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arXiv 2025年

作者： Yu, Xiaolong Zhao, Junqiao Song, Shuangfu Zhu, Zhongyang Yuan, Zihan Ye, Chen Feng, Tiantian School of Computer Science and Technology Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Using Quadrics as the object representation has the benefits of both generality and closed-form projection derivation between image and world spaces. Although numerous constraints have been proposed for dual quadric reconstruction, we found that many of them are imprecise and provide minimal improvements to localization. After scrutinizing the existing constraints, we introduce a concise yet more precise convex hull-based algebraic constraint for object landmarks, which is applied to object reconstruction, frontend pose estimation, and backend bundle adjustment. This constraint is designed to fully leverage precise semantic segmentation, effectively mitigating mismatches between complex-shaped object contours and dual quadrics. Experiments on public datasets demonstrate that our approach is applicable to both monocular and RGB-D SLAM and achieves improved object mapping and localization than existing quadric SLAM methods. The implementation of our method is available at https://***/tievtongji/convexhull-based-algebraic-constraint. © 2025, CC BY.

关键词： Semantic Segmentation

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ViDSOD-100: A New Dataset and A Baseline Model for RGB-D Video Salient Object Detection

arXiv

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arXiv 2024年

作者： Lin, Junhao Zhu, Lei Shen, Jiaxing Fu, Huazhu Zhang, Qing Wang, Liansheng The Hong Kong University of Science and Technology Guangzhou China The Hong Kong University of Science and Technology Hong Kong School of Informatics Xiamen University China Department of Computing and Decision Sciences Lingnan University Hong Kong Singapore School of Data and Computer Science Sun Yat-Sen University China

With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, the existing salient object detection (SOD) works only focus on either static RGB-D images or RGB videos, ignoring the collaborating of RGB-D and video information. In this paper, we first collect a new annotated RGB-D video SOD (ViDSOD-100) dataset, which contains 100 videos within a total of 9,362 frames, acquired from diverse natural scenes. All the frames in each video are manually annotated to a high-quality saliency annotation. Moreover, we propose a new baseline model, named attentive triple-fusion network (ATF-Net), for RGB-D video salient object detection. Our method aggregates the appearance information from an input RGB image, spatio-temporal information from an estimated motion map, and the geometry information from the depth map by devising three modality-specific branches and a multi-modality integration branch. The modality-specific branches extract the representation of different inputs, while the multi-modality integration branch combines the multi-level modality-specific features by introducing the encoder feature aggregation (MEA) modules and decoder feature aggregation (MDA) modules. The experimental findings conducted on both our newly introduced ViDSOD-100 dataset and the well-established DAVSOD dataset highlight the superior performance of the proposed *** performance enhancement is demonstrated both quantitatively and qualitatively, surpassing the capabilities of current state-of-the-art techniques across various domains, including RGB-D saliency detection, video saliency detection, and video object segmentation. Our data and our code are available at ViDSOD-100. Copyright © 2024, The Authors. All rights reserved.

关键词： Video analysis

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NLOS Identification and Ranging Trustworthiness for Indoor Positioning with LLM-based UWB-IMU Fusion

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Yang, Hongchao Wang, Yunjia Seow, Chee Kiat Li, Zengke Sun, Meng De Cock, Cedric Bi, Jingxue Joseph, Wout Plets, David China University of Ming and Technology Key Laboratory of Land Environment and Disaster Monitoring Xuzhou221116 China University of Glasgow School of Computing Science GlasgowG12 8Rz United Kingdom Ghent University Dept. of Information Technology Imec-Waves Ghent Belgium Shandong Jianzhu University School of Survering and Geo-Informatics Jinan250101 China

In the rapidly evolving Internet of Things (IoT) landscape, accurate indoor positioning is increasingly vital. The proposed algorithm synergizes Ultra-Wideband (UWB) sensor with Inertial measurement unit (IMU) and Artificial Intelligence to obtain precise positioning in Non-Line-of-Sight (NLOS) scenarios. In the proposed UWB module, Large language model (LLM) such as Bidirectional Encoder Representations from Transformers (BERT) algorithm is designed to utilize the Channel Impulse Response (CIR) for effective NLOS identification and UWB ranging trustworthiness evaluation. Concurrently, the IMU module is also designed with BERT to recognize various pedestrian activity states, thereby optimizing positioning. BERT's self attention mechanism and deep learning bidirectional training efficiently extract essential features from sequential data, capturing both local and global information. The integration of both UWB and IMU through a proposed tightly coupled algorithm significantly boosts positioning performance. Experiment campaigns demonstrate an average NLOS identification accuracy, Line-of-Sight (LOS) and F2 of 98.8%, 99.4% and 0.9926, respectively. These performance surpass the state-of-the-art Least Squares Support Vector Machine (LS-SVM), Convolutional Neural Network (CNN), CNN with Long Short-Term Memory (CNN-LSTM) up to 17.66% in NLOS identification. In terms of pedestrian activity recognition using BERT, the BERT algorithm achieves a precision(recall) of 99.3%(99.4%), notably outperforming CNN and CNN-LSTM by 17.9%(16.2%) and 11.9%(10.9%), respectively. Finally, the UWB-IMU algorithm significantly enhances positioning accuracy by 80.5%, outperforming Kalman, LSTM-EKF, and Particle filter methods by 68.1%, 48.3%, and 45.0%, respectively. The proposed approach presents a robust solution for indoor positioning for IoT applications, particularly in challenging NLOS environments. © 1963-2012 IEEE.

关键词： Impulse response

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Stress Severity Detection in College Students Using Emotional Pulse Signals and Deep Learning

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IEEE Transactions on Affective computing 2025年

作者： Li, Mi Li, Junzhe Chen, Yanbo Hu, Bin School of Information Science and Technology Beijing University of Technology Beijing International Collaboration Base on Brain Informatics and Wisdom Services Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing University of Technology School of Information Science and Technology Beijing China Beijing Institute of Technology Institute of Engineering Medicine Beijing China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China

College students face increasing stress from difficulties with studies, employment, and social interactions, which, if left unaddressed, may lead to depression and physical illnesses. Currently, the detection of stress severity relies on self-assessment scales, while machine learning or deep learning-based approaches primarily focus on classification. This study proposes an approach using pulse signals containing emotional cues and deep learning to automatically detect the severity of stress in college students. Firstly, pulse signals of 177 college students were collected using photoplethysmography (PPG) during they watched five virtual reality (VR) emotional scenes, including calm, sadness, happiness, fear, and tension. Pulse rate variability (PRV) and discrete PPG (dPPG) were extracted from these signals as input for detecting stress severity. Then, the proposed stress detection framework, 1DCNN-BiLSTM + Cross-Attention + XGBoost, was employed to detect stress severity, incorporating an emotional Cross-Attention mechanism. The impact of induced emotions on stress severity detection performance was examined. The results indicated that stress severity detection in emotional scenes outperformed in calm. Furthermore, the detection performance that integrates multiple emotions surpassed single emotions. The fusion of PRV and dPPG signals yielded the best detection performance. This study provides an end-to-end automated approach for detecting stress severity in college students. © 2010-2012 IEEE.

关键词： Photoplethysmography

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Dynamic Programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Half-Sweep Newton-SOR Iteration with Quadrature Scheme to Solve Nonlinear Fredholm Integral Equations 8th

Half-Sweep Newton-SOR Iteration with Quadrature Scheme to So...

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8th International Conference on Computational Science and technology, ICCST 2021

作者： Ali, L.H. Sulaiman, J. Saudi, A. Xu, M.M. Faculty of Science and Natural Resources Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia School of Mathematics and Information Technology Xingtai University Xingtai Hebei054000 China

ISBN: (纸本)9789811685149

This paper aims to propose an efficient and fast iterative method to solve the nonlinear Fredholm integral equations of the second kind (NFIE-2). For this starting point, we discretize the general NFIE-2 using a quadrature scheme into a corresponding nonlinear quadrature approximation equation to generate a system of nonlinear equations. Then, we convert the nonlinear system into a linear form using Newton’s method so the linear system can be solved using iterative methods. To speed up the convergence rate in solving the linear system, we proposed the hybridization of the Newton-Successive Overrelaxation (NSOR) method with the half-sweep (HS) complexity reduction technique to boost its convergence rate, called half-sweep NSOR (HSNSOR) method. Also, we used the standard form of Newton-Gauss–Seidel and NSOR called FSNGS and FSNSOR, respectively as control methods to test the ability of this iterative method. Based on the output of numerical experiments, we found that the application of half-sweep to the existing NSOR provides us with a method that converge faster than the FSNGS and FSNSOR. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Iterative methods

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Studying Self-Care with Generative AI Tools: Lessons for Design

arXiv

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arXiv 2024年

作者： Capel, Tara Ploderer, Bernd Bircanin, Filip Hanmer, Simon Yates, Jamie Wang, Jiaxuan Khor, Kai Ling Leong, Tuck Wah Wadley, Greg Newcomb, Michelle Institute for Design Informatics University of Edinburgh Edinburgh United Kingdom Department of Informatics King’s College London London United Kingdom Digital Wellbeing Lab Queensland University of Technology Brisbane Australia School of Computing Technologies RMIT University Melbourne Australia HCI Group University of Melbourne Melbourne Australia School of Public Health and Social Work Queensland University of Technology Brisbane Australia

The rise of generative AI presents new opportunities for the understanding and practice of self-care through its capability to generate varied content, including self-care suggestions via text and images, and engage in dialogue with users over time. However, there are also concerns about accuracy and trustworthiness of self-care advice provided via AI. This paper reports our findings from workshops, diaries, and interviews with five researchers and 24 participants to explore their experiences and use of generative AI for self-care. We analyze our findings to present a framework for the use of generative AI to support five types of self-care, – advice seeking, mentorship, resource creation, social simulation, and therapeutic self-expression – mapped across two dimensions – expertise and modality. We discuss how these practices shift the role of technologies for self-care from merely offering information to offering personalized advice and supporting creativity for reflection, and we offer suggestions for using the framework to investigate new self-care designs. © 2024, CC BY.

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Enhance Object Tracking on Augmented Reality Markerless using FAST Corner Detection 5

Enhance Object Tracking on Augmented Reality Markerless usin...

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5th IEEE International Conference on Information technology, Information Systems and Electrical Engineering, ICITISEE 2021

作者： Nurhadi Winanto, Eko Arip Saparudin Dinamika Bangsa University Faculty Of Engineering Sriwijaya University Dept. Of Informatics Engineering Indonesia University Of Technology Malaysia School Of Computing Malaysia Telkom University Faculty Of Computer Science Dept. Of Informatics Engineering Indonesia

ISBN: (纸本)9781665401968

Markerless-based is the method in Augmented Reality (AR) to augmentation 3D objects in the frame. One of the challenges in markerless AR is to extract features as input data training on markerless AR. One of algorithm for feature extraction of object target is FAST corner detection in image processing. This paper uses FAST corner detection for feature extraction on Augmented Reality Markerless based with the aim to improve performance of augmentation objects in AR. The User Defined Target and Extended Tracking are to improve convenience and reliability of the AR. The aim is can trace 3D objects even the 3D object invisible. The results show that maximum environment for the best augmentation is intensity in of 80-190 cd. © 2021 IEEE.

关键词： Augmented reality

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An Efficient Quantum Private Comparison Protocol Based on Cluster State and Bell State 8th

An Efficient Quantum Private Comparison Protocol Based on Cl...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Li, Chaoyang Qing, Hua Xu, Gang Chen, Xiubo Xin, Xiangjun Dong, Mianxiong Ota, Kaoru College of Software Engineering Zhengzhou University of Light Industry Zhengzhou450002 China School of Computing Science and Technology North China University of Technology Beijing100144 China Information Security Centre State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China Department of Sciences and Informatics Muroran Institution of Technology Muroran050-8585 Japan

ISBN: (纸本)9783031067631

Quantum private comparison (QPC) is the quantum method that can compare the equality of the distrustful participants’ private information without leaking them. The 4-particle cluster state has a strong violation of local reality and shows to be robust against decoherence than any other 4-particle state. In this paper, a new efficient QPC protocol based on the entanglement swapping between the 4-particle cluster state and Bell state has been proposed. With the pre-shared secret key sequence between the two participants, the semi-honest third party (TP) can only execute the protocol’s processes without obtaining information about the participants’ secrets and the final comparison results. Then, we show that the proposed QPC protocol is secure against outside or participant attacks and more efficient than similar literature. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Quantum cryptography

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S Priyadharshini G. Naveen Sundar D. Narmadha Senthil Kumar Thangavel Adeyemi Abel Ajibesin Selvanayaki Kolandapalayam Shanmugam Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Information Technology Faculty of Informatics and Design Cape Peninsula University of Technology South Africa Department of Mathematics and Computer Science Ashland University USA

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future.

关键词： Training Emotion recognition Accuracy Computational modeling Speech recognition Speech enhancement Predictive models Feature extraction Data models Convolutional neural networks

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