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arXiv 2024年

作者： Zhang, Lan Wan, Lipeng Deng, Weimin Hou, Liang Zou, Qiushun Wang, Tongbiao Zhao, Daomu Yu, Tianbao School of Physics and Material Science Nanchang University Nanchang330031 China Leiden Institute of Physics Leiden University LeidenCA 2333 Netherlands Faculty of Electrical Engineering and Computer Science Laboratory of Infrared Material and Devices Zhejiang Key Laboratory of Advanced Optical Functional Materials and Devices Advanced Technology Research Institute Ningbo University Zhejiang315211 China Zhejiang Province Key Laboratory of Quantum Technology and Device School of Physics Zhejiang University Hangzhou310027 China

Skyrmions are topological defects belonging to nontrivial homotopy classes in particle theory, and are recognized as a suitable unit in the high-density, low-dissipation microelectronic devices in condensed matter physics. Their remarkably stable topology has recently been observed in electromagnetic waves. For the evanescent fields near a surface, this has been realized so far only for elementary optical skyrmions, with a fixed skyrmion number. Here we introduce the concept of moiré optical skyrmion clusters, where multi-skyrmions are nested to form a large optical skyrmion cluster that is either crystallized or quasi-crystallized as a consequence of the twisted nanostructures. The rapid inverting of optical skyrmion number is achieved in the imperfectly aligned composite nanostructures. This moiré optical skyrmion interaction mechanism is described by a lattice model. Further, the nucleation and collapse of optical skyrmion are studied, where their nanoscale dynamics are revealed with a tiny change of the twist angle. The sudden reversal of the on-chip skyrmion can serve as a precise beacon of the relative alignment deviation between twisted composite nanostructures. Copyright © 2024, The Authors. All rights reserved.

关键词： Electromagnetic waves

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APC: Contactless healthy sitting posture monitoring with microphone array

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Smart Health 2024年 32卷

作者： Ma, Kaiyuan Song, Shunan An, Lingling Mao, Shiwen Wang, Xuyu Guangzhou Institute of Technology Xidian University Guangzhou China School of Computer Science and Technology Xidian University Xi'an China Department of Electrical and Computer Engineering Auburn University Auburn 36849 AL United States Knight Foundation School of Computing and Information Sciences Florida International University Miami 33199 FL United States

The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user's body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system. © 2024 Elsevier Inc.

关键词： Acoustic sensing Microphone array Posture detection Smart sensing

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Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

arXiv

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arXiv 2023年

作者： Liang, Xinnian Zhou, Zefan Huang, Hui Wu, Shuangzhi Xiao, Tong Yang, Muyun Li, Zhoujun Bian, Chao State Key Lab of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Northeastern University Shenyang China Faculty of Computing Harbin Institute of Technology Harbin China Lark Platform Engineering-AI Beijing China

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and completely ignore word information. Although Whole Word Masking can alleviate this, the semantics in words is still not well represented. In this paper, we revisit the segmentation granularity of Chinese PLMs. We propose a mixed-granularity Chinese BERT (MigBERT) by considering both characters and words. To achieve this, we design objective functions for learning both character and word-level representations. We conduct extensive experiments on various Chinese NLP tasks to evaluate existing PLMs as well as the proposed MigBERT. Experimental results show that MigBERT achieves new SOTA performance on all these tasks. Further analysis demonstrates that words are semantically richer than characters. More interestingly, we show that MigBERT also works with Japanese. Our code has been released here 1 and you can download our model here 2 © 2023, CC BY.

关键词： Semantics

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Development of CNN Model to Avoid the Food Spoiling Level

Development of CNN Model to Avoid the Food Spoiling Level

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： R. Usha R.Senthamil Selvan A Basi Reddy P. Chandrakanth Department of CSE Madanapalle Institute of Technology & Science Madanapalle India Department of Electronics and Communication Engineering nnamacharya Institute of Technology and Sciences Tirupati India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India Department of CSE N.B.K.R.I.S.T vidyanagar Kota India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Food grains are sensitive to deterioration owing to precipitation temperature, humidity, and several other variables; thus, researchers are working toward novel approaches to preserve the dietary value of food to improve the shelf lifespan of food. Therefore, reliable methods of monitoring food deterioration are necessary to maintain current food safety standards. A prototype has been developed to monitor food quality and manage storage systems within domestic settings. The CNN (Convolutional Neural Network) model was initially utilized for fruit and vegetable classification. The proposed system utilizes actuators and sensors to monitor the emission of gases, humidity, and the temperature of vegetables and fruits to assess the degree of food spoilage. Furthermore, this measure effectively regulates the surrounding conditions and minimizes the occurrence of food deterioration whenever feasible. Customers are notified through text message when the food they ordered is about to deteriorate based on the product’s freshness and condition. The used model has a confirmed accuracy of 96%. Some types of food could extend their storage life by two days, meaning the experiment succeeded.

关键词： Temperature sensors Temperature measurement Humidity Sensor systems Sensors Safety Convolutional neural networks

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Deep learning based Blockchain solution for preserving privacy in future vehicles

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International Journal of Hybrid Intelligent Systems 2020年第4期16卷 223-236页

作者： Varsha, R. Nair, Meghna Manoj Nair, Siddharth M. Tyagi, Amit Kumar School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India Research Division of Advanced Data Science Vellore Institute of Technology Tamilnadu Chennai India

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today's generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system. © 2020 - IOS Press and the authors. All rights reserved.

关键词： authentication Blockchain technology privacy preserving techniques recommender systems Trust in future vehicles

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Synergizing Generative Adversarial Networks and Pseudo-Labeling for Improved Knee Osteoarthritis Detection

Synergizing Generative Adversarial Networks and Pseudo-Label...

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Sustainable computing and Smart Systems (ICSCSS), International Conference on

作者： Sajeev Ram Arumugam Sheela Gowr P Geetha Ponnaian Maharajan K Sankar Ganesh Karuppasamy Divya Muralitharan Department of AI&DS Sri Krishna Collefe of Engineering and Technology Coimbatore India Department of CSE Vels Institute of Science Technology and Advanced Studies Chennai India Department of CSE Saveetha Institute of Medical and Technical Sciences Chennai India Department of CSE School of computing Kalasalingam Academy of research and education India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Progressive cartilage breakdown and joint inflammation are hallmarks of the common joint disease known as osteoarthritis (OA). The timely intervention and management of osteoarthritis (OA) depend on early detection. However, this can be difficult to achieve because labelled medical imaging data is scarce, and the disease's presentations are complex. A notable skew between the proportion of healthy and OA knee images can be seen in knee OA datasets, a phenomenon known as class imbalance. As a result, the model may underperform on the minority class (OA) and prioritize the majority class during training. The goal of the research is to further the development of more accurate and reliable techniques for knee OA detection. A novel method for knee osteoarthritis detection that first uses ResNet, DenseNet, VGG16, and VGG19 convolutional neural network (CNN) architectures for classification after generative adversarial networks (GANs) for data augmentation and pseudo-labeling is predicted. The aim of the work is to use labeled and unlabeled data to develop the robustness and accuracy of osteoarthritis detection. It is shown via thorough experimentation that the strategy is beneficial in enhancing classification performance, with ResNet obtaining the greatest accuracy and F1 score among the networks we studied. According to the research, pseudo-labeling and GAN-based data augmentation strategies can greatly improve osteoarthritis diagnosis accuracy and clinical significance. This work advances the field of medical image analysis and has potential benefits for bettering osteoarthritis patient treatment. The proposed approach performs well with an accuracy of 96.23%, precision of 0.963, recall of 0.959, and F1 score of 0.9246.

关键词： Training Accuracy Neural networks Medical treatment Generative adversarial networks Data augmentation Robustness

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Robotic Sensor Network: Achieving Mutual Communication Control Assistance With Fast Cross-Layer Optimization

arXiv

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arXiv 2024年

作者： Ji, Zhiyou Wan, Yujie Li, Guoliang Wang, Shuai Ye, Kejiang Wing, Derrick Ng, Kwan Xu, Chengzhong The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The University of Chinese Academy of Sciences China The Southern University of Science and Technology Shenzhen China The State Key Laboratory of IOTSC Department of Computer and Information Science University of Macau China The School of Electrical Engineering and Telecommunications The University of New South Wales Australia

Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. However, communication and control functionalities in RSNs are interdependent, for which existing approaches become inefficient, as they plan robot trajectories merely based on unidirectional impact between communication and control. This paper proposes the concept of mutual communication control assistance (MCCA), which leverages a model predictive communication and control (MPC2) design for intertwined optimization of motion-assisted communication and communication-assisted collision avoidance. The MPC2 problem jointly optimizes the cross-layer variables of sensor powers and robot actions, and is solved by alternating optimization, strong duality, and cross-horizon minorization maximization in real time. This approach contrasts with conventional communication control co-design methods that calculate an offline non-executable trajectory. Experiments in a high-fidelity RSN simulator demonstrate that the proposed MCCA outperforms various benchmarks in terms of communication efficiency and navigation time. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Psychological Analysis for Depression Detection from Social Networking Sites

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Computational Intelligence and Neuroscience 2022年第0期2022卷 4395358页

作者： Gupta, Sonam Goel, Lipika Singh, Arjun Prasad, Ajay Ullah, Mohammad Aman Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India School of Computing and Information Technology Manipal University Jaipur Jaipur India University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh

Rapid technological advancements are altering people's communication styles. With the growth of the Internet, social networks (Twitter, Facebook, Telegram, and Instagram) have become popular forums for people to share their thoughts, psychological behavior, and emotions. Psychological analysis analyzes text and extracts facts, features, and important information from the opinions of users. Researchers working on psychological analysis rely on social networks for the detection of depression-related behavior and activity. Social networks provide innumerable data on mindsets of a person's onset of depression, such as low sociology and activities such as undergoing medical treatment, a primary emphasis on oneself, and a high rate of activity during the day and night. In this paper, we used five machine learning classifiers - decision trees, K-nearest neighbor, support vector machines, logistic regression, and LSTM - for depression detection in tweets. The dataset is collected in two forms - balanced and imbalanced - where the oversampling of techniques is studied technically. The results show that the LSTM classification model outperforms the other baseline models in the depression detection healthcare approach for both balanced and imbalanced data. © 2022 Sonam Gupta et al.

关键词： Decision trees

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MAU: A Motion-Aware Unit for Video Prediction and Beyond 35

MAU: A Motion-Aware Unit for Video Prediction and Beyond

引用

35th Conference on Neural Information Processing Systems, NeurIPS 2021

作者： Chang, Zheng Zhang, Xinfeng Wang, Shanshe Ma, Siwei Ye, Yan Xiang, Xinguang Gao, Wen University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Digital Media Peking University China Institute of Digital Media Information Technology R&D Innovation Center Peking University China Alibaba Group China School of Computer Science and Engineering Nanjing University of Science and Technology China University of Chinese Academy of Sciences China

ISBN: (纸本)9781713845393

Accurately predicting inter-frame motion information plays a key role in video prediction tasks. In this paper, we propose a Motion-Aware Unit (MAU) to capture reliable inter-frame motion information by broadening the temporal receptive field of the predictive units. The MAU consists of two modules, the attention module and the fusion module. The attention module aims to learn an attention map based on the correlations between the current spatial state and the historical spatial states. Based on the learned attention map, the historical temporal states are aggregated to an augmented motion information (AMI). In this way, the predictive unit can perceive more temporal dynamics from a wider receptive field. Then, the fusion module is utilized to further aggregate the augmented motion information (AMI) and current appearance information (current spatial state) to the final predicted frame. The computation load of MAU is relatively low, and the proposed unit can be easily applied to other predictive models. Moreover, an information recalling scheme is employed into the encoders and decoders to help preserve the visual details of the predictions. We evaluate the MAU on both video prediction and early action recognition tasks. Experimental results show that the MAU outperforms the state-of-the-art methods on both tasks. © 2021 Neural information processing systems foundation. All rights reserved.

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Deterministic Algorithms for the Hidden Subgroup Problem

SSRN

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SSRN 2022年

作者： Ye, Zekun Li, Lvzhou Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China

We consider deterministic algorithms for the well-known hidden subgroup problem (HSP): for a finite group G and a finite set X, given a function f : G → X and the promise that for any (Formula Presented) for a subgroup H ≤ G, the goal of the decision version is to determine whether H is trivial, and the goal of the search version is to find H. An algorithm for the problem should query f(g) for g ∈ G at least as possible. Nayak [? ] asked whether there exist deterministic algorithms with (Formula Presented) query complexity for HSP. We answer this problem completely by proving the following results, which also extend the main results of Ref. [? ], since here the algorithms do not rely on any prior knowledge of H. (i) When G is a general finite Abelian group, there exists an algorithm with (Formula Presented) queries to decide the triviality of H and an algorithm to find H with (Formula Presented) queries.(ii) In general, there is no deterministic algorithm for the search version of HSP with query complexity of (Formula Presented), since there exists an instance of HSP that needs (Formula Presented) queries to find H. f(x) is said to be (Formula Presented) if for every positive constant C, there exists a positive constant N such that for (Formula Presented), which means g is a strict lower bound for f.} On the other hand, there exist instances of HSP with query complexity far smaller than (Formula Presented), whose query complexity is (Formula Presented) and even O(1). © 2022, The Authors. All rights reserved.

关键词： Computational complexity

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