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IEEE Workshop on High Performance Switching and Routing

作者： Alessio Sacco Antonino Angi Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Saint Louis University USA

Years of research on congestion controls have highlighted how end-to-end and in-network protocols might perform poorly in some contexts. Recent advances in data plane network programmability could also bring advantages in transport protocols, enabling mining and processing in-network congestion signals. However, the new machine learning-based congestion control class has only partially used data from the network, favoring a more sophisticated model design but neglecting possibly precious pieces of data. In this paper, we present HINT, an in-band network telemetry architecture designed to provide insights into network congestion to the end-host TCP algorithm during the learning process. In particular, the key idea is to adapt switches’ behavior via P4 and instruct them to insert simple device information, such as processing delay and queue occupancy, directly into transferred packets. Initial experimental results show that this approach comes with a little network overhead but can improve the visibility and, consequently, the accuracy of TCP decisions of the end-host. At the same time, the programmability of both switches and hosts also enables customization of the default behavior as the user’s needs change.

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WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

arXiv

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arXiv 2025年

作者： Bongini, Pietro Mandelli, Sara Montibeller, Andrea Casu, Mirko Pontorno, Orazio Ragaglia, Claudio Vittorio Zanchetta, Luca Aquilina, Mattia Wani, Taiba Majid Guarnera, Luca Tondi, Benedetta Boato, Giulia Bestagini, Paolo Amerini, Irene De Natale, Francesco Battiato, Sebastiano Barni, Mauro University of Siena Department of Information Engineering an Mathematics Italy Politecnico di Milano Department of Electronics Informatics and Bioengineering Italy University of Trento Department of Information Engineering and Computer Science Italy University of Catania Department of Mathematics and Computer Science Italy Sapienza University of Rome Departement of Computer Control and Management Engineering Italy

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD1 is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. Moreover, an assessment of seven baseline methodologies on closed and open set attribution is presented, including robustness tests with respect to post-processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Hide & Seek: Traffic Matrix Completion and Inference Using Hidden Information

Hide & Seek: Traffic Matrix Completion and Inference Using H...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Alessio Sacco Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Saint Louis University USA

Traffic matrices are used for many network management operations, from planning to repairing. Despite years of research on the topic, their estimation and inference on the Internet are still challenging and error-prone. For example, missing values are unavoidable due to flaws in the measurement systems and possible failure in data collection systems. It is thus helpful for many network operators to recover the missing data from the partial direct measurements. Some existing matrix completion methods do not fully consider network traffic behavior and hidden traffic characteristics, showing the inability to adapt to multiple scenarios. Others instead make assumptions on the matrix structure that may be invalid or impractical, curtailing the applicability. In this paper, we propose Hide & Seek, a novel matrix completion and prediction algorithm based on a combination of generative autoencoders and Hidden Markov Models. We demonstrate with an extensive experimental evaluation on real-world datasets how our algorithm can accurately reconstruct missing values while predicting their short-term evolution.

关键词： Hidden Markov models Estimation Telecommunication traffic Data collection Prediction algorithms Inference algorithms Encoding

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GLOBAL PATH PLANNING OF CLIMBING ROBOT FOR WELD QUALITY INSPECTION IN LARGE SCALE STORAGE TANKS

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International Journal of Robotics and Automation 2025年第3期40卷 242-253页

作者： Wang, Ruifeng Gu, Shichao Liu, Xiaoguang Zhang, Hao Xu, Zhihao Pan, Jia Zhou, Xuefeng Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology GuangDong Guangzhou510070 China School of Rail Transportation Wuyi University GuangDong Jiangmen529020 China School of Mechanical Automotive Engineering South China University of Technology Guangdong Guangzhou510006 China Department of Computer Science University of Hong Kong 999077 Hong Kong

Climbing robots are considered efficient solutions for conducting quality inspections of welds on large storage tanks. Nevertheless, planning the global shortest path along spatially complex cross distributed welds poses a significant challenge for climbing robots. This paper introduces a novel graph-based global shortest climbing path planning method, facilitating swift inspect all welds by the robot. The three-dimensional (3D) weld lines undergo conversion into a two-dimensional (2D) weighted connected graph using a 3D model equivalence mapping approach. The dimensionality-reduced connectivity map is required to be an Euler graph;otherwise, the adoption of the Floyd and KM algorithms is necessary to construct an undirected Euler graph with the minimum total weight. Subsequently, an Euler circuit is searched in the Euler graph, considering the presence of plus edges, through an improved Fleury algorithm to obtain the shortest global path for the climbing robot. The paper also introduces the idea of Euler graph segmentation to address the challenge of Euler circuits in large or symmetric models. Simulations are conducted to validate the effectiveness and performance of the proposed planners. The results demonstrate that our method effectively reduces the length global path and enhances inspection efficiency. © 2025 Acta Press. All rights reserved.

关键词： Motion planning

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Federated-Boosting: A Distributed and Dynamic Boosting-Powered Cyber-Attack Detection Scheme for Security and Privacy of Consumer IoT

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IEEE Transactions on Consumer Electronics 2024年

作者： Khan, Izhar Ahmed Pi, Dechang Kamal, Shaharyar Alsuhaibani, Mohammed Alshammari, Bandar Mzel Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China University of Chile Department of Electrical Engineering Chile Qassim University College of Computer Department of Computer Science Buraydah Saudi Arabia Technology Control Company Riyadh Saudi Arabia

Consumer Internet of Things (CIoT) is an emerging technology that took the traditional consumer electronics to next level with smarter devices and higher connectivity. However, the rapid boom of this technology has captured a great deal of hacker's attention in the past decade. Due to resource restricted nature of CIoT devices and limited computational abilities, security professionals are unable to strengthen the security and privacy measures. Since the existing security methods suffers from data imbalance and overfitting issues, this paper proposes a distributed and dynamic weighted boosting method named Federated-Boosting for the accurate and timely detection of cyber-Attacks in CIoT. Specifically, a weighting strategy is designed to overcome the data imbalance issue by dynamically adjusting the weights of weaker class samples. Additionally, a regularized loss function is designed that helps in controlling the overfitting of the model and provide enhance performance and generalization. Finally, a time-based and performance-based dynamic aggregation scheme is designed using distributed training model that keeps the privacy of the CIoT devices intact to the local server. Extensive experiments on two real-world datasets validates the superior performance of Federated-Boosting model against state-of-The-Art detection models. © 1975-2011 IEEE.

关键词： Network intrusion

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Designing a Secure Smart Remote Patient Monitoring and Warning System using Big Data and Internet of Things Ecosystems 10

Designing a Secure Smart Remote Patient Monitoring and Warni...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Ramesh, S.P. Abdulwahid, Ali Hadi Anjum, Afsana Venkatesh, N. Singh, Rajesh Chakravarthi, M. Kalyan Amet University Department of Computer Science and Engineering Tamil Nadu Kanathur India Southern Technical University Engineering Technical College Department of Control and Automation Basra Iraq Jazan University Department of Information Technology and Security Jazan Saudi Arabia School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Uttaranchal Institute of Technology Uttaranchal University India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9789380544472

The demand on healthcare is growing as a result of the expanding global population, rising demands for successful treatment, and an underlying improvement in life quality. Consequently, healthcare remains one of the most significant social and economic problems in the world, necessitating the development of new and more sophisticated scientific and technological treatments.E-health services are provided via personalised healthcare systems to meet the medical and support needs of the ageing population. An important development in the Big Data age is the Internet of Things, which facilitates several real-time engineering solutions through improved services. Healthcare systems are now using analytics over IoT data streams as a stream of user data to find new information, forecast rapid recognition, and make decisions on life-threatening situations. In this study, researchers have carried out a thorough review of the hottest recent revolutionary technologies in bespoke healthcare systems, with a focus on hypervisor, cloud infrastructure, big data analytic, the Web of Things, and smartphone apps. In order to make early disease detection and diagnosis possible, it is important to develop a better healthcare system. We have examined these problems and suggested potential solutions while offering secure e-health services. The purpose of this study is to design a smart and secure remote monitoring and warning system using the latest emerging technologies in 21stcentury such as big data analytics and Internet of Things (IoT) ecosystems. In this study the researchers discussed a detailed research methodology to meet the outcomes of the reerach. All the relevant findings and analysis are explained in descriptive manner. Moreover, this study highlights the constantly expanding requirements for better medical systems in the present and offers potential directions for future research. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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ScCADE: A Superior Tool for Predicting Perturbation Responses in Single-Cell Gene Expression Using Contrastive Learning and Attention Mechanisms

ScCADE: A Superior Tool for Predicting Perturbation Response...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ou, Jingfeng Li, Jiawei Xia, Zhiliang Dai, Shurui Ding, Yulian Guo, Yan Jiang, Limin Tang, Jijun Southern University of Science and Technology College of Engineering Shenzhen518055 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Miami Department of Public Health Sciences MiamiFL33136 United States

ISBN: (纸本)9798350386226

The advent of single-cell transcriptomics has revolutionized our ability to analyze cellular heterogeneity and dynamics at a fine resolution, yet covering the vast array of potential perturbations remains challenging due to biological variability. To address this, we propose scCADE, a novel computational approach utilizing contrastive learning and an attention mechanism to decouple gene expression signatures and predict cellular responses to perturbations. scCADE excels in predicting responses in cells to perturbations observed in other cells but not yet seen in the target cells. Through rigorous ablation studies and validation across three datasets involving drug and gene editing perturbations, scCADE consistently outperformed existing methods, underscoring its efficacy and potential to advance genomics and personalized medicine by accurately forecasting responses to novel perturbations. © 2024 IEEE.

关键词： Contrastive Learning

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Noise Reduction in Industry Based on Virtual Instrumentation

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computers, Materials & Continua 2021年第10期69卷 1073-1096页

作者： Radek Martinek Rene Jaros Jan Baros Lukas Danys Aleksandra Kawala-Sterniuk Jan Nedoma Zdenek Machacek Jiri Koziorek Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical EngineeringVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia Faculty of Electrical Engineering Automatic Control and InformaticsOpole University of TechnologyOpolePoland Faculty of Electrical Engineering and Computer Science Department of TelecommunicationsVSB–Technical University of Ostrava70800Ostrava-PorubaCzechia

This paper discusses the reduction of background noise in an industrial environment to extend *** the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G *** study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a *** experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from *** tested a hybrid algorithm for noise reduction and its impact on voice command recognition *** virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested *** welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+*** workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

关键词： 5G hybrid algorithms signal processing speech recognition

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels

Benchmarking Semantic Communications for Image Transmission ...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yanhu Wang Shuaishuai Guo Anming Dong Hui Zhao School of Control Science and Engineering Shandong University School of Computer Science and Technology Qilu University of Technology Communication Systems Department EURECOM

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-tonoise (SNR) regimes.

关键词： Wireless communication Training Transmitters Receivers Artificial neural networks Signal processing Semantic communication Transceivers Interference channels Signal to noise ratio

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