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4th International Conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Weng, Canbin Wan, Yitian Xie, Hongbo East China Normal University Software Engineering Institute Shanghai China East China Normal University Computer Science & Technology Institute Shanghai China

ISBN: (纸本)9798350394375

This paper introduces DeepWS, a dynamic scheduling algorithm that leverages the A2C (Actor-Critic) reinforcement learning algorithm and Graph Convolution Network (GCN) techniques. Unlike existing models, DeepWS does not necessitate prior knowledge of the entire Directed Acyclic Graph (DAG), enhancing scalability and enabling versatility across diverse DAG graphs. Employing reinforcement learning and GCN, DeepWS adeptly adapts task scheduling in dynamic environments. The primary focus of DeepWS is to address dynamic workflow scheduling in heterogeneous cloud computing clusters. The algorithm accounts for task dependencies, node and edge weights, as well as the computational power of host machines and router bandwidth. The optimization objective is to minimize makespan. Experimental validation conducted in a heterogeneous clustering environment, using classical datasets, demonstrates DeepWS's superior performance compared to benchmark algorithms like HEFT (Heterogeneous Earliest Finish Time) and MCT (Minimum Completion Time). Results illustrate that DeepWS not only outperforms traditional algorithms significantly but also exhibits enhanced scalability, making it adaptable to different DAG graphs without requiring complete graph information in advance. This research offers a flexible and efficient solution for complex task scheduling challenges in cloud computing, particularly in environments characterized by scheduling uncertainties. © 2024 IEEE.

关键词： Reinforcement learning

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Advancing Emotion Recognition in Visual Data with Artificial Intelligence

Advancing Emotion Recognition in Visual Data with Artificial...

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2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Anusree, K. Bhuvaneshvari, B. Balakrishnan, D. Nandhagopal, S. Chellakannu, Aarthy Logeshwari, S. K S R Institute for Engineering and Technology Department of Information Technology Namakkal India KGiSL Institute of Technology Department of Computer Science and Business Systems Coimbatore India K P R Institute of Engineering and Technology Department of Computer Science Engineering Coimbatore India Dhanalakshmi Srinivasan University Department of Computer Science & Engineering Tiruchirappalli India

ISBN: (纸本)9798350359688

The burgeoning discipline of affective computing, which sits at the nexus of AI and psychology, aims to improve our capacity to comprehend and analyze human emotions as they manifest themselves in visual data. This abstract provides a succinct overview of the problem, highlighting the key elements that characterize the discipline's significance and ramifications. Emotion recognition in visual data requires sophisticated artificial intelligence techniques, including computer vision algorithms and deep learning models. These technologies have made it feasible to accurately deduce emotional states from facial expressions, body language, and even speech sentiment. Facial expression analysis has gained popularity in particular because it may be used to recognize a variety of emotions © 2024 IEEE.

关键词： Convolutional neural networks

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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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Adaptive Context Based Road Accident Risk Prediction Using Spatio-Temporal Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2872-2883页

作者： Bhardwaj, Nishit Pal, Anupriya Bhumika Das, Debasis Indian Institute of Technology Jodhpur Department of Computer Science and Engineering Rajasthan 343020 India Samsung Semiconductor India Research Karnataka560048 India Verint Systems Karnataka560005 India

Traffic accidents are common urban events that pose significant risks to human safety, traffic management, and economic stability;consequently, the research community is paying increasing attention toward accident risk prediction. However, accident risk prediction is a challenging problem because accident occurrences are sparse and influenced by multiple contextual factors (e.g., POI, road structure, road type, hour of the day, and month). Therefore, in this article, we propose a novel architecture named Topographic-Weighted Context Category (TWCCnet) that adapts heterogeneous contextual category weights based on spatial-temporal correlations across sectors. Specifically, the framework consists of two parallel components: one uses convolution and stacked bidirectional gated recurrent unit (Bi-GRU) to capture spatial-temporal relationships between neighborhood sectors, while the other uses multiple graph convolution network (GCN) over resemblance graphs to capture spatial-temporal relationships between semantic sectors. At last, temporal attention is utilized on top of parallel components to learn important spatiotemporal features that have a substantial impact on traffic accidents. The extensive experiments on two publicly available citywide datasets, i.e., New York City and Chicago demonstrate the effectiveness of the proposed approach and showed (7.21, 10.7) Root Mean Squared Error (RMSE), (34.09, 20.75) Mean Average Precision (MAP), and (0.19, 0.09) Recall approximately over both datasets, respectively, outperforming baseline as well as state-of-the-art models. © 2020 IEEE.

关键词： Convolution

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On Configuration Sequences in Feature Models 24

On Configuration Sequences in Feature Models

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18th International Working Conference on Variability Modelling of software-Intensive systems, VaMoS 2024

作者： Meixner, Kristof Feichtinger, Kevin Greiner, Sandra Rabiser, Rick Cdl Sqi Ise Tu Wien Austria Crc 1608 Kastel Dependability of Software-intensive Systems Karlsruhe Institute of Technology Germany Software Engineering Group Institute of Computer Science University of Bern Switzerland Cdl VaSiCS Lit Cps Lab Jku Linz Austria

ISBN: (纸本)9798400708770

Feature models are the de-facto standard in product line engineering to capture the commonalities and variability of systems. However, feature models provide little user guidance during configuration and are unable to represent configuration sequences. A configuration sequence refers to the specific order and combination of features that can be selected when configuring a product line. While mechanisms, such as staged configuration and configuration workflows, exist, we argue that the effort to implement these mechanisms does not pay off for small feature models. This paper motivates the use of configuration sequences for feature models to support better user guidance even for a single feature model. We highlight the need for such user guidance and configuration sequences with a real-world use case. © 2024 ACM.

关键词： Configuration Sequence Feature Models User Guidance.

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Secure-JobChain: Revolutionizing Job Recruitment with Blockchain Technology

Secure-JobChain: Revolutionizing Job Recruitment with Blockc...

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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Pavithra, S. Geethanjali, S. Vidya, B. Suria, S. Rahul, B.S. Kishore, R. Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India SRM Institute of Science and Technology Data Science and Business Systems Kattankulathur India SRM Institute of Science and Technology Electronics and Communication Engineering Kattankulathur India

ISBN: (纸本)9798331539948

The traditional job recruitment process is time consuming and also undergoes certain difficulties such as data privacy, lack of transparency, and inefficient credential verification. These difficulties lead to mistrust and also delay the process of hiring. There is a need for improvement in this process that could benefit both the companies and the job applicants. In this paper, a decentralized recruitment platform based on blockchain technology is proposed. This proposed technique will overcome the above-mentioned difficulties by ensuring secure, transparent, and efficient connections between the recruitment companies and the prospective candidates. This solution uses the merits of blockchain i.e., immutability and automation to record job postings, applications, and hiring decisions on an immutable ledger, protecting personal data with cryptographic techniques and automating qualification verification with smart contracts. By implementing the decentralization concepts in Job recruitment, it is proven that the transparency in the interview process is improved by 9 0 and fraud risks are reduced considerably. Moreover, the efficiency of the entire job recruitment process is improved largely. Thus, the decentralized solution proposed in this paper demonstrates its potential in revolutionizing the job recruitment process by providing a secure, transparent and efficient hiring ecosystem. © 2024 IEEE.

关键词： Smart contract

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Effectiveness of Deep Learning Technologies to Assist Educational Inclusion for Children with Down Syndrome 5

Effectiveness of Deep Learning Technologies to Assist Educat...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Silva, Shehan Jayasinghe, Anjula Dilshan, Rishitha Dissanayake, Chandur Vidhanaarachchi, Samitha De Silva, Dilshan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Down Syndrome (DS) is a genetic disorder causing intellectual disability and developmental delays. Despite instances of discrimination, several individuals with DS have achieved success through proper education and community support. This research addresses the challenges faced by children with DS in terms of motor skill development, cognitive impairments, and speech difficulties with the use of Deep Learning Technologies such as LSTM, CNN machine learning models. To bridge the educational gap and improve speech intelligibility, this study focuses on oral motor exercises that enhance articulation skills. Cognitive development in individuals with DS is also a concern, prompting the proposal of a self-learning system to foster cognitive growth. Fine motor skills improvement is explored through yoga exercises, and interventions like interactive technologies are considered to enhance learning experiences. This paper discusses the development of a user-friendly mobile application for early intervention in DS, integrating interactive activities using advanced technologies and a parent therapy dashboard. The system's potential for improving developmental skills and future expansion is emphasized. © 2023 IEEE.

关键词： Speech intelligibility

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Security Aspects of Container Orchestration in Kubernetes Environments 24

Security Aspects of Container Orchestration in Kubernetes En...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanišić, Sava Vesković, Milan Ristić, Olga Dordević, Borislav Faculty of Technical Sciences Department of Computer and Software Engineering Čačak Serbia Mihajlo Pupin Institute Imp Computer Systems Belgrade Serbia

ISBN: (纸本)9798331515799

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described. © 2025 IEEE.

关键词： Docker Kubernetes security

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A Comparative Analysis of the Imputing of Missing Data on Air Pollution 2

A Comparative Analysis of the Imputing of Missing Data on Ai...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Gayathri, M. Kavitha, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution in the environment over the course of the past few years. Data analyst is commonly encountering the issue of data missing. It is crucial to handle them efficiently to get more accurate and dependable data analysis findings. This study aims to improve understanding of data missing mechanisms, data imputation techniques, and examine the efficacy of frequently utilized data imputation techniques on quantitative datasets. Analysts and data scientists frequently face the challenge of missing data in their investigations. Therefore, it is essential to handle them in an efficient manner so as to gain improved and more trustworthy results through the analysis of the data. The objective of this research is to enhance comprehension of data missing processes and data imputed methods, while also evaluating the effectiveness of data imputed approaches often employed in quantitative datasets. A complete comparison of 5 different data imputation methods is presented in this article. These approaches include mean imputation, median imputation, mode imputation, kNN imputation and Linear Regression. For the purpose of analysing and contrasting the efficacy of the various data imputation approaches, we have utilized five distinct numeric datasets that were gathered from several air pollution repositories. Standardized Root Mean Square Error (RMSE) is the technique for assessing the efficacy of data imputation techniques. According on the findings of the analysis, the kNN imputation approach offers superior performance than the other methods. There is no correlation between the length of the dataset or the number of values that are missing in the dataset and the accuracy of the data imputed approach. © 2024 IEEE.

关键词： Air pollution

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