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arXiv 2023年

作者： Han, Runzhou Zheng, Mai Byna, Suren Tang, Houjun Dong, Bin Dai, Dong Chen, Yong Kim, Dongkyun Hassoun, Joseph Thorsley, David Wolf, Matthew Department of Electrical and Computer Engineering Iowa State University United States Department of Computer Science and Engineering The Ohio State University United States Scientific Data Management Group Lawrence Berkeley National Laboratory United States Computer Science Department University of North Carolina Charlotte United States Systems Architecture Lab Samsung United States Samsung Advanced Institute of Technology Samsung Korea Republic of

Data provenance, or data lineage, describes the life cycle of data. In scientific workflows on HPC systems, scientists often seek diverse provenance (e.g., origins of data products, usage patterns of datasets). Unfortunately, existing provenance solutions cannot address the challenges due to their incompatible provenance models and/or system implementations. In this paper, we analyze four representative scientific workflows in collaboration with the domain scientists to identify concrete provenance needs. Based on the first-hand analysis, we propose a provenance framework called PROV-IO+, which includes an I/O-centric provenance model for describing scientific data and the associated I/O operations and environments precisely. Moreover, we build a prototype of PROV-IO+ to enable end-to-end provenance support on real HPC systems with little manual effort. The PROV-IO+ framework can support both containerized and non-containerized workflows on different HPC platforms with flexibility in selecting various classes of provenance. Our experiments with realistic workflows show that PROV-IO+ can address the provenance needs of the domain scientists effectively with reasonable performance (e.g., less than 3.5% tracking overhead for most experiments). Moreover, PROV-IO+ outperforms a state-of-the-art system (i.e., ProvLake) in our experiments. © 2023, CC BY-NC-SA.

关键词： Life cycle

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Facial Recognition Impact in Smartphone Sector 4

Facial Recognition Impact in Smartphone Sector

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Jha, Amaresh Sudhakar, P. Lokhande, Sharayu Ashish Kumar Ishodzhanova, Galina Venkatrao, K. Zhakupova, Aliya School of Digital Media Journalism & Mass Communication Gd Goenka University India Vignan's Nirula Institute of Technology and Science for Women Department of Ece India Computer Engineering Army Institute of Technology India International Education Corporation Department of Architecture Kazakhstan Srkr Engineering College Department of Ece India International Education Corporation Department of Linguistics Kazakhstan

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In recent times, technological advancement brings many benefits for people and facial recognition technology is one of them. Nowadays, S martphone users increasingly use facial recognition features that help in verifying identity of users for different purposes, for example, accessing their device before making any payment from their phones. This study paper has analysed impact of facial recognition systems in S martphone sectors by discussing its advantages and challenges to understand both its negative and positive impact. Literature review section has provided relevant information on selected topic as it has elaborated that different large organisation have their own face recognition software for Apple, Facebook, and Google. Additionally, it has mentioned that there are two different types of facial recognition software such as camera-based and infrared;however, regarding effectiveness, infrared works more appropriate rather than camera-based recognition technologies. The paper has also elaborated that this technology offers automating time-tracking systems and it has touch less sign options, which help them in limiting physical contact with others. Result of this study showed that facial recognition helps to protect any type of unauthorised access in a Smartphone as well as it could prevent cyber threats by recognising face of S martphone users. Additionally, due to this software's fast identification feature, it could successfully verify faces of people within a few seconds. However, as this software is based on artificial intelligence, unlike other applications, it is highly vulnerable as hackers can hack Smartphones if strong algorithms are more used. Lastly, to prevent this issue a strong AI infrastructure and model security is required in future. © 2022 IEEE.

关键词： Smartphones

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A Key Factor in Traffic Management - Vehicle Speed Prediction Using Machine Learning 4

A Key Factor in Traffic Management - Vehicle Speed Predictio...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Maurya, Ayush Dwivedi, Ashish Pratap, Saurabh Dubey, Suchi Zhou, Fuli Shukla, Vinod Kumar Shambhunath Institute of Engineering and Technology Department of Computer Science and Engineering U.P Prayagraj India Jindal Global Business School O.P. Jindal Global University Sonipat India Department of Mechanical Engineering Varanasi India Manipal Academy of Higher Education Dubai United Arab Emirates College of Economics and Management Zhengzhou University of Light Industry Zhengzhou450001 China Amity University Dubai Department of Engineering and Architecture United Arab Emirates

ISBN: (纸本)9798350393248

The analysis of transportation data is considered as an essential application for Machine Learning (ML) based-intelligent transportation design and control especially for safety and improving energy management. There is a requirement to identify efficient methods for optimizing transportation operations and enhance traffic management. This study adopts exploratory data analysis to analyse transportation data including year, month, vehicle type, time period, area type, functional classification. Predictive analytics is used to predict vehicle speed as dependent variable. The study adopts four supervised ML models such as Multiple Linear Regression (MLR), Random Forest Regression (RFR), Decision Tree (DT) and Artificial Neural Network (ANN) approach for analysing the speed prediction factors. Speed prediction assists as a foundation of advanced traffic management systems. The results from the findings suggest that ANN and RFR are the commendable model for performance analytics. © 2023 IEEE.

关键词： Machine learning

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Comparative Assessment Between Statistical and Soft Computing Methods for Accident Severity Classification

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Journal of The Institution of Engineers (India): Series A 2020年第1期101卷 27-40页

作者： Sarkar, Amrita Sarkar, Satyaki Department of Computer Science and Engineering Sarala Birla University Ranchi835103 India Department of Architecture Birla Institute of Technology Mesra Ranchi835215 India

Globally traffic scenario has experienced a vast change over the years as volumes of traffic on urban roads have increased by leaps and bounds. This increase has also marked an increase in the number of road accidents, both fatal and non-fatal. Accident prediction has a significant role in the improvement in traffic safety, and urban traffic management. Different statistical methods and various soft computing techniques are being used for developing accident prediction models. In this study, ensemble ranking method, comprising three filter-based ranking methods such as Information Gain, Gain Ratio, and Symmetric Uncertainty, has been employed to select significant features for prediction of accident severity. Through analysis, eight significant features have been selected for accident severity prediction. These are Annual Average Daily Traffic, Road Width, Percentage of Left-Turn Vehicles, Driver’s Age, Percentage of Right-Turn Vehicles, Types of Vehicles Involved, Spot Speed, and Cause of Accidents. Multiple logistic regression (MLGR) and artificial neural network (ANN) have been applied for accident severity prediction in urban roads. This analysis concludes that ANN model gives more accuracy and minimum errors compared with the MLGR method at the time of severity classification of a vehicular accident. © 2019, The Institution of Engineers (India).

关键词： Forecasting

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection 5

Recommendation System Towards Residential Energy Saving Base...

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5th International Conference on Signal Processing and Information Security, ICSPIS 2022

作者： Atalla, Shadi Himeur, Yassine Mansoor, Wathiq Amira, Abbes Fadli, Fodil Copiaco, Abigail Sohail, Shahab Saquib University of Dubai College of Engineering and IT Dubai United Arab Emirates University of Sharjah Department of Computer Science Sharjah United Arab Emirates Qatar University Dept. of Architecture & Urban Planning Doha2713 Qatar Jamia Hamdard School of Engineering Sciences & Technology New Delhi110062 India

ISBN: (纸本)9781665492652

This paper presents a recommender system to promote energy consumption reduction behaviors in residential buildings. The system exploits data stream processing methods jointly with machine learning algorithms on real-time residential data. Specifically, the data stream includes disaggregated power consumption, context, and weather conditions data. Internally the system converts time-series data streams into discrete ordered data points, which serve as inputs for training ML models. This method is used to predict power consumption anomalies. Gradually, the system helps to shape its users' activities into more energy-efficient ones. The experimental evaluation on real and simulated datasets demonstrates the promising performance of the proposed method, primarily when the K-neighbors neighbors' algorithm is used to classify the features extracted with interleaving current with the previous data points. The performance assessment of the machine learning algorithms shows the suitability of our implementation for Edge and Fog platforms in terms of accuracy, latency, and model size. © 2022 IEEE.

关键词： Electric power utilization

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Innovative Approaches in Network architecture: Enhancing Connectivity and Performance in the Era of 5G and Beyond

Innovative Approaches in Network Architecture: Enhancing Con...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Lakshmi Narasimhan Srinivasagopalan Balla Mounica B Srinivasa Rao Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Hyderabad Ace Engineering College Hyderabad Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study examines the evolving ecosystem of network applications for enhancing connectivity and performance in 5G and beyond (B5G) networks. The objective is to streamline large-scale deployment of vertical applications through a middleware layer, facilitating interactions among network operators and third parties based on varying trust levels. The proposed model addresses the complexity and computational demands of enabling adaptable, secure, and scalable applications for diverse 5G platforms. In particular, this study highlights the 5G-EPICENTRE project, which enables traffic management and dynamic control, especially for missioncritical Public Protection and Disaster Relief (PPDR) applications. By optimizing resource allocation, the model reduces computational costs while meeting the unique demands of PPDR services, such as high-quality video and data for emergency operations. The model's effectiveness will be evaluated through experiments leveraging 5G Core (5GC) control-plane capabilities, with a focus on quality of service (QoS) and latency. Practical limitations, including the integration challenges of multi-network APIs, are discussed in the conclusion.

关键词： Adaptation models 5G mobile communication Computational modeling Scalability Ecosystems Quality of service Telecommunications Complexity theory Resource management Protection

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An Efficient Authentication Protocol for Smart Grid Communication Based on On-Chip-Error-Correcting Physical Unclonable Function

arXiv

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arXiv 2023年

作者： Kaveh, Masoud Mosavi, Mohammad Reza Martín, Diego Aghapour, Saeed Department of Information and Communication Engineering Aalto University Espoo Finland Department of Electrical Engineering Iran University of Science and Technology Tehran Iran ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Department of Computer Science and Engineering University of South Florida Tampa United States

Security has become a main concern for the smart grid to move from research and development to industry. The concept of security has usually referred to resistance to threats by an active or passive attacker. However, since smart meters (SMs) are often placed in unprotected areas, physical security has become one of the important security goals in the smart grid. Physical unclonable functions (PUFs) have been largely utilized for ensuring physical security in recent years, though their reliability has remained a major problem to be practically used in cryptographic applications. Although fuzzy extractors have been considered as a solution to solve the reliability problem of PUFs, they put a considerable computational cost to the resource-constrained SMs. To that end, we first propose an on-chip-error-correcting (OCEC) PUF that efficiently generates stable digits for the authentication process. Afterward, we introduce a lightweight authentication protocol between the SMs and neighborhood gateway (NG) based on the proposed PUF. The provable security analysis shows that not only the proposed protocol can stand secure in the Canetti–Krawczyk (CK) adversary model but also provides additional security features. Also, the performance evaluation demonstrates the significant improvement of the proposed scheme in comparison with the state-of-the-art. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart power grids

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Memory-facilitated Joint-space Shift Adaptation in Traffic Forecasting

Memory-facilitated Joint-space Shift Adaptation in Traffic F...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ye Wei He Haitao Gerald Schaefer Zhigang Ji Yifan Wang Hui Fang Department of Computer Science Loughborough University Loughborough UK The Alan Turing Institute London UK School of Architecture Building and Civil Engineering Loughborough University Loughborough UK National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traffic forecasting, crucial for intelligent transport systems, faces significant challenges from distribution shifts due to the dynamic nature of traffic patterns. Although normalisation approaches have been proposed to address distribution shifts in other time-series forecasting tasks such as predicting electricity consumption load prediction or influenza-like illness patient number estimation, they fall short in handling the complex spatial and temporal shifts in traffic data. In this paper, we propose a novel memory-facilitated joint-space shift adaptation framework, ST-Align, to address this problem in traffic forecasting. ST-Align comprises two key components targeting the input and latent space, respectively: a memory-based data alignment module in the input space, and an end-to-end memory network structure dedicated to alignment within the latent space. This joint-space design enables our ST-Align framework to effectively capture and adapt to dynamic distribution shifts in both spatial and temporal dimensions, thus enhancing model performance. Extensive experiments on various real-world datasets and prediction backbones convincingly demonstrate the robustness and generalisability of our method.

关键词： Adaptation models Electricity Neural networks Estimation Traffic control Robustness Forecasting

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ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USING YOLOV3

ANALYSIS OF CONCRETE CRACKS AND FATIGUE IN SMART CITIES USIN...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Archana, U. Sharma, Sarvdaman Singh, Sitesh Kumar Sureshkumar, R. Senthilkumar, P. Harish Babu, T. Department of Computer Science and Design R.M.K. Engineering College Chennai India Department of Civil Engineering University Institute of Engineering and Technology University of Jammu Jammu & Kashmir India Department of Civil Engineering Wollega University Oromia Nekemte Ethiopia Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai India KL School of Architecture Koneru Lakshmaiah Education Foundation Greenfield Vaddeswaram Andhra Pradesh Guntur India Department of Electrical and Electronics Engineering Sethu Institute of Technology Tamilnadu India

ISBN: (纸本)9798350305708

This study focuses on the application of the YOLOv3 (You Only Look Once version 3) object detection algorithm for the analysis of concrete cracks and fatigue within Smart Cities. Concrete infrastructure plays a crucial role in urban environments, and its structural integrity is paramount for ensuring public safety and sustainable development. The proposed approach utilizes YOLOv3 to identify and localize instances of concrete cracks and signs of fatigue in images and videos captured from various urban settings. By leveraging deep learning techniques, this research aims to enhance the efficiency and accuracy of monitoring and maintaining concrete structures, contributing to the overall resilience and longevity of Smart Cities. The findings of this study present valuable insights into the potential of utilizing advanced computer vision methods for proactive infrastructure management in the urban landscape. © 2023 IEEE.

关键词： Concrete cracks Fatigue analysis Infrastructure monitoring Smart Cities YOLOv3

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