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50th International Conference on computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Usuki, Risa Usuki, Jun Department of Computer Science and Engineering Waseda University Tokyo Japan Department of Information Network and Communication Kanagawa Institute of Technology Kanagawa Japan

ISBN: (纸本)9781713886952

With the advancement of information technology, research on non-contact methods as well as contact methods using keyboards and controllers as methods of operating devices has been progressing. One such method is handwriting characters in the air (hereinafter called "KUSHO"), which allows users to input information to devices through characters written in the air. KUSHO enables users to transmit information by writing characters towards a camera. It involves three steps: tracking hand movements from camera images, converting tracking information into features, and recognizing input information from features. KUSHO offers advantages such as non-contact operation without special devices and intuitive operation using familiar characters. By the way, in a kitchen, workers confirm orders and cooking procedures, operate cooking equipment and transmit information between processes. Additionally, although display devices are used to transmit information in large-scale restaurants, the use of devices is not suitable for tasks that involve dirty hands due to hygiene concerns. Paper-based communication is also used, however, it can easily get dirty in a kitchen with water and oil. In addition, the use of voice dictation, which has been developed in recent years as a non-contact operation method, is difficult due to its tendency to generate noise, and considering the diversification of customer demands, it is necessary to consider new support methods in terms of information handling. Therefore, this study proposed a method of applying KUSHO in restaurant kitchens, where quick communication is necessary while maintaining hygiene. Following this, the proposed methods were implemented and their potential was discussed through a comparison of recognition rates between different methods, and experiments of command input by signals during KUSHO and KUSHO input for information management assuming kitchens. © 2023 computers and Industrial engineering. All rights reserved.

关键词： Antennas

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Evaluation of Distance Estimation Using Phase Information of OFDM Signal 100

Evaluation of Distance Estimation Using Phase Information of...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Liu, Jingwei Kikuchi, Noriyasu Kubo, Nobuaki Tang, Suhua The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan Kanagawa Institute of Technology Department of Mechanical Engineering Kanagawa Japan Tokyo University of Marine Science and Technology Information and Communication Engineering Laboratory Tokyo Japan

ISBN: (纸本)9798331517786

In urban areas, due to the obstruction/reflection of GNSS signals by roadside buildings, position accuracy is greatly degraded, or even outage occurs when no enough satellites are available. To address this problem, vehicles and roadside units have been used as positioning anchors to estimate pedestrian position. Although previous methods exploited channel state information, which is superior to received signal strength for distance estimation, they rely on signal attenuation characteristics, and the accuracy is limited by temporal resolution and multipath propagation. This work focuses on using the phase information of V2X OFDM signals to estimate distance differences more robustly. We investigated the impact of the sampling rate, a crucial parameter, and conducted simulation evaluations using 3D maps and ray-tracing. Additionally, we built a testbed and performed evaluations with real-world OFDM signal data, which demonstrates that phase information can significantly improve the accuracy of distance measurements. © 2024 IEEE.

关键词： Frequency division multiplexing

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Automated object recognition in high-resolution optical remote sensing imagery

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National Science Review 2023年第6期10卷 38-41页

作者： Yazhou Yao Tao Chen Hanbo Bi Xinhao Cai Gensheng Pei Guoye Yang Zhiyuan Yan Xian Sun Xing Xu Hai Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Aerospace Information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Key Laboratory of Network Information System Technology (NIST) Aerospace Information Research Institute Chinese Academy of Sciences Department of Computer Science and Technology Tsinghua University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION With the rapid development of remote sensing technology,high-quality remote sensing images have become widely *** automated object detection and recognition of these images,which aims to automatically locate objects of interest in remote sensing images and distinguish their specific categories,is an important fundamental task in the *** provides an effective means for geospatial object monitoring in many social applications,such as intelligent transportation,urban planning,environmental monitoring and homeland security.

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Achieving Privacy-Preserving Multi-View Consistency with Advanced 3D-Aware Face De-identification 5

Achieving Privacy-Preserving Multi-View Consistency with Adv...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Cao, Jingyi Liu, Bo Wen, Yunqian Xie, Rong Song, Li Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science University of Technology Sydney Sydney Australia

ISBN: (纸本)9798400702051

The widespread application of face recognition technology has exacerbated privacy threats. Face de-identification is an effective means of protecting visual privacy by concealing identity information. While deep learning-based methods have greatly improved de-identification results, most existing algorithms rely on 2D generative models that struggle to produce identity-consistent results for multiple views. In this paper, we focus on identity disentanglement within the latest 3D-aware face generation model, and propose an advanced face de-identification framework that can be applied to various scenarios. Our proposed framework disentangles identity from other facial features, modifies only the former and generates the de-identified face using a 3D generator. This approach results in high-quality, identity-consistent de-identification that preserves other facial features. We demonstrate our approach on StyleNeRF, one of the most widely-used style-based neural radiation field models. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving face de-identification both for a single image and group images with the same identity. Our work is a significant step forward in the field of face de-identification, opening up new possibilities for practical applications. © 2023 Copyright held by the owner/author(s).

关键词： Deep learning

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A Boosting-Type Convergence Result for *** with Factorized Multi-Class Classifiers 38

A Boosting-Type Convergence Result for *** with Factorized M...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Xin Zhou, Zhengyu Xu, Jingyuan Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ADABOOST is a well-known algorithm in boosting. Schapire and Singer propose, an extension of ADABOOST, named ***, for multi-class classification problems. Kégl shows empirically that *** works better when the classical one-against-all base classifiers are replaced by factorized base classifiers containing a binary classifier and a vote (or code) vector. However, the factorization makes it much more difficult to provide a convergence result for the factorized version of ***. Then, Kégl raises an open problem in COLT 2014 to look for a convergence result for the factorized ***. In this work, we resolve this open problem by presenting a convergence result for *** with factorized multi-class classifiers. © 2024 Neural information processing systems foundation. All rights reserved.

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The Reliability of OKRidge Method in Solving Sparse Ridge Regression Problems 38

The Reliability of OKRidge Method in Solving Sparse Ridge Re...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Xiyuan Wang, Youjun Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

Sparse ridge regression problems play a significant role across various domains. To solve sparse ridge regression, [1] recently proposes an advanced algorithm, Scalable Optimal K-Sparse Ridge Regression (OKRidge), which is both faster and more accurate than existing approaches. However, the absence of theoretical analysis on the error of OKRidge impedes its large-scale applications. In this paper, we reframe the estimation error of OKRidge as a Primary Optimization (PO) problem and employ the Convex Gaussian min-max theorem (CGMT) to simplify the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for OKRidge based on the AO problem. This error analysis improves the theoretical reliability of OKRidge. We also conduct experiments to verify our theorems and the results are in excellent agreement with our theoretical findings. © 2024 Neural information processing systems foundation. All rights reserved.

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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Mobility-Aware Multi-Task Migration and Offloading Scheme for Internet of Vehicles

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Chinese Journal of Electronics 2023年第6期32卷 1192-1202页

作者： LI Xujie TANG Jing XU Yuan SUN Ying College of Computer and Information Hohai University State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Key Lab for Wireless Sensor Network and Communication Shanghai Institute of Microsystem School of Information Engineering Jiangsu Open University

In Internet of vehicles, vehicular edge computing(VEC) as a new paradigm can effectively accomplish various tasks. Due to limited computing resources of the roadside units(RSUs), computing ability of vehicles can be a powerful supplement to computing resources. Then the task to be processed in data center can be offloaded to the vehicles by the RSUs. Due to mobility of the vehicles, the tasks will be migrated among the RSUs. How to effectively offload multiple tasks to the vehicles for processing is a challenging problem. A mobility-aware multi-task migration and offloading scheme for Internet of vehicles is presented and analyzed. Considering the coupling between migration and offloading, the joint migration and offloading optimization problem is formulated. The problem is a NP-hard problem and it is very hard to be solved by the conventional methods. To tackle the difficult problem, the idea of alternating optimization and divide and conquer is introduced. The problem can be decoupled into two sub-problems: computing resource allocation problem and vehicle node selection problem. If the vehicle node selection is given, the problem can be solved based on Lagrange function. And if the allocation of computing resource is given, the problem turns into a 0-1 integer programming problem, and the linear relaxation of branch bound algorithm is introduced to solve it. Then the optimization value is obtained through continuous iteration. Simulation results show that the proposed algorithm can effectively improve system performance.

关键词： Costs Computational modeling System performance Simulation Programming Multitasking Resource management

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Predicting the Unseen: A Novel Dataset for Hidden Intention Localization in Pre-abnormal Analysis 24

Predicting the Unseen: A Novel Dataset for Hidden Intention ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Qi, Zehao Zhang, Ruixu Hu, Xinyi Liu, Wenxuan Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798400706868

Our paper introduces a novel video dataset specifically for Temporal Intention Localization (TIL), aimed at identifying hidden abnormal intention in densely populated and complex environments. Traditional Temporal Action Localization (TAL) frameworks, focusing on overt actions within constrained temporal intervals, often miss subtle pre-abnormal actions that unfold over extended periods. Our dataset comprises 228 videos with 5790 clips, each annotated to capture fine-grained actions within ambiguous temporal boundaries using the Joint-Linear-Assignment methodology. This approach enables detailed analysis of the evolution of abnormal intention over time. To detect subtle, hidden intention, we developed the Intention-Action Fusion module, an creative approach integrating dynamic feature fusion across 11 behavioral subcategories, significantly enhancing the model's ability to discern nuanced intention. This enhancement has led to performance improvements of up to 139% in specific scenarios, dramatically boosting the model's sensitivity and interpretability, crucial for advancing proactive surveillance systems. By pushing the boundaries of technology, our dataset and methodologies foster proactive surveillance systems capable of preemptively identifying potential threats from nuanced behavioral patterns, encouraging further exploration into the complexities of intention beyond observable actions. The dataset is available at https://***/Zzz99999/Hidden-Abnormal-Intention. © 2024 ACM.

关键词： Security systems

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Perceptual Quality Assessment of TTS-Synthesized Speech 9th

Perceptual Quality Assessment of TTS-Synthesized Speech

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9th International Forum on Digital Multimedia communication, IFTC 2022

作者： Chen, Zidong Min, Xiongkuo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819908554

The evaluation of a Text-to-Speech (TTS) system is typically labor-intensive and highly biased because there is no golden standard of the generated speech or objective evaluation metrics. To improve the performance of TTS systems, it is highly desirable to explore the perceptual quality assessment of TTS-synthesized speech and propose a relatively valid evaluation method. In this paper, we introduce a deep-learning-based approach to predict human labeled perceptual quality scores of the generated speech. Our approach is based on ResNet and self-attention, where the former addresses the issue of deep feature extraction and integration and the latter takes advantage of the natural relationship between the input sequences. The experiment results indicate that the proposed method performs better on test tasks in terms of various accuracy evaluation criteria than the state-of-the-art methods. Additionally, the experiment demonstrates a strong correlation between the predicted scores and the true (human) MOS scores. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality control

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