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arXiv 2022年

作者： Song, Xianxin Xu, Jie Liu, Fan Han, Tony Xiao Eldar, Yonina C. Shenzhen518172 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The Wireless Technology Lab 2012 Laboratories Huawei Shenzhen518129 China The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

This paper investigates intelligent reflecting surface (IRS) enabled non-line-of-sight (NLoS) wireless sensing, in which an IRS is dedicatedly deployed to assist an access point (AP) to sense a target at its NLoS region. It is assumed that the AP is equipped with multiple antennas and the IRS is equipped with a uniform linear array. We consider two types of target models, namely the point and extended targets, for which the AP aims to estimate the target's direction-of-arrival (DoA) and the target response matrix with respect to the IRS, respectively, based on the echo signals from the AP-IRS-target-IRS-AP link. Under this setup, we jointly design the transmit beamforming at the AP and the reflective beamforming at the IRS to minimize the Cramér-Rao bound (CRB) on the estimation error. Towards this end, we first obtain the CRB expressions for the two target models in closed form. It is shown that in the point target case, the CRB for estimating the DoA depends on both the transmit and reflective beamformers;while in the extended target case, the CRB for estimating the target response matrix only depends on the transmit beamformers. Next, for the point target case, we optimize the joint beamforming design to minimize the CRB, via alternating optimization, semi-definite relaxation, and successive convex approximation. For the extended target case, we obtain the optimal transmit beamforming solution to minimize the CRB in closed form. Finally, numerical results show that for both cases, the proposed designs based on CRB minimization achieve improved sensing performance in terms of mean squared error, as compared to other traditional schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Beamforming

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GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning

arXiv

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arXiv 2023年

作者： Wang, Qiannan Yin, Changchun Liu, Zhe Fang, Liming Wang, Run Lin, Chenhao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics Astronautics Shenzhen Research Institute Shenzhen China Research Institute of Basic Theories Zhejiang Lab Hangzhou China School of Cyber Science and Engineering Wuhan University Wuhan China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

Within the realm of computer vision, self-supervised learning (SSL) pertains to training pre-trained image encoders utilizing a substantial quantity of unlabeled images. Pre-trained image encoders can serve as feature extractors, facilitating the construction of downstream classifiers for various tasks. However, the use of SSL has led to an increase in security research related to various backdoor attacks. Currently, the trigger patterns used in backdoor attacks on SSL are mostly visible or static (sample-agnostic), making backdoors less covert and significantly affecting the attack performance. In this work, we propose GhostEncoder, the first dynamic invisible backdoor attack on SSL. Unlike existing backdoor attacks on SSL, which use visible or static trigger patterns, GhostEncoder utilizes image steganography techniques to encode hidden information into benign images and generate backdoor samples. We then fine-tune the pre-trained image encoder on a manipulation dataset to inject the backdoor, enabling downstream classifiers built upon the backdoored encoder to inherit the backdoor behavior for target downstream tasks. We evaluate GhostEncoder on three downstream tasks and results demonstrate that GhostEncoder provides practical stealthiness on images and deceives the victim model with a high attack success rate without compromising its utility. Furthermore, GhostEncoder withstands state-of-the-art defenses, including STRIP, STRIP-Cl, and SSL-Cleanse. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Comparison of Random Forest and Extreme Gradient Boosting Fingerprints to Enhance an indoor Wifi Localization System

Comparison of Random Forest and Extreme Gradient Boosting Fi...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Niang, Mariame Ndong, Massa Dioum, Ibra Diop, Idy Mashaly, Maggie Ghany, Mohamed A. Abd El Cheikh Anta Diop University of Dakar Faculty of Science and Technology Department of Physics Dakar Senegal Virtual University of Senegal Science and Technology Dakar Senegal Cheikh Anta Diop University of Dakar Polytechnic High School of Dakar Researcher of Electronic and Telecommunications Computer Science Department Dakar Senegal German University in Cairo Networking Department Cairo Egypt German University in Cairo Cairo Faculty of Information Engineering and Technology Dept. of Electronics Egypt

ISBN: (纸本)9781665412438

Machine Learning framework adds a new dimension to the localization estimation problem. It tries to find the most likely position using processed features in a radio map. This paper compares the performance of two machine learning tools, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), in exploiting the multipath information for Indoor localization problem. The investigation was carried out in a noisy indoor scenario, where Non-Line-Of-Sight (NLOS) between target and sensors may affect the location of Wi-Fi Access point strongly. It is possible to improve the position system performance by using fingerprints techniques that employ multipath information in a Machine Learning framework, which operates a dataset generated by real time. Usually, real measurements produce the fingerprints localization features, and there is mismatching with the simulated data. Another drawback of NLOS features extraction is the noise level that occurs in position processing. Random Forest algorithm uses fully grown decision trees to classify possible emitter position, trying to achieve error mitigation by reducing variance. On the other hand, XGBoost approach uses weak learners, defined by high bias and low variance. The results of the simulation show that XGBoost reaches a Mean Square Error (MSE) of 1.77m while RF has 1.83m, as shown in Fig. (4). In real time analysis, 40 points were used for testing. RF has an MSE 1.85m and XGBoost has 1.82m. The results are compared to the state of the art and recently published papers. © 2021 IEEE.

关键词： Mean square error

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Proposing System to Recognize Emotions in Public Network Using Phobert Deep Learning Model

Proposing System to Recognize Emotions in Public Network Usi...

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National Foundation for Science and technology Development Conference on Information and computer Science (NICS)

作者： Phat Nguyen Huu Tuan Nguyen Anh Long Hoang Phi Dinh Dang Dang Chau Nguyen Le Bao Quang Tran Minh School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam Hanoi-Amsterdam Highschool for the Gifted Hanoi Vietnam Department of Information Systems Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Hochiminh Vietnam Vietnam National University HoChiMinh City Hochiminh Vietnam

People can receive information faster especially in the 4.0 revolution with the continuous development of revolutions. The information can affect our emotional, psychological and spiritual well-being, especially in the recent high school graduation exam across the country. Therefore, we propose to build a user emotion analysis system in a public network with the PhoBert training model in the paper. Besides, we built our dataset aggregated from social networks, articles, blogs, etc. We next use the PhoBert model to solve the processing data problems. The simulation results have shown an accuracy of 86.5% on the training and 81.32% on the validation dataset with a training time of 3 hours (about 180 minutes). The results also show that we can build a warning system to avoid health and psychological effects with great emotion.

关键词： Training Deep learning computer science Emotion recognition Social networking (online) Simulation Psychology

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Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview

arXiv

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arXiv 2024年

作者： Hu, Yingbai Abu-Dakka, Fares J. Chen, Fei Luo, Xiao Li, Zheng Knoll, Alois Ding, Weiping Multi-Scale Medical Robotics Centre Ltd. The Chinese University of Hong Kong Hong Kong School of Computation Information and Technology Technical University of Munich Munich85748 Germany Electronic and Computer Science Department Faculty of Engineering Mondragon Unibertsitatea Arrasate20500 Spain Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Department of Surgery The Chinese University of Hong Kong Hong Kong School of Information Science and Technology Nantong University Nantong226019 China

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers' comprehension of dynamical systems' foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration. © 2024, CC BY.

关键词： Reinforcement learning

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Maximal quantum interaction between free electrons and photons

arXiv

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arXiv 2024年

作者： Xie, Zetao Chen, Zeling Li, Hao Yan, Qinghui Chen, Hongsheng Lin, Xiao Kaminer, Ido Miller, Owen D. Yang, Yi Department of Physics HK Institute of Quantum Science and Technology The University of Hong Kong Pokfulam Hong Kong Department of Applied Physics Energy Sciences Institute Yale University New HavenCT06520 United States Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Solid State Institute Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa32000 Israel

The emerging field of free-electron quantum optics enables electron-photon entanglement and holds the potential for generating nontrivial photon states for quantum information processing. Although recent experimental studies have entered the quantum regime, rapid theoretical developments predict that qualitatively unique phenomena only emerge beyond a certain interaction strength. It is thus pertinent to identify the maximal electron-photon interaction strength and the materials, geometries, and particle energies that enable one to approach it. We derive an upper limit to the quantum vacuum interaction strength between free electrons and single-mode photons, which illuminates the conditions for the strongest interaction. Crucially, we obtain an explicit energy selection recipe for electrons and photons to achieve maximal interaction at arbitrary separations and identify two optimal regimes favoring either fast or slow electrons over those with intermediate velocities. We validate the limit by analytical and numerical calculations on canonical geometries and provide near-optimal designs indicating the feasibility of strong quantum interactions. Our findings offer fundamental intuition for maximizing the quantum interaction between free electrons and photons and provide practical design rules for future experiments on electron-photon and electron-mediated photon-photon entanglement. They should also enable the evaluation of key metrics for applications such as the maximum power of free-electron radiation sources and the maximum acceleration gradient of dielectric laser accelerators. © 2024, CC BY.

关键词： Photons

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A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9403-9418页

作者： Baghdadi, Asma Fourati, Rahma Aribi, Yassine Daoud, Sawsen Dammak, Mariem Mhiri, Chokri Chabchoub, Habib Siarry, Patrick Alimi, Adel Esprit School of Business Computer Science and Applied Mathematics Department Tunis Tunisia University of Sfax BP 1173 Sfax3038 Tunisia Laboratoire Images Signaux et Systèmes Intelligents Université Paris Est Créteil 120 Rue Paul Armangot Vitry sur Seine94400 France University of Jendouba Jendouba Tunisia Sciences and Technology Department University College of Ranyah Taif University Taif Saudi Arabia Department of Neurology Habib Bourguiba University Hospital Sfax Tunisia Neuroscience Laboratory Sfax university LR-12-SP-19 Sfax Tunisia College of Business Al Ain University Abu Dhabi United Arab Emirates LiSSi Université de Paris 12 Créteil94010 France Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a crucial patient-dependent step for the treatment selection process. The selection of the proper treatment relies on the correct identification of the seizure type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Patient treatment

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Exploring genetic mechanisms underlying EEG endophenotypes via summary-data-based Mendelian randomization

Exploring genetic mechanisms underlying EEG endophenotypes v...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jing Chen Pengfei Zhu Jinghui Pan Tao Wang School of Computer Science and Engineering Xi’an University of Technology Xi’an China Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science Northwestern Polytechnical University Xi’an China School of Electronic Information Wuhan University Wuhan China Department of Radiation Oncology Renmin Hospital of Wuhan University Wuhan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Oscillations in neuronal activity play a crucial role in neural processing. Electroencephalogram (EEG) records the amplitudes of oscillations at different frequencies. The robust interindividual variation and the high intraindividual stability of EEG oscillations can be explained by genetic variants. However, our knowledge of how genetic variants affect EEG endophenotypes is still limited. In this paper, we aim to identify the linkage between genes and EEG oscillations at different frequencies. We integrate the summary-level datasets from the genome-wide association studies (GWASs) of seven EEG endophenotypes and expression quantitative trait loci (eQTL) study of the human brain, to explore the genetic mechanisms underlying EEG using summary-data-based two-sample Mendelian randomization. As a result, twelve candidate causal genes are prioritized across four EEG endophenotypes. These genes offer opportunities for a deeper understanding of the molecular mechanisms whereby genomic variation leads to variable brain activities.

关键词： Couplings Brain Genomics Electroencephalography Stability analysis Gene expression Bioinformatics

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TEA-IHPCR: Temporal Augmented-Invariant Vehicle-Infrastructure Cooperative Heterogeneous Point Cloud Registration

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Zhang, Shiyan Zhang, Xu Zhang, Xinyu Chen, Yuxi Zhu, Dongwei Wu, Fan Jin, Dafeng Li, Jun Tsinghua University State Key Laboratory of Automotive Safety and Energy School Vehicles and Mobility Beijing China Tsinghua University Suzhou Automotive Research Institute Suzhou China Nanyang Technological University School of Electrical and Electronic Engineering Singapore Singapore Tsinghua University State Key Laboratory of Automotive Safety and Energy Beijing China Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Forestry University School of Technology Beijing China Shanghai Polytechnic University School Faculty of Intelligent Manufacturing and Control Engineering Shanghai China Tsinghua University State Key Laboratory of Automotive Safety and Energy The School of Vehicle and Mobility Beijing China

In the field of autonomous driving, relying solely on environmental information captured by single vehicle's LiDAR often falls short of perception requirements in complex scenarios. Adopting a vehicle-infrastructure cooperation approach can effectively address information limitations. Therefore, the development of an algorithm that can register heterogeneous LiDAR point clouds is crucial. This paper proposes a vehicle-infrastructure cooperative heterogeneous point cloud registration network (TEA-IHPCR) based on temporal augmented information and rotation invariance. By capturing temporal augmented information between continuous frames of point clouds, data augmentation is performed on the point cloud data from both vehicle and infrastructure sides, mitigating issues of poor heterogeneous point cloud registration caused by perspective differences. The rotation invariance property can address the issue of aligning coordinate systems between the vehicle and infrastructure sides that have not been collaboratively calibrated. Simultaneously, the use of a local-level encoder capable of extracting point cloud features with rotation invariance, combined with a global-level attention mechanism that can learn cross-frame features, improves the model's performance in low-overlap rate point cloud registration. The experiments were conducted on a vehicle-infrastructure collaborative dataset while the results demonstrate the effectiveness of the proposed module in the network, as well as its superiority and efficiency compared to other networks. © 1963-2012 IEEE.

关键词： Spatio-temporal data

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A Deep Learning Approach for Recognizing the Cursive Tamil Characters in Palm Leaf Manuscripts

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Computational Intelligence and Neuroscience 2022年 2022卷 3432330页

作者： Devi S, Gayathri Vairavasundaram, Subramaniyaswamy Teekaraman, Yuvaraja Kuppusamy, Ramya Radhakrishnan, Arun School of Computing Sastra Deemed University Thanjavur613401 India Department of Electronic and Electrical Engineering The University of Sheffield SheffieldS1 3JD United Kingdom Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Bangalore562 106 India Faculty of Electrical and Computer Engineering Jimma Institute of Technology Jimma University Jimma Ethiopia

Tamil is an old Indian language with a large corpus of literature on palm leaves, and other constituents. Palm leaf manuscripts were a versatile medium for narrating medicines, literature, theatre, and other subjects. Because of the necessity for digitalization and transcription, recognizing the cursive characters found in palm leaf manuscripts remains an open problem. In this research, a unique Convolutional Neural Network (CNN) technique is utilized to train the characteristics of the palm leaf characters. By this training, CNN can classify the palm leaf characters significantly on training phase. Initially, a preprocessing technique to remove noise in the input image is done through morphological operations. Text Line Slicing segmentation scheme is used to segment the palm leaf characters. In feature processing, there are some major steps used in this study, which include text line spacing, spacing without obstacle, and spacing with an obstacle. Finally, the extracted cursive characters are given as input to the CNN technique for final classification. The experiments are carried out with collected cursive Tamil palm leaf manuscripts to validate the performance of the proposed CNN with existing deep learning techniques in terms of accuracy, precision, recall, etc. The results proved that the proposed network achieved 94% of accuracy, where existing ResNet achieved 88% of accuracy. © 2022 Gayathri Devi S et al.

关键词： Convolutional neural networks

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