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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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Implementation of Authentic Resources for E-Learning in Education 1st

Implementation of Authentic Resources for E-Learning in Educ...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Mushtaq, Muhammad Yousaf Irfan, Hafiz Muhammad Niaz, Muhammad Ahmad Iqbal, Muhammad Waseem Mazhar, Anas Akhtar, Adnan Jamshaid, Amir Department of Software Engineering Superior University Lahore Lahore5400 Pakistan Department of Computer Science The Islamia University Bahawalpur Bahawalnagar62300 Pakistan

ISBN: (纸本)9783031776199

Students at all educational levels benefit from authentic learning, which helps them to connect the concepts they learn in the classroom and environments happening in the real world. Authentic learning is applied in schools. Elearning is an effective method of conveying knowledge both spoken and visually and has become more dominant in recent years. When conventional teaching methods are combined with online learning, an innovative and beneficial new educational model may be created for both the instructors and students. This research explores whether or not having experience in the real world may be helpful while learning in a virtual classroom environment. After the material has been presented to the participants using a prototype application, a survey questionnaire is used to evaluate the participants’ knowledge levels. The variables included in the survey analyze the correlations between the ages of the respondents, the levels of education that they possess, and the knowledge that the respondents were able to recall and grasp. The results are consistent across all parameters, demonstrating that conventional training in a classroom setting and online courses have value in their respective fields. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Lessons from the Field: Practical Frameworks for CTF Competition Success

Lessons from the Field: Practical Frameworks for CTF Competi...

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Annual IEEE Systems Conference

作者： Dylan Middendorf Suman Bhunia Arthur Carvalho Department of Computer Science and Software Engineering Miami University Oxford Ohio Farmer School of Business Miami University Oxford Ohio

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Capture the Flag (CTF) competitions have become increasingly popular over the previous decade. This research aims to systematically approach organizing CTF competitions by developing two frameworks. The first framework utilizes design science research with a stakeholder approach to generate a definition of success. By defining success, an actionable set of design principles was derived to guide competition organizers. These design principles are structured to provide actionable insight to organizers to help solve the multi-objective problem of stakeholder requirements. The second framework identifies the challenge development life cycle through a temporal approach while describing the current best practices in challenge development. These models were then evaluated through a CTF competition case study hosted for an intra-collegiate audience. This competition utilized both frameworks to assess them in a critical light. To our knowledge, this paper is the first to provide a design science approach for hosting a successful CTF. Based on the findings, a brief discussion on refining the frameworks and potential areas for improvement is provided.

关键词： Adaptation models Systematics Biological system modeling Refining Ecosystems Organizations Stakeholders computer security Best practices Testing

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Usability Impact of Conversational Chatbot 1st

Usability Impact of Conversational Chatbot

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Rasheed, Maryam Iqbal, Muhammad Waseem Fatima, Anam Saleem, Maryam Bhatti, Sohail Masood Ahsan, Muhammad Junaid Department of Computer Science Superior University Lahore Lahore54000 Pakistan Department of Software Engineering Superior University Lahore Lahore54000 Pakistan

ISBN: (纸本)9783031776199

The resurgence of the chatbot has been facilitated by recent developments in machine learning. Recently, chatbot technology has been more widely available on the web and on mobile devices. According to recent statistics, 57% of businesses have already used or plan to execute a chatbot in the close to future. As a result, chatbots will be crucial in a future powered by AI. The goal of chatbots, which can be text-based computer systems, is to mimic human communication skills. In numerous industries, including customer service, e-coaching, and entertainment, they are spreading like wildfire. The effectiveness of these virtual agents cannot yet be evaluated in a consistent manner. As an alternative, a variety of individuals and groups have developed their guiding principles, either exclusively for their chatbot project or with some inspiration from other groups. In this paper, we explore the trends and evaluation techniques currently in use for chatbots. We investigate chatbot evaluation techniques and rank them in accordance with the ISO 9214 usability characteristics of efficiency, effectiveness and satisfaction. After that, we examine the techniques applied in the literature between 1970 and 2022 and contrast the outcomes. We find a clear trend toward examining the efficacy of chatbots in several recent studies, which is likely due to the growing popularity of task-based chatbots, which are increasingly being deployed in many business situations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Chatbots

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Assessing Customer Satisfaction and Loyalty in the Energy Sector Through Service Quality: A Modified SERVQUAL Analysis 24th

Assessing Customer Satisfaction and Loyalty in the Energy Se...

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24th International Symposium for Production Research, ISPR 2024

作者： Çiçeklidağ, Paşa Nebati, Emine Elif Gülen, Kemal Güven Zaim, Selim Computer Science and Software Engineer The University of Western Australia Perth Australia Department of Industrial Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Department of Industrial Engineering Tekirdag Namık Kemal University Tekirdag Turkey Department of Management Ibn Haldun University Istanbul Turkey

ISBN: (纸本)9783031836107

This study examines the impact of service quality on customer satisfaction and loyalty in the energy sector using the SERVQUAL model. Key dimensions such as reliability, empathy, and physical features were found to significantly influence customer satisfaction. The research involved an online survey, with data analyzed using Structural Equation Modeling (SEM) to identify both direct and indirect effects of service quality dimensions on customer satisfaction and loyalty. Results show that improving reliability and empathy can notably enhance customer satisfaction, leading to stronger customer loyalty. However, the study acknowledges limitations, such as the generalizability of results due to the online nature of the survey and the study's focus on a specific geographical region. The use of verbal data may also introduce bias, which future research could address by applying more detailed data collection methods and examining sub-factors. The study concludes that energy companies should refine their service strategies to better meet customer expectations, suggesting further research in different regions and sectors. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Customer satisfaction Energy sector Loyalty Service quality SERVQUAL Sustainability

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Comparative Analysis of Differential Privacy Implementations on Synthetic Data

Comparative Analysis of Differential Privacy Implementations...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Huwida E. Said Qusay H. Mahmoud Mandeep Goyal Faiza Hashim Department of Computing and Applied Technolgy College of Technological Innovation Zayed University Dubai UAE Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Computer Science and Software Engineering College of Information Technology UAE University AI-Ain UAE

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Differential privacy offers a promising solution to balance data utility and user privacy. This paper compares two prominent differential privacy tools-PyDP and IBM's diffprivlib-that are applied to a synthetic dataset with medical attributes. We evaluate these tools based on their effectiveness in maintaining data privacy while preserving the statistical integrity of the data. Our results reveal that PyDP provides synthetic data that closely matches real-world data, making it appropriate for tasks requiring accuracy while striking a better balance between data utility and privacy. However, IBM's diffprivlib is more suitable for privacy-critical applications due to its stronger privacy guarantees, but at the cost of increased noise and decreased data utility. This paper contributes to the practical understanding of implementing differential privacy in machine learning and software applications and enhances the tools available for developers in sensitive data environments.

关键词： Differential privacy Costs Accuracy Conferences Noise Machine learning software Synthetic data

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A Survey on Evolutionary Feature Selection in Multi-Label Classification

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IEEE Transactions on Evolutionary Computation 2025年

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Burdur Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multi-label classification (MLC) involves assigning multiple labels to each instance from a predefined set of labels. With the increasing prevalence of multi-label datasets in real-world problems, MLC has become a popular area of research. These datasets frequently contain irrelevant, redundant, or noisy features, highlighting the importance of feature selection in MLC tasks. As a result, numerous multi-label feature selection (MLFS) approaches have been introduced in the literature. Given their effective search capabilities, evolutionary computation (EC) techniques have been adopted to develop MLFS approaches. However, there is a notable absence of a comprehensive survey dedicated to EC-based MLFS approaches to MLC tasks. Although few attempts have been made to address this gap, they do not provide detailed discussions on EC-based approaches. They also tend to overlook the strengths and weaknesses of existing approaches, especially regarding search, optimization, and evaluation processes. This paper aims to fill this gap by presenting a comprehensive survey of EC-based MLFS approaches, focusing on the latest advancements, current challenges, and future directions. To be specific, we categorize EC-based MLFS approaches based on different criteria and provide detailed descriptions for each category. © 1997-2012 IEEE.

关键词： Evolutionary Computation Multi-Label Classification Multi-Label Feature Selection Multi-Objective Optimization

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Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

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IEEE Transactions on Big Data 2025年

作者： Cai, Zeju Chen, Jianguo Fan, Yuting Zheng, Zibin Li, Keqin Sun Yat-sen University School of Software Engineering China State University of New York Department of Computer Science New PaltzNY United States

Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system. © 2015 IEEE.

关键词： Federated learning

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HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets

arXiv

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arXiv 2025年

作者： Ban, Lulu Zhu, Tao Lu, Xiangqing Qiu, Qi Han, Wenyong Li, Shuangjian Chen, Liming Wang, Kevin I-Kai Nie, Mingxing Wan, Yaping School of Computer Science University of South China 421001 China School of Computer Science and Technology Dalian University of Technology 116024 China Department of Electrical Computer and Software Engineering University of Auckland New Zealand

Cross-dataset Human Activity Recognition (HAR) suffers from limited model generalization, hindering its practical deployment. To address this critical challenge, inspired by the success of DoReMi in Large Language Models (LLMs), we introduce a data mixture optimization strategy for pre-training HAR models, aiming to improve the recognition performance across heterogeneous datasets. However, directly applying DoReMi to the HAR field encounters new challenges due to the continuous, multi-channel and intrinsic heterogeneous characteristics of IMU sensor data. To overcome these limitations, we propose a novel framework HAR-DoReMi, which introduces a masked reconstruction task based on Mean Squared Error (MSE) loss. By raplacing the discrete language sequence prediction task, which relies on the Negative Log-Likelihood (NLL) loss, in the original DoReMi framework, the proposed framework is inherently more appropriate for handling the continuous and multichannel characteristics of IMU data. In addition, HAR-DoReMi integrates the Mahony fusion algorithm into the self-supervised HAR pre-training, aiming to mitigate the heterogeneity of varying sensor orientation. This is achieved by estimating the sensor orientation within each dataset and facilitating alignment with a unified coordinate system, thereby improving the cross-dataset generalization ability of the HAR model. Experimental evaluation on multiple cross-dataset HAR transfer tasks demonstrates that HAR-DoReMi improves the accuracy by an average of 6.51%, compared to the current state-of-the-art method with only approximately 30% to 50% of the data usage. These results confirm the effectiveness of HAR-DoReMi in improving the generalization and data efficiency of pre-training HAR models, underscoring its significant potential to facilitate the practical deployment of HAR technology. Copyright © 2025, The Authors. All rights reserved.

关键词： Data assimilation

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Hierarchical Partitioning-Based Inter-Chip Redistribution Layer Routing for Fan-Out Wafer-Level Packaging

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, Haoyang Huang, Xing Zhuang, Zhen Yu, Zhiwen Guo, Bin Chao, Kai-Yuan Yu, Bei Ho, Tsung-Yi Wong, Martin D. F. Northwestern Polytechnical University School of Computer Science Xi’an China Harbin Engineering University College of Computer Science and Technology Harbin China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Siemens Digital Industries Software WilsonvilleOR United States Hong Kong Baptist University Hong Kong

In modern advanced fan-out wafer-level packaging (FOWLP), redistribution layers (RDLs) are used to implement efficient interconnections among chips, where both flexible vias and irregular pad structures can be deployed to realize highly integrated heterogeneous systems. With the sharp increase in chip densities, however, it becomes challenging to fully utilize the routing resources in RDLs. The routing graph without optimized resource planning can lead to significant deterioration in the solution quality of RDL design. Furthermore, prior work determines via locations in the RDLs before identifying wire routes, thus offsetting the advantages of flexible vias and irregular pads. To systematically solve the RDL routing problem in FOWLP, in this paper, we propose a hierarchical partitioning-based inter-chip routing method considering both flexible vias and irregular pads, so that optimized RDL solutions can be generated automatically and efficiently. The proposed method includes the following key techniques: 1) a channel planning method for optimized routing region partition and a dynamic routing graph-based pathfinding method for efficient global routing, 2) a binarysearch-based hierarchical tile segmentation method for conflict removal and wirelength reduction, 3) a novel monotonicityanalysis-based access point optimization method and a staggered via planning strategy for both wirelength and detour reduction, and 4) a geometry-based detailed routing algorithm for innertile wire routing. The experimental results demonstrate that the proposed algorithm leads to 100% routablility and 4.8% wirelength reduction on average compared to the state-of-the-art work, with a maximum reduction rate of 10.3% across all the benchmarks. © 1982-2012 IEEE.

关键词： Chip scale packages

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