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IoTwins: Design and Implementation of a Platform for the Management of Digital Twins in Industrial Scenarios 21

IoTwins: Design and Implementation of a Platform for the Man...

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21st IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Borghesi, Andrea Di Modica, Giuseppe Bellavista, Paolo Gowtham, Varun Willner, Alexander Nehls, Daniel Kintzler, Florian Cejka, stephan Tisbeni, Simone Rossi Costantini, Alessandro Galletti, Matteo Antonacci, Marica Ahouangonou, Jean Christian Univ Bologna DISI Bologna Italy TU Berlin Fraunhofer FOKUS Berlin Germany Siemens AG Austria Vienna Austria Ist Nazl Fis Nucl CNAF Bologna Italy Ist Nazl Fis Nucl Bari Italy ESI GRP Rungis France

ISBN: (纸本)9781728195865

With the increase of the volume of data produced by IoT devices, there is a growing demand of applications capable of elaborating data anywhere along the IoT-to-Cloud path (Edge/Fog). In industrial environments, strict real-time constraints require computation to run as close to the data origin as possible (e.g., IoT Gateway or Edge nodes), whilst batch-wise tasks such as Big Data analytics and Machine Learning model training are advised to run on the Cloud, where computing resources are abundant. The H2020 IoTwins project leverages the digital twin concept to implement virtual representation of physical assets (e.g., machine parts, machines, production/control processes) and deliver a software platform that will help enterprises, and in particular SMEs, to build highly innovative, AI-based services that exploit the potential of IoT/Edge/Cloud computing paradigms. In this paper, we discuss the design principles of the IoTwins reference architecture, delving into technical details of its components and offered functionalities, and propose an exemplary software implementation.

关键词： Training Technical requirements Cloud computing Digital twin computer architecture Machine learning Software

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An Integrated Framework for Team Formation and Winner Prediction in the FIRst Robotics Competition: Model, Algorithm, and Analysis 25

An Integrated Framework for Team Formation and Winner Predic...

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25th IEEE international Conferences on high performance computing and Communications, 9th international Conference on Data Science and Systems, 21st IEEE international Conference on Smart City and 9th IEEE international Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Galbiati, Federico Gran, Ranier X. Jacques, Brendan D. Mulhern, Sullivan J. Ngan, Chun-Kit Worcester Polytechnic Institute Department of Computer Science Worcester United States Worcester Polytechnic Institute Data Science Program Worcester United States

ISBN: (纸本)9798350330014

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRst Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRst Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy. © 2023 IEEE.

关键词： Predictive analytics

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LIMOCE: Live Migration of Containers in the Edge 21

LIMOCE: Live Migration of Containers in the Edge

引用

21st IEEE/ACM international symposium on Cluster, Cloud and Internet computing (CCGrid)

作者： Das, Rohit Sidhanta, Subhajit IIT Bhilai Sejbahar India

ISBN: (纸本)9781728195865

Being comprised of resource-constrained edge devices, live migration is a necessary feature in edge clusters for migrating state of an entire machine in case of machine failures and network partitions without disrupting continued availability of services. While most of the prior work in this area has provided solutions for live migration on clusters comprised of resource-rich servers or fog servers with high computing power, there is a general lack of research in live migration on the low-end ARM devices comprised in edge clusters. To that end, we propose a lightweight algorithm for performing live migration on resource constrained edge clusters. We provide an open source implementation of the above algorithm for migrating containers in Linux. We demonstrate that our algorithm outperforms state-of-the-art live migration algorithms on resource constrained edge clusters with network partitions and device failures.

关键词： performance evaluation Cloud computing Image edge detection Linux Computational modeling Clustering algorithms Containers

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VPFL: A Verifiable Property Federated Learning Framework Against Invisible Attacks in Distributed IoT 25

VPFL: A Verifiable Property Federated Learning Framework Aga...

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作者： Wu, Yuanxiang Cheng, Hang Guan, Lin Liu, Peihao Chen, Fei Wang, Meiqing College of Computer Science and Big Data Fuzhou University Fuzhou China

ISBN: (纸本)9798350330014

The development of Federated Learning (FL) offers an efficient Machine Learning (ML) approach with privacy protection to solve the data island issue in distributed Internet of Things (IoT). However, existing FL frameworks still suffer from invisible attacks in IoT environments, such as free-rider attacks, backdoor attacks, and model theft. In this paper, we propose a Verifiable Property Federated Learning (VPFL) framework to overcome the above invisible attacks. We present a black-box watermarking task distribution mechanism to prevent free-rider attacks by verifying the property of local models. Our adversarial fine-tuning embedding technique can not only eliminate backdoors in global models, but also simultaneously embed white-box watermarks into model parameters to prevent model theft. Comprehensive experimental evaluations demonstrate that our framework outperforms state-of-the-art schemes in terms of security and feasibility against invisible attacks. © 2023 IEEE.

关键词： Internet of things

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Professional Capital Generation Process and Value-added Path of Rural Teachers Based on the DIKWP Model 25

Professional Capital Generation Process and Value-added Path...

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作者： Liu, Lifang Wei, Xiaojie Cao, Zike You, Xinya Duan, Yucong Liu, Ruolin Liu, Xufeng School of Marxism Wuhan Institute of Technology Hubei China Qianhu Academic Silk Road Cultural Exchange Center Hubei China School of Computer Science and Technology Hainan University Haikou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei China

ISBN: (纸本)9798350330014

Professional capital is an important research perspective for measuring teachers' teaching effectiveness and professionalism. Currently, China's rural teachers are trapped in the predicament of professional capital loss, such as limited professional development, disconnection of social networks, and dissolution of professional discourse. Remodeling the professional capital of rural teachers, continuously promoting the effective value-added of professional capital, enhancing the professional happiness and satisfaction of rural teachers, and realizing the sustainable development of rural education in remote, isolated, and impoverished areas are the only ways to ensure the teaching level of rural basic education. Based on the modeling system of DIKWP, the data, information and knowledge objectively contained in teachers' individual resources are minutely mined, and combined with the subjective professional cognition and content cognition to complement and improve each other, forming a relatively complete knowledge framework, which can effectively alleviate the problems of inconsistency, incompleteness, inaccuracy, and inability to express the urban and rural educational resources, and effectively break down the barriers of value-adding of professional capital in the rural field. © 2023 IEEE.

关键词： Rural areas

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Representing the Intangible Cultural Heritage Knowledge Graph with Vector Embedding 25

Representing the Intangible Cultural Heritage Knowledge Grap...

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作者： Han, Mao Wang, Qing Chen, Hong Chen, Wei Zhang, Junchang Wang, Guang College of Information and Electrical Engineering China Agricultural University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China Century College Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350330014

The construction and effective application of an intangible cultural heritage knowledge graph (ICH KG) can realize the knowledge integration, and optimize the ICH knowledge management. However, high complexity and low computational efficiency of ICH KG make its application face challenges. We propose a multi-source knowledge graph embedding (KGE) model named ICHMKGE to convert the ICH KG into the vector representations to improve the computational efficiency of ICH KG and promote the digital sustainable development of ICH. Firstly, we take the Chinese ICH project 24 solar terms as an example and combine multiple official data sources to construct the Chinese ICH KG as a basis for this study. Secondly, as the ICH project is being further explored with the limited coverage of ICH knowledge, entity sparsity poses a serious challenge for ICH KGE. This paper employs the BERT model to encode the complete description information of entities, and establishes connection between triples entities and ontology concepts via cross-view modeling. Finally, the proposed ICHMKGE model is compared with the baseline models, and the experimental results demonstrate that the model exhibits superiority. © 2023 IEEE.

关键词： Vector spaces

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Few-Shot Segmentation Network Robust to Background Interference 25

Few-Shot Segmentation Network Robust to Background Interfere...

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作者： Ji, Enze Chen, Yunxiao Chen, Anni Chen, Zhikui Computer Network Information Center Chinese Academy of Sciences Beijing China School of Software Technology Dalian University of Technology Dalian China University of Wollongong Computer Science Australia

ISBN: (纸本)9798350330014

Few-shot segmentation has gained significant attention owning to the effectiveness in segmenting unseen classes with a few annotated images. However, there exist two challenges in previous works. 1) They focus on extracting foreground features of support images to guide the segmentation of unseen classes, which causes the loss of useful information and obtains a limited representation of the overall context. 2) They inevitably produce a bias towards base (seen) classes due to the meta-training on the base dataset. That is, the segmentation performance of models can not be guaranteed when predicting images whose backgrounds are similar to classes in the base dataset. To this end, a few-shot segmentation network robust to the background interference (RB-net) is proposed. Specifically, RB-net utilizes middle layers of feature extractors to extract multi-level representations for enhancing the generalization of features. Then, a self-guided prototype correlation learning is designed via modelling correlation matrix encoding between support-query image pairs, to learn foreground and background prototypes with the help of the pyramid pooling encoder, which effectively guides the segmentation of the query image. Meanwhile, a base-class learner based on meta learner is added to predict backgrounds similar to base classes in the query image, for alleviating the bias problem of seen classes. They seamlessly cooperate to suppress the interference of background noises and enhance the segmentation to unseen classes with blurry edges and complex backgrounds. Finally, extensive experiments demonstrate the superiority and effectiveness of RR-net. © 2023 IEEE.

关键词： Signal encoding

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Traffic Speed Prediction Based on Multi Spatial-Temporal Graph Fusion Convolution Network 25

Traffic Speed Prediction Based on Multi Spatial-Temporal Gra...

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作者： Zhang, Anqin Gu, Zhao Ling School of Computer Science Shanghai University of Electric Power Shanghai China

ISBN: (纸本)9798350330014

Traffic speed prediction is an important and challenging task in urban planning. Although there are many existing traffic prediction methods, most of them use two separate modules to capture temporal and spatial correlation respectively, which will limit the effectiveness and efficiency of spatial-temporal feature learning, so they cannot get satisfactory prediction results. In order to capture spatial-temporal correlation at the same time with one module, a new model of Multi Spatial-Temporal Graph Fusion Convolution Networks (Mst-FGCN) for traffic speed prediction is proposed. Mst-FGCN module first use a data-driven method to generate a temporal graph to capture the temporal correlation and the historical data of the same time period and adjacent time periods are processed by fusing multiple different graph. Then, by integrating the graph convolution module and the gated multi-branch convolution module into a unified layer, the fused spatialtemporal fusion graph is processed. Finally, Mst-FGCN can also learn long-term spatial-temporal correlations by stacking deeper layers. Experiments on three real public data sets show that our method is superior to the most advanced baseline. © 2023 IEEE.

关键词： Convolution

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A Shared Bottleneck Aware Multi - Path Selection Scheme for NDN 25

A Shared Bottleneck Aware Multi - Path Selection Scheme for ...

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作者： Wang, Yong Yang, Wang Zhou, Jieyu School of Computer Science and Engineering Central South University China

ISBN: (纸本)9798350330014

Different from the traditional TCP/IP network, the Named Data Networking (NDN) naturally supports consumers to obtain content from multi-source through multi-path. In NDN, the number of available sub-paths of consumers is significantly increased, which means that NDN has the potential to provide better multi-path transmission. However, excessive sub-path use can create shared bottlenecks, leading to increased queuing delays. Also, shared bottleneck detection is not an easy task due to the path lag problem. Therefore, we propose SBAMPS, a Shared Bottleneck Aware Multi-Path Selection scheme designed for NDN. SBAMPS contains CDDTW-SBD, a Shared Bottleneck Detection algorithm based on Congestion Degree (CD) and Dynamic Time Warping (DTW). CDDTW-SBD defines the max queuing ratio of intermediate nodes on the sub-path as its CD, and then calculates the CD similarity of every two sub-paths based on DTW to determine whether they share a bottleneck. Afterward, SBAMPS selects a better sub-path among every two shared bottleneck sub-paths based on their performance evaluation. Simulation results show that CDDTW-SBD can mitigate the interference of path lag and improve detection accuracy by an average of 26 % as compared to the benchmark. SBAMPS can reduce the latency by up to 47.13% and achieve comparable throughput as compared to benchmarks not considering shared bottleneck. © 2023 IEEE.

关键词： Traffic congestion

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Construction of Index System of international Economic Cooperation Innovation Model in Digital Era-based on TIF Theory and DIKWP Model 25

Construction of Index System of International Economic Coope...

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作者： Shanqi, Zhan Tang, Fuliang Li, Yongmei Qiurong, Mei Zining, Lu Xing-Sen, Li Biyun, Zhang School of Software and Microelectronics Peking University Beijing China Dikwp Research Group School of Computer and Technology Hainan University China College of Engineering Peking University China Center of Chinese and Foreign Think Tanks China Beijing Financial Holdings Group China Research Institute of Extenics and Innovation Methods Guangdong University of Technology China China

ISBN: (纸本)9798350330014

With the deepening of globalization, international economic cooperation has become a key factor in promoting economic growth and development. However, the rapid changes and technological advances brought about by the digital age are changing the rules and methods of international economic cooperation. In this digital age, data is no longer just a resource, it is more like a new economic currency, and the development of information technology has made it easier to move data across borders, providing unprecedented opportunities for all kinds of economic activities. However, international economic cooperation in the digital age also comes with a series of challenges. Data privacy and security issues are more prominent, the digital divide persists, and regulatory and legal issues become more complex. The issues of fairness, fairness, efficiency and dialectical openness of digital platforms have become major issues that attract the attention of all countries. Therefore, in these turbulent times, we need innovative methods and indicators to assess and guide international economic cooperation to ensure that it leads to sustainable, beneficial, and balanced growth that benefits everyone around the world. © 2023 IEEE.

关键词： Economics

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