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检索条件"任意字段=2023 IEEE Visualization in Data Science, VDS 2023"

共 1363 条记录，以下是1181-1190 订阅

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Augmenting the Problem Frames Approach with Explicit data Descriptions Using ChatGPT

Augmenting the Problem Frames Approach with Explicit Data De...

引用

ieee International Requirements Engineering Conference Workshops (REW)

作者： Ling Xie Hongbin Xiao Zhi Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

Problem Frames (PF) as a mainstream requirements analysis method has attracted extensive attention and research in the field of requirements engineering. However, the current problem diagram as a PF visualization tool is more abstract and lacks the ability to express data descriptions. In this paper, we propose a method to extend problem diagrams based on ChatGPT. The aim is to detail the requirements to ensure a more accurate and comprehensive representation of the complex requirements of the system. We combine problem diagrams and ChatGPT by letting them interact with each other, and obtain the cause table describing causal relationships that exist within shared phenomena, then we model the cause table and calculate the structural coefficient of each cause and rank the causes from the largest to the smallest. Finally the ranked causes are used as an extension of the shared phenomenon, thus using the property that they can help improve the detailed descriptions of the requirements. A case study of an insulin injection control system demonstrates the applicability and viability of this method.

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data Governance: Status, Technologies, Applications and Development Trends

Data Governance: Status, Technologies, Applications and Deve...

引用

ieee International Conference on data science in Cyberspace (DSC)

作者： Haiyang Zhu Xiaofeng Chen Suyan Li Yejin Wang Xinwei Fang Minfeng Zhu The state Key Lab of CAD&CG Zhejiang University Wuchan Zhongda Digital Technology Co. Ltd. Hangzhou China The state Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou Qulian Technology Co. Ltd. Hangzhou China Zhejiang Institute of Mechanical & Electrical Engineering Hangzhou China Wuchan Zhongda Digital Technology Co. Ltd. Hangzhou China Department of Internet and Artificial Intelligence Sir Run Run Shaw Hospital School of Medicine Zhejiang University Hangzhou China Zhejiang University Hangzhou China

data governance involves legal, policy, technologies, and standards measures to ensure security, accuracy, integrity, consistency, and accessibility of data, with the primary objective of unlocking its latent value. In this context, the study initiates by scrutinizing the present status of data governance, subsequently identifying pivotal challenges through the lens of computer sciences. Moreover, to effectively grapple with the intricacies of high-dimensional data, data incompletion, and data redundancy within the realm of data governance, we present a compendium of data governance technology methodologies, encompassing data dimensionality reduction, data imputation, data deduplication, and data visualization. Further enhancing our discourse, we shed light on the typical scenarios of large-scale supply chain enterprises, elucidating data analysis and data intelligence applications. The convergence of these insights seeks to provide a comprehensive overview both data governance technology research and real-world application.

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Intelligent Soft Robotic Gripper Enabled by Multimodal Sensors and Deep Learning

Intelligent Soft Robotic Gripper Enabled by Multimodal Senso...

引用

ieee International Conference on Nano/Micro Engineered and Molecular Systems (NEMS)

作者： Qiongfeng Shi Zhongda Sun Xianhao Le Jin Xie Chengkuo Lee Joint International Research Laboratory of Information Display and Visualization School of Electronic Science and Engineering Southeast University Nanjing China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China

Here we report an intelligent soft robotic gripper enabled by the integration of an ultrasonic remote sensor and triboelectric sensors. Due to the noncontact distance sensing ability, the ultrasonic sensor is used to find the object’s visual information including position and height by lateral scanning. The information is then used for adjusting the robotic gripper to an appropriate grasp location, after which grasp operation is performed to obtain the object’s tactile information through triboelectric bending and tactile sensors. To efficiently analyze the multimodal information, a deep-learning neural network based on feature-level data fusion is constructed, which is able to achieve a high accuracy of 99.3% in classifying 14 objects, enabling the intelligent soft robotic gripper for various smart applications.

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Application of Convolutional Neural Networks to Shape Classification of Cloud Particles

Application of Convolutional Neural Networks to Shape Classi...

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ieee International Conference on Computer and Communication Engineering Technology (CCET)

作者： Yangdi Chen Yefeng Xu Ruil Jiao Minsong Huang School of Information and Communication Engineering Beijing Information Science and Technology University Beijing China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics CAS Beijing China

Cloud particle images taken by two-dimensional stereo cloud particle Imager (2D-S) and high resolution cloud particle imager (CPI) play an important role in the study of cloud particle morphology. This paper proposes a method of cloud particle shape classification based on convolutional neural network, constructs a 21-layer convolutional neural network model, designs a visualization App based on MATLAB App Designer toolbox, and realizes the classification of cloud particle shapes measured by 2D-S and CPI instruments. By exploring the performance of the convolutional neural network model classification results under different parameter Settings, and comparing with the traditional convolutional neural network, the optimal network model under experimental data is determined. The results show that this model has an accuracy of 90.03% and a shorter training time than the transfer learning method, which suggests that the presented model can better assist the research of cloud precipitation physics and weather modification.

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Acquisition and Remote Transfer of Operative Field View During Open Surgery

Acquisition and Remote Transfer of Operative Field View Duri...

引用

ieee Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS)

作者： Malek Anabtawi Jhasketan Padhan Abdulla Al-Ansari Zhigang Deng Elias Yaacoub Amr Mohammed Nikhil V. Navkar Department of Surgery Hamad Medical Corporation Doha Qatar Department of Computer Science University of Houston Houston Texas USA Department of Computer Science Qatar University Doha Qatar

Telementoring in open surgery has emerged as an alternative to on-site teaching. To ensure effective communication between the mentee and the mentor, the view of surgical operations in the operating room needs to be shared remotely in real-time. This study is carried out to propose a multi-threaded system that takes input from multi-color/depth cameras, aligns and creates a single point cloud representing the operative field in three-dimension, transfers it over the network, and reconstructs it for visualization for mentors. The system's performance is measured in terms of execution time of each threaded unit, and latency for transfer of multi-color/depth data over the network. The achieved registration accuracy is benchmarked for multiple cameras. The results show the potential usage of the proposed system for near real-time telementoring during open surgeries.

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PRODG: Practical Overlapping Community Detection in Weighted Graphs using Deep GCNs

PRODG: Practical Overlapping Community Detection in Weighted...

引用

Computer science and data Engineering (CSDE), ieee Asia-Pacific Conference on

作者： Md Nurul Muttakin Muhammad Anwarul Azim Md Iqbal Hossain Md. Saidur Rahman Department of Computer Science and Engineering Graph Drawing & Information Visualization Lab Bangladesh University of Engineering and Technology Dhaka - 1205 Bangladesh Research Innovation & Impact (RII) The University of Arizona Tucson Arizona USA

Graph-based neural networks, including transformers, are the most used neural networks. Recently, overlapping community detection problems have been tackled using graph convolutional networks (GCN), achieving superior performance than classical methods. However, some of these methods have limited practical use as they require ground truth information to optimize the hyper-parameters. In addition, most existing works, including GCN-based methods, either ignore the edge weight of weighted graphs or fail to leverage the edge weight information to achieve better performance. To this end, we propose PRODG as a practical overlapping community detection method in edge-weighted graphs using deep GCNs. Specifically, we design weighted dilated aggregation algorithms to incorporate the information of edge weights to achieve a deep-weighted residual GCN (WResGCN) model. Moreover, we perform a completely unsupervised modularity-based depth search for deep GCNs, making our method available for practical applications. We apply our model to real-world Facebook graphs with synthetic edge weights, having reliable ground truth information. Experimentation and evaluation show that our methods effectively leverage the edge-weight in weighted graphs. Our completely unsupervised depth search strategy in deep GCNs performs competitively or better than state-of-the-art methods, bolstering the practical use of deep GCNs in overlapping community detection.

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FL-HSDP: Client-Side Defense for Federated Learning via Hessian Optimization and Shuffled Differential Privacy

FL-HSDP: Client-Side Defense for Federated Learning via Hess...

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ieee International Conference on High Performance Computing and Communications (HPCC)

作者： Yuting Li Guojun Wang Tian Wang Weijia Jia School of Computer Science and Cyber Engineer Guangzhou University Guangzhou China Institute of AI and Future Networks Beijing Normal University & UIC China Zhuhai China

Federated learning (FL) is a prominent paradigm for distributed training that can decrease data leakage by not explicitly sharing private information. Lots of recent research has revealed, however, that malicious clients can easily launch model poisoning attacks and the private information of benign clients can still be divulged in the setting of federated learning. As the existing works are either incapable of providing quantitative privacy for user data or vulnerable to attacks that result in aggregated model misclassifications, we present a framework, FL-HSDP, that uses hessian matrix optimization to creatively involve perturbation into the process of updating gradients for local iterations. Instead of using conventional noise adding method for high-dimensional parameters, we improve the system's performance by employing differential privacy to the regularization term of the Hessian matrix during each local training epoch. In order to further enhance the security of our system, we introduce the privacy amplification effect brought by the shuffler between the clients and server to bolster the confidentiality while minimizing any adverse impact on algorithm performance. The extensive experimental results on FashionMNIST and CIFAR10 datasets demonstrate the effectiveness and efficiency of our approach, almost maintaining the accuracy and effectively mitigating tar-geted model poisoning attack by up to 73.7%. Both the similarity scores and the visualization of the reconstructed images have been demonstrated to be effective against the state-of-the-art weight inversion attacks.

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AI-Generated Content for Academic visualization and Communication in Maker Education

AI-Generated Content for Academic Visualization and Communic...

引用

Educational Technology (ICET), ieee International Conference on

作者： Qingqing Xing Chenghong Zheng Nan Zhu David Yip College of Education Sciences The Hong Kong University of Science and Technology Guangzhou Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou Innovation Policy and Entrepreneurship Thrust The Hong Kong University of Science and Technology Guangzhou Computational Media and Arts Thrust The Hong Kong University of Science and Technology Guangzhou

Collaborative learning in higher education, such as the emerging makerspaces, has contributed to research on innovation and participant expertise. However, there is little research on knowledge management in makerspaces or how learners transfer their individual tacit knowledge in the collaborative space. In addition, the functionality of maker education to promote individualized and personalized learning still needs to be explored. This study is based on Chinese STEM graduate students’ experiences with project-based learning in a Maker Education environment to test how AIGC tools help to acquire and transfer students’ individual tacit knowledge. The sample was formed from 266 MPhil students taking the "Design Thinking and Effective Academic Communication" course in their first academic year at the world’s first interdisciplinary university in southern China. The AIGC teaching intervention was based on the four-phase knowledge management model. In a cycle of socialization-externalization-combination-internalization,learners first explore expertise in a research area in project teams and formulate their research interest. Then, storytelling, analogy, and metaphor data were first used to assess students’ abilities to communicate their research topic in text and images on an A4 paper. The subsequent AIGC-supported instructional intervention consisted of 20-minute instructional Sprints with two instructors from Computational Media Arts and Academic Communication over two consecutive weeks. The students’ work shows that combining AIGC tools allows for a higher quality of visualization of tacit knowledge in the combination phase -.

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Indexing and visualization of Climate Change Narratives Using BERT and Causal Extraction

Indexing and Visualization of Climate Change Narratives Usin...

引用

ieee International Conference on Big data

作者： Hiroki Sakaji Noriyasu Kaneda Research Faculty of Information Science and Technology Hokkaido University Bank of Japan

In this study, we propose a methodology to extract, index, and visualize “climate change narratives” (stories about the connection between causal and consequential events related to climate change). We use two natural language processing methods, BERT (Bidirectional Encoder Representations from Transformers) and causal extraction, to textually analyze newspaper articles on climate change to extract “climate change narratives.” The novelty of the methodology could extract and quantify the causal relationships assumed by the newspaper’s writers. Looking at the extracted climate change narratives over time, we find that since 2018, an increasing number of narratives suggest the impact of the development of climate change policy discussion and the implementation of climate change-related policies on corporate behaviors, macroeconomics, and price dynamics. We also observed the recent emergence of narratives focusing on the linkages between climate change-related policies and monetary policy. Furthermore, there is a growing awareness of the negative impacts of natural disasters (e.g., abnormal weather and severe floods) related to climate change on economic activities, and this issue might be perceived as a new challenge for companies and governments. The methodology of this study is expected to be applied to a wide range of fields, as it can analyze causal relationships among various economic topics, including analysis of inflation expectation or monetary policy communication strategy.

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CN-CVS: A Mandarin Audio-Visual dataset for Large Vocabulary Continuous Visual to Speech Synthesis

CN-CVS: A Mandarin Audio-Visual Dataset for Large Vocabulary...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen Chen Dong Wang Thomas Fang Zheng Center for Speech and Language Technologies BNRist Tsinghua University China Department of Computer Science and Technology Tsinghua University China

Research on Video to Speech Synthesis (VTS) surges recently and the focus is gradually shifting from small-vocabulary short-phrase VTS to large-vocabulary continuous VTS (LVC-VTS). A large-scale dataset with sufficient speakers and utterances is a prerequisite for such research, and the database is certainly language *** this paper, we introduce CN-CVS, a large-scale Mandarin continuous visual-speech dataset, to support LVC-VTS research. The dataset contains about 200k utterances from more than 2500 individuals, amounting to more than 300 hours of visual-speech data. We built a state-of-the-art VTS model with the new dataset and conducted preliminary studies. Our results show that models that achieve good performance on small vocabulary tasks may perform very poor on CN-CVS, indicating that continuous VTS is indeed a challenging task, and the main challenge comes from the unconstrained vocabulary. The dataset and baseline code can be downloaded for free from http://***.

关键词： Vocabulary visualization Codes Signal processing Cameras Speech synthesis Visual databases

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