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检索条件"任意字段=Proceedings of the Sixth Conference on Computer Supported Cooperative Work in Design"

共 3225 条记录，以下是21-30 订阅

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Hybrid Ensemble Polynomial Neural Network Classifier: Analysis and design 27

Hybrid Ensemble Polynomial Neural Network Classifier: Analys...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Gao, Mingjie Huang, Wei Xu, Zhilei Sungkwun, Oh Tianjin Univ Sch Comp Sci Tianjin Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China Univ Suwon Dept Elect & Elect Engn Gyeonggi South Korea

ISBN: (纸本)9798350349184;9798350349191

In this paper, we propose a hybrid ensemble polynomial neural network (HEPNN) with the aid of polynomial neural network (PNN) and hybrid ensemble polynomials neurons (HEPNs). Two types of HEPNs including ensemble radial-based-function polynomial neuron (ERPN) and ensemble polynomial neuron (EPN) are proposed. ERPN and EPN are generalized polynomial neurons based on ensemble architecture. To address the problem of multiple covariance in traditional PNN neural networks, correlation coefficients and performance are utilized to select neurons replacing the original selection of nodes by performance only. The main strategies of HEPNN design are as follows: First, the first layer of the network consists of ERPN that are utilized to reflect the structure encountered between the data, while the second and higher layers consists of EPN, which reflect higher polynomial-order relationships between input and output data. Second, particle swarm optimization (PSO) is adopted to optimize the architecture of HEPNN. A comparative study shows the proposed HEPNN has better performance than other state-of-art models reported in literature.

关键词： Polynomial neural network (PNN) Hybrid ensemble polynomial neural network (HEPNN) Particle swarm optimization (PSO)

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Neural ODE-Guided Imputation Computing for Building IoT Time Series with High Missing Gaps 27

Neural ODE-Guided Imputation Computing for Building IoT Time...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Zhao, Ruixuan Chen, Chuyi Li, Ruoyu Liu, Shenghang Liu, Jie Wang, Hongan Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Inst Architectural Design Co Ltd Beijing 100045 Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Imputing missing data in Building Internet of Things (BIoT) poses a significant challenge due to the complex multi-dimensional variations and the inevitable occurrence of long-span missing gaps during maintenance or repair periods. In this paper, we introduce ODE-MI, a novel neural ODE-guided Multiple Imputation framework tailored for BIoT time series with high missing gaps. ODE-MI integrates shapelet-wise convolution, node embeddings, and dynamic graph embeddings with transfer delay to capture compound spatio-temporal (ST) volatilities, trends, and high-order impact transfer. Our contributions include the design of a multi-faceted backbone network capable of capturing long-distance ST characteristics in BIoT, and the development of the ODE-MI framework to handle long-term missing data within networked dynamic attributes. Empirical assessments using open datasets demonstrate that ODE-MI outperforms existing imputation methods, especially in scenarios involving continuous long-span missing gaps.

关键词： data imputation missing data neural ordinary differential equation multi-variate time series

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Cognitive Operations and Patterns in Remote design Collaboration: A Protocol Study 16

Cognitive Operations and Patterns in Remote Design Collabora...

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16th ACM conference on Creativity and Cognition (C and C)

作者： Lee, Ju Hyun Univ New South Wales Fac Arts Design & Architecture Sch Built Environm Sydney NSW 2052 Australia

ISBN: (纸本)9798400704857

While remote computer supported cooperative work (CSCW) offers unprecedented opportunities for collaboration and innovation, it also posts unique challenges in shaping design team cognition (DTC). To address this knowledge gap in understanding DTC during remote teamwork, this research introduces a new coding scheme and conducts protocol analysis on nine online design sessions. The findings of this study focus on examining the dynamics of design operations and patterns during remote teamwork, highlighting the interconnected nature of the three cognitive dimensions - 'representation' (R), 'communication' (C), and 'allocation' (A) - in the R-C-A framework. The study demonstrates that the R-C-A framework offers a valuable approach to examining cognitive operations and patterns in remote design collaboration. Furthermore, the insights gained from this research hold the potential to enrich and optimise design collaboration in remote CSCW, thereby contributing to advancements in design practice and education.

关键词： Remote design Collaboration design Team Cognition computer supported cooperative work (CSCW) Protocol Analysis

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Knowledge Graph-based Optimization of Multi-AGV cooperative Handling in Flexible workshops 27

Knowledge Graph-based Optimization of Multi-AGV Cooperative ...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Dong, Xiao Zheng, Xiaojun Song, Yaning Dalian Jiaotong Univ Sch Mech Engn Dalian Peoples R China

ISBN: (纸本)9798350349184;9798350349191

The modern flexible workshop is characterized by the diversification of AGV equipment and a large number of production tasks. The traditional multi-AGV collaborative handling method has been unable to meet the needs of modern users who pursue high efficiency and high precision. The knowledge graph is an important part of the knowledge management field which has great advantages in creating an efficient and interconnected knowledge base. The knowledge graph can manage the performance parameters and other information of AGV equipment in modern flexible workshops efficiently. In order to solve the problem of low collaboration efficiency of AGV in flexible workshops, initially, this paper constructs the AGV performance parameter spectrum based on the knowledge graph in a combination of topdown and bottom-up methods. Then, based on the experience of factory experts, this paper constructs a multi-AGV collaborative handling paradigm and maps the paradigm to the performance parameter spectrum of the workpiece handling AGV to form an AGV collaborative chain spectrum. Secondly, this paper constructs a multi-AGV collaborative handling link network topology graph using a complex network to dynamically simulate the AGV and station location of the flexible workshop. We query relevant performance data from the AGV performance parameter spectrum and match the AGV collaborative chain to the production task. Finally, we aim to minimize the completion time of the handling task and realize the AGV collaborative handling optimization problem through linear programming to improve the production efficiency of modern flexible workshops.

关键词： Knowledge Graph AGV AGV path planning problem Flexible workshops

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cooperative Simulation of RDMA-based Network and Storage 27

Cooperative Simulation of RDMA-based Network and Storage

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Jiang, Wanchun Chen, Hanyu Chen, Jialiang Wang, Kai Yang, Jiarui Han, Xiao Mang, Xingping Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Huawei Beijing Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Nowadays, the NVMe SSDs and the NVMe-oF (NVMe over Fabric) protocols are popular, and RDMA (Remote Direct Memory Access) has become one of the de-facto network fabric. Meanwhile, as the network bandwidth rapidly expands, potential bottlenecks can occur in both the RDMA-based network and the SSDs of the host. Accordingly, traffic management has become a hot research topic. However, the RDMA-based network and SSDs cannot be simulated at the same time, although simulators play an important role in research. To address this issue, we developed RSNet, a simulator for both the RDMA-based network and storage. Specifically, RSNet implements the RDMA and NVMe-oF protocols and integrates the open-source SSD simulator MQSim into the NS3 platform. The results validate that RSNet can simultaneously treat both network and SSD, as well as realistically simulate the collaboration between network and SSD. Moreover, RSNet is also capable of replicating certain issues within RDMA-based networks and storage. In total, we believe that RSNet would facilitate further research on traffic management in RDMA-based networks and storage.

关键词： Simulation RDMA NVMe-oF SSD

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MedMatch: design of Semi-supervised learning Model with Curriculum Pseudo-Labels for Medical Image Classification 27

MedMatch: Design of Semi-supervised learning Model with Curr...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Gao, Yufei Chen, Zhe Shi, Lei Wang, QingXian Zhang, Yameng Zhengzhou Univ Sch Cyber Sci & Engn Zhengzhou Peoples R China SongShan Lab Zhengzhou Peoples R China Henan Prov Peoples Hosp Dept Pathol Zhengzhou Peoples R China Zhengzhou Univ Peoples Hosp Zhengzhou Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Deep neural networks are extensively employed in CAD (computer-aided diagnosis) to enhance diagnostic efficiency, accuracy, and reliability. However, relying on manually annotation, labeling medical images is costly and time-consuming. Consequently, obtaining labeled data of sufficient scale and high quality poses a challenge. In the field of data science, semi-supervised learning (SSL) methods have significantly reduced the burden of dataset labeling. However, two primary issues exist: (1) the multi-classification challenge involving simultaneous classification of multiple lesions and diagnosis of multiple diseases;(2) data imbalance results in significant variations in disease prevalence rates. In this paper, the design of Curriculum Pseudo-labels (CPL) methods is adopted to overcome the limitations of applying SSL methods across data science and CAD. Additionally, a novel strategy called MedAugment is proposed for enhancing the model's robustness and generalization ability. Experimental results show that the design of MedMatch model reaches 94.21% in AUC score compared with state-of-the-art SSL methods on CAD task.

关键词： Medical image classification Semi-supervised learning Pseudo-labels Data augmentation

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MLWF-Net: Multiple lung windows based fusion network for segmentation of small infected areas in COVID-19 CT slices 27

MLWF-Net: Multiple lung windows based fusion network for seg...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Han, Chenxu Het, Xuelei Het, Xiaowei Huang, Zhixin Zhang, Chuang Huang, Yong Northwest Univ Network & Data Ctr Xian Shanxi Peoples R China Northwest Univ Sch Informat Sci & Technol Xian Shanxi Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Xian Chest Hosp Xian Shanxi Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Automatic infected segmentation on CT enables rapid quantitative analysis of lung involvement in COVID-19 infections. However, it is a challenge to use limited information for segmentation of small infected regions. To deal with the problem, a Multi-Lung Window Fusion Network (MLWF-Net) is proposed to sufficiently utilize CT information. In the first part of MLWF-Net, the multi-lung window block is introduced to address the long-tailed distribution of CT Hounsfield Unit for more features. Then, the adaptive feature aggregation block which combines the attention mechanism with the message passing mechanism is developed to fuse the infection features adaptively from different lung windows CT. The MLWF-Net model is evaluated on a publicly available dataset and a private dataset, compared with a variety of widely-used segmentation models. The MLWF-Net holds on better performance with some benchmarks, especially for small infected areas. The proposed model is an efficient method to alleviate the long tail problem of CT Hounsfield Unit and improve the performance of small infected areas, which can be used for the grading assessment and predictive treatment of COVID-19 patients, with significant clinical value.

关键词： COVID-19 Image segmentation Computed tomography computer-aided diagnosis

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LFAS: An electricity load forecasting framework assisted by cooperative multi-task learning-based spike occurrence prediction 27

LFAS: An electricity load forecasting framework assisted by ...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Shi, Wei Ma, Jianhua Wang, Yufeng Jin, Qun Nanjing Univ Posts & Telecomm Nanjing Peoples R China Hosei Univ Tokyo Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9798350349184;9798350349191

The accurate power load forecasting is beneficial in reasonably arranging the power supply load, and greatly promoting the safety and smooth operation of smart grid. However, on one hand, high volatility exists in electricity load, especially, in which a few spike loads suddenly occurred has significantly affected the accuracy of short term load forecasting. On the other hand, most existing forecasting schemes follows one-size-fits-all paradigm, i.e., train and utilize a unique model to forecast the whole loads, irrespective of the spike or normal loads. Based on the above observation, we propose a novel short-term load forecasting scheme assisted by cooopeative Multi-task learning (MTL)-based spike occurrence prediction, LFAS, which is composed of two stages. At the first stage, a MTL-based spike occurrence prediction is explicitly proposed to forecast whether the future loads would be spike or not. Then, instead of one-size-fits-all scheme, the second stage intentionally selects the suitable data pre-processing technologies and deep neural network (DNN) prediction models for respectively forecasting spike and normal loads. The experimental results on the Europe electricity load dataset from ENTSO-E Transparency Platform demonstrate that our proposed scheme increases the accuracy of spike occurrence prediction, significantly improves both the forecasting accuracy of spike and normal electricity loads, in comparison with the existing one-size-fits-all load forecasting schemes.

关键词： Electricity load forecasting Spike occurrence prediction Multi-task learning (MTL) Deep neural networks (DNN) Variance stabilization transformation (VST)

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Research on Bandwidth-Oriented Distributed Task Allocation Mechanism for Multi-UAV 27

Research on Bandwidth-Oriented Distributed Task Allocation M...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Lu, Xinpeng Song, Heng Ma, Huailing Li, Xueqing Zhu, Junwu Yangzhou Univ Dept Informat Engn Yangzhou Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Dept Artificial Intelligence Nanjing Peoples R China

ISBN: (纸本)9798350349184;9798350349191

Bandwidth resources are crucial for the internal communication and task execution of multi-UAV systems. However, traditional allocation models for multi-UAV system are inefficient and result in low utility of UAVs. To overcome this challenge, this paper proposes a novel bandwidth-oriented task allocation model that accounts for the supply-demand relationship between UAVs and tasks for bandwidth resources. To solve this model, this paper presents a Double Auction-based Task Allocation Mechanism, which can achieve distributed allocation of tasks and bandwidth among multi-UAV. The main steps of this mechanism are as follows: (1) each UAV privately submits its bandwidth demand and bids for each task;(2) each task determines the bandwidth price and initial allocation based on the bids, and announces them to each UAV;(3) each UAV selects the task that maximizes its utility, and obtains the final allocation. Furthermore, this paper proves that this mechanism satisfies desirable economic properties such as individual rationality, budget balance, and incentive compatibility. Finally, experimental results show that the proposed mechanism exhibits excellent performance in terms of UAV and task utility, as well as the rate of bandwidth allocation.

关键词： Multi-UAV Distributed double auction Task allocation Mechanism design

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement 27

A Novel Text Matching Model Based on Multilayer Coding and F...

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27th International conference on computer supported cooperative work in design (CSCWD)

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Qilu Univ Technol Shandong Acad Sci Key Lab Comp Power Network & Informat Secur Minist EducShandong Comp Sci Ctr Jinan Peoples R China Qilu Univ Technol Shandong Acad Sci Fac Comp Sci & Technol Shandong Engn Res Ctr Big Data Appl Technol Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China China Mobile Commun Grp Design Inst Co Shandong Branch Jinan Peoples R China

ISBN: (纸本)9798350349184;9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： text matching multilayer encoding attention mechanism information retrieval

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