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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fuquan Wu, Yu Xu, Xinxin Wang, Chuansheng Ma, Minjia Grau, Antoni Huang, Jiayan School of Computer and Big Data Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (纸本)9798350386226

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices. © 2024 IEEE.

关键词： Electrocardiography

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Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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Spatial and Contextual Path Network for Image Inpainting

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Intelligent Automation & Soft Computing 2024年第2期39卷 115-133页

作者： Dengyong Zhang Yuting Zhao Feng Li Arun Kumar Sangaiah Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinTaiwan Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Image inpainting is a kind of use known area of information technology to repair the loss or damage to the *** feature extraction is the core of image *** enough space for information and a larger receptive field is very important to realize high-precision image ***,in the process of feature extraction,it is difficult to meet the two requirements of obtaining sufficient spatial information and large receptive fields at the same *** order to obtain more spatial information and a larger receptive field at the same time,we put forward a kind of image restoration based on space path and context path *** the space path,we stack three convolution layers for 1/8 of the figure,the figure retained the rich spatial *** the context path,we use the global average pooling layer,where the accept field is the maximum of the backbone network,and the pooling module can provide global context information for the maximum accept *** order to better integrate the features extracted from the spatial and contextual paths,we study the fusion module of the two *** fusionmodule first path output of the space and context path,and then through themass normalization to balance the scale of the characteristics,finally the characteristics of the pool will be connected into a feature vector and calculate the weight *** of images in order to extract context information,we add attention to the context path refinement *** modules respectively from channel dimension and space dimension to weighted images,in order to obtain more effective *** show that our method is better than the existing technology in the quality and quantity of themethod,and further to expand our network to other inpainting networks,in order to achieve consistent performance improvements.

关键词： Image inpainting attention deep learning convolutional network

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A Switch Architecture for Time-Triggered Transmission with Best-Effort Delivery

arXiv

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arXiv 2023年

作者： Li, Zonghui Zhu, Wenlin Shin, Kang G. Wan, Hai Song, Xiaoyu Yang, Dong Ai, Bo The Beijing Key Laboratory of Transportation Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109-2121 United States The Software School Tsinghua University Beijing100084 China The Department of Electrical and Computer Engineering Portland State University PortlandOR United States The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

In Time-Triggered (TT) or time-sensitive networks, the transmission of a TT frame is required to be scheduled at a precise time instant for industrial distributed real-time control systems. Other (or best-effort (BE)) frames are forwarded in a BE manner. Under this scheduling strategy, the transmission of a TT frame must wait until its scheduled instant even if it could have been transmitted sooner. On the other hand, BE frames are transmitted whenever possible but may miss deadlines or may even be dropped due to congestion. As a result, TT transmission and BE delivery are incompatible with each other. To remedy this incompatibility, we propose a synergistic switch architecture (SWA) for TT transmission with BE delivery to dynamically improve the end-to-end (e2e) latency of TT frames by opportunistically exploiting BE delivery. Given a TT frame, the SWA generates and transmits a cloned copy with BE delivery. The first frame arriving at the receiver device is delivered with a configured jitter and the other copy ignored. So, the SWA achieves shorter latency and controllable jitter, the best of both worlds. We have implemented SWA using FPGAs in an industry-strength TT switches and used four test scenarios to demonstrate SWA's improvements of e2e latency and controllable jitter over the state-of-the-art TT transmission scheme. Copyright © 2023, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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An algorithm for multi-armed bandit based on variance change sensitivity

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engineering Research Express 2024年第2期6卷 025209-025209页

作者： Zhu, Canxin Yang, Jingmin Zhang, Wenjie Zheng, Yifeng School of Computer Science Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology Taipei106344 Taiwan

The Multi-Arm Bandit problem is becoming increasingly popular as it enables real-world sequential decision making across application domains, including clinical trials, recommender systems, and online decision making. The Multi-Arm Bandit problem is a classical problem of exploration and exploitation dilemma in reinforcement learning and it needs to decide on the optimal strategy based on the reward situation of each rocker arm. However, the existing Multi-Arm Bandit algorithms have many shortcomings, such as blind exploration, weak generalization ability and endless exploration. Aiming at the shortcomings of the existing Multi-Armed Bandit algorithms, this paper proposes a Multi-Armed Bandit algorithm based on variance change sensitivity. The algorithm takes the variance change of reward as a clue, and adjusts the exploration probability by the average variance change of all actions, and selects the action with the largest variance change when exploring. At the same time, in order to reduce the waste of action selection times and maximize the cumulative reward, A parameter N c o n was introduced to record the number of consecutive selections of the same action. The exploration stops when N c o n reaches a certain value. Experiments show that the exploration algorithm can obtain higher reward value and lower regret value in the end. © 2024 IOP Publishing Ltd.

关键词： Reinforcement learning

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Advanced Channel Characterization and Performance Forecasting for Hybrid mmWave and LTE-A Networks in Metropolitan Environments

Advanced Channel Characterization and Performance Forecastin...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： R. Gowtham Raj P. Aswini H. Pal Thethi Deepika Arora Taqi Mohammed Khattab Al-Rubaye Department of Mechanical Engineering New Horizon College of Engineering Bengaluru India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Lovely Professional University Phagwara Punjab India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Department of Medical Laboratory Technology College of Medical Technologies The Islamic University Najaf Iraq

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Mainly in metropolitan areas that attract interference and restriction due to the fast growth of wireless communication, it is essential to understand the channel characteristics and throughput predictions. Important for the development of the future 5G and beyond networks such as millimeter-wave (mmWave) and LTE-Advanced (LTE-A) networks are under discussion in this paper. This paper evaluates crucial channel characteristics which include route loss, delay spread and angle spread at mmWave and LTE-A frequencies in many urban settings through the use of current ray tracing formulas and actual data. The research also applies real environmental objects such as building density, vehicles, plants, etc, and employs various machine learning algorithm to predict the network throughput The findings suggest that mmWave networks have a higher throughput than LTE-A under line-of-sight situations although they offer a higher route loss with less diffraction. On the other hand, LTE-A's characteristic of diffraction is better, and the threshold that proves its superiority in non-line-of-sight situations. For the network designers, this research provides fresh outlooks which assists for the optimal organization of hybrid mmWave and LTE-A thus achieving the best performances pointed out in the urban zones. The results prove that it is necessary to incorporate methods of adaptive beamforming and other complex methods of modulation in order to overcome the obstacles related to signal transmission in urban environment.

关键词： Wireless communication Diffraction Urban areas Millimeter wave technology Modulation Ray tracing Predictive models Throughput Delays Millimeter wave communication

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Harmonic Long-Range Backscatter with Frequency-Shifted Lightweight Tag

Harmonic Long-Range Backscatter with Frequency-Shifted Light...

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IEEE Conference on Vehicular Technology (VTC)

作者： Junliang Lin Xiannan Zhang Rongtao Xu Gongpu Wang Tony Q. S. Quek Beijing Key Laboratory of Transportation Data Analysis and Mining Beijing Jiaotong University Beijing China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

In this paper, we introduce a simplified long-range (LoRa) backscatter system that enables a lightweight tag to communicate with a remote transceiver using chirp carrier and harmonic backscatter. The key idea is twofold: first, we delegate the chirp waveform generation from the tag to the transceiver, thereby maintaining lightweight tag design; second, we introduce a frequency shift on the tag during its carrier modulation to create harmonic backscatter, thereby mitigating self-interference. To refine the framework, we first detail the system model, including the design for harmonic backscatter. We then present an efficient method to address synchronization and detection issues at the transceiver. Finally, we prototype the proposed system and evaluate its performance. Experimental results demonstrate that in a corridor environment with a carrier power of 0 dBm, the tag can backscatter a signal from the transceiver at a maximum bit rate of 4 kb/s over a distance of 50 meters.

关键词： Meters Vehicular and wireless technologies Interference cancellation Chirp Prototypes LoRa Harmonic analysis Transceivers Synchronization Backscatter

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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A Median Filtering Forensics CNN Approach Based on Local Binary Pattern 11th

A Median Filtering Forensics CNN Approach Based on Local Bin...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Zhu, Tao Gu, Haiyan Chen, Zenan Department of Computer Information and Cyber Security Jiangsu Police Institute Nanjing210031 China Jiangsu Province Electronic Data Forensics and Analysis Engineering Research Center Nanjing210031 China Key Laboratory of Digital Forensics of Jiangsu Provincial Public Security Department Nanjing210031 China

ISBN: (纸本)9789811665530

Median filtering forensics has gradually become a research hotspot because of the wide application of the traditional median filtering (MF) in image tampering and anti-forensics. The difficulty of traditional median filtering based on machine learning forensics is feature extraction which is a manual selection process, and the Convolutional Neural Network (CNN) cannot also well perceive the traces left by median filtering straightly. By taking the Local Binary Pattern (LBP) data of an image as a presentation of the streaking artifact that is a very strong indication for median filtering, a median filtering forensics CNN approach with LBP is proposed, which can automatically learn and get median filtering features from image. Different from traditional CNN, an LBP perception layer is added before the following deep learning layers. Then, five convolution layers constitute the feature extraction group. Finally, the classifier group is composed of three full connected layers to decide whether the image is median filtered or not. The proposed approach is tested on several experiments and the experimental results demonstrate its effectiveness. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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VERO: Verification and Zero-Shot Feedback Acquisition for Few-Shot Multimodal Aspect-Level Sentiment Classification 39

VERO: Verification and Zero-Shot Feedback Acquisition for Fe...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Kai Wu, Hao Shi, Bin Mensah, Samuel Liu, Peng Dong, Bo Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China School of computer science and engineering Guangxi Normal University Guilin China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Continuing Education Xi’an Jiaotong University China

ISBN: (纸本)157735897X

Deep learning approaches for multimodal aspect-level sentiment classification (MALSC) often require extensive data, which is costly and time-consuming to obtain. To mitigate this, current methods typically fine-tune small-scale pretrained models like BERT and BART with few-shot examples. While these models have shown success, Large Vision-Language Models (LVLMs) offer significant advantages due to their greater capacity and ability to understand nuanced language in both zero-shot and few-shot settings. However, there is limited work on fine-tuning LVLMs for MALSC. A major challenge lies in selecting few-shot examples that effectively capture the underlying patterns in data for these LVLMs. To bridge this research gap, we propose an acquisition function designed to select challenging samples for the few-shot learning of LVLMs for MALSC. We compare our approach, Verification and ZERO-shot feedback acquisition (VERO), with diverse acquisition functions for few-shot learning in MALSC. Our experiments show that VERO outperforms prior methods, achieving an F1 score improvement of up to 6.07% on MALSC benchmark datasets. © 2025, Association for the Advancement of Artificial.

关键词： Zero-shot learning

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