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6th Smart Cities Symposium, SCS 2022

作者： Adnane, Asma Hardy, James Ahmad, Farhan Morris, Andrew Filtness, Ashleigh Department of Computer Science Loughborough University United Kingdom College of Science and Engineering University of Derby United Kingdom Systems Security Group Centre for Future Transport and Cities Coventry University United Kingdom Loughborough University Transport Safety Research Centre School of Design and Creative Arts Loughborough United Kingdom

ISBN: (纸本)9781839538544

The purpose of this paper is to analyse the human factors in cyberattacks targeting connected vehicles. To achieve this purpose, we utilised qualitative research methods and analysed multiple attack cases against connected vehicles. These cases demonstrated a number of different attack vectors and vulnerabilities. Our paper demonstrated that the most widely used Risk Assessment standards are flawed in this case;each assumes that certain considerations are either managed by other standards or that they are common knowledge and do not necessitate standardisation. This problem results in unresolved voids in the overall standards structure. The main gap identified was around Human Factors;none of the regulations or standards considered the impact of the owner/user/driver on the connected elements of vehicle safety and cybersecurity. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Risk assessment

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Oil Spill Detection in Calm Ocean Conditions: A U-Net Model Novel Solution

Oil Spill Detection in Calm Ocean Conditions: A U-Net Model ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Bilal Hammoud Charbel Bou Maroun Mohamed Moursi Norbert Wehn Microelectronic Systems Design Research Group (EMS) EIT Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern Germany Department of Electrical and Computer Engineering SoE Lebanese American University (LAU) Byblos Lebanon

Oil spills severely damage marine life and coastal environments. To reduce their polluting effect on the ecosystem, it is important to promptly react to potential spills for early detection and monitoring. In this paper, we propose a drone-based solution with a deep-learning U-net model. It processes the radar backscattering dominated by the specular component in calm ocean conditions to detect contaminated sea surfaces with oil spills. Results show that our approach achieves a high detection rate exceeding 90% for thick oil slicks in the range of 1-10 mm.

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Quantum-Safe Cloud-Based Safety Methods

Quantum-Safe Cloud-Based Safety Methods

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Z. Beknozarova Mehul Manu Saloni Bansal Simranjit Kaur Ramy Riad Al-Fatlawy Shweta Goyal Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan School of Allied Sciences Graphic Era Hill University Uttarakhand Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department of Electrical Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

As cloud computing has evolved into mainstream practice, data security is arguably one of the most critical concerns. Even with a number of guidelines in place, data breaches still happen. Encryption is nowadays the leading method of protecting data in cloud but this entails any argument developed with quantum computing must be resistant against it - and a bridge between traditional encryption safety and top-tier security for future cases. Therefore, some cryptosystems will survive the test of quantum computing and others become weakened and obsolesced. The paper introduces a model consisting of two important parts, an approach using polynomial ring units encryption variantally utilising the N-th level reduced cryptosystem to protection cloud user data and access control safeguardal cryptography utilizing another emerging post quantum thriller for future replacing Rivest-Shamir-Adleman in Quantum-computing era supporting McEliece. Based on the simulation, we find that the suggested McEliece algorithm is better in time complexity comparing to actual McEliece cryptosystem. In this way, a variable is realized for creative modification of S and P on the one hand to further upgrade suggested algorithms' security level and recommend NTRU cryptosystem over-existent which simulation evinced that time complexity better than previous existed NTRU.

关键词： Cloud computing Quantum computing Data protection computer architecture Resists Polynomials Encryption Telecommunication computing Time complexity Protection

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SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

arXiv

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arXiv 2025年

作者： Li, Nan Wang, Xiaolu Du, Xiao Cai, Puyu Wang, Ting Engineering Research Center of Software/Hardware Co-Design Technology and Application Ministry of Education the Shangh ai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Software Engineering Institute East China Normal University Shanghai200050 China Computer Science Department New York University New YorkNY10012 United States

Federated Learning (FL) enables clients to collaboratively train machine learning models without sharing local data, preserving privacy in diverse environments. While traditional FL approaches preserve privacy, they often struggle with high computational and communication overhead. To address these issues, model pruning is introduced as a strategy to streamline computations. However, existing pruning methods, when applied solely based on local data, often produce sub-models that inadequately reflect clients’ specific tasks due to data insufficiency. To overcome these challenges, this paper introduces SAFL (Structure-Aware Federated Learning), a novel framework that enhances personalized federated learning through client-specific clustering and Similar Client Structure Information (SCSI)-guided model pruning. SAFL employs a two-stage process: initially, it groups clients based on data similarities and uses aggregated pruning criteria to guide the pruning process, facilitating the identification of optimal sub-models. Subsequently, clients train these pruned models and engage in server-based aggregation, ensuring tailored and efficient models for each client. This method significantly reduces computational overhead while improving inference accuracy. Extensive experiments demonstrate that SAFL markedly diminishes model size and improves performance, making it highly effective in federated environments characterized by heterogeneous data. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Joint Deployment and Resource Allocation for Multi-AeBS Networks: A Two-Timescale Optimization Framework Using MADRL

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IEEE Transactions on Communications 2024年

作者： Zhao, Yikun Zhou, Fanqin Feng, Lei Sun, Yao Li, Wenjing Lim, Wei Yang Bryan Xiong, Zehui Mao, Shiwen Han, Zhu State Key Laboratory of Networking and Switching Technology Beijing100876 China University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom Nanyang Technological University Alibaba Group and Alibaba-NTU Joint Research Institute Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the space-air-ground integrated network, aerial base station (AeBS) systems have gained significant attention for their flexibility in mobility and cost-effective construction. Nevertheless, the scarce spectrum resources and difficulty in accessing global information bring necessity and challenges to the deployment and resource allocation of AeBSs. In this paper, we propose a practical two-timescale framework to solve the resource allocation and deployment optimization problem in multi-AeBS networks. Specifically, the subcarrier allocation problem is first transformed into a many-to-one matching game coupled with power allocation and solved in a small timescale. Then, in a large timescale, the AeBS deployment subproblem is transformed into a distributed partially observable Markov decision process (Dec-POMDP), and then a novel multi-agent hypergraph convolutional deep reinforcement learning (MAHGCDRL) is proposed to solve this problem. The proposed MAHGCDRL extracts features of neighboring AeBSs through hypergraph convolutional networks, enabling AeBS agents to achieve better coordination in a distributed manner. Simulation results show that our proposed approach can attain a higher sum rate, and the proposed MAHGCDRL algorithm achieves better learning performance compared to the existing benchmarks in the literature. © 1972-2012 IEEE.

关键词： Deep reinforcement learning

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GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations

arXiv

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arXiv 2023年

作者： Ranasinghe, Nisal Senanayake, Damith Seneviratne, Sachith Premaratne, Malin Halgamuge, Saman AI Optimization and Pattern Recognition Research Group Dept. of Mechanical Eng. University of Melbourne Australia Melbourne School of Design University of Melbourne Australia Department of Electrical and Computer Systems Engineering Monash University Australia

Traditional machine learning is generally treated as a black-box optimization problem and does not typically produce interpretable functions that connect inputs and outputs. However, the ability to discover such interpretable functions is desirable. In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial. This is facilitated by a new type of interpretable neural network block, named the "power-term approximator block", consisting of logarithmic and exponential activation functions. GINN-LP is end-to-end differentiable, making it possible to use backpropagation for training. We propose a neural network growth strategy that will enable finding the suitable number of terms in the Laurent polynomial that represents the data, along with sparsity regularization to promote the discovery of concise equations. To the best of our knowledge, this is the first model that can discover arbitrary multivariate Laurent polynomial terms without any prior information on the order. Our approach is first evaluated on a subset of data used in SRBench, a benchmark for symbolic regression. We first show that GINN-LP outperforms the state-of-the-art symbolic regression methods on datasets generated using 48 real-world equations in the form of multivariate Laurent polynomials. Next, we propose an ensemble method that combines our method with a high-performing symbolic regression method, enabling us to discover non-Laurent polynomial equations. We achieve state-of-the-art results in equation discovery, showing an absolute improvement of 7.1% over the best contender, by applying this ensemble method to 113 datasets within SRBench with known ground-truth equations. © 2023, CC BY-NC-ND.

关键词： Polynomials

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Robot-mediated intervention can assist children with autism to develop visual perspective taking skills

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Paladyn 2021年第1期12卷 87-101页

作者： Lakatos, Gabriella Wood, Luke Jai Syrdal, Dag Sverre Robins, Ben Zaraki, Abolfazl Dautenhahn, Kerstin Adaptive Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom Cardiff University CardiffCF24 3AA United Kingdom Departments of Electrical and Computer Engineering Systems Design Engineering University of Waterloo WaterlooON N2L 3G1 Canada

In this work, we tested a recently developed novel methodology to assist children with Autism Spectrum Disorder (ASD) improve their Visual Perspective Taking (VPT) and Theory of Mind (ToM) skills using the humanoid robot Kaspar. VPT is the ability to see the world from another person's perspective, drawing upon both social and spatial information. Children with ASD often find it difficultto understand that others might have perspectives, viewpoints and beliefs that are different from their own, which is a fundamental aspect of both VPT and ToM. The games we designed were implementedas the first attempt to study if these skills can be improved in children with ASD through interacting with a humanoid robot in a series of trials. The games involved a number of different actions with the common goal of helping the children to see the world from the robot's perspective. Childrenwith ASD were recruited to the study according to specific inclusion criteria that were determined in a previous pilot study. In order to measure the potential impact of the games on the children, three pre- and post-tests (Smarties, Sally-Anne and Charlie tests) were conducted with the children Our findings suggest that children with ASD can indeed benefit from this approach of robot-assistedtherapy. © 2021 Gabriella Lakatos et al., published by De Gruyter.

关键词： Anthropomorphic robots

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Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network research

arXiv

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arXiv 2024年

作者： Hille, Tobias Stubbemann, Maximilian Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Kassel Germany Information Systems and Machine Learning Lab University of Hildesheim Hildesheim Germany Institute of Computer Science University of Hildesheim Hildesheim Germany

Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning research results are sound and reliable requires reproducibility, which verifies the reliability of research findings using the same code and data. This promotes open and accessible research, robust experimental workflows, and the rapid integration of new findings. Evaluating the degree to which research publications support these different aspects of reproducibility is one goal of the present work. For this we introduce an ontology of reproducibility in machine learning and apply it to methods for graph neural networks. Building on these efforts we turn towards another critical challenge in machine learning, namely the curse of dimensionality, which poses challenges in data collection, representation, and analysis, making it harder to find representative data and impeding the training and inference processes. Using the closely linked concept of geometric intrinsic dimension we investigate to which extend the used machine learning models are influenced by the intrinsic dimension of the data sets they are trained *** Codes 68T01 68T07 68T09 51F99 Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Multi-GNSS simulation and its positioning performance for static and dynamic users 6

Multi-GNSS simulation and its positioning performance for st...

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6th International Symposium on Autonomous Systems, ISAS 2023

作者： Li, Kun Gao, Mengzhi Zheng, Shuaiyong Wang, Xuanwen Jin, Xiaoqin Yang, Peng Yang, Jianlei Lu, Jing Yao, Gaole Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin China Nankai University College of Artificial Intelligence Tianjin China Tianjin University of Technology School of Computer Science and Engineering Tianjin China The 54th Research Institute of China Electronics Technology Group Corporation State Key Laboratory of Satellite Navigation System and Equipment Technology Shijiazhuang China Land Satellite Remote Sensing Application Center Ministry of Natural Resources Beijing China Tianjin AtoN Division The Navigation Guarantee Centre of North China Sea Tianjin China

ISBN: (纸本)9798350316155

Global navigation satellite system plays an important role in military and civil fields, providing global positioning, navigation and timing services in real time. Unfortunately, the services provided by single constellation or double constellations cannot meet the requirements of high-level navigation, and there are still some problems such as low positioning accuracy and low number of visible satellites at specific circumstances to be solved. In this paper, multi-GNSSs are constructed and their global positioning performance is analyzed with respect to static and dynamic users. Results show that with more constellations, the position dilution of precision is decreased, and the number of visible satellites and the positioning accuracy are improved, which reveals that compared with single constellation or double constellations, multi-GNSSs can obviously improve the positioning performance. The methodology proposed can serve as a technical reference for national integrated PNT systems. © 2023 IEEE.

关键词： Navigation

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A flexible analytic wavelet transform and ensemble bagged tree model for electroencephalogram-based meditative mind-wandering detection

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Healthcare Analytics 2024年 5卷 100286-100286页

作者： Dadhich, Ajay Patel, Jaideep Tiwari, Rovin Verma, Richa Mishra, Pratha Jain, Jay Kumar Department of Electronics Instrumentation and Control Engineering Engineering College Ajmer Rajasthan Ajmer India Department of Computer Science and Engineering Sagar Institute of Research & Technology MP Bhopal India Department of Electronics & Communication Engineering Amity University MP Gwalior India Department of Computer Science and Application Makhanlal Chaturvedi University of Journalism and Mass Communication MP Bhopal India Department of Electronics & Communication Engineering Technocrats TIT Group Madhya Pradesh Bhopal India Department of Computer Science & Engineering Indian Institute of Information Technology (IIIT) MP Bhopal India

Mind-wandering (MW) is when an individual's concentration drifts away from the task or activity. researchers found a greater variability in electroencephalogram (EEG) signals due to MW. Collecting more nuanced information from raw EEG data to examine the harmful effects of MW is time-consuming. This study proposes a multi-resolution assessment of EEG signals using the flexible analytic wavelet transform (FAWT). The FAWT algorithm decomposes raw EEG data into more representative sub-bands (SBs). Several statistical characteristics are derived from the obtained SBs, and the effects of MW during meditation on the EEG signals are investigated. A set of significant characteristics is chosen and fed into the machine learning modules using a 10-fold validation approach to detect MW subjects automatically. Our proposed framework attained the highest classification accuracy of 92.41%, the highest sensitivity of 93.56%, and the highest specificity of 91.97%. The proposed framework can be used to design a suitable brain-computer interface (BCI) system to reduce MW and increase meditation depth for holistic and long-term health in society. © 2023 The Author(s)

关键词： Brain–computer interface Classification Electroencephalogram signals Flexible analytic wavelet transform Machine learning Mind-wandering

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