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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Zafar, Saniya Alshehri, Mohammed S. Shah, Syed Aziz Ahmad, Jawad Zafar, Adnan Khattak, Aizaz Ahmad Institute of Space Technology Department of Electrical Engineering Islamabad Pakistan Najran University College of Computer Science and Information Systems Department of Computer Science Saudi Arabia Coventry University Research Centre for Intelligent Healthcare United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Edinburgh Napier University School of Computing Engineering and the Built Environment United Kingdom

ISBN: (纸本)9798350373011

The rapid advancement in mobile broadband technology has evolved wireless infrastructure influencing internet-of-things (IoTs). This evolution has made mobile IoTs (mIoTs) as centre of attention due to its potential of providing seamless connectivity to enable intelligent transportation systems with huge data demands of vehicular users. To address the ever-escalating data demands of vehicular mobile users, vehicular edge computing (VEC) and moving small cells (mSCs) play a pivotal role. mSCs together with VEC are capable to provide improved service quality for mIoTs. However, incorporating the mSCs in vehicles for mIoTs presents challenges while assigning the resources in mIoT-mSC network. This is due to the existence of time varying interference among mSCs. This work explores the distributed resource assignment mechanism for RSU assisted mIoT-mSC network formed in vehicular environment. In the proposed work, location prediction of city buses deployed with mSCs is done using federated learning (FL) which is subsequently utilized for efficient assignment of resources in mIoT-mSC network. Numerical results are provided to validate the proposed resource assignment mechanism. © 2024 IEEE.

关键词： intelligent systems

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An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling 38

An Autoencoder-Like Nonnegative Matrix Co-Factorization for ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

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computer Systems Science & engineering 2023年第9期46卷 3939-3958页

作者： Sareer Ul Amin Yongjun Kim Irfan Sami Sangoh Park Sanghyun Seo Department of Computer Science and Engineering Chung-Ang UniversitySeoul06974Korea Intelligent Convergence Research Lab. ETRIDaeJeon34129Korea Department of Electrical and Electronics Engineering Chung-Ang UniversitySeoul06974Korea College of Art and Technology Chung-Ang UniversityAnseong17546Korea

In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision *** is difficult to search for anomalous events manually in thesemassive video records since they happen infrequently and with a low probability in real-world monitoring ***,intelligent surveillance is a requirement of the modern day,as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision *** this article,we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video(ADSV).At the input of the ADSV,a shots boundary detection technique is used to segment prominent ***,The Lightweight ConvolutionNeuralNetwork(LWCNN)model receives the segmented frames to extract spatial and temporal information from the intermediate *** that,spatial and temporal features are learned using Long Short-Term Memory(LSTM)cells and Attention Network from a series of frames for each anomalous activity in a *** detect motion and action,the LWCNN received chronologically sorted ***,the anomaly activity in the video is identified using the proposed trained ADSV *** experiments are conducted on complex and challenging benchmark *** addition,the experimental results have been compared to state-ofthe-artmethodologies,and a significant improvement is attained,demonstrating the efficiency of our ADSV method.

关键词： Attention-based anomaly detection video shots segmentation video surveillance computer vision deep learning smart surveillance system violence detection attention model

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A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

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Control Theory and Technology 2021年第2期19卷 283-294页

作者： Behzad Farzanegan Mohsen Zamani Amir Abolfazl Suratgar Mohammad Bagher Menhaj Distributed Intelligent Optimization Research Lab Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran Computational Intelligence Lab Department of Electrical EngineeringAmirkabir University of TechnologyTehranIran The School of Electrical Engineering and Computer Science The University of NewcastleNewcastleAustralia Department of Medical Physics and Engineering Shiraz University of Medical SciencesShirazIran

In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input ***–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control *** consists of both analytical and algebraic *** the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB *** the algebraic part,the optimal control input that does not exceed the saturation bounds is *** weights of two NNs associated with observer and controller are simultaneously updated in an online *** ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct ***,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.

关键词： Input constraints Optimal control Neural networks Nonaffine nonlinear systems Reinforcement learning Unknown dynamics

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An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

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Digital Communications and Networks 2022年第6期8卷 900-910页

作者： M.S.Mekala Gautam Srivastava Ju H.Park Ho-Youl Jung Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38544South Korea RLRC LAb for Autonomous Vehicle Parts and Materials Innovation Yeungnam UniversityGyeongsan38544South Korea Department of Math and Computer Science Brandon UniversityBrandonCanada Department of Electrical Engineering Yeungnam UniversityGyeongsanSouth Korea Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

Social Edge Service(SES)is an emerging mechanism in the Social Internet of Things(SIoT)orchestration for effective user-centric reliable communication and *** services are affected by active and/or passive attacks such as replay attacks,message tampering because of sharing the same spectrum,as well as inadequate trust measurement methods among intelligent devices(roadside units,mobile edge devices,servers)during computing and *** issues lead to computation and communication overhead of servers and computation *** address this issue,we propose the HybridgrAph-Deep-learning(HAD)approach in two stages for secure communication and ***,the Adaptive Trust Weight(ATW)model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication ***,a Quotient User-centric Coeval-Learning(QUCL)mechanism to formulate secure channel selection,and Nash equilibrium method for optimizing the communication to share data over edge *** simulation results confirm that our proposed approach has achieved effective communication and computation performance,and enhanced Social Edge Services(SES)reliability than state-of-the-art approaches.

关键词： Edge computing Adaptive trust weight(ATW)model Quotient user-centric coeval-learning(QUCL)mechanism Deep learning Service reliability

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Enhanced Multidimensional Harmonic Retrieval in MIMO Wireless Channel Sounding

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IEEE Internet of Things Journal 2025年第11期12卷 16243-16255页

作者： Zhang, Yanming Xu, Wenchao Jin, A-Long Tang, Tianquan Li, Min Ma, Peifeng Jiang, Lijun Gao, Steven The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Xi'an Jiaotong-Liverpool University Department of Communications and Networking Suzhou China HKU Zhejiang Institute of Research and Innovation Lab for Aerodynamics and Acoustics Hangzhou China Tianjin University School of Microelectronics Tianjin China The Chinese University of Hong Kong Institute of Space and Earth Information Science Department of Geography and Resource Management Hong Kong Hong Kong Missouri University of Science and Technology Department of Electrical and Computer Engineering RollaMO United States

This paper introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a two-dimensional matrix format along the r-th dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the r-th dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding. © 2014 IEEE.

关键词： Eigenvalues and eigenfunctions

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Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

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IEEE International Conference on Robotics and Biomimetics

作者： Wen Wang Zheyuan Lin Shanshan Ji Te Li Jason Gu Minhong Wan Chunlong Zhang Zhejiang Lab and Zhejiang Engineering Research Center for Intelligent Robotics Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Canada

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset.

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CHAN: Cross-Modal Hybrid Attention Network for Temporal Language Grounding in Videos

CHAN: Cross-Modal Hybrid Attention Network for Temporal Lang...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen Wang Ling Zhong Guang Gao Minhong Wan Jason Gu Zhejiang Lab Research Center for Intelligent Robotics China Department of Electrical and Computer Engineering Dalhousie University Canada

The goal of temporal language grounding (TLG) task is to temporally localize the most semantically matched video segment with respect to a given sentence query in an untrimmed video. How to effectively incorporate the cross-modal interactions between video and language is the key to improve grounding performance. Previous approaches focus on learning correlations by computing the attention matrix between each frame-word pair, while ignoring the global semantics conditioned on one modality for better associating the complex video contents and sentence query of the target modality. In this paper, we propose a novel Cross-modal Hybrid Attention Network, which integrates two parallel attention fusion modules to exploit the semantics of each modality and interactions in cross modalities. One is Intra-Modal Attention Fusion, which utilizes gated self-attention to capture the frame-by-frame and word-by-word relations conditioned on the other modality. The other is Inter-Modal Attention Fusion, which utilizes query and key features derived from different modalities to calculate the co-attention weights and further promote inter-modal fusion. Experimental results show that our CHAN significantly outperforms several existing state-of-the-arts on three challenging datasets (ActivityNet Captions, Charades-STA and TACOS), demonstrating the effectiveness of our proposed method.

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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