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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Tang, Ming Wong, Vincent W. S. Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350334142

In federated learning, clients cooperatively train a global model by training local models over their datasets under the coordination of a central server. However, clients may sometimes be unavailable for training due to their network connections and energy levels. Considering the highly non-independent and identically distributed (non-IID) degree of the clients' datasets, the local models of the available clients being sampled for training may not represent those of all other clients. This is referred as system induced bias. In this work, we quantify the system induced bias due to time-varying client availability. The theoretical result shows that this bias occurs independently of the number of available clients and the number of clients being sampled in each training round. To address system induced bias, we propose a FedSS algorithm by incorporating stratified sampling and prove that the proposed algorithm is unbiased. We quantify the impact of system parameters on the algorithm performance and derive the performance guarantee of our proposed FedSS algorithm. Theoretical and experimental results on CIFAR-10 and MNIST datasets show that our proposed FedSS algorithm outperforms several benchmark algorithms by up to 5.1 times in terms of the algorithm convergence rate. © 2023 IEEE.

关键词： Learning systems

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Analysis of Security Mechanisms Used in ETL Tools 32

Analysis of Security Mechanisms Used in ETL Tools

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32nd Telecommunications Forum, TELFOR 2024

作者： Jeftovic, Milica Vuletic, Pavle Stanisavljevic, Zarko Capgemini Engineering Belgrade Serbia University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Belgrade Serbia

ISBN: (纸本)9798350391053

This paper analyses current best practices for using security mechanisms in extract, transform, and load (ETL) tools. The results of the analysis are visually represented in a form of an optimal security model used in these tools at the moment. In order to test the recommended model, it was implemented in a selected open source ETL tool. At the end, advantages and disadvantages of the recommended model are enlisted and directions for further improvement of the model are given. © 2024 IEEE.

关键词： ETL tools Security Mechanisms Security Model

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Odometry-Less Indoor Dynamic Object Detection and Localization with Spatial Anticipation

Odometry-Less Indoor Dynamic Object Detection and Localizati...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Shen, Zhengcheng Gao, Yi Kästner, Linh Lambrecht, Jens Berlin Institute of Technology Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350312072

The continually evolving image segmentation methods in computer vision can further broaden the cognitive abilities of the robot. As humans, we won't judge if the object is moving by accuracy speed estimation but through semantic information based on visual input. With the existing video segmentation method, the robot can pay more attention to the foreground objects, which are more important for path planning and navigation in most cases. The proposed methods try to deploy the first-person view segmentation method with an RGBD sensor and get the location information and locally egocentric map. The error of the segmentation methods will cause unexpected objects in the egocentric map, and the self-occlusion will influence the location and distance estimation. To further improve the methods, a spatial anticipate unit is embedded into the framework. We improve 5.6% the localization accuracy(RMSE), 12.9% the distance estimation(MAE) with the trained object, and 28.9% the recall for the trained object. The methods also show the margin on the untrained object dataset for similar objects. © 2024 IEEE.

关键词： Semantics

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Hybrid attention residual deep convolution learning network for bio medical image analysis

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Journal of Intelligent and Fuzzy Systems 2025年第4期48卷 537-554页

作者： Sundar Raj, A. Naidu, Paruchuri Chandra Babu Sivakumar, S. Senthilkumar, S. Department of Biomedical Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India Department of Electrical and Electronics Engineering BSA Crescent Institute of Science and Technology Vandalur Tamilnadu Chennai India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Tamilnadu Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Biomedical image analysis has progressed significantly with the integration of artificial intelligence, presenting new opportunities for early diagnosis and treatment of diseases with high mortality rates, such as skin cancer. This research work introduces a novel Hybrid Attention Residual Deep Convolution Learning (HARDCL) Model designed to enhance the accuracy of skin lesion classification, which is often challenging due to the subtle characteristics of lesions. The HARDCL Network employs an innovative hybrid attention mechanism within a residual learning framework, effectively distinguishing between benign and melanoma skin lesions with a high degree of precision. The encoder-decoder network segments the lesion from dermoscopic images, followed by feature extraction and classification, achieving a maximum accuracy of 97.11%, precision of 98.10%, recall of 98.81%, and an F1-score of 98.45%. This performance surpasses conventional deep learning algorithms such as AlexNet, InceptionV3, and ResNet18, demonstrating the effectiveness of the attention-based features in identifying critical lesion characteristics. However, despite its strengths, the HARDCL Network’s computational intensity and the need for a substantial dataset for optimal training highlight areas for further refinement. The proposed network advances the field of skin cancer diagnostics, offering a robust tool for medical professionals and setting a foundation for future enhancements in automated biomedical image analysis techniques. © 2025 – IOS Press. All rights reserved.

关键词： Deep learning

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DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPEECH WAVEFORM GENERATION 12

DIFFAR: DENOISING DIFFUSION AUTOREGRESSIVE MODEL FOR RAW SPE...

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12th International Conference on Learning Representations, ICLR 2024

作者： Benita, Roi Elad, Michael Keshet, Joseph Department of Electrical and Computer Engineering Technion - Israel Institute of Technology Haifa Israel Department of Computer Science Technion - Israel Institute of Technology Haifa Israel

Diffusion models have recently been shown to be relevant for high-quality speech generation. Most work has been focused on generating spectrograms, and as such, they further require a subsequent model to convert the spectrogram to a waveform (i.e., a vocoder). This work proposes a diffusion probabilistic end-to-end model for directly generating the raw speech waveform. The proposed model is autoregressive, generating overlapping frames sequentially, where each frame is conditioned on a portion of the previously generated one. Hence, our model can effectively synthesize an unlimited speech duration while preserving high-fidelity synthesis and temporal coherence. We implemented the proposed model for unconditional and conditional speech generation, where the latter can be driven by an input sequence of phonemes, amplitudes, and pitch values. Working directly on the waveform has some empirical advantages. Specifically, it allows the creation of local acoustic behaviors, like vocal fry, which makes the overall waveform sounds more natural. Furthermore, the proposed diffusion model is stochastic and not deterministic;therefore, each inference generates a slightly different waveform variation, enabling abundance of valid realizations. Experiments show that the proposed model generates speech with superior quality compared with other state-of-the-art neural speech generation systems. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Diffusion

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A UWB Antenna for Wireless Body Area Network and its Characteristics Analysis 3

A UWB Antenna for Wireless Body Area Network and its Charact...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Rahman, H M Arifur Shovon, Md. Nakib Alam Hoque, Md. Ariful Khan, Mohammad Monirujjaman Yeboah-Akowuah, Bright North South University Department of Electrical and Computer Engineering Dhaka Bangladesh Kwame Nkrumah University of Science and Technology Department of Computer Engineering Kumasi Ghana

ISBN: (纸本)9798350388282

A newly designed small, almost square shaped Ultra Wideband antenna is presented in this paper. Free space simulations provided promising results with a wider bandwidth and high efficiency up to 97.68% within the UWB range. The antenna was intended for Wireless Body Area Network (WBAN) application. To verify its human on-body performance, a 3D human torso phantom was created and re-simulated the results by placing the antenna in its near field. To determine the effects of the presence of the human body, further distance-based studies were done by placing the antenna in five different distances from the torso phantom. The results were compared with each other and with free space simulations to determine how the distance influences its behaviors. Highest gain was recorded while the antenna was placed on 4mm and above with maximum value of 8.13dB. The results were summarized in terms of antenna parameters such as return loss, voltage standing wave ratio, farfield and 2D radiation patterns, efficiency, and gains. The antenna shows operability in wide frequency range within the UWB spectrum with optimistic gain values. © 2024 IEEE.

关键词： Microstrip antennas

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Photonics Applications and Web engineering – WILGA 2024

Photonics Applications and Web Engineering – WILGA 2024

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Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2024

作者： Romaniuk, Ryszard S. Smolarz, Andrzej Wójcik, Waldemar Warsaw University of Technology Institute of Electronic Systems Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Poland

ISBN: (数字)9781510685796

ISBN: (纸本)9781510685789

Wilga 2024 Summer Symposium on Photonics Applications and Web engineering was the 52th edition of the research and technical meetings series. Traditionally, the annual series of technical conferences and topical sessions lasted the whole week. They usually were split to one winter meeting in January and one or two summer events in May/June, and in September. The Covid epidemic and the war in Ukraine has changed a lot. The hybrid meetings were held in Wilga resort near Warsaw, owned by the Warsaw University of technology. Now the meetings since 2022 were shifted to Lublin. Nearly 100 participants took part in all of the Wilga 2022 events, most of them being young researchers active in all aspects of photonics, electronics and ICT science and technology. Over 60 participants took part in Wilga 2024 meetings. Wilga Symposium embraces hardware and software technologies associated with photonics like optics, optical engineering, optoelectronics, electronics and electrical engineering, mechatronics, chemical and material engineering, applied physics, industrial solutions and applications. Around 50 papers were presented during Wilga 2024, oral and poster. Out of this number, some papers chosen by authors are published in this volume of Proc. SPIE mostly related to photonics. The major change was that during Wilga 2020, 2021, 2022 and 2024 Symposia, during summer editions, no traditional large young researcher sessions at large were possible to be organized. We hope that this will change and Wilga 2025 will return to a good tradition to organize concurrent social research sessions full of young researchers like was during Wilga 2019 and earlier. © 2024 SPIE.

关键词： Photonic devices

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Robust Sparse Regression with Non-Isotropic Designs 38

Robust Sparse Regression with Non-Isotropic Designs

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Chih-Hung Novikov, Gleb Department of Electrical Engineering National Taiwan University Taiwan Lucerne School of Computer Science and Information Technology Switzerland

We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive. Consider the model y∗ = X∗β∗ + η where X∗ is an n × d random design matrix, β∗ ∈ Rd is a k-sparse vector, and the noise η is independent of X∗ and chosen by the oblivious adversary. Apart from the independence of X∗, we only require a small fraction entries of η to have magnitude at most 1. The adaptive adversary is allowed to arbitrarily corrupt an Ε-fraction of the samples (X1∗, y1∗), ..., (Xn∗, yn∗ ). Given the Ε-corrupted samples (X1, y1), ..., (Xn, yn), the goal is to estimate β∗. We assume that the rows of X∗ are iid samples from some d-dimensional distribution D with zero mean and (unknown) covariance matrix Σ with bounded condition number. We design several robust algorithms that outperform the state of the art even in the special case of Gaussian noise η ∼ N(0, 1)n. In particular, we provide a polynomial-time algorithm that with high probability recovers β∗ up to error O(√Ε) as long as n ≥ O∼ (k2/Ε), only assuming some bounds on the third and the fourth moments of D. In addition, prior to this work, even in the special case of Gaussian design D = N(0, Σ) and noise η ∼ N(0, 1), no polynomial time algorithm was known to achieve error o(√Ε) in the sparse setting n 2. We show that under some assumptions on the fourth and the eighth moments of D, there is a polynomial-time algorithm that achieves error o(√Ε) as long as n ≥ O∼ (k4/Ε3). For Gaussian distribution D = N(0, Σ), this algorithm achieves error O(Ε3/4). Moreover, our algorithm achieves error o(√Ε) for all log-concave distributions if Ε ≤ 1/polylog(d). Our algorithms are based on the filtering of the covariates that uses sum-of-squares relaxations, and weighted Huber loss minimization with 1 regularizer. We provide a novel analysis of weighted penalized Huber loss that is suitable for heavy-tailed designs in the presence of two adversaries

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Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

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computer Systems science & engineering 2022年第9期42卷 935-948页

作者： M.Manikandan R.Subramanian M.S.Kavitha S.Karthik Department of Computer Science&Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatore641035India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

In today’s world, Cloud Computing (CC) enables the users to accesscomputing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located inremote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and taskscheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud isemployed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the currentresearch work develops a Cost-Effective Optimal Task Scheduling Model(CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) modelis used in the proposed work for hybrid clouds. Moreover, the algorithm workson the basis of multi-intentional task completion process with optimal resourceallocation. The model was successfully simulated to validate its effectivenessbased on factors such as processing time, make span and efficient utilization ofvirtual machines. The results infer that the proposed model outperformed theexisting works and can be relied in future for real-time applications.

关键词： Cost effectiveness hybrid cloud optimal task scheduling virtual machine resource allocation make span

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Adaptive Trajectory Planning in Autonomous Vehicles: A Hierarchical Reinforcement Learning Approach with Soft Actor-Critic 18

Adaptive Trajectory Planning in Autonomous Vehicles: A Hiera...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Amit Kumar Choudhary, Amit Chaudhary, Rajat Bhardwaj, Aditya Aslam, Anjum Mohd Sir Chhotu Ram Institute of Engineering and Technology Electronics and Communication Uttar Pradesh Meerut India Kth Royal Institute of Technology Electrical Engineering and Computer Science Stockholm Sweden Bennett University School of Computer Science and Engineering Technology Greater Noida India

ISBN: (纸本)9798350391725

This study introduces a methodology enabling automated vehicles to perform lane changes effectively within complex road systems. It emphasizes a hierarchical driver behavior framework that integrates decision-making with trajectory planning to enhance safety. The approach utilizes reinforcement learning (RL) agents for automated vehicles and the MOBIL model for human-operated vehicles, aiming to optimize the lane change process. The paper introduces the Soft Actor-Critic (SAC), an off-policy actor-critic algorithm, to improve training stability and effectiveness in real-world robotics applications. Additionally, it offers a comprehensive review of existing planning and control algorithms for self-driving vehicles, offering a comprehensive survey of techniques and their strengths and limitations to aid in informed design choices. © 2024 IEEE.

关键词： Reinforcement learning

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