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8th International Conference on Information Technology and Nanotechnology, ITNT 2022

作者： Saurabh, Raman Bhasme, Sarang Balasaheb Saeed, Muhammad Salman Kumar, Chanchal Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Jamia Millia Islamia Department of Computer Science New Delhi India Ciena India Pvt. Ltd Haryana India Federal Research Centre Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9781665485715

ISBN: (纸本)9781665485715

The email which contains suspicious links or malicious files is one of the considerable threats in the digital world nowadays and because of these types of emails, enormous social and financial losses occur. The different approaches of detection of masquerading emails, which are already proposed, are unceasingly being renovated, the outcomes of that are not more impressive. Moreover, the masquerading email crisis is intensifying at a higher rate in the past few years. Because of the rapid increase in masquerading email threats, there is a need to develop new techniques to eliminate these types of masquerading email which is the present need of the hour. In our proposed work, the structure of the email was thoroughly analysed and then fed on recurrent convolutional neural networks. After that the Recurrent convolutional neural network model processes the email header and body of the email for detecting the suspicious or malicious files, elements and codes at character and word level of particular email;if the suspicious elements, file extension is detected then it will automatically transfer to the spam folder. For increasing the veracity of the design, the training of the model is performed with a balanced and unbalanced data set due to which improves the efficiency to distinguish between the legitimate and non-legitimate emails. Thus, the result of the experiment implies that the filtering or recognition probability of non-legitimate email is much closer to the value one, which also symbolizes that certainty of the proposed model enhances. As a result, classification of the spam and non-spam email is done by using the neural network classifier. The masquerading emails contain the suspicious links when the user clicks on the link the redirection takes place and the user reaches the suspicious website where the confidential information is asked from the user. The details shared by users results in financial and social losses. © 2022 IEEE.

关键词： Support vector machines

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Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1612-1626页

作者： Yixin Wang Shuang Qiu Dan Li Changde Du Bao-Liang Lu Huiguang He the Research Center for Brain-inspired Intelligence National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of ScienceBeijing 100190 the University of Chinese Academy of Sciences Beijing 100049 the Beijing Institute of Control and Electronic Technology Beijing 100038China the School of Mathematics and Information Sciences Yantai UniversityYantai 264003China the Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceBeijingChina the Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China

Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in *** attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration *** solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal *** method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common *** adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of *** experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed *** work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.

关键词： Cycle-consistency domain adaptation electroencephalograph(EEG) multi modality variational autoencoder

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic control Society.

关键词： Trucks

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A survey on secure decentralized optimization and learning

arXiv

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arXiv 2024年

作者： Liu, Changxin Bastianello, Nicola Huo, Wei Shi, Yang Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology and Digital Futures Stockholm Sweden Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Mechanical Engineering University of Victoria VictoriaBC Canada

Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. However, this paradigm introduces new privacy and security risks, with malicious agents potentially able to infer private data or impair the model accuracy. Over the past decade, significant advancements have been made in developing secure decentralized optimization and learning frameworks and algorithms. This survey provides a comprehensive tutorial on these advancements. We begin with the fundamentals of decentralized optimization and learning, highlighting centralized aggregation and distributed consensus as key modules exposed to security risks in federated and distributed optimization, respectively. Next, we focus on privacy-preserving algorithms, detailing three cryptographic tools and their integration into decentralized optimization and learning systems. Additionally, we examine resilient algorithms, exploring the design and analysis of resilient aggregation and consensus protocols that support these systems. We conclude the survey by discussing current trends and potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Multi-Dataset Multi-Task Learning for COVID-19 Prognosis

arXiv

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arXiv 2024年

作者： Ruffini, Filippo Tronchin, Lorenzo Wu, Zhuoru Chen, Wenting Soda, Paolo Shen, Linlin Guarrasi, Valerio Unit of Computer Systems & Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Hong Kong

In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue, we introduce a novel multi-dataset multi-task training framework that predicts COVID-19 prognostic outcomes from chest X-rays (CXR) by integrating correlated datasets from disparate sources, distant from conventional multi-task learning approaches, which rely on datasets with multiple and correlated labeling schemes. Our framework hypothesizes that assessing severity scores enhances the model's ability to classify prognostic severity groups, thereby improving its robustness and predictive power. The proposed architecture comprises a deep convolutional network that receives inputs from two publicly available CXR datasets, AIforCOVID for severity prognostic prediction and BRIXIA for severity score assessment, and branches into task-specific fully connected output networks. Moreover, we propose a multi-task loss function, incorporating an indicator function, to exploit multi-dataset integration. The effectiveness and robustness of the proposed approach are demonstrated through significant performance improvements in prognosis classification tasks across 18 different convolutional neural network backbones in different evaluation strategies. This improvement is evident over single-task baselines and standard transfer learning strategies, supported by extensive statistical analysis, showing great application potential. © 2024, CC BY-NC-SA.

关键词： COVID-19

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Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models

arXiv

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arXiv 2025年

作者： Martini, Luca Zolezzi, Daniele Iacono, Saverio Vercelli, Gianni Viardo Department of Languages and Modern Culture University of Genova Genova16124 Italy Department of Computer Science and Technology Bioengineering Robotics and Systems Engineering University of Genova Genova16145 Italy

The reconstruction of low-resolution football broadcast images presents a significant challenge in sports broadcasting, where detailed visuals are essential for analysis and audience engagement. This study introduces a multi-stage generative upscaling framework leveraging Diffusion Models to enhance degraded images, transforming inputs as small as 64 × 64 pixels into high-fidelity 1024 × 1024 outputs. By integrating an image-to-image pipeline, controlNet conditioning, and LoRA fine-tuning, our approach surpasses traditional upscaling methods in restoring intricate textures, sharp edges, and domain-specific elements such as player details and jersey logos. The custom LoRA is trained on a custom football dataset, ensuring adaptability to sports broadcast needs. Experimental results demonstrate substantial improvements over conventional models, with controlNet refining fine details and LoRA enhancing task-specific elements. These findings highlight the potential of diffusion-based image reconstruction in sports media, paving the way for future applications in automated video enhancement and real-time sports analytics. © 2025, CC BY-NC-ND.

关键词： Image reconstruction

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries 13

Exponential Weighted Moving Average-Based Variational Mode D...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Climate Time Series Prediction with Deep Learning and LSTM

Climate Time Series Prediction with Deep Learning and LSTM

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Ravi Sha Tapas Guha Department of Computer Systems Tata Consultancy Services Melbourne Victoria Australia Department of Computer Science and Technology University of Engineering and Management Kolkata West Bengal India

Climate time series prediction is a critical task for understanding and forecasting complex climate patterns and trends. Traditional methods have shown limited accuracy and struggle to capture the intricacies of climate data. In this study, a deep learning-based approach is proposed to improve the accuracy of climate time series prediction and address the challenges posed by previous works. The main objective of this work is to train Deep Learning models from collected data to generate climate predictors. For this public data on climate indicators is collected; Deep Learning models are trained; climate forecasts from the models trained from the collected data is carried out. The innovation in proposed approach lies in the integration of a novel temporal attention mechanism that allows the model to focus on relevant time steps, improving interpretability and prediction accuracy. Furthermore, transfer learning is adopted with pretrained models to capitalize on their knowledge and accelerate convergence on climate-specific tasks. The comparison with most recent related work showcases the superiority of the proposed approach in capturing complex climate patterns and delivering precise long-term forecasts. The model's interpretability enables researchers to gain valuable insights into the underlying climate dynamics, facilitating informed decision-making and policy planning.

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A New Radio-Frequency Microfluidic Injection-Locked Oscillator Sensor for Loss Tangent Measurement of Liquid Material

A New Radio-Frequency Microfluidic Injection-Locked Oscillat...

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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Chien-Heng Chen Chao-Hsiung Tseng Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Institute of Space Systems Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798331510473

ISBN: (纸本)9798331510480

A new radio-frequency microfluidic injectionlocked oscillator (ILO) sensor is proposed in this paper for loss tangent measurement of the liquid material. The proposed sensor comprises a passive T-shape LC resonator (TLCR) sensing structure and an ILO. When an RF signal passes through the TLCR loaded by a test liquid, its amplitude will be decreased by the liquid's loss. The degraded signal is injected into an ILO to vary the output power. By loading different test liquids into the microfluidic channel, one can measure the ILO's output powers to relate the corresponding liquids' loss tangents. The $7.5-\mu \mathbf{L}$ water-ethanol mixtures with ethanol volume fractions of 20 % to 80 % in increments of 20 % are used as the test liquids to evaluate the sensor performance. Compared with the results obtained from a dielectric probe, the maximum error of the loss tangent measured by the proposed sensor is $-3.8 \%$ .

关键词： Radio frequency Liquids Permittivity measurement Dielectric loss measurement Biomedical measurement Loss measurement Dielectrics Probes Microfluidics Power generation

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An Optimal Distributed Algorithm with Operator Extrapolation for Stochastic Aggregative Games 4

An Optimal Distributed Algorithm with Operator Extrapolation...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Wang, Tongyu Yi, Peng Tongji University Department of Control Science and Engineering Shanghai201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201804 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

This work studies Nash equilibrium seeking for a class of stochastic aggregative games, where each player has an expectation-valued objective function depending on its local strategy and the aggregate of all players' strategies. We propose a distributed algorithm with operator extrapolation, in which each player maintains an estimate of this aggregate by exchanging this information with its neighbors over a time-varying network, and updates its decision through the mirror descent method. An operator extrapolation at the search direction is applied such that the two step historical gradient samples are utilized to accelerate the convergence. Under the strongly monotone assumption on the pseudo-gradient mapping, we prove that the proposed algorithm can achieve the optimal convergence rate of for Nash equilibrium seeking of stochastic games. © 2022 IEEE.

关键词： Extrapolation

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