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7th International Conference on Innovative Computing, IC 2024

作者： Chu, Chao-Ting Chang, Jia-Wei Chunghwa Telecom Laboratories Internet of Things Laboratory No. 99 Dianyan Rd. Yangmei District Taoyuan City32661 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd North Dist. Taichung City404 Taiwan

ISBN: (纸本)9789819741816

This paper introduces a gesture recognition system for proximity inductance, featuring fuzzy identification. The realm of smart home security is experiencing rapid expansion, wherein the Internet of Things (IoT) is assuming an escalating significance. Security algorithms implemented in real-time processors facilitate user identification and improve the overall security of smart homes. Novel technologies in the realm of inductance sensors have emerged, designed for the proximity-based detection of electronic fluctuations in the vicinity of sensor detection points. The increasing prevalence of affordable gadgets in the intelligent home sector renders inductance sensors an especially enticing choice for gesture recognition systems. In this manuscript, we introduce a proximity inductance gesture identification system featuring fuzzy categorization. We utilize a microprocessor equipped with fuzzy expert knowledge identification algorithms to distinguish diverse signals and configurations associated with gestures. The outcomes of our experimental trials substantiate the efficacy of the system we put forth. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Microcontrollers

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A Noise Extraction Method for Cryo-EM Single-Particle Denoising

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Journal on Big Data 2022年第1期4卷 61-76页

作者： Huanrong Tang Sihan Wang Jianquan Ouyang Tianming Liu Key Laboratory of Intelligent Computing Information Processing Ministry of EducationSchool of Computer Science and School of Cyberspace ScienceXiangtan UniversityXiangtan411100China Department of Computer Science The University of GeorgiaGeorgia30301USA

Cryo-Electron Microscopy(cryo-EM)has become a powerful method to study the structure and function of biological ***,in clustering tasks based on the projection angle of particles in cryoEM,the noise considerably affects the clustering *** denoising algorithms are ineffective due to the extremely low signal-to-noise ratio(SNR)of cryo-EM images and the complexity of noise *** noise of a single particle greatly influences the orientation estimation of the subsequent clustering task,and the result of the clustering task directly affects the accuracy of the 3D *** this paper,we propose a construction method of cryo-EM denoising dataset that uses U-Net to extract noise blocks from cryoEM images,superimpose the noise block with the projected pure particles to construct our simulated *** we adopt a supervised generative adversarial network(GAN)with perceptual loss to train on our simulated dataset and denoise the real cryo-EM single *** method can solve the problem of poor denoising performance caused by assuming that the noise of the Gaussian distribution does not conform to the noise distribution of cryo-EM,and it can retain the useful information of particles to a great *** compared traditional image filtering methods and the classic deep learning denoising algorithm DnCNN on the simulated and real *** results show that the method based on deep learning has more advantages than traditional image denoising *** is worth mentioning that our method achieves a competitive peak signal to noise ratio(PSNR)and structural similarity(SSIM).Moreover,visualization results,indicate that our method can retain the structure information and orientation information of particles to a greater extent compared with other state-of-the-art image denoising *** means that our denoising task can provide considerable help for subsequent cryo-EM clustering tasks.

关键词： Cryo-EM noise extraction denoising GAN

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PDSketch: integrated planning domain programming and learning 22

PDSketch: integrated planning domain programming and learnin...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jiayuan Mao Tomas Lozano-Pérez Joshua B. Tenenbaum Leslie Pack Kaelbling MIT Computer Science & Artificial Intelligence Laboratory MIT Computer Science & Artificial Intelligence Laboratory and MIT Department of Brain and Cognitive Sciences and Center for Brains Minds and Machines

ISBN: (纸本)9781713871088

This paper studies a model learning and online planning approach towards building flexible and general robots. Specifically, we investigate how to exploit the locality and sparsity structures in the underlying environmental transition model to improve model generalization, data-efficiency, and runtime-efficiency. We present a new domain definition language, named PDSketch. It allows users to flexibly define high-level structures in the transition models, such as object and feature dependencies, in a way similar to how programmers use TensorFlow or PyTorch to specify kernel sizes and hidden dimensions of a convolutional neural network. The details of the transition model will be filled in by trainable neural networks. Based on the defined structures and learned parameters, PDSketch automatically generates domain-independent planning heuristics without additional training. The derived heuristics accelerate the performance-time planning for novel goals.

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REVISIT AND OUTSTRIP ENTITY ALIGNMENT: A PERSPECTIVE OF GENERATIVE MODELS

arXiv

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arXiv 2023年

作者： Guo, Lingbing Chen, Zhuo Chen, Jiaoyan Fang, Yin Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China Zhejiang University Ant Group Joint Laboratory of Knowledge Graph China Donghai Laboratory China Department of Computer Science The University of Manchester United Kingdom School of Software Technology Zhejiang University China

Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study embedding-based entity alignment (EEA) from a perspective of generative models. We show that EEA shares similarities with typical generative models and prove the effectiveness of the recently developed generative adversarial network (GAN)-based EEA methods theoretically. We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i.e., generating new entities). We mitigate this problem by introducing a generative EEA (GEEA) framework with the proposed mutual variational autoencoder (M-VAE) as the generative model. M-VAE enables entity conversion between KGs and generation of new entities from random noise vectors. We demonstrate the power of GEEA with theoretical analysis and empirical experiments on both entity alignment and entity synthesis tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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Obstacle Detection and Avoidance based on 3D Stereoscopic Technique for Autonomous Drone 13

Obstacle Detection and Avoidance based on 3D Stereoscopic Te...

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13th International Conference on Information Technology and Electrical Engineering, ICITEE 2021

作者： Nabandit, Thanawut Saiyod, Saiyan Nonsakhoo, Woottichai Sirisawat, Piyanat Khon Kaen University Hardware-Human Interface and communication laboratory Department of Computer Science Khon Kaen Thailand

ISBN: (纸本)9781665443067

Autonomous drones are drones that can fly on their own without the need for human supervision. Once the drone is assigned a flight mission, it will automatically fly to its destination. The ability to quickly maneuver obstacles is essential to complete the task assigned to it. Obstacle analysis processes need to be efficient and require fewer computing resources due to the hardware and power constraints of drones. This work presents a simple but effective way to detect obstacles using a 3D Stereoscopic camera and a less complicated image processing method, which can run on a small single-board computer. On-site flight tests show that the proposed method can be used efficiently. It can detect obstacles at a distance of 2 meters, 5 frames per second, 79.55% of detection accuracy, and maneuver effectively. © 2021 IEEE.

关键词： Drones

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RF Fingerprinting-Based IoT Node Authentication Using Mahalanobis Distance Correlation Theory

IEEE Networking Letters

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IEEE Networking Letters 2022年第2期4卷 78-81页

作者： Nguyen, Dinh Duc Nha Sood, Keshav Nosouhi, Mohammad Reza Xiang, Yong Gao, Longxiang Chi, Lianhua Deakin University Centre of Cyber Security Research Innovation GeelongVIC3220 Australia Deakin University Deakin Blockchain Innovation Laboratory School of Information Technology GeelongVIC3220 Australia Qilu University of Technology Shandong Academy of Sciences Jinan250316 China National Supercomputer Center in Jinan Shandong Computer Science Center Jinan250101 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia

The wireless Internet of Things (IoT) node authentication approaches also used Radio Frequency (RF) fingerprinting or physical unclonable features (PUF) of IoT devices for node authentication. Machine learning based models play vital role in these approaches. In this letter, we introduce an effective and novel IoT node authentication approach using Mahalanobis Distance correlation and Chi-square distribution theories. Further, it has lower computational time to determine node authenticity. The comparative results of our proposal with three recent machine learning based approaches and PUF based approaches are promising which validates the effectiveness and the novelty of our proposal. © 2019 IEEE.

关键词： Next generation networks

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VARIATIONAL AUTO-ENCODER BASED SOLUTIONS TO INTERACTIVE DYNAMIC INFLUENCE DIAGRAMS

arXiv

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arXiv 2024年

作者： Pan, Yinghui Ma, Biyang Zhang, Hanyi Zeng, Yifeng National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science Minnan Normal University Zhangzhou China Department of Computer and Information Sciences Northumbria University Newcastle United Kingdom

Addressing multiagent decision problems in AI, especially those involving collaborative or competitive agents acting concurrently in a partially observable and stochastic environment, remains a formidable challenge. While Interactive Dynamic Influence Diagrams (I-DIDs) have offered a promising decision framework for such problems, they encounter limitations when the subject agent encounters unknown behaviors exhibited by other agents that are not explicitly modeled within the I-DID. This can lead to sub-optimal responses from the subject agent. In this paper, we propose a novel data-driven approach that utilizes an encoder-decoder architecture, particularly a variational autoencoder, to enhance I-DID solutions. By integrating a perplexity-based tree loss function into the optimization algorithm of the variational autoencoder, coupled with the advantages of Zig-Zag One-Hot encoding and decoding, we generate potential behaviors of other agents within the I-DID that are more likely to contain their true behaviors, even from limited interactions. This new approach enables the subject agent to respond more appropriately to unknown behaviors, thus improving its decision quality. We empirically demonstrate the effectiveness of the proposed approach in two well-established problem domains, highlighting its potential for handling multi-agent decision problems with unknown behaviors. This work is the first time of using neural networks based approaches to deal with the I-DID challenge in agent planning and learning problems. © 2024, CC BY.

关键词： Decoding

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Difficulty-Aware Data Augmentor for Scene Text Recognition

Difficulty-Aware Data Augmentor for Scene Text Recognition

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guanghao Meng Tao Dai Bin Chen Naiqi Li Yong Jiang Shu-Tao Xia Tsinghua Shenzhen International Graduate School Tsinghua University Peng Cheng Laboratory Shenzhen China College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Technology Harbin Institute of Technology Shenzhen

Deep neural network (DNN) based scene text recognition (STR) methods usually require a large amount of annotated data for training, which is time-consuming and cost-expensive in practice. To address this issue, many data augmentation methods have been developed to train recognizers by improving the diversity of training samples. However, most existing methods neglect the difficulty inherent in samples, and easily suffer from the problem of over-diversity, i.e., the distribution of the augmented data significantly deviates from that of clean data. In this paper, we propose a novel difficulty-aware data augmentation framework for scene text recognition, which jointly considers the difficulty of samples and the strength of augmentations. Specifically, our framework first predicts the sample difficulty, followed by an adaptive data augmentation strategy. Furthermore, we build a more diverse set of augmentation methods for STR and integrate it into our augmentation framework. Extensive experiments on scene text recognition benchmarks show that our augmentation framework significantly improves the performance of recognizers.

关键词： Training Measurement Deep learning Text recognition Neural networks Speech recognition Signal processing

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Facesight: Enabling hand-to-face gesture interaction on ar glasses with a downward-facing camera vision 21

Facesight: Enabling hand-to-face gesture interaction on ar g...

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2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

作者： Weng, Yueting Yu, Chun Shi, Yingtian Department of Computer Science and Technology Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education Beijing China

ISBN: (纸本)9781450380966

We present FaceSight, a computer vision-based hand-to-face gesture sensing technique for AR glasses. FaceSight fxes an infrared camera onto the bridge of AR glasses to provide extra sensing capability of the lower face and hand *** obtained 21 hand-to-face gestures and demonstrated the potential interaction benefts through fve AR applications. We designed and implemented an algorithm pipeline that segments facial regions, detects hand-face contact (f1 score: 98.36%), and trains convolutional neural network (CNN) models to classify the hand-to-face gestures. The input features include gesture recognition, nose deformation estimation, and continuous fngertip movement. Our algorithm achieves classifcation accuracy of all gestures at 83.06%, proved by the data of 10 users. Due to the compact form factor and rich gestures, we recognize FaceSight as a practical solution to augment input capability of AR glasses in the future. © 2021 ACM.

关键词： Glass

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Polymerization-Amplified Photoacoustic Signal by Enhancing Near-Infrared Light-Harvesting Capacity and Thermal-to-Acoustic Conversion

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Chinese Journal of Polymer science 2022年第9期40卷 1090-1100页

作者： He-Qi Gao Jing-Tian Zhang Xin-Wen Qi Di Jiao Yu-Ning Hong Ke Shan Xiang-Long Kong Dan Ding Key Laboratory of Bioactive Materials Ministry of Educationand College of Life SciencesNankai UniversityTianjin 300071China Center for AIE Research Shenzhen Key Laboratory of Polymer Science and TechnologyGuangdong Research Center for Interfacial Engineering of Functional MaterialsCollege of Materials Science and EngineeringCollege of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China Department of Chemistry and Physics La Trobe Institute for Molecular ScienceLa Trobe UniversityMelbourneVictoria3086Australia Shandong Artificial intelligence Institute and Shandong Computer Science Center Qilu University of TechnologyJinan 250353China

As a frontier imaging technique for biomedical applications,photoacoustic(PA)imaging has been developed *** development of new design strategies and excellent PA imaging reagents to boost PA conversion is eagerly desirable for high quality PA imaging but complicated to ***,we develop a new strategy in which PA imaging reagents with better properties can be easily optimized by polymerization.A series of new PA imaging reagents were designed and *** polymerization strategy can effectively promote the PA signal by specifically increasing the thermal-to-acoustic conversion *** these materials shared the same building units,the optimized effectiveness of polymerization strategy in terms of near-infrared light-harvesting capacity and thermal-to-acoustic conversion efficiency are discussed,*** polymers with intense intramolecular motion exhibit an amplified PA signal by elevating thermal-to-acoustic conversion and its higher light-harvesting capability at redshifted *** simultaneously strong PA signal and photothermal conversion efficiency of p-TTmB NPs enable precise PA imaging and effective photothermal *** work highlights a simple and available design guideline of polymerization for amplifying the PA effect and optimizing existing materials.

关键词： Intramolecular motion Thermal-to-acoustic conversion Photoacoustic imaging Photothermal conversion efficiency Polymerization

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