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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Rajesh Sharma, R. Naikwade, Imamhussain Sungheetha, Akey Adere, Ketema Gandasala, G.S. Pradeep Banshal, Sumit Kumar Alliance University Department of Computer Science and Engineering Bangalore India Alliance University Department of Data Science Bangalore India Adama Science and Technology University Department of Computer Science & Engineering Adama Ethiopia

ISBN: (纸本)9798331518592

This paper examines the convergence of cloud computing, facts science, and facts engineering, providing a primer for college kids getting into those fields. The examine highlights the synergistic courting among those domains, displaying how cloud infrastructure complements collaboration and efficiency. By illuminating those interconnections, the paper presents college students with a holistic view of the current facts landscape, making ready them to leverage current equipment withinside the cloud era. © 2024 IEEE.

关键词： data science

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Bridging the Gap: Evaluating the Symbiotic Relationship of Cloud Computing with data science and data engineering

Bridging the Gap: Evaluating the Symbiotic Relationship of C...

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Delhi Section Conference (DELCON), IEEE

作者： Rajesh Sharma R. Imamhussain Naikwade Akey Sungheetha Ketema Adere G. S. Pradeep Gandasala Sumit Kumar Banshal Department of Computer Science and Engineering Alliance University Bangalore India Department of Data Science Alliance University Bangalore India Department of Computer Science & Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

关键词： Symbiosis Cloud computing Quantum computing Soft sensors Decision making data science data engineering Automated machine learning Real-time systems Servers

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Understanding and improving fairness in cognitive diagnosis

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science China(Information sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information Engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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SGformer:Boosting transformers for indoor lighting estimation from a single image

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Computational Visual Media 2024年第4期10卷 671-686页

作者： Junhong Zhao Bing Xue Mengjie Zhang Centre for Data Science and Artificial Intelligence&School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand

Estimating lighting from standard images can effectively circumvent the need for resourceintensive high-dynamic-range(HDR)lighting ***,this task is often ill-posed and challenging,particularly for indoor scenes,due to the intricacy and ambiguity inherent in various indoor illumination *** propose an innovative transformer-based method called SGformer for lighting estimation through modeling spherical Gaussian(SG)distributions—a compact yet expressive lighting *** from previous approaches,we explore underlying local and global dependencies in lighting features,which are crucial for reliable lighting ***,we investigate the structural relationships spanning various resolutions of SG distributions,ranging from sparse to dense,aiming to enhance structural consistency and curtail potential stochastic noise stemming from independent SG component *** harnessing the synergy of local–global lighting representation learning and incorporating consistency constraints from various SG resolutions,the proposed method yields more accurate lighting estimates,allowing for more realistic lighting effects in object relighting and *** code and model implementing our work can be found at https://***/junhong-jennifer-zhao/SGformer.

关键词： lighting estimation transformer spherical Gaussian(SG) augmented reality

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Identification of Human Activity from Video Streaming Smartphone data Using Intensified VGG16

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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Multi-UAV path planning for multiple emergency payloads delivery in natural disaster scenarios

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Journal of Electronic science and Technology 2025年第2期23卷 1-18页

作者： Zarina Kutpanova Mustafa Kadhim Xu Zheng Nurkhat Zhakiyev Department of Computer Engineering Astana IT UniversityAstana010000Kazakhstan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Department of Computational and Data Science Astana IT UniversityAstana010000Kazakhstan

Unmanned aerial vehicles(UAVs)are widely used in situations with uncertain and risky areas lacking network *** natural disasters,timely delivery of first aid supplies is *** UAVs face risks such as crashing into birds or unexpected *** systems with parachutes risk dispersing payloads away from target *** objective here is to use multiple UAVs to distribute payloads cooperatively to assigned *** civil defense department must balance coverage,accurate landing,and flight safety while considering battery power and *** Q-network(DQN)models are commonly used in multi-UAV path planning to effectively represent the surroundings and action *** strategies focused on advanced DQNs for UAV path planning in different configurations,but rarely addressed non-cooperative scenarios and disaster *** paper introduces a new DQN framework to tackle challenges in disaster *** considers unforeseen structures and birds that could cause UAV crashes and assumes urgent landing zones and winch-based airdrop systems for precise delivery and return.A new DQN model is developed,which incorporates the battery life,safe flying distance between UAVs,and remaining delivery points to encode surrounding hazards into the state space and ***,a unique reward system is created to improve UAV action sequences for better delivery coverage and safe *** experimental results demonstrate that multi-UAV first aid delivery in disaster environments can achieve advanced performance.

关键词： Deep Q-network First aid delivery Multi-UAV path planning Reinforcement learning Unmanned aerial vehicle(UAV)

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Event-triggered tracking control for a class of nonholonomic systems in chained form

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science China(Information sciences) 2023年第7期66卷 147-161页

作者： Liang XU Youfeng SU He CAI Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University College of Computer and Data Science Fuzhou University School of Automation Science and Engineering South China University of Technology

In this study, the event-triggered asymptotic tracking control problem is considered for a class of nonholonomic systems in chained form for the time-varying reference input. First, to eliminate the ripple phenomenon caused by the imprecise compensation of the time-varying reference input, a novel time-varying event-triggered piecewise continuous control law and a triggering mechanism with a time-varying triggering function are developed. Second, an explicit integral input-to-state stable Lyapunov function is constructed for the time-varying closed-loop system regarding the sampling error as the external input. The origin of the closed-loop system is shown to be uniformly globally asymptotically stable for any global exponential decaying threshold signals, which in turn rules out the Zeno behavior. Moreover, infinitely fast sampling can be avoided by appropriately tuning the exponential convergence rate of the threshold signal. A numerical simulation example is provided to illustrate the proposed control approach.

关键词： event-triggered nonholonomic systems strict Lyapunov function tracking integral input-to-state stable

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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

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Multimedia Tools and Applications 2025年第17期84卷 18351-18371页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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BATON: symphony of random testing and concolic testing through machine learning and taint analysis

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science China(Information sciences) 2023年第3期66卷 97-117页

作者： Bihuan CHEN Yang LIU Xin PENG Yijian WU Shengchao QIN School of Computer Science Fudan University Shanghai Key Laboratory of Data Science Fudan University School of Computer Science and Engineering Nanyang Technological University School of Computing Teesside University

Random testing is scalable but often fails to hit corner program behaviors,while systematic testing （e.g.,concolic execution） is promising to cover corner program behaviors but is not scalable to explore all program *** attempts to integrate random testing with systematic testing lack targeted *** this paper,we propose a guided hybrid testing approach,named BATON,to synergize random testing with concolic *** integrates the knowledge inside test cases and their executions into a conditional execution graph,and uses such knowledge to guide test case ***,we learn classification models for some conditionals in the conditional execution graph in a demand-driven *** models are used to guide random testing to reach and cover partially-covered *** further employ targeted concolic testing to cover conditionals that cannot be fully covered by guided random *** implemented BATONfor Java and evaluated it on three *** results show that BATONimproved branch coverage and mutation score over random testing by 16.2%–29.4%and 19.0%–30.0%,over adaptive random testing by 16.8%–33.8%and 19.4%–34.2%,over concolic testing by 2.3%–29.9%and 2.9%–30.1%,and over simple hybrid testing by 1.6%–14.5%and 1.4%–18.7%.

关键词： system testing random testing concolic testing

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