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Constructing Semantical Structure by Segmentation Integrated Video Embedding for Temporal Action Detection

IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5980-5992页

作者： Zhao, Zixuan Liu, Shuming Zhao, Chengze Zhao, Xu Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China King Abdullah University of Science and Technology Computer Science Program Thuwal23599 Saudi Arabia

Video embedding is the pivot in Temporal Action Detection (TAD). Once the video embedding can robustly capture the essence of actions and perceive activities in complex scenes, the TAD model can more accurately localize action boundaries. Currently, video embedding is typically based on rule-based pixel convolution or cube-based transformer, wherein structured semantic information is intertwined, leading to the submergence of crucial spatial semantic information, such as the intrinsic motion of key semantic objects and interactions among semantic objects. To address these limitations, it is imperative to explore alternative approaches. With the remarkable performance of general semantic segmentation models in visual representation, we introduce the general segmentation model SEEM into the video embedding paradigm, constructing a semantically structured representation from perceptual semantics to cognitive semantics. To more effectively utilize SEEM for structured video representation, we designed the Semantic Adapter (Sem-Adapter) as a bridge to connect the two models. Firstly, we design a Self-Motion Module (SMM) to pay attention to the self-motion of key semantic regions. Secondly, we propose a Mutual Relation Module (MRM) to construct the interactions between semantic regions. Extensive experiments on ActivityNet-1.3, THUMOS-14 and EPIC-Kitchens-100 reveal that our method significantly outperforms state-of-the-art methods under the same input modality, and our method improves the average mAP from 60.6% to 64.2% on THUMOS-14 with the same backbone. The code is available on https://***/shouxiaozixuan/semtad. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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ACTUAL SITUATION OF REPAIR AND ENHANCEMENT OF FACADE DESIGN ON THE MACHIYA CERTIFICATION SYSTEM INCLUDING BUILDINGS WITH FEW HISTORIC VALUE － A case of Komatsu City －

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AIJ Journal of technology and Design 2024年第74期30卷 376-381页

作者： Nishiki, Koshi Matsui, Daisuke Abe, Yukari Graduate School of Science and Technology Niigata Univ. Japan Program of Arch. Faculty of Eng. Niigata Univ. Japan

This study aims to clarify the actual situation of repair and enhancement of buildings and planning tasks from the perspective of authenticity of streetscape on Komatsu Machiya certification system. The results are as follows;1) 123 buildings certified as Komatsu Machiya. 2) The allocation of repair, enhancement and newly built are 25 buildings, 4 buildings and 4 buildings. 3) There’s room for improvement which are "the standardization of facade design" and "repair and enhancement with conjecture". 4) We propose to reconsider the operation of design rules in subsidy system and establish the consulation bank of facade design. © 2024 Architectural Institute of Japan. All rights reserved.

关键词： Repair

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Analysing Relaxation Effects Through Natural Experiences Using Natural Sounds and VR Devices 14

Analysing Relaxation Effects Through Natural Experiences Usi...

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14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Suzuki, Yukizi Sekine, Yusuke Tago, Kiichi Chiba Institute of Technology Graduate School of Information and Computer Science Narashino Japan

ISBN: (纸本)9798350356670

In recent years, studies have been conducted on virtual reality (VR) relaxation using VR devices to reduce stress. Our previous study suggested that VR relaxation using a virtual world, a 360-degree image, and a 360-degree video could produce comparable relaxation effects. However, in our study, the 360-degree image had no sound. The addition of natural sounds to the image may potentially enhance the relaxation effect. Therefore, this study aims to investigate the impact of incorporating natural sounds into the 360-degree image for relaxation. We employ five evaluation metrics to measure the relaxation effect: electroencephalography (EEG), heart rate, salivary amylase, State-Trait Anxiety Inventory (STAI), and a subjective stress questionnaire. These metrics are used to calculate the relaxation scores. The experimental results revealed a significant difference in relaxation scores derived from the EEG data. It suggests that adding natural sounds to 360-degree images can enhance the relaxation effect of VR relaxation. © 2024 IEEE.

关键词： Electroencephalography

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sEMG-based Sarcopenia risk classification using empirical mode decomposition and machine learning algorithms

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Mathematical Biosciences and Engineering 2024年第2期21卷 2901-2921页

作者： Kumar, Konki Sravan Lee, Daehyun Jamsrandoj, Ankhzaya Soylu, Necla Nisa Jung, Dawoon Kim, Jinwook Mun, Kyung Ryoul Center for Artificial Intelligence Korea Institute of Science and Technology Seoul Korea Republic of KHU-KIST Department of Converging Science and Technology Graduate School Kyung Hee University Seoul Korea Republic of Department of Human Computer Interface and Robotics Engineering University of Science and Technology Daejeon Korea Republic of Department of Computer Science Ozyegin University Istanbul Turkey

Early detection of the risk of sarcopenia at younger ages is crucial for implementing preventive strategies, fostering healthy muscle development, and minimizing the negative impact of sarcopenia on health and aging. In this study, we propose a novel sarcopenia risk detection technique that combines surface electromyography (sEMG) signals and empirical mode decomposition (EMD) with machine learning algorithms. First, we recorded and preprocessed sEMG data from both healthy and at-risk individuals during various physical activities, including normal walking, fast walking, performing a standard squat, and performing a wide squat. Next, electromyography (EMG) features were extracted from a normalized EMG and its intrinsic mode functions (IMFs) were obtained through EMD. Subsequently, a minimum redundancy maximum relevance (mRMR) feature selection method was employed to identify the most influential subset of features. Finally, the performances of state-of-the-art machine learning (ML) classifiers were evaluated using a leave-one-subject-out cross-validation technique, and the effectiveness of the classifiers for sarcopenia risk classification was assessed through various performance metrics. The proposed method shows a high accuracy, with accuracy rates of 0.88 for normal walking, 0.89 for fast walking, 0.81 for a standard squat, and 0.80 for a wide squat, providing reliable identification of sarcopenia risk during physical activities. Beyond early sarcopenia risk detection, this sEMG-EMD-ML system offers practical values for assessing muscle function, muscle health monitoring, and managing muscle quality for an improved daily life and well-being. © 2024 American Institute of Mathematical sciences. All rights reserved.

关键词： Empirical mode decomposition

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Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12528-12537页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Tariq, Tayyaba Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Sichuan University Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Yunlin Douliu64002 Taiwan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

Vehicular network technology has made substantial advancements in recent years in the field of Intelligent Transportation Systems. Vehicular Cloud Computing (VCC) has emerged as a novel paradigm with a substantial increase in data exchange within Vehicular ad-hoc networks (VANETs). VCC integrates cloud computing, vehicular networking, and Internet of Things (IoT) technologies. It enables Infrastructure-to-Vehicle (I2V), Vehicle-to-Vehicle (V2V), and Vehicle-to-Device (V2D) communication. VCC optimizes vehicle, cloud infrastructure, and IoT resources while addressing significant communication security and vehicle-user privacy challenges. To address these issues, we developed an RFID-based authentication protocol for VCC based on a Henon map using a hash function. In addition, we also incorporated a Physical Unclonable Function (PUF) to resist physical tampering attacks. We validate the protocol's security formally and informally. The formal security analysis is conducted through a widely used RoR model. We use the Scyther simulation tool to verify the proposed protocol's security against various attacks. Moreover, we compare the performance of our protocol with similar existing protocols across important performance parameters such as communication and computation overheads and security attributes. The proposed protocol yields substantial improvements, demonstrating a 40.74% reduction in computation overhead and a 13.03% decrease in communication overhead as compared to related protocols, delivering both enhanced performance and resource efficiency. The analysis demonstrates its capacity to support secure communication in the VCC environment and satisfy desirable security attributes. © 2024 IEEE.

关键词： Authentication

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Improved encoding structure and decoding algorithms for spinal codes

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Journal of Systems Engineering and Electronics 2022年第4期33卷 856-866页

作者： HUANG Wensha WANG Lina School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingBeijing 100083China

To improve the error correction performance, an innovative encoding structure with tail-biting for spinal codes is designed. Furthermore, an adaptive forward stack decoding(A-FSD) algorithm with lower complexity for spinal codes is proposed. In the A-FSD algorithm, a flexible threshold parameter is set by a variable channel state to narrow the scale of nodes accessed. On this basis, a new decoding method of AFSD with early termination(AFSD-ET) is further proposed. The AFSD-ET decoder not only has the ability of dynamically modifying the number of stored nodes, but also adopts the early termination criterion to curtail complexity. The complexity and related parameters are verified through a series of simulations. The simulation results show that the proposed spinal codes with tail-biting and the AFSD-ET decoding algorithms can reduce the complexity and improve the decoding rate without sacrificing correct decoding performance.

关键词： spinal code tail-biting encoding lower decoding complexity early termination strategy

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Synonym Replacement Augmentation for Handling Data Imbalance in Personality Classification 8

Synonym Replacement Augmentation for Handling Data Imbalance...

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8th International Conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Pradana, Rilo Chandra Suhartono, Derwin Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350368970

The Myers-Briggs Type Indicator (MBTI) classification is a widely utilized instrument for personality assessment. However, it frequently encounters challenges due to imbalanced data distributions across personality dimensions. It is paramount to address this issue to enhance the accuracy and reliability of personality predictions. Most current research in this field is focused on data balancing techniques, such as oversampling and undersampling, which have been demonstrated to enhance model performance. Nevertheless, there is a dearth of research exploring text augmentation methods, particularly synonym replacement, for this purpose. This study examines the efficacy of synonym replacement as a data augmentation technique for MBTI classification. Experiments are conducted with varying levels of synonym replacement (10%, 30%, 50%, 70%, and 90%) to assess its impact on model accuracy and F1 scores across the four MBTI dimensions. Our findings indicate that low levels of synonym replacement, particularly at 10% to 50%, can enhance the performance of the model in predicting MBTI dimensions. On the other hand, a higher number of replaced words in the synonym replacement augmentation can harm the model's performance. These observations suggest that synonym replacement can effectively address data imbalance in MBTI classification, although its application must be tailored to specific personality dimensions. © 2024 IEEE.

关键词： Data accuracy

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Dataset Genreratoin for Semantic Segmentation from 3D Scanned Data Considering Domain Gap 9

Dataset Genreratoin for Semantic Segmentation from 3D Scanne...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Wada, Marin Adachi, Miho Ueda, Yuriko Miyamoto, Ryusuke Graduate School of Science and Technology Dept. of Computer Science Japan School of Science and Technology Meiji University Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350311402

An autonomous moving scheme with semantic information extracted from images captured by the monocular camera was proposed, providing accurate results of semantic segmentation. A training dataset must accommodate the moving environment to train a classifier for autonomous moving. However, preparing a large-scale dataset composed of images having pixel-wise manually-annotated class labels is impractical. We generated datasets automatically from 3D point clouds, for reducing manpower. The dataset had significant problems: domain gap and shadows. Therefore, style transfer is incorporated in the proposed scheme to bridge the gap to resolve this problem. Moreover, pseudo shadows were added to improve the classification accuracy of testing images with shadows. Experimental results using 3D point clouds and testing images taken around Tsukuba City showed that classification accuracy was improved by the proposed scheme. The classification accuracy of the sidewalk, the most significant class for autonomous moving at the Tsukuba Challenge, was 95.7%. © 2023 IEEE.

关键词： Semantic Segmentation

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Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

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computer Systems science & Engineering 2023年第9期46卷 3471-3489页

作者： Jae-Seok Jang Hyuk-Yoon Kwon Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Korea Department of Industrial Engineering/Graduate School of Data Science Seoul National University of Science and TechnologySeoul01811Korea

Question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given *** studies for the approach performed on the entire dataset or datasets within a category that the question writer manually *** contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the *** to the text classification model,we can effectively reduce the search space for finding the answers to a given ***,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference ***,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding *** real-world QA data sets,we evaluate the performance of the proposed QA matching *** a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%***,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.

关键词： Question-answering text classification model data augmentation text embedding

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Comparative usability analysis across different driving control scenarios in a driving simulation game 9th

Comparative usability analysis across different driving cont...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： David Davis, Juan Lewis, Leon Oliver, Christian Fajar, Muhamad Pratama, Galih Dea Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

This study aims to address the gap in the evaluation of driving simulation games by further assessing and developing ProjectStir (a Driving Simulation Game) through its adaptation to two different driving control devices: a steering wheel and an arcade stick. Despite the growing popularity of simulation games, limited research has been conducted on how different control devices impact user experience and usability. Usability was evaluated through variables including Easiness of Game Control, Learnability of Gameplay, Remembrance, Challenge, Efficiency, Tension, Familiarity, and Satisfaction. Involving 30 respondents from the computer science program at BINUS University, the results revealed no significant differences in usability between the two scenarios (p © 2024 The Authors.

关键词： Automobile driver simulators

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