhou510006 China Institute of Computing Technology Chinese Academy of Sciences Beijing100045 China

ISBN: (纸本)9798350365221

WiFi backscatter communication has gained many applications, but its performance characteristics remain to be analyzed. While existing research has investigated the success probability of backscatter tags in contention-based WiFi backscatter networks (CWBNs), it has focused solely on the first-order statistic of the signal-to-interference-plus-noise ratio (SINR). In this paper, we present a meta distribution-based fine-grained analysis that provides high-order statistics of SINR and characterizes the disparity among backscatter transmission links in CWBNs. Leveraging stochastic geometry, we, for the first time, derive mathematical expressions for the b-th moments of conditional success probability and its meta distribution. The extensive Monte-Carlo simulation results validate the accuracy of our proposed theoretical model and demonstrate its outstanding value to help us understand the overall performance of CWBNs. © 2024 IEEE.

关键词： Wi-Fi

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GNSS/IμUWB-Based Train Integrity Monitoring Using Fuzzy Reasoning 4

GNSS/IμUWB-Based Train Integrity Monitoring Using Fuzzy Rea...

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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Zhong, Tianyu Liu, Jiang Cai, Baigen Wang, Jian School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350359589

Integrity of a train is a significant characteristic parameter towards a railway train control system in order to guarantee the railway operational safety. The decoupling event between a locomotive and a carriage or two adjacent carriages has become a serious threat for the following train operating along the same track. Conventional train integrity monitoring solutions based on state detection with specific sensors, like the Global Navigation Satellite System (GNSS) receiver and tail wind pressure unit, may not perform effectively and safely under constrained or difficult operation environments. This paper presents a Train Integrity Monitoring System (TIMS) architecture with integration of GNSS, Inertial Measurement Unit (IMU) and Ultra-wide Band (UWB) ranging technique. To realize the effective determination of the train integrity state with multiple detection channels, the fuzzy reasoning theory is adopted for decision-making. By using the simulated Head-of-Train (HoT) and End-of-Train (EoT) platforms, both the normal and decoupling scenarios are investigated through experiments. With the practically collected sensor datasets, the different single-sensor-based methods are compared with the presented fuzzy reasoning-based solution. The comparison results illustrate the advanced performance level under the given experimental conditions, which indicate great potentials of the presented solution in novel train control systems. © 2024 IEEE.

关键词： fuzzy reasoning Global Navigation Satellite System (GNSS) railway transportation sensor integration train integrity monitoring Ultra-wide Band

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Analyzing Sentiments of Global Workforce Towards Hybrid Work Culture in the New Normal 4th

Analyzing Sentiments of Global Workforce Towards Hybrid Wor...

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Vaghela, Hardika Jadeja, Mahipal K.S. School of Business Management and Information Technology Ahmedabad India Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (纸本)9789819736898

The Covid-19 pandemic has substantially influenced human existence, impacting not just our social and economic aspects but also our professional lives. This effect is readily evident on social media platforms. Amidst the exchange of information and discussions concerning the Covid-19 pandemic, one can discern a growing wave of support for a hybrid work culture when closely examining social media activities. With this perspective in mind, we conducted an analysis of approximately 16,000 tweets to gauge public sentiment regarding the hybrid work culture. We chose Twitter as our platform for this study because it offers a wealth of data that can be effectively leveraged to gain a comprehensive insight into people’s sentiments. We have considered tweets containing the following keywords (hashtags): WFH, WorkFromHome, Remotework, and Hybridwork. By analyzing the Twitter dataset, we extract insights and ascertain the ratio of negative tweets to positive (or supportive) tweets concerning the hybrid work culture. Graphical visualizations of the polarity of the tweets support the study. We conclude that a significant percentage (around 50%) of people support hybrid work culture and show positive sentiments via tweets, whereas 30% and 20% of tweets are neutral and negative, respectively. This research aids in gaining insight into how public opinions align with the shifts in the "new normal." This study can greatly assist strategic decision-makers in planning for future organizational decisions. We combined the interpretation of the results with Wordmap (using WordCloud library of Python) in order to add more sense to the results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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AI-Inspired Algorithm for the Automated Recognition of the World’s Oldest Script “Brahmi”

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SN computer science 2025年第1期6卷 1-12页

作者： Jain, Trang Jain, Arpit Dwivedi, Rakesh Kumar Saxena, Rakhi College of Computing Sciences and Information Technology Teerthanker Mahaveer University UP Moradabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada India Department of Computer Science Deshbandhu College University of Delhi New Delhi India

One of the internationally known oldest script is Brahmi whose digitisation may be helpful for the archaeologists as well as it may help in the digitisation of other languages. Optical character recognition techniques with genetic algorithms will be quite useful for this purpose. In this paper, a review of the work, done on the images of Brahmi letters using CNN till now, is presented. It will be useful to check the possibility of improvement in the performance of a model used for character recognition. A model is designed where tuning of hyper parameters is done using genetic algorithms which improves the accuracy of recognition to 97%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ashok shilalekh Brahmi script CNN Kharavela shilalekh OCR

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Real-time road occupancy and traffic measurements using unmanned aerial vehicle and fundamental traffic flow diagrams

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Personal and Ubiquitous Computing 2023年第5期27卷 1669-1680页

作者： Ahmed, Afzal Outay, Fatima Farooq, Muhammad Umer Saeed, Sehrish Adnan, Muhammad Ismail, Muhammad Ali Qadir, Adnan Department of Urban & Infrastructure Engineering NED University of Engineering & Technology Karachi Pakistan Zayed University Dubai United Arab Emirates Department of Computer Science and Information Technology NED University of Engineering & Technology Karachi Pakistan Exascale Open Data Analytics Lab NED University of Engineering & Technology Karachi Pakistan Hasselt University Diepenbeek Belgium

The technologies and sensors developed for standard traffic streams often fail to accurately measure the heterogeneous traffic with no lane discipline. This research proposes an efficient framework to measure traffic flow parameters in real-time using an unmanned aerial vehicle (UAV). The proposed framework in this research estimates road area occupancy from the images extracted from the video recorded using a UAV by applying the image segmentation technique. The road area occupancy is converted into traffic density using an occupancy-density model developed for the local heterogeneous traffic. A speed-density fundamental diagram is developed, which estimates the average speed corresponding to the measured traffic density. The fundamental relation between flow rate, average speed, and density is applied to compute traffic flow rate using the estimated average speed and density. The proposed framework is computationally less demanding in comparison with other computer vision and artificial intelligence–based techniques. The proposed framework accurately measured road area occupancy with an average accuracy of 97.6%. Traffic density was measured with an average accuracy of 88.8%. Similarly, the average speed and flow rate were measured with an average accuracy of 85.4% and 84.2%, respectively. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： Image segmentation

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Single-to-Multi Music Track Composition Using Interactive Chaotic Evolution 13th

Single-to-Multi Music Track Composition Using Interactive Ch...

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13th International Conference on Frontier Computing, FC 2023

作者： Hung, Ying Kai Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819993413

This research presents a new music generation model and a novel MIDI data format for MIDI music generation. This innovative data format allows us to process MIDI music in a manner analogous to video analysis. Initially, the model employs Convolutional Neural Networks (CNN) as an encoder to effectively capture local and global features within the musical data. Subsequently, we utilize a Transformer as a decoder, leveraging its self-attention mechanism to handle the long-term dependencies present in music data. In the training process, an interactive chaotic algorithm is introduced to update the model’s weights, assisting the model in avoiding entrapment in local optima. This enhances the learning efficiency of the model and improves the quality of the generated output, enabling the model to generate music, including accompaniment, that aligns with human aesthetics from any given melody. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Music

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Rice Disease Recognition using Improved Visual Geometry Group Deep Learning Algorithms 23

Rice Disease Recognition using Improved Visual Geometry Grou...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Lee, Shu-Hung Cheng, Chia-Hsin Tsai, Yu-Shun Huang, Yung-Fa Liu, Shing-Hong Chen, Ching-Mu School of Intelligent Manufacturing and Automotive Engineering Guangdong Business and Technology University Zhaoqing526238 China National Formosa University Department of Electrical Engineering Yunlin Taiwan Chaoyang University of Technology Department of Information and Communication Engineering Taichung Taiwan Chaoyang University of Technology Department of Computer Science and Information Engineering Taichung Taiwan National Penghu University of Science and Technology Department of Electrical Engineering Penghu Taiwan

ISBN: (纸本)9798331528041

Rice diseases are one of the major factors affecting rice production. Traditionally, the identification and assessment of rice diseases have been done manually by experts and farmers, which is time-consuming and cannot provide real-time disease prediction, leading to significant losses. Therefore, there is a need for faster and more efficient methods for rice disease recognition. In this paper, we propose a solution for rice disease detection using artificial intelligence in image recognition. We investigate the performance of the visual geometry group (VGG) deep learning algorithms in recognizing rice diseases. Simulation results show that the VGG16 and VGG19 pretrained models achieve validation accuracies of 67% and 72.5%, respectively. © 2024 IEEE.

关键词： Artificial Intelligence Rice Diseases Visual Geometry Group

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