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arXiv 2025年

作者： Li, Zhenglai Wang, Jun Tang, Chang Zhu, Xinzhong Zhang, Wei Liu, Xinwang Faculty of data science City University of Macau 999078 China School of computer National University of Defense Technology Changsha410073 China School of computer China University of Geosciences Wuhan430074 China College of Mathematics Physics and Information Engineering Zhejiang Normal University Jinhua China Research Institute of Ningbo Cixing Co. Ltd Ningbo China Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250000 China

Multi-view clustering (MvC) aims to integrate information from different views to enhance the capability of the model in capturing the underlying data structures. The widely used joint training paradigm in MvC is potentially not fully leverage the multi-view information, since the imbalanced and under-optimized view-specific features caused by the uniform learning objective for all views. For instance, particular views with more discriminative information could dominate the learning process in the joint training paradigm, leading to other views being under-optimized. To alleviate this issue, we first analyze the imbalanced phenomenon in the joint-training paradigm of multi-view clustering from the perspective of gradient descent for each view-specific feature extractor. Then, we propose a novel balanced multi-view clustering (BMvC) method, which introduces a view-specific contrastive regularization (VCR) to modulate the optimization of each view. Concretely, VCR preserves the sample similarities captured from the joint features and view-specific ones into the clustering distributions corresponding to view-specific features to enhance the learning process of view-specific feature extractors. Additionally, a theoretical analysis is provided to illustrate that VCR adaptively modulates the magnitudes of gradients for updating the parameters of view-specific feature extractors to achieve a balanced multi-view learning procedure. In such a manner, BMvC achieves a better trade-off between the exploitation of view-specific patterns and the exploration of view-invariance patterns to fully learn the multi-view information for the clustering task. Finally, a set of experiments are conducted to verify the superiority of the proposed method compared with state-of-the-art approaches on eight benchmark MvC datasets. The demo code of this work is publicly available at https://***/guanyuezhen/BMvC. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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An Integrated Strategy Based on Radiomics and Quantum Machine Learning: Diagnosis and Clinical Interpretation of Pulmonary Ground-Glass Nodules

SSRN

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SSRN 2025年

作者： Huang, Xianzhi Xu, Fangyi Zhu, Wenchao Zhao, Wending Yao, Lin He, Jiahuan Su, Junhao Hu, Hongjie Institute for Quantum Technology and Engineering Computing School of JiaYang Zhejiang Shuren University Hangzhou China Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Medical imaging international scientific and technological cooperation base of Zhejiang province Hangzhou China Quantum Technology Co. Ltd. Beijing China Shulan International Medical College Zhejiang Shuren University Hangzhou China College of Information Science and Technology Zhejiang Shuren University Hangzhou China

Background and Purpose: Accurate classification of pulmonary pure ground-glass nodules (pGGNs) is essentialfor distinguishing invasive adenocarcinoma (IVA) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA), which significantly influences treatment decisions. This study aims to develop a high-precisionintegrated strategy by combining radiomics-based feature extraction, Quantum Machine Learning (QML) models,and SHapley Additive exPlanations (SHAP) analysis to improve diagnostic accuracy and interpretability in *** and methods: A total of 322 pGGNs from 275 patients were retrospectively analyzed. The CT images wasrandomly divided into training and testing cohorts (80:20), with radiomic features extracted from the training *** QML models—Quantum Support Vector Classifier (QSVC), Pegasos QSVC, and Quantum Neural Network(QNN)—were developed and compared with a classical Support Vector Machine (SVM). SHAP analysis was appliedto interpret the contribution of radiomic features to the models’ ***: All three QML models outperformed the classical SVM, with the QNN model achieving the highest improvements (p © 2025, The Authors. All rights reserved.

关键词： Computerized tomography

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Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps

arXiv

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arXiv 2023年

作者： Wei, Yaonai Zhang, Tuo Zhang, Han Zhong, Tianyang Zhao, Lin Liu, Zhengliang Ma, Chong Zhang, Songyao Shang, Muheng Du, Lei Li, Xiao Liu, Tianming Han, Junwei School of Automation Northwestern Polytechnical University Xi’an China Network and Data Center Northwest university Xi’an China School of Automation Orthwestern Polytechnical University Xi’an China School of Computing University of Georgi GA United States School of Information Science&Technology Northwest University Xi’an China

Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that successfully establishes a link from text queries to brain activation maps using these research results, but it still relies on an ideal query environment. In practical applications, text queries used for meta-analyses may encounter issues such as semantic redundancy and ambiguity, resulting in an inaccurate mapping to brain images. On the other hand, large language models (LLMs) like ChatGPT have shown great potential in tasks such as context understanding and reasoning, displaying a high degree of consistency with human natural language. Hence, LLMs could improve the connection between text modality and neuroscience, resolving existing challenges of meta-analyses. In this study, we propose a method called Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain, to map open-ended semantic queries to brain activation maps in data-scarce and complex query environments. By utilizing the understanding and reasoning capabilities of LLMs, the performance of the mapping model is optimized by transferring text queries to semantic queries. We demonstrate that Chat2Brain can synthesize anatomically plausible neural activation patterns for more complex tasks of text queries. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Multi-Scale Features Fusion with YOLOv3 for Detecting Small and Fine Tumors in MRI images

Multi-Scale Features Fusion with YOLOv3 for Detecting Small ...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： R. Balaji G Prabaharan A. Robert Singh Suganya Athisayamani Velliangiri Sarveshwaran S Daniya Department of ECE B V Raju Institute of Technology Narasapur Telangana India Department of Computational Intelligence SRM Instiute of Science and Technology chennai india School of Computing Sastra Deemed to be University Thanjavur India Department of Information Technology GMR Institute of Technology Rajam india

ISBN: (纸本)9781665482721

Radiologists or other clinical professionals must spend a lot of time and effort segmenting, detecting, and extracting the infected tumor area from magnetic resonance (MR) images, and their accuracy is solely dependent on their experience. Computer-aided technology becomes essential to get beyond these constraints. Single layer network's feature information suffers from issues such as a lack of contextual information, which could make the model insensitive to small tumors and have poor detection accuracy. This research study proposes an MSF-YOLO model based on the YOLOv3 algorithm to address the abovementioned problems. The multi-scale aspects of the image are first combined. The single convolutional scale of the original ResNet cell is extended to four convolutional scales, and the characteristics under each distinct perceptual field are merged to get rich hierarchical information from images. Then the first anchor box is optimized. The size of the initial anchor box is optimized twice using K-means clustering techniques to increase the overlap of the anchor box and boost model accuracy. Finally, the model's convergence is sped up. The model's training procedure is optimized, and the model's convergence is hastened by introducing the weight parameters learned through training on the COCO dataset. Experimental results on two open datasets demonstrate that MSF-YOLO outperforms YOLOv3 with an average accuracy of 98.67% and 97.51 % and outperforms state-of-the-art models in mAP and IoU measures.

关键词： Training Computational modeling Magnetic resonance imaging Refining Medical services Feature extraction Brain modeling

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A Comparative Study of the Performance of Personnel Detection based on Visible and Infrared Images 22

A Comparative Study of the Performance of Personnel Detectio...

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Proceedings of the 2022 4th International Conference on Video, Signal and Image Processing

作者： Futao Liu Zhenling Li Yuezhong Wan Weida Cao Duanwei Ma Xiaolei Li Peng Yan Maintenance Departments Liaocheng Power Supply Company State Grid Corporation of China China School of Control Science and Engineering Shandong University China Trusted Cloud Computing Lab Shandong TCCA Information Technology Research Institute China

ISBN: (纸本)9781450397810

Personnel security has always been a hot topic of research especially in power systems. Effective detection of people in power scenes has become a high priority. Currently, algorithms for personnel detection have been developed considerably, and they are mainly used for visible images. Since infrared images imaged by thermal radiation can detect living blood creatures easily, they can be used in personnel detection at night. To investigate the effect of visible and infrared images on the performance of personnel detection algorithms in different environments, this thesis uses two object detection algorithms, YOLOv5 and Faster RCNN, to train personnel detection models for visible daytime, visible nighttime, infrared daytime and infrared nighttime datasets respectively. Comparative experiments are conducted to quantitatively and qualitatively analyze the performance advantages and disadvantages of the personnel detection algorithms for visible images and infrared images during the day and night. The experimental results for both models consistently show that the visible images are similar to the infrared images during daylight hours with a good target detection model, the visible image is slightly better in performance. While the infrared image is significantly better than the visible image at night, and the [email protected] can be improved by 0.2. For small human target detection, the infrared image has a significant advantage over the visible image at night, with an average detection size reduction of 48.0% and a minimum detection size reduction of 64.6%.

关键词： Visible images Infrared images Different light conditions YOLOv5 and Faster RCNN Personnel detection

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Schrödinger-Heisenberg Variational Quantum Algorithms

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Physical Review Letters 2023年第6期131卷 060406-060406页

作者： Zhong-Xia Shang Ming-Cheng Chen Xiao Yuan Chao-Yang Lu Jian-Wei Pan Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230026 China Shanghai Branch CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China Shanghai Research Center for Quantum Sciences Shanghai 201315 China Center on Frontiers of Computing Studies Peking University Beijing 100871 China School of Computer Science Peking University Beijing 100871 China

Recent breakthroughs have opened the possibility of intermediate-scale quantum computing with tens to hundreds of qubits, and shown the potential for solving classical challenging problems, such as in chemistry and condensed matter physics. However, the high accuracy needed to surpass classical computers poses a critical demand on the circuit depth, which is severely limited by the non-negligible gate infidelity, currently around 0.1%–1%. The limited circuit depth places restrictions on the performance of variational quantum algorithms (VQA) and prevents VQAs from exploring desired nontrivial quantum states. To resolve this problem, we propose a paradigm of Schrödinger-Heisenberg variational quantum algorithms (SHVQA). Using SHVQA, the expectation values of operators on states that require very deep circuits to prepare can now be efficiently measured by rather shallow circuits. The idea is to incorporate a virtual Heisenberg circuit, which acts effectively on the measurement observables, into a real shallow Schrödinger circuit, which is implemented realistically on the quantum hardware. We choose a Clifford virtual circuit, whose effect on the Hamiltonian can be seen as efficient classical processing. Yet, it greatly enlarges the state’s expressivity, realizing much larger unitary t designs. Our method enables accurate quantum simulation and computation that otherwise are only achievable with much deeper circuits or more accurate operations conventionally. This has been verified in our numerical experiments for a better approximation of random states, higher-fidelity solutions to the XXZ model, and the electronic structure Hamiltonians of small molecules. Thus, together with effective quantum error mitigation, our work paves the way for realizing accurate quantum computing algorithms with near-term quantum devices.

关键词： Quantum algorithms Quantum simulation

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FACOS: Enabling Privacy Protection Through Fine-Grained Access Control with On-chain and Off-chain System

arXiv

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arXiv 2024年

作者： Liu, Chao Hou, Cankun Jiang, Tianyu Ning, Jianting Qiao, Hui Wu, Yusen Fujian Provincial Key Laboratory of Data Intensive Computing the Key Laboratory of Intelligent Computing and Information Processing Fujian China The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China The Department of Computer Science University of Miami MiamiFL33146 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China The Faculty of Data Science City University of Macau China

Data-driven landscape across finance, government, and healthcare, the continuous generation of information demands robust solutions for secure storage, efficient dissemination, and fine-grained access control. Blockchain technology emerges as a significant tool, offering decentralized storage while upholding the tenets of data security and accessibility. However, on-chain and off-chain strategies are still confronted with issues such as untrusted off-chain data storage, absence of data ownership, limited access control policy for clients, and a deficiency in data privacy and auditability. To solve these challenges, we propose a permissioned blockchain-based privacy-preserving fine-grained access control on-chain and off-chain system, namely FACOS. We applied three fine-grained access control solutions and comprehensively analyzed them in different aspects, which provides an intuitive perspective for system designers and clients to choose the appropriate access control method for their systems. Compared to similar work that only stores encrypted data in centralized or non-fault-tolerant IPFS systems, we enhanced off-chain data storage security and robustness by utilizing a highly efficient and secure asynchronous Byzantine fault tolerance (BFT) protocol in the off-chain environment. As each of the clients needs to be verified and authorized before accessing the data, we involved the Trusted Execution Environment (TEE)-based solution to verify the credentials of clients. Additionally, our evaluation results demonstrated that our system1 offers better scalability and practicality than other state-of-the-art designs. We deployed our system on Alibaba Cloud and Tencent Cloud and conducted multiple evaluations. The results indicate that it takes about 2.79 seconds for a client to execute the protocol for uploading and about 0.96 seconds for downloading. Compared to other decentralized systems, our system exhibits efficient latency for both download and upload operations.

关键词： Access control

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A Survey of Error Analysis and Calibration Methods for MEMS Triaxial Accelerometers

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Computers, Materials & Continua 2020年第7期64卷 389-399页

作者： Bo Xiao Yinghang Jiang Qi Liu Xiaodong Liu Mingxu Sun Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and TechnologyNanjing210044China School of Computer&Software Nanjing University of Information Science&TechnologyNanjingChina School of Computing Edinburgh Napier UniversityEdinburghUK Centre for Health Sciences Research University of SalfordSalfordM54WTUK Shandong Beiming Medical Technology Ltd. Jinan250101China

MEMS accelerometers are widely used in various fields due to their small size and low cost,and have good application ***,the low accuracy limits its range of *** ensure data accuracy and safety we need to calibrate MEMS *** authors have improved accelerometer accuracy by calculating calibration parameters,and a large number of published calibration methods have been *** this context,this paper introduces these techniques and methods,analyzes and summarizes the main error models and calibration procedures,and provides useful ***,the content of the accelerometer calibration method needs to be overcome.

关键词： MEMS accelerometer calibration error analysis accelerometer calibration

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Graph-level Neural Networks: Current Progress and Future Directions

arXiv

引用

arXiv 2022年

作者： Zhang, Ge Wu, Jia Yang, Jian Xue, Shan Hu, Wenbin Zhou, Chuan Peng, Hao Sheng, Quan Z. Aggarwal, Charu School of Computing Macquarie University Sydney Australia School of Computing and Information Technology University of Wollongong Australia School of Computer Science Wuhan University Wuhan China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China School of Cyber Science and Technology Beihang University China IBM T. J. Watson Research Center Yorktown Heights NY United States

Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-level learning methods used to be the mainstream. However, with the increasing scale and complexity of graphs, Graph-level Neural Networks (GLNNs, deep learning-based graph-level learning methods) have been attractive due to their superiority in modeling high-dimensional data. Thus, a survey on GLNNs is necessary. To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling. The representative and state-of-the-art models in each category are focused on this survey. We also investigate the reproducibility, benchmarks, and new graph datasets of GLNNs. Finally, we conclude future directions to further push forward GLNNs. The repository of this survey is available at https://***/GeZhangMQ/Awesome-Graph-level-Neural-Networks. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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