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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

共 5114 条记录，以下是81-90 订阅

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ESBMC v7.7: Efficient Concurrent Software verification with Scheduling, Incremental SMT and Partial Order Reduction: (Competition Contribution) 31th

ESBMC v7.7: Efficient Concurrent Software Verification with ...

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31st International conference on Tools and Algorithms for the Construction and analysis of Systems, TACAS 2025, which was held as part of the International Joint conferences on Theory and Practice of Software, ETAPS 2025

作者： Wu, Tong Li, Xianzhiyu Manino, Edoardo Menezes, Rafael Sá Gadelha, Mikhail R. Xiong, Shale Tihanyi, Norbert Petoumenos, Pavlos Cordeiro, Lucas C. The University of Manchester Manchester United Kingdom Federal University of Amazonas Manaus Brazil Igalia A Coruña Spain Eötvös Loránd University Budapest Hungary Arm® Cambridge United Kingdom

ISBN: (纸本)9783031906596

ESBMC v7.7 improves the verification of concurrent C programs by incorporating techniques such as dynamic thread scheduling, incremental SMT solving, and partial order reduction (POR). These improvements enhance the tool’s performance, particularly in exploring complex multi-threaded executions. The new scheduler prioritizes higher-thread identifiers during context switches, which helps explore deeper program states. The use of incremental SMT solving and a refined POR algorithm reduces the exploration of unreachable interleavings and redundant states. These updates enable ESBMC to detect bugs faster, making it a more effective tool for ensuring the safety of multi-threaded applications. © The Author(s) 2025.

关键词： Scheduling algorithms

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vMambaDA: visual State Space Model with Unsupervised Domain Adaptation in Cervical Cancer 6

VMambaDA: Visual State Space Model with Unsupervised Domain ...

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6th IEEE International conference on Image Processing, Applications and Systems, IPAS 2025

作者： vu, Hoang-Nguyen Nguyen, Thi-Ngoc-Truc Bui, Minh-Duc Huynh, Kim-Ngan Ngoc Minh Nguyen, Thanh Minh, Hoang Tran Nguyen v, Duy AI Lab - AI Vietnam Ho Chi Minh City Viet Nam Ho Chi Minh City University of Science Ho Chi Minh City Viet Nam Ho Chi Minh City Medicine and Pharmacy University Ho Chi Minh City Viet Nam Trung Vuong Hospital Ho Chi Minh City Viet Nam Industrial University of Ho Chi Minh City Ho Chi Minh City Viet Nam

ISBN: (纸本)9798331506520

Cervical cancer remains a major public health concern in vietnam, ranking second only to breast cancer among women. Early detection through screening, particularly HPv testing and Pap smears, is critical in reducing cervical cancer mortality. While deep learning has shown great promise in medical image analysis, particularly for detecting cervical cancer, challenges remain, especially when models are applied to real-world datasets with limited labeled data due to privacy concerns and expensive annotations. This research offers an effective cervical cancer diagnostic approach that combines Sliced Wasserstein Distance (SWD), Maximum Classifier Discrepancy (MCD), and the vMamba model. The vMambaDA model learns domain-invariant features and adjusts to domain shifts to address the problems of accuracy, generalization, and speed in cervical cancer screening. vMambaDA has proven to be more adept at handling the intricacies of medical images than earlier models by exhibiting better classification accuracy and sensitivity through extensive testing on both public and private datasets. This automated method could enhance the early detection of cervical cancer and intervention by pushing the limits of domain adaptation in cytopathology. © 2025 IEEE.

关键词： Lung cancer

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AI-Driven Personalized Skincare Recommendations

AI-Driven Personalized Skincare Recommendations

引用

Electronics and Renewable Systems (ICEARS), International conference on

作者： Iyyanar Perumal Kalaivani P Naveenkumar A Saran v Sriram K Suruthi N Department of Information Technology Sona College of Technology Salem India Department of CSE Kongu Engineering College Perundurai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study investigates the utilization of artificial intelligence (AI) to transform skincare product recommendations and the formulation of individualized routines through the analysis of user inputs and characteristics. The system utilizes data including skin type, acne count, oiliness level, and Uv exposure, using KNearest Neighbors (KNN) for personalized product suggestions. Natural Language Processing (NLP) approaches are utilized to develop tailored 7-day skincare regimen recommendations by examining product and user problems, converting unstructured data into actionable insights. Moreover, deep learning algorithms are employed for real-time image-based assessments of skin conditions, facilitating precise acne diagnosis and oiliness evaluation. The system integrates visual data with textual analysis, continually adapting through user feedback to improve accuracy and relevance. This article highlights the capacity of AI-driven methods to automate routine generation and provide tailored product recommendations, fostering innovation in the cosmetics sector through a thorough, data-oriented strategy.

关键词： Deep learning visualization Accuracy Transforms Nearest neighbor methods Skin Natural language processing Real-time systems Artificial intelligence Wearable devices

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data analysis on the comparison of IC vs Ev vehicles

Data Analysis on the comparison of IC vs EV Vehicles

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International conference on

作者： Harishitha S v Priyadharshini S Aswini J J Dhivya vijeta Iyar Department of Automobile Engineering Kumaraguru College of Technology Coimbatore India Department of Mathematics Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This study undertakes a comprehensive exploration of consumer behavior in the automotive sector, focusing on the intricate details of purchasing decisions between Evs and IC engine vehicles. Leveraging extensive data analytics, this research aims to unearth nuanced patterns that shape the preferences and considerations of contemporary automobile buyers. The analysis encompasses a multifaceted examination, delving into the economic, environmental, and technological dimensions influencing the choices between Evs and IC engines. By dissecting intricate purchase data, our study illuminates distinct trends and evolving paradigms in the automotive market. Moreover, this research scrutinizes the role of governmental incentives, the maturation of charging infrastructure, and the broader contextual shifts in societal attitudes towards sustainable mobility. The presented findings not only offer a panoramic view of current purchase dynamics but also serve as a predictive compass for manufacturers, policymakers, and industry influencers navigating the path toward a more sustainable automotive future. This paper provides a comprehensive overview of the study’s methodology, key findings, and potential implications for stakeholders across the automotive landscape, fostering a deeper understanding of the intricate interplay between consumer preferences and the evolving automotive market.

关键词： Costs data analysis Shape Transportation Telecommunication computing Stakeholders vehicle dynamics Sustainable development Engines Automotive engineering

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Personalized Content-based Movie Recommendation System: A Comparative analysis

Personalized Content-based Movie Recommendation System: A Co...

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visual Analytics and data visualization (ICvADv), International conference on

作者： B Nithya Asha v Sreeja S P Abhay Aditya Sinha Akash P Akshay Bharadwaj S Department of Master of Computer Applications New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

A recommendation system is an analytical tool designed to provide personalized suggestions to users for specific resources such as literature, films, or music, derived from a comprehensive dataset. Movie recommendation systems aim to predict movies a user might enjoy based on attributes of previously liked movies. These systems are particularly advantageous for organizations that manage data from extensive customer bases and seek to deliver optimal suggestions. Factors such as genre, cast, and filmmakers significantly influence recommendations, which may be based on a single attribute or a combination of features. This paper focuses on a genre-based recommendation system employing content-based filtering to cater to user preferences. Using Python, the design tools algorithms like Cosine Similarity, Jaccard Similarity, and Pearson Correlation on a large dataset to identify patterns and preferences eIectively. Through a detailed evaluation of these approaches, the system provides robust and applicable movie suggestions. By smoothing user-friendly navigation and aligning recommendations with user interests, the system enhances user engagement and satisfaction. Also, this study examines the strengths and limitations of the applied algorithms, emphasizing the implicit for combining multiple similarity measures to enhance accuracy. The findings emphasize the significance of substantiated content delivery in optimizing user experience.

关键词： Technological innovation Correlation Smoothing methods Sensitivity Filtering visual analytics Motion pictures vectors User experience Recommender systems

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analysis of visual Information to Determine the Emotional State of People

Analysis of Visual Information to Determine the Emotional St...

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International Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Svetlana M. Kutsenko Elena A. Saltanaeva Tatiana v. Lapteva Nikolay S. Kiselev Information Technology and Information Systems Department Kazan State Power Engineering University Kazan Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The demand for emotion recognition software in various fields of human endeavour is undeniable. Research has focused on the application of various technologies and methods, including machine learning, neural networks and big data analytics. Available software in the field of emotion recognition is characterised by a number of drawbacks: high cost; not always transparent data privacy and ethics; dependence on data quality; and narrow specialisation of data processing. Thus, it is relevant to develop the software taking into account the minimisation of the above disadvantages. In implementing the application, data collection and preparation was carried out, a recognition model was developed using neural networks and machine learning, and the accuracy of the model was evaluated. The application implements user authorisation using encryption algorithms, file uploading, file analysis, saving and viewing the results. The developed software allows recognising six basic emotions: joy, sadness, anger, fear, surprise and neutral state.

关键词： Training Emotion recognition visualization Analytical models Accuracy Text recognition Neural networks Software algorithms data models Software

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AutoGaze: A very Initial exploration in A SAM2-based Pipeline for Automated Eye-Object Interaction analysis in First-Person videos

AutoGaze: A Very Initial Exploration in A SAM2-based Pipelin...

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virtual Reality and 3D User Interfaces Abstracts and Workshops (vRW), IEEE conference on

作者： Qing Zhang Yifei Huang Jun Rekimoto The University of Tokyo Sony CSL Kyoto

ISBN: (数字)9798331514846

ISBN: (纸本)9798331525637

This paper presents a novel automated workflow for analyzing eye-tracking data in first-person videos. Our system uses the Segment Anything Model 2 (SAM2) to segment and track objects, correlating them with gaze information to provide a detailed understanding of visual attention and hand-object interactions. The pipeline outputs a structured JSON file containing rich information about gaze behavior, object interactions, and their temporal relationships. We demonstrate the system’s effectiveness in a life science operational context and compare its performance with a multimodal Large Language Model (LLM) for automated annotation. Our findings highlight the potential of combining advanced computer vision techniques with LLMs for comprehensive and scalable analysis of gaze behavior in real-world scenarios.

关键词： visualization Solid modeling Three-dimensional displays Pipelines Gaze tracking virtual reality User interfaces Life sciences Trajectory videos

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visual Analyze the Impact of Structural Features and Node Attributes on Collaborative Relationships 19th

Visual Analyze the Impact of Structural Features and Node...

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19th CCF conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Lu, Qiang Ma, Cheng Wu, Hongfeng Zhang, Gaofeng Yu, Ye School of Computer Science and Information Hefei University of Technology Hefei China School of Software Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9789819623754

Collaborative relationships are becoming more complex, diversified and multi-organization cross, which poses new challenges to collaborative relationship analysis. In order to analyze the effects of different structural features and node attributes on the overall cooperative relationships, and to support the exploration at the global, group and individual levels to promote the comprehensive analysis of cooperative relationships, we propose a visual analysis method based on graph contrast learning(GCL). The first step is to perturb the structural features and node attributes of the collaboration relationships through data augmentation. The second step is to use the graph convolutional network(GCN) to represent the structural features and node attributes as dense vectors in the high-dimensional vector space. The third step is to explore the influence of different structural features and node attributes through classification task outcome indicators. Based on this method, GCL4vis, an interactive visual analysis system is designed, which is equipped with a variety of visual views to help users analyze the effects of different structural features and node attributes. Finally, this paper verified the usability and effectiveness of the GCL4vis visual analysis system through specific case studies and structured interviews with users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Research on Early Warning of Drug Crime Behaviors Based on data-driven Method 24

Research on Early Warning of Drug Crime Behaviors Based on D...

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Proceedings of the 2024 4th International conference on Big data, Artificial Intelligence and Risk Management

作者： Shaobing Wu Changmei Wang Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Kunming Yunnan China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCv. It provides a useful exploration for combating Drug Crime.

关键词： data-driven

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Haemodynamic Parameters analysis through Audio Signal Processing analysis of Doppler Audio Signals for Carotid Artery Assessment 24

Haemodynamic Parameters Analysis through Audio Signal Proces...

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Proceedings of the 11th International conference on Bioinformatics Research and Applications

作者： vidya Gopal T v Inayathullah Ghori Avinash Eranki Renu John Department of Biomedical Engineering Indian Institute of Technology Hyderabad Hyderabad Telangana India Department of Cardiology Kamineni Hospital Hyderabad Hyderabad Telangana India

ISBN: (纸本)9798400717536

Limitations of traditional auscultation for vascular assessment prompt the exploration of alternative methods. This study investigates the potential of Doppler ultrasound signal analysis as a non-invasive and potentially cost-effective tool for screening carotid artery health. We analyzed Doppler data from 216 individuals (18-59 years) and identified distinct frequency ranges associated with blood flow. Statistical analysis revealed significant correlations between these frequencies and hemodynamic parameters, suggesting a link to vascular health. These findings hold promise for developing a novel screening method for early detection of carotid artery stenosis.

关键词： Carotid artery

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