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arXiv 2024年

作者： Yan, Sheng Fan, Cunhang Zhang, Hongyu Yang, Xiaoke Tao, Jianhua Lv, Zhao Anhui Province Key Laboratory of Multimodal Cognitive Computations School of Computer Science and Technology Anhui University China Department of Automation Tsinghua University China

At a cocktail party, humans exhibit an impressive ability to direct their attention. The auditory attention detection (AAD) approach seeks to identify the attended speaker by analyzing brain signals, such as EEG signals. However, current AAD algorithms overlook the spatial distribution information within EEG signals and lack the ability to capture long-range latent dependencies, limiting the model’s ability to decode brain activity. To address these issues, this paper proposes a dual attention refinement network with spatiotemporal construction for AAD, named DARNet, which consists of the spatiotemporal construction module, dual attention refinement module, and feature fusion & classifier module. Specifically, the spatiotemporal construction module aims to construct more expressive spatiotemporal feature representations, by capturing the spatial distribution characteristics of EEG signals. The dual attention refinement module aims to extract different levels of temporal patterns in EEG signals and enhance the model’s ability to capture long-range latent dependencies. The feature fusion & classifier module aims to aggregate temporal patterns and dependencies from different levels and obtain the final classification results. The experimental results indicate that DARNet achieved excellent classification performance, particularly under short decision windows. While maintaining excellent classification performance, DARNet significantly reduces the number of required parameters. Compared to the state-of-the-art models, DARNet reduces the parameter count by 91%. Code is available at: https://***/fchest/***. © 2024, CC BY.

关键词： Audition

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Key Expansion Based on Elliptic Curve Cryptography for Anonymous Voting

Key Expansion Based on Elliptic Curve Cryptography for Anony...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Abel C. H. Chen Chia-Yu Lin Information & Communications Security Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan Department of Computer Science & Information Engineering National Central University Taoyuan Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

This study designs a security credential management system, including a certificate authority (CA), a registration authority (RA), and other individuals outside the CA and the RA. An anonymous voting method using elliptic curve cryptography (ECC)-based key expansion has been proposed to achieve anonymity to CA, RA, and individuals. In experiments, the performance of the proposed method based on different security strengths has been evaluated.

关键词： Elliptic curve cryptography Cryptography Consumer electronics

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DASIV: Directional Acoustic Sensing based Intelligent Vehicle Interaction System

DASIV: Directional Acoustic Sensing based Intelligent Vehicl...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Dinghua Zhao Juntao Zhou Dian Ding Yu Lu Yijie Li Hang Yang Yi-Chao Chen Guangtao Xue Department of Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation Governance and Web3

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

With the increase in motor vehicles, more convenient and accurate interactions are expected while retaining a high standard of safe driving. However, complex and dynamic vehicle environments challenge sensing tasks such as breathing monitor and hand gesture recognition. In this paper, we propose DASIV, which utilizes the highly directional nature of ultrasonic signals to achieve fine-grained directional acoustic sensing in vehicle environments. Due to air nonlinearity, the system enables synchronized directional acoustic communication to transmit information (e.g., navigation) to the driver without affecting other passengers. By optimizing the frequency of the Frequency Modulated Continuous Wave (FMCW) signals, DASIV avoids mutual interference between the sensing and communication signals and achieves breathing detection and hand gesture recognition for the driver. Specifically, the system extracts breathing-induced weak thoracic bullying through the signal phase, captures and analyses breathing patterns using bandpass and Gaussian filters, and develops a breathing model. Then, the system defines 10 interaction hand gestures to meet daily interaction needs, uses spectral features to mine complex and fast hand movement features, and proposes a hand gesture recognition model. Extensive experiments in real environments show that DASIV achieves high-precision breathing monitor (Pearson correlation coefficient of 0.89) and hand gesture recognition (Precision of 91.7%).

关键词： Hands Frequency modulation Navigation Gesture recognition Acoustics Sensors Synchronization Vehicle dynamics Monitoring Standards

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Learning-based Detection of GPS Spoofing Attack for Quadrotors

arXiv

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arXiv 2025年

作者： Wang, Pengyu Yang, Zhaohua Li, Jialu Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Safety-critical cyber-physical systems (CPS), such as quadrotor UAVs, are particularly prone to cyber attacks, which can result in significant consequences if not detected promptly and accurately. During outdoor operations, the nonlinear dynamics of UAV systems, combined with non-Gaussian noise, pose challenges to the effectiveness of conventional statistical and machine learning methods. To overcome these limitations, we present QUADFormer, an advanced attack detection framework for quadrotor UAVs leveraging a transformer-based architecture. This framework features a residue generator that produces sequences sensitive to anomalies, which are then analyzed by the transformer to capture statistical patterns for detection and classification. Furthermore, an alert mechanism ensures UAVs can operate safely even when under attack. Extensive simulations and experimental evaluations highlight that QUADFormer outperforms existing state-of-the-art techniques in detection accuracy. (Video1) Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Model-Mediated Teleoperation for Remote Haptic Texture Sharing: Initial Study of Online Texture Modeling and Rendering

Model-Mediated Teleoperation for Remote Haptic Texture Shari...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mudassir Ibrahim Awan Tatyana Ogay Waseem Hassan Dongbeom Ko Sungjoo Kang Seokhee Jeon Department of Computer Science and Engineering Kyung Hee University South Korea AI Research Laboratory ETRI South Korea

While model-mediated teleoperation (MMT) is an effective alternative for ensuring both transparency and stability, its potential in transmitting surface haptic texture is not yet explored. This paper introduces the first MMT framework capable of sharing surface haptic texture. The follower side collects physical signals contributing to haptic texture perception, e.g., high frequency acceleration, and streams them to the leader side. The leader side uses the signals to build and update a local measurement-based texture simulation model that reflects the remote surface. At the same time, the leader runs local simulation using the model, resulting in non-delayed, stable, and accurate feedback of texture. Considering that rendering haptic texture needs tougher real-time requirements, e.g., higher update rate and lower action-feedback latency, MMT can be a perfect platform for remote texture sharing. An initial proof-of-concept system supporting single and homogeneous surface is implemented and evaluated, demonstrating the potential of the approach.

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Fuzzy Logic and Deep learning Techniques for Covid-19 Detection 23

Fuzzy Logic and Deep learning Techniques for Covid-19 Detect...

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Hassani, Belkis Akram, Khelili Mohamed Okba, Kazar Sihem, Slatnia Harous, Saad Athamena, Belkacem Houhamdi, Zina Smart Computer Science Laboratory Department of Computer Science Biskra Algeria University of Mohamed Khider Biskra Algeria College of Information Technology United Arab Emirate University Department of Information Systems and Security United Arab Emirates College of Computing and Informat Ics Department of Computer Science AL Ain United Arab Emirates University of Sharjah AL Ain United Arab Emirates College of Business Al Ain University Al Ain United Arab Emirates University of Al Ain Al Ain United Arab Emirates

ISBN: (纸本)9798350320244

With the development of ICT and its adoption in various domains, it gained remarkable intention in the healthcare sector which introduce the telemedicine term. The coronavirus pandemic has created several challenges for researchers to develop an accurate and fast detection system. In this paper, we present a new telemedicine application to predict Covid-19 using CNN and Fuzzy set techniques. The evaluation of the system indicates high performance with a 98% F1 score, 99% of recall, 98% for precision, and 97% of accuracy. © 2022 IEEE.

关键词： Telemedicine

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Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

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computers, Materials & Continua 2021年第2期66卷 2041-2059页

作者： Gopi Krishna Durbhaka Barani Selvaraj Mamta Mittal Tanzila Saba Amjad Rehman Lalit Mohan Goyal School of Electrical and Electronics Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering G.B.Pant Government Engineering CollegeNew Delhi110020India Artificial Intelligence and Data Analytics(AIDA)Laboratory CCISPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Engineering J.C.Bose University of Science and TechnologyFaridabad121006India

Nowadays,renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling *** of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult *** monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches,practices and technology during the last *** mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the *** paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the conventional Long Short-Term Memory(LSTM)model in classifying the faults from the vibration signals data acquired from the *** helps to analyze the performance and behavioral patterns of the system more effectively and efficiently which helps to suggest for replacement of the unit with higher *** results have demonstrated that the proposed hybrid modeling approach is effective in classifying the faults of the gearbox from the time series data and achieve higher diagnostic accuracy in comparison to the conventional LSTM methods.

关键词： Gearbox long short term memory fault classification swarm intelligence optimization condition monitoring

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Accelerating VQE Algorithm via Parameters and Measurement Reuse

Accelerating VQE Algorithm via Parameters and Measurement Re...

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IEEE International Conference on Rebooting Computing (ICRC)

作者： Xinpeng Li Ji Liu Ethan H. Hansen Shuai Xu Paul Hovland Vipin Chaudhary Department of Computer and Data Science Case Western Reserve University Mathematics and Computational Research Division Argonne National Laboratory

The Variational Quantum Eigensolver (VQE), a quantum-classical hybrid algorithm, holds significant potential for advancements in quantum chemistry and optimization tasks. Our study focuses on accelerating the VQE in combinatorial optimization tasks such as Max-Cut, k-Clique, and k-SAT, while addressing the challenges of scalability and measurement overhead in quantum devices. Notably, while solving one problem, it allows us to concurrently compute essential information like expectation values and gradients for multiple other problems. This capability is made feasible because combinatorial optimization problems often share the same measurement basis. Drawing inspiration from this insight, we introduce a method that efficiently solves multiple problems by appropriately selecting their initial points. Subsequently, we propose two selection strategies for the method. Employing these strategies can, to some extent, help avoid the issue of becoming trapped in local optima, thus leading to improved accuracy. Our tests on 5-node and 10-node graphs for Max-Cut and k-Clique show that, in some cases, our method outperforms random initialization in terms of both accuracy and number of iterations.

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Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction 25

Seeing the Unseen: Learning Basis Confounder Representations...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Jiahao Ji Wentao Zhang Jingyuan Wang Chao Huang School of Computer Science and Engineering Beihang University Beijing China SCSE Beihang University Beijing China & MIIT Key Laboratory of Data Intelligence and Management SEM Beihang University Beijing China Department of Computer Science Musketeers Foundation Institute of Data Science University of Hong Kong Hong Kong SAR China

ISBN: (纸本)9798400712456

Traffic prediction is essential for intelligent transportation systems and urban computing. It aims to establish a relationship between historical traffic data X and future traffic states Y by employing various statistical or deep learning methods. However, the relations of X → Y are often influenced by external confounders that simultaneously affect both X and Y, such as weather, accidents, and holidays. Existing deep-learning traffic prediction models adopt the classic front-door and back-door adjustments to address the confounder issue. However, these methods have limitations in addressing continuous or undefined confounders, as they depend on predefined discrete values that are often impractical in complex, real-world scenarios. To overcome this challenge, we propose the Spatial-Temporal sElf-superVised confoundEr learning (STEVE) model. This model introduces a basis vector approach, creating a base confounder bank to represent any confounder as a linear combination of a group of basis vectors. It also incorporates self-supervised auxiliary tasks to enhance the expressive power of the base confounder bank. Afterward, a confounder-irrelevant relation decoupling module is adopted to separate the confounder effects from direct X → Y relations. Extensive experiments across four large-scale datasets validate our model's superior performance in handling spatial and temporal distribution shifts and underscore its adaptability to unseen confounders. Our model implementation is available at https://***/bigscity/STEVE_CODE.

关键词： continuous and undefined confounder

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Two-Stage Estimation and Variance Modeling for Latency-Constrained Variational Quantum Algorithms

arXiv

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arXiv 2024年

作者： Ha, Yunsoo Shashaani, Sara Menickelly, Matt Department of Industrial and Systems Engineering North Carolina State University United States Mathematics and Computer Science Division Argonne National Laboratory United States

The Quantum Approximate Optimization Algorithm (QAOA) has enjoyed increasing attention in noisy intermediate-scale quantum computing due to its application to combinatorial optimization problems. Because combinatorial optimization problems are NP-hard, QAOA could serve as a potential demonstration of quantum advantage in the future. As a hybrid quantum-classical algorithm, the classical component of QAOA resembles a simulation optimization problem, in which the simulation outcomes are attainable only through the quantum computer. The simulation that derives from QAOA exhibits two unique features that can have a substantial impact on the optimization process: (i) the variance of the stochastic objective values typically decreases in proportion to the optimality gap, and (ii) querying samples from a quantum computer introduces an additional latency overhead. In this paper, we introduce a novel stochastic trust-region method, derived from a derivative-free adaptive sampling trust-region optimization (ASTRO-DF) method, intended to efficiently solve the classical optimization problem in QAOA, by explicitly taking into account the two mentioned characteristics. The key idea behind the proposed algorithm involves constructing two separate local models in each iteration: a model of the objective function, and a model of the variance of the objective function. Exploiting the variance model allows us to both restrict the number of communications with the quantum computer, and also helps navigate the nonconvex objective landscapes typical in the QAOA optimization problems. We numerically demonstrate the superiority of our proposed algorithm using the SimOpt library and Qiskit, when we consider a metric of computational burden that explicitly accounts for communication costs. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum computers

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