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

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The 1st International Workshop on Federated Learning with Graph data (FedGraph) 22

The 1st International Workshop on Federated Learning with Gr...

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31st ACM International conference on Information and Knowledge Management (CIKM)

作者： Yang, Carl Li, Xiaoxiao Baracaldo, Nathalie Shah, Neil He, Chaoyang Lyu, Lingjuan Sun, Lichao Avestimehr, Salman Emory Univ Atlanta GA 30322 USA Univ British Columbia Vancouver BC Canada IBM Res San Jose CA USA Snap Res Seattle WA USA FedML Inc Los Angeles CA USA Sony AI Tokyo Japan Lehigh Univ Bethlehem PA 18015 USA Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781450392365

The field of graph data mining, one of the most important AI research areas, has been revolutionized by graph neural networks (GNNs), which benefit from training on real-world graph data with millions to billions of nodes and links. Unfortunately, the training data and process of GNNs involving graphs beyond millions of nodes are extremely costly on a centralized server, if not impossible. Moreover, due to the increasing concerns about data privacy, emerging data from realistic applications are naturally fragmented, forming distributed private graphs of multiple "data silos", among which direct transferring of data is forbidden. The nascent field of federated learning (FL), which aims to enable individual clients to jointly train their models while keeping their local data decentralized and completely private, is a promising paradigm for large-scale distributed and private training of GNNs. FedGraph2022 aims to bring together researchers from different backgrounds with a common interest in how to extend current FL algorithms to operate with graph data models such as GNNs. FL is an extremely hot topic of large commercial interest and has been intensively explored for machine learning with visual and textual data. The exploration from graph mining researchers and industrial practitioners is timely catching up just recently. There are many unexplored challenges and opportunities, which urges the establishment of an organized and open community to collaboratively advance the science behind it. The prospective participants of this workshop will include researchers and practitioners from both graph mining and federated learning communities, whose interests include, but are not limited to: graph analysis and mining, heterogeneous network modeling, complex data mining, large-scale machine learning, distributed systems, optimization, meta-learning, reinforcement learning, privacy, robustness, explainability, fairness, ethics, and trustworthiness.

关键词： graph mining distributed learning data privacy federated learning graph neural networks

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visual analysis Tool for a BLE Technology based Tracking data 23

Visual Analysis Tool for a BLE Technology based Tracking Dat...

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23rd International conference on Enterprise Information Systems (ICEIS)

作者： Schneider, Flavia A. Branco, Adriano Rodrigues, Ariane M. B. Carvalho, Felipe Barbosa, Simone D. J. Endler, Markus Lopes, Helio Pontificia Univ Catolica Rio de Janeiro Dept Comp Rua Marques Sao Vicente 225 Rio de Janeiro Brazil

ISBN: (纸本)9789897585098

Several systems deal with human mobility. Most of them are for outdoor environments and use mobile phones to capture data. However, there is a growing interest of enterprises to consider indoor movement to take employees and client classes into account. Moreover, they usually want to assign semantics to the visited locations. We propose a visual exploration tool for analyzing the dynamics of individual movements in an indoor environment in this work. We present the use of suitable charts and animations to explore these complex data better. Finally, we argue that one could use our solution to monitor social distancing in indoor environments, which is a sensible thing during the current COvID-19 pandemic.

关键词： Internet of Mobile Things data visualization Indoor Positioning data Mining Information Systems

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visual Question Answering Model Based on CAM and GCN 5

Visual Question Answering Model Based on CAM and GCN

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5th International conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Wen, Ping Li, Ma Zhen, Zhang Ze, Wang Xi'an University of Posts and Telecommunications Shaanxi Provincial Key Laboratory of Network Data Analysis and Intelligent Processing China

ISBN: (纸本)9781450396899

visual Question Answering (vQA) is a challenging problem that needs to combine concepts from computer vision and natural language processing. In recent years, researchers have proposed many methods for this typical multimodal problem. Most existing methods use a two-stream strategy, i.e., compute image and question features separately and fuse them using various techniques, rarely relying on higher-level image representations, to capture semantic and spatial relationships. Based on the above problems, a visual question answering model (CAM-GCN) based on Cooperative Attention Mechanism (CAM) and Graph Convolutional Network (GCN) is proposed. First, the graph learning module and the concept of graph convolution are combined to learn the problem-specific graph representation of the input image and capture the interactive image representation of the specific problem. Image region dependence, and finally, continue to optimize the fused features through feature enhancement. The test results on the vQA v2 dataset show that the CAM-GCN model achieves better classification results than the current representative models. © 2022 ACM.

关键词： Semantics

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Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates 36

Diverse, Global and Amortised Counterfactual Explanations fo...

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36th AAAI conference on Artificial Intelligence / 34th conference on Innovative Applications of Artificial Intelligence / 12th Symposium on Educational Advances in Artificial Intelligence

作者： Ley, Dan Bhatt, Umang Weller, Adrian Univ Cambridge Cambridge England Alan Turing Inst London England

ISBN: (纸本)9781577358763

To interpret uncertainty estimates from differentiable probabilistic models, recent work has proposed generating a single Counterfactual Latent Uncertainty Explanation (CLUE) for a given data point where the model is uncertain, identifying a single, on-manifold change to the input such that the model becomes more certain in its prediction. We broaden the exploration to examine delta-CLUE, the set of potential CLUEs within a delta ball of the original input in latent space. We study the diversity of such sets and find that many CLUEs are redundant;as such, we propose DIverse CLUE (del-CLUE), a set of CLUEs which each propose a distinct explanation as to how one can decrease the uncertainty associated with an input. We then further propose GLobal AMortised CLUE (GLAM-CLUE), a distinct and novel method which learns amortised mappings on specific groups of uncertain inputs, taking them and efficiently transforming them in a single function call into inputs for which a model will be certain. Our experiments show that delta-CLUE, del-CLUE, and GLAM-CLUE all address shortcomings of CLUE and provide beneficial explanations of uncertainty estimates to practitioners.

关键词： Uncertainty analysis

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Developing a Comprehensive Task Framework for Effective Workforce analysis 25

Developing a Comprehensive Task Framework for Effective Work...

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Companion Proceedings of the ACM on Web conference 2025

作者： Rebecca Dew Mingzhao Li Weidong Liu Sandya Baratha Raj Pearson Melbourne Australia Pearson Sydney Australia

ISBN: (纸本)9798400713316

Effective workforce analysis, planning, and management require a deep understanding of the tasks and skills associated with different roles. This paper introduces a novel methodology for developing a comprehensive task framework that leverages Large Language Models (LLMs) and large-scale job ads data. We first propose an innovative approach to task taxonomy design, which involves the decomposition and reconstruction of tasks into a hierarchical structure based on action-object pairings, systematically refined using LLMs. The methodology extends to integrating the taxonomy with occupation and skill linkages derived from job ads, ensuring alignment with real-world workforce dynamics. Finally, we demonstrate the practical value of this framework through a visual analytics system that enables interactive exploration and analysis of tasks, occupations, and associated skills, highlighting its potential to transform workforce analysis. Demo video: https://***/41txBZK

关键词： gpt

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Autism Spectrum Disorder Detection Using Convolutional Neural Network

Autism Spectrum Disorder Detection Using Convolutional Neura...

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2025 International conference on visual Analytics and data visualization, ICvADv 2025

作者： Petricia, v. Angel Jacksy, M. Bhavadharani, R. St. Joseph's Institute of Technology Dept. of Computer Science and Engineering Chennai119 India

ISBN: (纸本)9798331521394

Autism Spectrum Disorder(ASD) is a complex neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Early detection is critical to enable timely intervention and improve outcomes for individuals with ASD. This research proposes an innovative framework that integrates Convolutional Neural Networks (CNNs) with advanced data preprocessing techniques, facial imaging, and behavioral analysis to enhance the accuracy and efficiency of ASD diagnosis. Leveraging data augmentation and transfer learning, the model achieves robust performance with high sensitivity and specificity. The novelty of this approach lies in its ability to identify subtle facial and behavioral patterns indicative of ASD, using a multidisciplinary strategy that combines neuroimaging, machine learning, and real-time diagnostic feedback. By advancing beyond traditional methods, this comprehensive framework holds the potential to revolutionize early ASD detection, making it more accessible, scalable, and precise. © 2025 IEEE.

关键词： Convolutional neural networks

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Image Enhancement Based on Multi-Scale Transform Domain Technique for visual Surveillance Application 6

Image Enhancement Based on Multi-Scale Transform Domain Tech...

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conference on Counterterrorism, Crime Fighting, Forensics, and Surveillance Technologies vI

作者： Zelensky, A. voronin, v. Gapon, N. Semenishchev, E. voroninaa, S. Ilyukhin, Yu Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510655546

Image enhancement refers to processing images to make them more suitable for display or further image analysis. An enhancement procedure improves future automated image-processing steps (detection, segmentation, and recognition) for efficient system decision-making. This paper presents a new method of visual surveillance image enhancement that improves the visual quality of digital images that exhibit dark shadows due to the limited dynamic range of imaging. The proposed method base on 3-D block-rooting multi-scale transform domain technique, comprising: finding similar blocks in the image by block-matching;block-grouping for different block sizes;applying 3-D block-matching parametric image enhancement;calculating the quality measure of enhancement;optimizing parameters of image enhancement method through the quality measure of enhancement;fusing different enhanced images. Experimental results from test data set show that the proposed technique performs well and can improve the quality during the sharpening of the image details.

关键词： block-rooting enhancement multi-scale processing the measure of enhancement visual surveillance

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Low valence Low Arousal Stimuli: An Effective Candidate for EEG-Based Biometrics Authentication System 33

Low Valence Low Arousal Stimuli: An Effective Candidate for ...

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33rd Medical Informatics Europe conference (MIE) - Caring is Sharing - Exploiting the value in data for Health and Innovation

作者： Jeswani, Jahanvi Govarthan, Praveen Kumar Selvaraj, Abirami Prince, Amalin Thomas, John Kalathe, Mohanavelu Sreeraj, v. S. Subramaniam, venkat Ronickom, Jac Fredo Agastinose Indian Inst Technol BHU Sch Biomed Engn Varanasi Uttar Pradesh India BITS Pilani Dept Elect & Elect Engn KK Birla Goa Campus Pilani Goa India McGill Univ Montreal Neurol Inst Montreal PQ Canada DRDO Def Bioengn & Electro Med Lab Minist Def Bangalore India NIMHANS Clin Res Ctr Neuromodulat Psychiat Bangalore India NIMHANS Translat Psychiat Lab Bangalore India

ISBN: (纸本)9781643683898

Electroencephalography (EEG) has recently gained popularity in user authentication systems since it is unique and less impacted by fraudulent interceptions. Although EEG is known to be sensitive to emotions, understanding the stability of brain responses to EEG-based authentication systems is challenging. In this study, we compared the effect of different emotion stimuli for the application in the EEG-based biometrics system (EBS). Initially, we preprocessed audio-visual evoked EEG potentials from the 'A database for Emotion analysis using Physiological Signals' (DEAP) dataset. A total of 21 time-domain and 33 frequency-domain features were extracted from the considered EEG signals in response to Low valence Low arousal (LvLA) and High valence low arousal (HvLA) stimuli. These features were fed as input to an XGBoost classifier to evaluate the performance and identify the significant features. The model performance was validated using leave-one-out cross-validation. The pipeline achieved high performance with multiclass accuracy of 80.97% and a binary-class accuracy of 99.41% with LvLA stimuli. In addition, it also achieved recall, precision and F-measure scores of 80.97%, 81.58% and 80.95%, respectively. For both the cases of LvLA and LvHA, skewness was the stand-out feature. We conclude that boring stimuli (negative experience) that fall under the LvLA category can elicit a more unique neuronal response than its counterpart the LvHA (positive experience). Thus, the proposed pipeline involving LvLA stimuli could be a potential authentication technique in security applications.

关键词： Audio-visual evoked potential Biometric authentication Electroencephalography Time and frequency features XGBoost

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CKD NET: Chronic Kidney Disorders Distinction Model

CKD NET: Chronic Kidney Disorders Distinction Model

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International conference on

作者： Arpana Prasad v. Asha A.P. Nirmala Akash v Chethan S G Chintan S Naik Master of Computer Application New Horizon College of Engineering Bengaluru India

Habitual Renal complaint is a condition that causes a progressive drop in renal function. It refers to a clinical reality that causes renal dysfunction, indecorous complaint opinion, and treatment can affect in unrecoverable renal complaint with veritably short chances of survival. Artificial Intelligence (AI) technologies equip healthcare providers with a rich set of algorithms to prognosticate the habitual development of conditions. previous information helps to enable visionary corrective measures. This exploration study intends to develop an effective tool for prognosticating CKD using machine learning approaches. A machine literacy model is proposed and presented to address the on-uniform distribution of cases through class balancing, point ranking and analysis, and classifier training.

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Research on Interior Design Applications Incorporating Artificial Intelligence Technology

Research on Interior Design Applications Incorporating Artif...

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Intelligent Design (ICID), International conference on

作者： Ning Xian Yazhen Liu College of Design and Art Shenyang Jianzhu University Hunnan Shenyang China

ISBN: (数字)9798331541491

ISBN: (纸本)9798331541507

The purpose of this paper is to discuss the application of interior design incorporating artificial intelligence technology. The rapid development of artificial intelligence technology in the new era has produced innovative contributions to various industries, and interior design is no exception. This paper describes the concepts and development status of artificial intelligence and interior design, and analyzes the advantages and scope of its application in interior design according to the artificial intelligence technology that can be used in interior design. Indepth exploration of the potential of AI technology in the field of interior design, with the help of machine learning, big data analysis, virtual reality, augmented reality and intelligent customer feedback system and other tools, significantly improve the efficiency and quality of the design process, strengthen the interactivity and visual impact of the design.

关键词： Industries Computer science Technological innovation visualization Design methodology Catalysts Collaboration Machine learning Big data Augmented reality

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