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10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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An Experimental Study of Passive UAV Tracking with Digital Arrays and Cellular Downlink Signals

arXiv

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arXiv 2024年

作者： Sun, Yifei Yu, Chao Luo, Yan Han, Tony Xiao Tan, Haisheng Wang, Rui Lau, Francis C.M. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science School of Computing and Data Science The University of Hong Kong Hong Kong Huawei Technologies Company Ltd. Shenzhen518063 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230027 China

Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically, a passive sensing receiver with two digital antenna arrays is proposed and developed to capture both the line-of-sight (LoS) signal and the scattered signal off a target UAV. From their cross ambiguity function, the bistatic range, Doppler shift and angle-of-arrival (AoA) of the target UAV can be detected in a sequence of time slots. In order to address missed detections and false alarms of passive sensing, a multi-target tracking framework is adopted to track the trajectory of the target UAV. It is demonstrated by experiments that the proposed UAV tracking system can achieve a meter-level accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Long Term Evolution (LTE)

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Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud computing

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Computers, Materials & Continua 2022年第11期73卷 2223-2239页

作者： Abdul Saboor Mohd Fadzil Hassan Rehan Akbar Erwin Susanto Syed Nasir Mehmood Shah Muhammad Aadil Siddiqui Saeed Ahmed Magsi High-Performance Cloud Computing Centre(HPC3) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia School of Electrical Engineering Telkom UniversityBandungIndonesia KICSIT Institute of Space Technology(IST)IslamabadPakistan Department of Electrical and Electronics Engineering Universiti Teknologi PETRONASSeri IskandarPerakMalaysia

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud ***,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location ***,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud ***,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security *** this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and *** underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center *** preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

关键词： Root of Trust(RoT) Trusted Platform Module(TPM) cryptoprocessor microservices Hardware Security Modules(HSM) DevOps

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TEACHING engineering ETHICS IN THE DISCIPLINES: STRATEGIES TO INTEGRATE ETHICS INTO THE CLASSROOM 52

TEACHING ENGINEERING ETHICS IN THE DISCIPLINES: STRATEGIES T...

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52nd Annual Conference of the European Society for engineering: Educating Responsible Engineers, SEFI 2024

作者： Børsen, T. Cruz, C.C. Giménez, E. Johnson, A.W. Josa, I. Lord, S. Lunn, S. Hazewindus, I. Mehlich, J. Mitchell, J.E. Prasad, P. Ramachandran, V. Rodrigues, R.D. Department of Sustainability and Planning Aalborg University Aalborg Denmark Universidade Federal de São João del-Rei São João del-Rei Brazil University of Michigan Michigan United States The Bartlett School of Sustainable Construction UCL London United Kingdom University of San Diego San Diego United States School of Universal Computing Construction and Engineering Education Florida International University Florida United States 10Ethicist at Filosofie in Actie and De Wijze Haas Universiteit van Amsterdam Amsterdam Netherlands Center for Life Ethics/Hertz Chair TRA 4 Rheinische Friedrich-Wilhelms-Universität Bonn Germany UCL Centre for Engineering Education London United Kingdom School of Computing and Data Sciences FLAME University Pune India University of Manitoba Manitoba Canada

ISBN: (纸本)9782873520274

The importance of ethics education within the engineering curriculum cannot be *** such, it has received considerable emphasis in progressive engineering education curricula ***, it has been noted that many of the examples used relate to generic issues in engineering, for example, failure to follow codes of ethics, rather than issues that are related specifically to engineering *** argue that while these generic issues are important, it is vital that discipline specific examples are also available for educators to embed ethics issues and concerns within technical *** part of the development of the Routledge International Handbook of engineering Ethics Education (Chance et al., forthcoming), the authors represented here have developed a series of chapters to highlight ethics issues and discussions that are pertinent to Chemical engineering, Civil engineering, Electronic and Electrical engineering, Mechanical and Aerospace engineering and Software *** this workshop we engaged educators in an exploration of the ways that discipline specific ethical topics may be embedded in the technical subjects within their *** workshop aimed to support those that are looking to make sure of the handbook to enhance their coverage of ethics within their teaching and will help promote the open-access publication within the engineering education community. © 2024 SEFI 2024 - 52nd Annual Conference of the European Society for engineering, Proceedings: Educating Responsible Engineers. All rights reserved.

关键词： engineering education

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ML-driven Emotion Identification For Feedback Analysis In E-learning Platforms

ML-driven Emotion Identification For Feedback Analysis In E-...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Chengamma Chitteti Runvika Kopparam B.V.S.S. Ganesh Sukerthi Sutraya V Kamakshi Shiridinath Jangam Dept. of Data Science School of Computing Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of CSSE Mohan babu university (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of Data Science G. Narayanamma Institute of Technology and Science for women Hyderabad India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

With an emphasis on the cognitive and emotional effects of e-learning platforms, this study fills a research gap concerning user experience studies. The study suggests a nonintrusive technique to evaluate the psychological impacts of e-learning platforms by considering feedback data of users in the form of videos and analyzes emotions from video and decides the user feedback level. This process automates the understanding of feedback in a short amount of time for anyone, without requiring specific knowledge of the system. Feedback analysis for E-Learning platforms using deep learning methods for facial expression analysis is proposed in this work. Convolutional Neural Networks (CNNs), a category of deep learning, has become increasingly popular for its ability to precisely analyze and comprehend emotions from facial photos. These models may efficiently learn and extract essential features by being trained on large datasets of tagged facial expression photos. This application takes video feedback as input for various E-learning platforms and analyzes emotions and displays the predicted emotions on video. A publicly accessible dataset is used to test the proposed technique, and the results show that it achieves ${9 0. 3 \%}$ accuracy. This indicates that the approach is highly effective in accurately recognizing facial emotions.

关键词： Deep learning Training Technological innovation Electronic learning Accuracy Face recognition Feature extraction

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Evaluating Conditional Handover for 5g Networks with Dynamic Obstacles

SSRN

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SSRN 2024年

作者： Deb, Souvik Rathod, Megh Balamurugan, Rishi Singh, Rajeev Kumar Sanyal, Samriddha Ghosh, Shankar K. Advanced Computing & Microelectronics Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Engineering Shiv Nadar Institution of Eminence Delhi201314 India School of Computing and Data Science FLAME University Maharashtra Pune412115 India Shiv Nadar University India

To enhance seamless connectivity in millimetre wave New radio networks, Conditional handover has evolved as a promising solution. Unlike A3 handover where handover execution is certain after receiving handover command from the serving access network, in conditional handover, handover execution is conditional on the Reference signal received power measurements from both current and target access networks, as well as on mobility parameters such as preparation and execution offsets. Presence of dynamic obstacles may block the signal from serving and (or) target access networks, which results in violation of the conditions for the handover preparation/execution. Moreover, signal blockage by dynamic obstacles may cause radio link failure, which may cause handover failure as well. Analytic evaluations of conditional handover performance in the presence of dynamic obstacles are quite limited in the preceding literature. In this work, handover performance of conditional handover has been carried out in terms of handover latency, handover packet loss and handover failure probability. A Markov model accounting the effect of dynamic obstacles, different mobility parameters (e.g., execution offset, preparation offset, time-to-preparation and time-to-execution), user velocity and channel fading characteristics has been proposed to characterize handover failure. Results obtained from the analytic model has been validated against extensive simulation results. Our study reveal that optimal configuration of the mobility parameters is actually conditional on the presence of dynamic obstacles, user velocity and fading characteristics. This study will be helpful for the mobile operators to configure the mobility parameters for New radio systems where dynamic obstacles are present. © 2024, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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A New Federated Learning Framework Against Gradient Inversion Attacks 39

A New Federated Learning Framework Against Gradient Inversio...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Pengxin Zeng, Shuang Chen, Wenhao Zhang, Xiaodan Ren, Weihong Zhou, Yuyin Qu, Liangqiong School of Computing and Data Science The University of Hong Kong Hong Kong Department of Mathematics The University of Hong Kong Hong Kong College of Computer Science Beijing University of Technology China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering UC Santa Cruz United States

ISBN: (纸本)157735897X

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL to thwart such attacks, such as Secure Multi-party computing (SMC), Homomorphic Encryption (HE), and Differential Privacy (DP). Despite their ability to protect data privacy, these approaches inherently involve substantial privacy-utility trade-offs. By revisiting the key to privacy exposure in FL under GIA, which lies in the frequent sharing of model gradients that contain private data, we take a new perspective by designing a novel privacy preserve FL framework that effectively "breaks the direct connection" between the shared parameters and the local private data to defend against GIA. Specifically, we propose a Hypernetwork Federated Learning (HyperFL) framework that utilizes hypernetworks to generate the parameters of the local model and only the hypernetwork parameters are uploaded to the server for aggregation. Theoretical analyses demonstrate the convergence rate of the proposed HyperFL, while extensive experimental results show the privacy-preserving capability and comparable performance of HyperFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Real-time partitioned scheduling: Exploiting the inter-resource affinity for task allocation on multiprocessors

Real-time partitioned scheduling: Exploiting the inter-resou...

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作者： Akram, Naveed Li, Jianxin Bai, Yan Zhang, Yangyang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Engineering and Technology University of Washington Tacoma WA United States

Real-time edge computing is forging its place in cloud computing rapidly, and requirements for high-performance edge devices are becoming increasingly complex. Multiprocessor edge devices are an attractive choice to meet these higher performance requirements. However, multiprocessor devices encounter inherent challenges when handling on-chip shared resources. The concurrent access to these resources by the tasks requesting more than one shared resource and running on multiple processors may face huge blocking times, which can lead to missed hard real-time deadlines and cause a catastrophic system failure. To reduce the task blocking time, we propose a task allocation algorithm that takes advantage of inter-resource affinity and allocates all the tasks accessing multiple shared resources having inter-resource affinity on the same processor of the multiprocessor. The proposed algorithm reduces global resources and remote blocking, which subsequently increase schedulability of task sets and reduce the processor utilization. In our experiments, we compare the proposed task allocation algorithm SRTA with existing well-known task allocation strategies SPA, ROP, and blocking-agnostic FFD. The experimental results reveal that the SRTA, on the average, can allocate 1.80, 2.34, and 3.14 times more task sets as compared to ROP, SPA, and FFD, respectively and reduces the number of globally shared resources significantly. © 2019 John Wiley & Sons, Ltd.

关键词： Scheduling

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Privacy-Aware Energy Consumption Modeling of Connected Battery Electric Vehicles using Federated Learning

arXiv

引用

arXiv 2023年

作者： Yan, Sen Fang, Hongyuan Li, Ji Ward, Tomas O’Connor, Noel Liu, Mingming The School of Electronic Engineering SFI Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland The School of Computing SFI Insight Centre for Data Analytics Dublin City University Dublin 9 Ireland The Department of Mechanical Engineering University of Birmingham BirminghamB15 2TT United Kingdom

Battery Electric Vehicles (BEVs) are increasingly significant in modern cities due to their potential to reduce air pollution. Precise and real-time estimation of energy consumption for them is imperative for effective itinerary planning and optimizing vehicle systems, which can reduce driving range anxiety and decrease energy costs. As public awareness of data privacy increases, adopting approaches that safeguard data privacy in the context of BEV energy consumption modeling is crucial. Federated Learning (FL) is a promising solution mitigating the risk of exposing sensitive information to third parties by allowing local data to remain on devices and only sharing model updates with a central server. Our work investigates the potential of using FL methods, such as FedAvg, and FedPer, to improve BEV energy consumption prediction while maintaining user privacy. We conducted experiments using data from 10 BEVs under simulated real-world driving conditions. Our results demonstrate that the FedAvg-LSTM model achieved a reduction of up to 67.84% in the MAE value of the prediction results. Furthermore, we explored various real-world scenarios and discussed how FL methods can be employed in those cases. Our findings show that FL methods can effectively improve the performance of BEV energy consumption prediction while maintaining user privacy. © 2023, CC BY.

关键词： Learning systems

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Device-Level Learning-Based Distributed Power Control for Uplink NOMA in IoT Networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Raza, Muhammad Ahmad Abolhasan, Mehran Lipman, Justin Ni, Wei Faculty of Engineering and Information Technology Sydney Australia National University of Computer and Emerging Sciences School of Computing Lahore Pakistan Commonwealth Scientific and Industrial Research Organization Data61 Sydney Australia

Non-orthogonal multiple access (NOMA) addresses the massive connectivity issues in Internet of Things (IoT) networks operating under limited radio resources. This paper proposes a statistical learning-based novel power control mechanism for uplink data transmission in dense IoT networks where energy-constrained devices employ the power domain NOMA. The proposed mechanism has two stages and follows a distributed approach. The first stage uses an orthogonal multiple access (OMA) scheme, enabling devices to predict the number of active devices (network load) and device activation probability. In the second stage, devices utilize their network load prediction to determine the optimal number of transmission power levels for NOMA-based data transmission that meets the required latency-reliability constraint. Moreover, by using the probability of exception in network load prediction, devices are enabled to compute the optimal duration of the first phase. Results show that the average battery life in the proposed network load-aware adaptive NOMA scheme outperforms the conventional power-domain NOMA, where devices are unaware of the current network load and randomly select power levels. Moreover, when device activation probability is increased, the number of optimal power levels increases, consequently average battery life decreases. Furthermore, for the given number of installed devices, normalized mean square error (NMSE) in the device-level estimation of the current network load decreases as device activation probability increases. © 2015 IEEE.

关键词： Mean square error

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