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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是241-250 订阅

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11th World conference on Information Systems and Technologies, WorldCIST 2023

11th World Conference on Information Systems and Technologie...

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11th World conference on Information Systems and Technologies, WorldCIST 2023

ISBN: (纸本)9783031456442

The proceedings contain 210 papers. The special focus in this conference is on Information Systems and Technologies. The topics include: Decision Support System Based on Machine Learning Techniques to Diagnosis Heart Disease Using Four-Lead ECG Recordings;cognitive computing in the Travel and Tourism Industry;expectations and Knowledge Sharing by Stakeholders in a Bioproducts Community of Practice: An Exploratory Study;gamification in Higher Education Assessment Through Kahoot;education and Characteristics of Computational Thinking: A Systematic Literature Review;blockchain and Artificial Intelligence in a Business Context: A Bibliometric Analysis;preliminary Research to Propose a Master data Management Framework Aimed at Triggering data Governance Maturity;Adoption of GDPR – In the Intermunicipal Community of Terras de Trás-os-Montes, Portugal;drivers of Long-Term Work from Home Adoption in Small and Medium-Sized Enterprises: A Managerial Perspective;Optimizing Topic Modelling for Comments on Social Networks: Reactions to Science Communication on COVID;study of Digital Maturity Models Considering the European Digital Innovation Hubs Guidelines: A Critical Overview;a Comparison Between the Most Used Process Mining Tools in the Market and in Academia: Identifying the Main Features Based on a Qualitative Analysis;anonymisation Methods for complex data Based on Privacy Models;a Mobile Application for Wooden House Fire Risk Notifications Based on Edge computing;diagnosis Model for Detection of e-threats Against Soft-Targets;x 2OMSAC-An Ontology Population Framework for the Ontology of Microservices Architecture Concepts;calibration Techniques and Analyzing the Website Design with Eye Tracking Glasses;healthCare Fake News Detection: A New Approach Using Feature Selection Concept.

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11th World conference on Information Systems and Technologies, WorldCIST 2023

11th World Conference on Information Systems and Technologie...

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11th World conference on Information Systems and Technologies, WorldCIST 2023

ISBN: (纸本)9783031456503

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11th World conference on Information Systems and Technologies, WorldCIST 2023

11th World Conference on Information Systems and Technologie...

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11th World conference on Information Systems and Technologies, WorldCIST 2023

ISBN: (纸本)9783031456473

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Online Multi-Object Tracking Based On Global Feature Enhancement Network And Historical Feature Complement Iteration

Online Multi-Object Tracking Based On Global Feature Enhance...

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cognitive computing and complex data (iccd), international conference on the

作者： Zunwang Ke Puping An Gang Wang Yugui Zhang School of Software Xinjiang University Urumqi China School of Computing and Data Engineering NingboTech University Ningbo China Institute of Semiconductors Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

Multi-object tracking is an important part of computer vision and plays a crucial role in many real-world application scenarios. The existing integrated tracking framework loses the key features of the target when fusing features and iteratively updating the trajectory apparent features, resulting in tracking instability. In order to solve the above problems, this paper proposes an online multi-object tracking method based on global feature enhancement network and historical feature complement iteration. In this method, a dual global feature fusion unit is used to replace the global feature enhancement network of summation or splicing operation, which is used to extract more discriminative key features and enhance the recognition ability of objects in multiple scenes. In addition, the historical features are used to supplement the iterative mechanism, which introduces random features and average features from the historical appearance features stored in the trajectory as supplementary features to supplement the lack of object features in the current frame. Comprehensive experiments are carried out on MOT15, MOT16, MOT17, and MOT20 datasets, in which the MOTA score of 73.2% is achieved on MOT16 dataset, indicating that the proposed method has good tracking performance. Source code: http://***/Anbping/gfen-hfci

关键词： Computer vision Target tracking Splicing Source coding Computational modeling Feature extraction Trajectory Iterative methods Object tracking cognitive systems

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Do Body Expressions Leave Good Impressions? - Predicting Investment Decisions based on Pitcher's Body Expressions 25

Do Body Expressions Leave Good Impressions? - Predicting Inv...

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25th international conference on Multimodal Interaction, ICMI 2023 Companion

作者： Jung, Merel M. Van Vlierden, Mark Liebregts, Werner Önal Ertuǧrul, Itlr Department of Cognitive Science and Artifcial Intelligence Tilburg University Tilburg Netherlands Jheronimus Academy of Data Science s-Hertogenbosch Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands

ISBN: (纸本)9798400703218

This work aims to investigate whether displayed body expressions during venture pitches hold predictive value over the success of gathering funding. To this end the performance of several traditional regression models trained on aggregated body expression features is compared to that of deep recurrent models. Moreover, the impact of features and the effect of oversampling to counteract data imbalance are investigated. The results showed that temporal modeling of body expressions with GRU yielded the best performance (average MAE = 16.9). Oversampling was found to not improve models' performance. The analysis of the impact of features suggest that higher levels of expressed body movements negatively affect investment scores whereas a more open body posture can have a positive effect. These findings show that body expressions can be predictive of investment decisions. Future work will be directed towards integrating these cues into a multimodal model to aid entrepreneurs in improving their pitching skills and further the understanding of the investment decision-making process. © 2023 Owner/Author.

关键词： Investments

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Improved model for image tampering monitoring based on fast-RCNN 2

Improved model for image tampering monitoring based on fast-...

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2nd international conference on data Analytics, computing and Artificial Intelligence, ICDACAI 2023

作者： Chen, Jiaqi Chen, Jiasheng Wang, Weitong Zhu, Yunlong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Software Institute East China Normal University Shanghai200062 China Beijing Jiaotong University Weihai International College Weihai100044 China Shanghai Experimental Foreign Language School Puxi Campus Shanghai200093 China

ISBN: (纸本)9798350339765

Image tampering detection is a method to detect if an image has been edited or moved. The most common tampering methods are copy, paste and splicing. The current techniques are divided into two main categories: traditional feature extraction-based tampering detection technology and convolutional neural network-based tampering detection technology. In this paper, we train images based on a dual-stream faster R-CNN network to detect the operation images given end-to-end in the tempered area. We replace VGG16 in the convolutional layer model with VGG19, smaller convolutional kernels, smaller pooling kernels and deeper layers are used to make wider feature mapping, learn the appropriate features from a large group of samples more efficiently. And in that case, we avoid unnecessary complex feature extraction process. We also demonstrate our approach through a set of experiments, and the experimental results have shown the effectiveness of using VGG19 instead of VGG 16. © 2023 IEEE.

关键词： Feature extraction

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Chimp Optimization Algorithm with Fuzzy cognitive Map for Vibration-based Damage Detection 2

Chimp Optimization Algorithm with Fuzzy Cognitive Map for Vi...

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2nd international conference on Sustainable computing and data Communication Systems, ICSCDS 2023

作者： Babu, Uppara Raghu Gehlot, Tarun Thenmozhi, S. Saravanakumar, P. Ravitheja, A. Upadhye, Nishant Anantrao Srinivasa Ramanujan Institute of Technology Civil Engineering Department Rotarypuram Village BK Samudram Mandal Anantapur India College of Technology and Agriculture Engineering Agriculture University Department of Civil Engineering Rajasthan jodhpur India St. Joseph's College of Engineering Department of Civil Engineering Tamilnadu Chennai India Sri Krishna College of Engineering and Technology Civil Engineering Department Tamilnadu Coimbatore India Svr Engineering College Department of Civil Engineering Nandyal Kurnool Andhra Pradesh Ayyalur India Bharati Vidyapeeth's Jawaharlal Nehru Institute of Technology Department of Civil Engineering Mahrashtra Pune India

ISBN: (纸本)9781665491990

In the past, Structural health monitoring (SHM) and vibration-oriented structural damage detection gain the great attention of mechanical, aerospace, and civil engineers. Initial and meticulous damage recognition has been one main aim of SHM applications. One key difficulty for structural damage detection utilizing observing dataset was to gain features that are delicate to damage but insensitive to noise (for example sensor measurement noise) along with the operational and environmental effects (for instance temperature effect). The performance of traditional damage detection techniques mainly relies upon the choice of the classifier and the features. Therefore, this study develops a Chimp optimization Algorithm with Fuzzy cognitive Map for Vibration-based Damage Detection (COAFCM-VDD) technique. The presented COAFCM-VDD technique determines cross-correlation functions of vibration data as fundamental features as input. The proposed COAFCM-VDD technique intends to derive damage features from the field measurement under the impact of noisy uncertainty. For detecting the damages, the FCM model is exploited in this work. At last, the performance of the FCM model can be improvised by the COA. The experimental result analysis of the COAFCM-VDD technique is tested using vibration dataset and the obtained outcomes signify the improved performance of the COAFCM-VDD technique. © 2023 IEEE.

关键词： Damage detection

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Multi-task Joint Prediction of Infant Cortical Morphological and cognitive Development 26th

Multi-task Joint Prediction of Infant Cortical Morphological...

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26th international conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Yuan, Xinrui Cheng, Jiale Zhao, Fenqiang Wu, Zhengwang Wang, Li Lin, Weili Zhang, Yu Li, Gang Southern Med Univ Sch Biomed Engn Guangzhou Guangdong Peoples R China Univ North Carolina Chapel Hill Dept Radiol Chapel Hill NC 27515 USA Univ North Carolina Chapel Hill Biomed Res Imaging Ctr Chapel Hill NC 27515 USA

ISBN: (纸本)9783031439957;9783031439964

During the early postnatal period, the human brain undergoes rapid and dynamic development. Over the past decades, there has been increased attention in studying the cognitive and cortical development of infants. However, accurate prediction of the infant cognitive and cortical development at an individual-level is a significant challenge, due to the huge complexities in highly irregular and incomplete longitudinal data that is commonly seen in current studies. Besides, joint prediction of cognitive scores and cortical morphology is barely investigated, despite some studies revealing the tight relationship between cognitive ability and cortical morphology and suggesting their potential mutual benefits. To tackle this challenge, we develop a flexible multi-task framework for joint prediction of cognitive scores and cortical morphological maps, namely, disentangled intensive triplet spherical adversarial autoencoder (DITSAA). First, we extract the mixed representative latent vector through a triplet spherical ResNet and further disentangles latent vector into identity-related and age-related features with an attention-based module. The identity recognition and age estimation tasks are introduced as supervision for a reliable disentanglement of the two components. Then we formulate the individualized cortical profile at a specific age by combining disentangled identity-related information and corresponding age-related information. Finally, an adversarial learning strategy is integrated to achieve a vivid and realistic prediction of cortical morphology, while a cognitive module is employed to predict cognitive scores. Extensive experiments are conducted on a public dataset, and the results affirm our method's ability to predict cognitive scores and cortical morphology jointly and flexibly using incomplete longitudinal data.

关键词： cognitive Prediction Cortical Morphology Prediction Disentanglement

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Pooling Techniques in Hybrid Quantum-Classical Convolutional Neural Networks 4

Pooling Techniques in Hybrid Quantum-Classical Convolutional...

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4th IEEE international conference on Quantum computing and Engineering, QCE 2023

作者： Monnet, Maureen Gebran, Hanady Matic-Flierl, Andrea Kiwit, Florian Schachtner, Balthasar Bentellis, Amine Lorenz, Jeanette Miriam Fraunhofer Institute for Cognitive Systems Iks Munich Germany Lmu University Hopsital Munich Germany

ISBN: (纸本)9798350343236

Quantum machine learning has received significant interest in recent years, with theoretical studies showing that quantum variants of classical machine learning algorithms can provide good generalization from small training data sizes. However, there are notably no strong theoretical insights about what makes a quantum circuit design better than another, and comparative studies between quantum equivalents have not been done for every type of classical layers or techniques crucial for classical machine learning. Particularly, the pooling layer within convolutional neural networks is a fundamental operation left to explore. Pooling mechanisms significantly improve the performance of classical machine learning algorithms by playing a key role in reducing input dimensionality and extracting clean features from the input data. In this work, an in-depth study of pooling techniques in hybrid quantum-classical convolutional neural networks (QCCNNs) for classifying 2D medical images is performed. The performance of four different quantum and hybrid pooling techniques is studied: mid-circuit measurements, ancilla qubits with controlled gates, modular quantum pooling blocks and qubit selection with classical postprocessing. We find similar or better performance in comparison to an equivalent classical model and QCCNN without pooling and conclude that it is promising to study architectural choices in QCCNNs in more depth for future applications. © 2023 IEEE.

关键词： Convolutional neural networks

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Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation 26

Graph Contrastive Learning with Adaptive Augmentation for Kn...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Mei Ding, Zhaoyuan Yu, Jian Zhang, Wenbin Yang, Ming Zhao, Mankun College of Intelligence and Computing Tianjin University Tianjin China Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Information and Network Center Tianjin University Tianjin China College of Computing and Software Engineering Kennesaw State University MariettaGA United States

ISBN: (纸本)9798350331684

Knowledge concept recommendation is a kind of fine-grained recommendation in massive open online courses (MOOCs) scenario, user interaction data has the characteristics of strong collaborative signals and imbalanced interactions. This leads to a single recommendation and reduced accuracy. Recently, the ability of contrastive learning (CL) in mitigating interaction imbalance in recommender systems has received widespread attention. CL requires the use of augmentation methods to generate different views. Existing augmentation methods (1) augment only at the topology or feature level ignoring semantic or structural information, and (2) undifferentiated augmentation tends to lose the critical information. In this paper, we propose Graph Contrastive Learning with Adaptive Augmentation for Knowledge Concept Recommendation (GCARec). Specifically, (1) topology level adaptive augmentation based on degree centrality captures critical structural information, and then (2) feature level adaptive augmentation based on degree centrality captures critical semantic information. Comprehensive experiments show that our proposed approach can outperform other baselines. Our implementations are available at https://***/DingZhaoyuan/GCARec. © 2023 IEEE.

关键词： Topology

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