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arXiv 2023年

作者： Xing, Yu Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm Sweden

We study community detection based on state observations from gossip opinion dynamics over stochastic block models (SBM). It is assumed that a network is generated from a two-community SBM where each agent has a community label and each edge exists with probability depending on its endpoints’ labels. A gossip process then evolves over the sampled network. We propose two algorithms to detect the communities out of a single trajectory of the process. It is shown that, when the influence of stubborn agents is small and the link probability within communities is large, an algorithm based on clustering transient agent states can achieve almost exact recovery of the communities. That is, the algorithm can recover all but a vanishing part of community labels with high probability. In contrast, when the influence of stubborn agents is large, another algorithm based on clustering time average of agent states can achieve almost exact recovery. Numerical experiments are given for illustration of the two algorithms and the theoretical results of the paper. © 2023, CC BY.

关键词： Recovery

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FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder

FedVAE: Trajectory privacy preserving based on Federated Var...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yuchen Jiang Ying Wu Shiyao Zhang James J.Q. Yu Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science University of York United Kingdom

The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized services tailored to their location information. However, this trajectory data contains sensitive information about users’ movement patterns and habits, necessitating confidentiality and protection from unknown collectors. To address this challenge, privacy-preserving methods like K-anonymity and Differential Privacy have been proposed to safeguard private information in the dataset. Despite their effectiveness, these methods can impact the original features by introducing perturbations or generating unrealistic trajectory data, leading to suboptimal performance in downstream tasks. To overcome these limitations, we propose a Federated Variational AutoEncoder (FedVAE) approach, which effectively generates a new trajectory dataset while preserving the confidentiality of private information and retaining the structure of the original features. In addition, FedVAE leverages Variational AutoEncoder (VAE) to maintain the original feature space and generate new trajectory data, and incorporates Federated Learning (FL) during the training stage, ensuring that users’ data remains locally stored to protect their personal information. The results demonstrate its superior performance compared to other existing methods, affirming FedVAE as a promising solution for enhancing data privacy and utility in location-based applications.

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Synthetic Text Generation for Training Large Language Models via Gradient Matching

arXiv

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arXiv 2025年

作者： Nguyen, Dang Li, Zeman Bateni, Mohammadhossein Mirrokni, Vahab Razaviyayn, Meisam Mirzasoleiman, Baharan Department of Computer Science University of California Los Angeles United States Google United States Department of Industrial & Systems Engineering University of Southern California United States

Synthetic data has the potential to improve the performance, training efficiency, and privacy of real training examples. Nevertheless, existing approaches for synthetic text generation are mostly heuristics and cannot generate human-readable text without compromising the privacy of real data, or provide performance guarantees for training Large Language Models (LLMs). In this work, we propose the first theoretically rigorous approach for generating synthetic human-readable text that guarantees the convergence and performance of LLMs during fine-tuning on a target task. To do so, we leverage Alternating Direction Method of Multipliers (ADMM) that iteratively optimizes the embeddings of synthetic examples to match the gradient of the target training or validation data, and maps them to a sequence of text tokens with low perplexity. In doing so, the generated synthetic text can guarantee convergence of the model to a close neighborhood of the solution obtained by fine-tuning on real data. Experiments on various classification tasks confirm the effectiveness of our proposed approach. © 2025, CC BY.

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ChatGPT vs. Stack Overflow: An Exploratory Comparison of Programming Assistance Tools

ChatGPT vs. Stack Overflow: An Exploratory Comparison of Pro...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Jinrun Liu Xinyu Tang Linlin Li Panpan Chen Yepang Liu Southern University of Science and Technology Shenzhen Guangdong China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Programmers often seek help from Q&A websites to resolve issues they encounter during programming. Stack Overflow has been a widely used platform for this purpose for over a decade. Recently, revolutionary AI-powered platforms like ChatGPT have quickly gained popularity among programmers for their efficient and personalized programming assistance via natural language interactions. Both platforms can offer valuable assistance to programmers, but it's unclear which is more effective at enhancing programmer productivity. In our paper, we conducted an exploratory user study to compare the performance of Stack Overflow and ChatGPT in enhancing programmer productivity. Two groups of students with similar programming abilities were instructed to use the two platforms to solve three different types of programming tasks: algorithmic challenges, library usage, and debugging. During the experiments, we measured and compared the quality of code produced and the time taken to complete tasks for the two groups. The results show that, concerning code quality, ChatGPT outperforms Stack Overflow significantly in helping complete algorithmic and library-related tasks, while Stack Overflow is better for debugging tasks. Regarding task completion speed, the ChatGPT group is obviously faster than the Stack Overflow group in the algorithmic challenge, but the two groups have a similar performance in the other two tasks. Additionally, we conducted a post-experiment survey with the participants to understand how the platforms have helped them complete the programming tasks. We analyzed the questionnaires to summarize ChatGPT and Stack Overflow's strengths and weaknesses pointed out by the participants. By comparing these, we identified the reasons behind the two platforms' divergent performances in programming assistance.

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Structural health monitoring system design for historical heritage building

Structural health monitoring system design for historical he...

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2021 IEEE International Conference on Metrology for Archaeology and Cultural Heritage, MetroArchaeo 2021

作者： Carnì, D.L. Scuro, C. Olivito, R.S. Lamonaca, Francesco Alì, G. Calà, S. Department of Computer Science Modelling Electronics and Systems Engineering University of Calabria CS Rende87043 Italy Physics Department University of Calabria CS Rende87043 Italy Department of Civil Engineering University of Calabria CS Rende87043 Italy

Structural Health Monitoring (SHM) systems play an important role in the defence of historical heritage. In fact, it is necessary to monitor and analyse the information that highlight the state of health of the structure. Unfortunately, in the realization of the monitoring system it is necessary to place several sensor nodes in place where it is not simple or possible the connection to the mains power supply. Moreover, it is necessary the fast detection of critical situation in which the state of conservation of an historical building can deteriorate. For these reasons in the paper is proposed the design of a low power consumption long range communication system. This design is proposed as an innovation of previous work, and the use of machine learning an algorithm to analyse the data. © Published under licence by IOP Publishing Ltd.

关键词： Sensor nodes

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Enhancing Road Safety with Real-Time Helmet Detection and E-Challan Issuance using YOLO and OCR

Enhancing Road Safety with Real-Time Helmet Detection and E-...

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Cognitive Robotics and Intelligent systems (ICC - ROBINS), International Conference on

作者： Deepa S Vinoth Kumar C N S Vivek Duraivelu Balamurugan Easwaran Suguna R Aparna H Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India School of IT Texila American University Zambia Africa Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Tamilnadu M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This study proposes an integrated system combining Optical Character Recognition (OCR) and YOLO (You Only Look Once) for enforcing helmet laws and improving traffic safety. A user-friendly PyQT GUI facilitates vehicle registration, creating an organized CSV database for future reference. YOLOv5, an advanced object detection algorithm, identifies key elements like motorcycles, helmets, and number plates from input images. The primary objective is detecting motorcyclists without helmets, followed by OCR extraction of text from license plates. The extracted text is compared with registered bike information in the CSV file, generating e-challans and fines for matches. Automatic email notifications ensure timely communication and compliance with traffic regulations. The system's implementation not only aids helmet enforcement but also acts as a deterrent for future violations, fostering a safer driving environment. With scalability and practicality, this approach can integrate seamlessly into existing traffic control systems, leveraging AI technologies like YOLOv5 and OCR for effective problem-solving and enhanced road safety.

关键词： YOLO Head Traffic control Road safety Real-time systems Safety Data mining

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Enabling IoT Continuous Connectivity in Smart Spaces 22

Enabling IoT Continuous Connectivity in Smart Spaces

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Dobre, Ciprian Markakis, Evangelos Mastorakis, George University of Nicosia University of Nicosia Research Foundation Departement of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University Politehnica of Bucharest National Institute for Research and Development in Informatics Automatic Control and Computers Bucharest Romania Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Agios Nikolaos Greece

ISBN: (纸本)9798350341270

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area. © 2023 IEEE.

关键词： Genetic algorithms

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Robust Authentication Protocols for IoT Devices in High-Density Networks

Robust Authentication Protocols for IoT Devices in High-Dens...

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Distributed systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Senthilkumar Thangavel Arnav Kotiyal Ancy Thomas Harshal Patil Shirish Kulkarni Natrayan L Distributed Systems Cloud Solutions & Machine Learning Expert Paypal Inc California USA Department of Computer Science & Engineering Graphic Era Dehradun India Department of Computer Science and Engineering RNSIT Bangalore India School of Computer Science & Engineering IILM University Greater Noida India Department of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Kuthambakkam India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Internet of things (IoT) gadgets have transformed several industries, including engineering, medicine, and more, thanks to the meteoric rise in the number of connected smart devices. The core principle of using the Internet of Things is that it expedites the delivery of information while consuming very little energy. In a nutshell, the Internet of Things (IoT) is a system of interconnected computing devices, services, and data that allows everyday objects to communicate with one another and with their physical environments. Prior to being saved on the server, all data sent across the IoT network must first be aggregated in the Queuing Telemetry Transport protocol (QTTP) broker. Access control, authorization, data storage, secrecy, authentication, system construction, and organization are some of the primary security concerns surrounding the Internet of Things (IoT). Three innovative security frameworks—RSA, Advanced Encryption Standard, and Elliptical Curve Cryptography—are created to enhance the protection of data in powered IoT devices. The security of IoT data is ensured by integrating all cryptic algorithms with the Constrained Queue Telemetry Transport Protocol (CQTTP). Data transmission from each IoT node undergoes cryptographic processes before being aggregated in the CQTTP. Exchange of key approvals, confirmation, and preservation of application data contrast are all accomplished by this cryptic action. Encryption keys are crucial to the safety of the data ix. The primary security component that reveals how many distinct key values a key in a protocol may take is the key's size. As a result, this study uses a new random key size method to give keys of varying sizes to the data sent by each node. Cryptography is harder when key sizes are set to be variable and random. In order to prevent script XSS commands and incorrect characteristics, all communications are encrypted using a technology that often permits employing TLS instead of plain TCP. Consequently, th

关键词： Transport protocols Wireless sensor networks Authentication Elliptic curve cryptography Encryption Internet of Things Security Telemetry Smart devices Radiofrequency identification

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Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying

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International Journal of Agricultural and Biological engineering 2023年第1期16卷 260-272页

作者： Dalong Jiang Congcong Li Zifan Lin Yuntian Wu Hongjuan Pei Magdalena Zielinska Hongwei Xiao School of Computer and Control Engineering Yantai UniversityYantai 264005ShandongChina Hebei Agricultural University Baoding 071001HebeiChina Hebei Key Laboratory of Agricultural Big Data Baoding 071001HebeiChina Department of Electrical and Electronic Engineering University of Western AustraliaPerth 6000Australia BeiGene Guangzhou Biologics Manufacturing Co. LtdGuangzhou 510555China Neuroscience and Intelligent Media Institute Communication University of ChinaBeijing 100024China Department of Systems Engineering University of Warmia and MazuryOlsztyn 004889Poland College of Engineering China Agricultural UniversityBeijing 100083China

In order to further understand the mechanism of material volume change in the drying process,numerical simulations(considering or neglecting shrinkage)of heat and mass transfer during convective drying of carrot slices under constant and controlled temperature and relative humidity were carried *** results were validated with experimental *** results of the simulation show that the Quadratic model fitted well to the moisture ratio and the material temperature data trend with average relative errors of 5.9%and 8.1%,***,the results of the simulation considering shrinkage show that the moisture and temperature distributions during drying are closer to the experimental data than the results of the simulation disregarding *** material moisture content was significantly related to the shrinkage of dried *** and relative humidity significantly affected the volume shrinkage of carrot *** volume shrinkage increased with the rising of the constant temperature and the decline of relative *** model can be used to provide more information on the dynamics of heat and mass transfer during drying and can also be adapted to other products and dryers devices.

关键词： carrot drying numerical simulation heat and mass transfer shrinkage

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