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International Conference on Network Protocols

作者： Yangfei Lin Celimuge Wu Muhammad Luqman Fikri Jiale Wu Jie Li Lei Zhong Yusheng Ji Department of Computer and Network Engineering University of Eletronic-communication Chofu Tokyo Japan Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai China Toyota Motor Corporation Tokyo Japan National Institute of Informatics (NII) Tokyo Japan

Remote driving, an emergent technology enabling remote operation of vehicles, presents a significant challenge due to the necessity of transmitting substantial volumes of image data from the vehicle to a central server. This requirement outpaces the capacity of traditional communication methods, emphasizing the need for efficient data communication. We propose a framework using semantic communication, specifically through a semantic segmentation-based method, which reduces the communication cost by transmitting meaningful semantic information rather than bit-wise data. Addressing the challenge of inconsistencies across knowledge bases in semantic communication, we present a blockchain-based, edge-assisted knowledge base management system. This system leverages edge nodes to manage multiple, geographically and contextually diverse knowledge bases while ensuring security through blockchain's tamper-resistant nature. Furthermore, blockchain sharding is employed to manage different knowledge bases for varying tasks, thereby enhancing the blockchain's throughput. Experimental results showed a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

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Proactive aging mitigation in CGRAs through utilization-aware allocation 57

Proactive aging mitigation in CGRAs through utilization-awar...

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57th ACM/IEEE Design Automation Conference, DAC 2020

作者： Brandalero, Marcelo Lignati, Bernardo Neuhaus Carlos Schneider Beck, Antonio Shafique, Muhammad Hubner, Michael Chair of Computer Engineering Cottbus Germany Institute of Informatics Porto Alegre Brazil Institute of Computer Engineering Vienna Austria

ISBN: (纸本)9781450367257

Resource balancing has been effectively used to mitigate the long-term aging effects of Negative Bias Temperature Instability (NBTI) in multi-core and Graphics Processing Unit (GPU) architectures. In this work, we investigate this strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel application-to-CGRA allocation approach. By introducing important extensions to the reconfiguration logic and the datapath, we enable the dynamic movement of configurations throughout the fabric and allow overutilized Functional Units (FUs) to recover from stress-induced NBTI aging. Implementing the approach in a resource-constrained state-of-the-art CGRA reveals 2.2× lifetime improvement with negligible performance overheads and less than 10% increase in area. © 2020 IEEE.

关键词： Negative bias temperature instability

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Federated learning: Applications, challenges and future directions

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 19-35页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions. © 2022 - IOS Press. All rights reserved.

关键词： Artificial intelligence computing methodologies federated learning healthcare machine learning privacy protection

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A Preprocessor for Creation of Large-Scale Computational Applications

A Preprocessor for Creation of Large-Scale Computational App...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Victor S. Simonov Marat S. Khairetdinov dept. Computer Engineering Novosibirsk State Technical University Novosibirsk Russian Federation dept. Geophysical Informatics Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russian Federation

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

The emergence of large-scale computing paradigms required the evolution of software development methods, moreover, the need to effectively transform traditionally sequential applications into parallelizable and scalable architectures. This transformation is key in harnessing the computing power of modern multicore and distributed systems. Python, with its vast ecosystem and simplicity, is becoming a powerful language that facilitates this transition. This article discusses the methodologies and development of a Python preprocessor tool aimed at converting sequential applications into forms suitable for large-scale computing environments. Through detailed analysis and implementation, we demonstrate the use of Spark functions and third-party libraries to automate this transition, making large-scale computing more accessible to a wider range of applications and developers. The tool includes a set of rules that identify different patterns of code input (for example, list-sequential looping) and transform consistent code. This article introduces a Python preprocessor designed to automate the transformation of sequential Python applications into ones that harness the power of large-scale computing environments.

关键词： Performance evaluation Codes Multicore processing Scalability Transforms computer architecture Libraries

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DECOR 2024: Where Data engineering Meets Intelligent Food and Cooking Innovation

DECOR 2024: Where Data Engineering Meets Intelligent Food an...

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International Conference on Data engineering Workshops

作者： Frederic Andres Farshad Fotouhi Gheorghita Ghinea Steve Tanimoto Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo Japan Dpt of Computer Science College of Engineering Wayne State University Detroit MI USA Department of Computer Science Brunel University London London UK Paul G. Allen School of Computer Science & Engineering University of Washington Seattle WA USA

ISBN: (数字)9798350384031

ISBN: (纸本)9798350384048

This extended abstract presents the 7th International Workshop on Data engineering Meets Intelligent Food & Cooking Recipes 2024, which will be held in conjunction with the 40th IEEE International Conference on Data engineering 2024 (ICDE). The primary aim of this workshop is to provide a comprehensive overview of the latest advancements and existing challenges at the intersection of Data engineering and Intelligent Food & Cooking Recipes.

关键词： Technological innovation Conferences Data engineering

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Variational Weighted p−q Regularization for Hyperspectral Image Restoration Under Mixed Noise

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IET Image Processing 2025年第1期19卷

作者： Aetesam, Hazique Prasath, V. B. Surya Department of Computer Science and Engineering Birla Institute of Technology Mesra Bihar Patna India Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center CincinnatiOH United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH United States Department of Biomedical Informatics University of Cincinnati CincinnatiOH United States Department of Computer Science University of Cincinnati CincinnatiOH United States

In this paper, we propose to use weighted (Formula presented.) -norm for approximating the solution of general (Formula presented.) -norm regularization problem for recovering hyperspectral images (HSI) corrupted by a mixture of Gaussian-impulse noise. As a special case of (Formula presented.), we design an optimization framework to accommodate the combined effect of different noise sources. An initial impulse noise pre-detection phase decouples the raw noisy HSI data into impulse and Gaussian corrupted pixels. Gaussian corrupted pixels are handled by data-fidelity term in (Formula presented.) while impulse corrupted pixels possess more Laplacian like behavior;modeled using (Formula presented.). Solutions of problems involving (Formula presented.) in data fidelity and regularization terms complicate the optimization process but are less sensitive to the outlier pixels. On the other hand, the least square solutions for the data misfit are computationally efficient but generates solutions which are quite sensitive to the outlier pixels;which is the characteristic of impulse corrupted pixels. Therefore, in this paper, we decouple the set of pixels into two distinct parts;handled using two separate data fidelity terms. Total variation (TV) is used on the Casorati matrix representation of the input data to exploit similarity along both spatial and spectral dimensions. The resulting optimization problem is reformulated as iteratively reweighted least square for the general (Formula presented.) -norm problem for (Formula presented.) for data fidelity terms and (Formula presented.) for the TV regularization term. Experiments conducted over synthetically corrupted HSI data and images obtained from real HSI sensors confirm the suitability of the proposed weighted norm optimization framework (WNOF) over a wide range of degradation scenarios. © 2025 The Author(s). IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Image denoising

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Mathematical Modelling of Nonlinear Heat Conduction with Relaxing Boundary Conditions 1

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10th International Conference on Numerical Methods and Applications, NMA 2022

作者： Filipov, Stefan M. Faragó, István Avdzhieva, Ana Department of Computer Science Faculty of Chemical and System Engineering University of Chemical Technology and Metallurgy Sofia Bulgaria Department of Differential Equations Institute of Mathematics Budapest University of Technology and Economics & MTA-ELTE Research Group Budapest Hungary Faculty of Mathematics and Informatics "St. Kl. Ohridski" University of Sofia Sofia Bulgaria

ISBN: (数字)9783031324123

ISBN: (纸本)9783031324116

This paper considers one dimensional unsteady heat condition in a media with temperature dependent thermal conductivity. When the thermal conductivity depends on the temperature, the corresponding heat equation is nonlinear. At one or both boundaries, a relaxing boundary condition is applied. It is a time dependent condition that approaches continuously, as time increases, a certain time independent condition. Such behavior at the boundaries arises naturally in some physical systems. As an example, we propose a simple model system that can give rise to either Dirichlet or convective relaxing boundary condition. Due to the dependence of the thermal conductivity on the temperature, the convective condition is nonlinear. For the solution of the problem, we propose a new numerical approach that first discretizes the heat equation in time, whereby a sequence of two-point boundary value problems (TPBVPs) is obtained. We use implicit time discretization, which provides for unconditional stability of the method. If the initial condition is given, we can solve consecutively the TPBVPs and get approximations of the temperature at the different time levels. For the solution of the TPBVPs, we apply the finite difference method. The arising nonlinear systems are solved by Newton’s method. A number of example problems are solved demonstrating the efficiency of the proposed approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Boundary conditions

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Energy-Aware Heterogeneous Federated Learning via Approximate DNN Accelerators

arXiv

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arXiv 2024年

作者： Pfeiffer, Kilian Balaskas, Konstantinos Siozios, Kostas Henkel, Jörg Embedded Systems Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Computer Engineering and Informatics University of Patras Patras26504 Greece Department of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece

In Federated Learning (FL), devices that participate in the training usually have heterogeneous resources, i.e., energy availability. In current deployments of FL, devices that do not fulfill certain hardware requirements are often dropped from the collaborative training. However, dropping devices in FL can degrade training accuracy and introduce bias or unfairness. Several works have tackled this problem on an algorithm level, e.g., by letting constrained devices train a subset of the server neural network (NN) model. However, it has been observed that these techniques are not effective w.r.t. accuracy. Importantly, they make simplistic assumptions about devices’ resources via indirect metrics such as multiply accumulate (MAC) operations or peak memory requirements. We observe that memory access costs (that are currently not considered in simplistic metrics) have a significant impact on the energy consumption. In this work, for the first time, we consider on-device accelerator design for FL with heterogeneous devices. We utilize compressed arithmetic formats and approximate computing, targeting to satisfy limited energy budgets. Using a hardware-aware energy model, we observe that, contrary to the state of the art’s moderate energy reduction, our technique allows for lowering the energy requirements (by 4×) while maintaining higher accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Lane Detection System for Driver Assistance in Vehicles

arXiv

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arXiv 2024年

作者： Mariano, Kauan Divino Pouso de Castro Fernandes, Fernanda Oliveira, Luan Gabriel Silva Rodrigues, Lyan Eduardo Sakuno Brandão, Matheus Andrade Institute of Informatics Federal University of Goiás Goiás Goiânia Brazil School of Electrical Mechanical and Computer Engineering Federal University of Goiás Goiás Goiânia Brazil

This work presents the development of a lane detection system aimed at assisting the driving of conventional and autonomous vehicles. The system was implemented using traditional computer vision techniques, focusing on robustness and efficiency to operate in real-time, even under adverse conditions such as worn-out lanes and weather variations. The methodology employs an image processing pipeline that includes camera calibration, distortion correction, perspective transformation, and binary image generation. Lane detection is performed using sliding window techniques and segmentation based on gradients and color channels, enabling the precise identification of lanes in various road scenarios. The results indicate that the system can effectively detect and track lanes, performing well under different lighting conditions and road surfaces. However, challenges were identified in extreme situations, such as intense shadows and sharp curves. It is concluded that, despite its limitations, the traditional computer vision approach shows significant potential for application in driver assistance systems and autonomous navigation, with room for future improvements. © 2024, CC BY.

关键词： Binary images

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