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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming Shanghai Key Lab. of Trustworthy Computing East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application China China

ISBN: (纸本)9798350333732

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN. © 2023 IEEE.

关键词： Radio access networks

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Rubber Buddy: A Mobile Application to Empower Rubber Planters of Sri Lanka 4

Rubber Buddy: A Mobile Application to Empower Rubber Planter...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Jayawardena, Aparna Ganegoda, Kasuni Imbulana, Sakuni Gunapala, Gavin Kodagoda, Nuwan Jayasinghe, Thilini Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

This research was conducted to develop a mobile application that provides expert solutions for the common problems faced by rubber planters in Sri Lanka. The application developed consists of four components, namely, identification of pests in immature rubber plantations and rubber nurseries;leaf disease identification;cover crop identification;and weed identification. Images taken using the mobile phone cameras are recognized using machine learning models developed using several convolutional neural network (CNN) architectures such as mobile net version 2 (MobileNet v2), VGG 16, VGG19, and residual networks (ResNet). After the images were recognized, the application will provide expert solutions and management strategies to the rubber planters. As most of the rubber plantations are located in areas with low network coverage, the application was designed to be operated in offline mode using TensorFlow lite technology. © 2022 IEEE.

关键词： Rubber

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A Graph Convolution Model for Intelligent Datum Features Selection 18

A Graph Convolution Model for Intelligent Datum Features Sel...

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18th CIRP Conference on Computer Aided Tolerancing, CAT 2024

作者： Lv, Wenbo Zhang, Chaolong Xu, Yuanping Kong, Chao Jin, Jin Li, Tukun Jiang, Xiangqian Tang, Dan Huang, Jian Zhang, Zongzheng School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Shandong Industrial Technology Research Institute of Zhejiang University Shandong China

The datum is a crucial component in tolerance specification, which is the foundation for the selections of geometric tolerances and tolerance principles. Currently, intelligent datum reasoning is largely based on logical rules that are mainly extracted from human experience, resulting in the high uncertainty and low efficiency. To tickle these issues, this study proposes a data selection model based on the GCN (Graph Convolutional Networks), In the devised model, the different geometric features of a workpiece are represented in a graph structure. The geometric, spatial, and assembly relationships, as well as positioning features are computed to obtain vectorized representations of the different geometric features, which serve as inputs to the constructed GCN model. Then, based on the GCN, a datum discriminant classifier has been developed on the training samples. To enhance the classifier accuracy, multiple GCN layers are employed for training, with the output of each GCN module added to a list. Ultimately, the outputs of all GCN modules are concatenated and subjected to classification prediction through fully connected layers. Datum specifications are established based on the classification of geometric features. The effectiveness and feasibility of this method are validated through case studies, e.g. rear floor crossbeams, with comparative results indicating a similarity rate of 85.19% with manually designed outcomes. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolution

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Federated Graph Learning for Cross-Domain Recommendation 38

Federated Graph Learning for Cross-Domain Recommendation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Ziqi Peng, Zhaopeng Wang, Zihui Qi, Jianzhong Chen, Chaochao Pan, Weike Wen, Chenglu Wang, Cheng Fan, Xiaoliang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computing and Information Systems The University of Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer between source and target domains. However, many recent CDR models overlook crucial issues such as privacy as well as the risk of negative transfer (which negatively impact model performance), especially in multi-domain settings. To address these challenges, we propose FedGCDR, a novel federated graph learning framework that securely and effectively leverages positive knowledge from multiple source domains. First, we design a positive knowledge transfer module that ensures privacy during inter-domain knowledge transmission. This module employs differential privacy-based knowledge extraction combined with a feature mapping mechanism, transforming source domain embeddings from federated graph attention networks into reliable domain knowledge. Second, we design a knowledge activation module to filter out potential harmful or conflicting knowledge from source domains, addressing the issues of negative transfer. This module enhances target domain training by expanding the graph of the target domain to generate reliable domain attentions and fine-tunes the target model for improved negative knowledge filtering and more accurate predictions. We conduct extensive experiments on 16 popular domains of the Amazon dataset, demonstrating that FedGCDR significantly outperforms state-of-the-art methods. We open source the code at https://***/LafinHana/FedGCDR. © 2024 Neural information processing systems foundation. All rights reserved.

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A study of geometry teaching mode based on Van Hiele theory and NetPad 23

A study of geometry teaching mode based on Van Hiele theory ...

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7th International Conference on Education and Multimedia Technology, ICEMT 2023

作者： Gao, Jinghui Wang, Ying Ding, Qianhui Xu, Zhangtao Guan, Hao Institute of Computing Science and Technology Guangzhou University China Software Engineering Institute of Guangzhou Guangzhou China School of Mathematics and Statistics of CCNU Central China Normal University China

ISBN: (纸本)9798400709142

It is one of the main goals of the mathematics curriculum reform to cultivate and develop geometric thinking in junior high school students. Geometric thinking skills have a very profound impact on middle school students learning of mathematics. Van Hiele's theory is dedicated to reducing the gap between the level of difficulty defined by the material and the thinking of the students. It provides strong theoretical support for analyzing the development process of students' geometric thinking and evaluating their level of geometric thinking. With the combination of dynamic geometry software (NetPad) and Van Hiele theory, this study proposes a geometry teaching mode for junior high school students' geometric thinking cultivation, which includes five teaching sessions of Interview, Direct Orientation, Explication, Activities, and Integration. An effective teacher-led, student inquiry-based teaching process, it helps students discover geometric patterns, understand geometric properties, and improve their geometric thinking. The effectiveness of the mode was verified by seventh graders using NetPad to explore Euler's theorem. The results verified the effectiveness of this geometry teaching model to a certain extent. On the other hand, it proves that this model can help improve teaching efficiency. The teaching practice provides a program reference for teachers to design a scientific and reasonable geometry lesson. © 2023 ACM.

关键词： Geometry

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Utilization-Aware Data Variable Allocation on NVM- Based SPM in Real-Time Embedded Systems

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Journal of Electronic Science and Technology 2021年第2期19卷 163-172页

作者： Jin-Yu Zhan Yi-Xin Li Wei Jiang Jun-Huan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054 State Key Lab of Mathematical Engineering and Advanced Computing Wuxi 214000

With the development of the nonvolatile memory(NVM),using NVM in the design of the cache and scratchpad memory(SPM)has been *** paper presents a data variable allocation(DVA)algorithm based on the genetic algorithm for NVM-based SPM to prolong the *** lifetime can be formulated indirectly as the write counts on each SPM *** the differences between global variables and stack variables,our optimization model has three *** constraints of the central processing unit(CPU)utilization and size are used for all variables,while no-overlay constraint is only used for stack *** satisfy the constraints of the optimization model,we use the greedy strategy to generate the initial population which can determine whether data variables are allocated to SPM and distribute them evenly on SPM ***,we use the Mälardalen worst case executive time(WCET)benchmark to evaluate our *** experimental results show that the DVA algorithm can not only obtain close-to-optimal solutions,but also prolong the lifetime by 9.17% on average compared with SRAM-based SPM.

关键词： Data variables allocation genetic algorithm nonvolatile momory(NVM) scratchpad memory(SPM)

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Cryptography of Blockchain 7th

Cryptography of Blockchain

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Long, Ying Gong, Yinyan Huang, Weihong Cai, Jiahong Xu, Nengxiang Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

With the development of digital currencies and 5G technology, blockchain has gained widespread attention and is being used in areas such as healthcare, industry and smart vehicles. Many security issues have also been exposed in the course of blockchain applications. Cryptography can ensure the security of data on the blockchain, the integrity and validity of data as well as the ability to authenticate users and anonymize them. This article therefore examines the cryptography underlying blockchain security issues, providing an overview of cryptographic homomorphic encryption, zero-knowledge proofs and secure multi-party computation commonly used in blockchains. At the same time, the development of quantum computing is bound to affect existing cryptographic systems, and blockchains applying these cryptographic systems are bound to be hit hard, so this article discusses four of the most promising post-quantum cryptography techniques available: hash-based public key cryptography, code-based public key cryptography, multivariate public key cryptography, and lattice-based public key cryptography. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Public key cryptography

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Semantic feature extraction and classification inference of ship trajectories using coding sequences: A case study of the Taiwan strait 7

Semantic feature extraction and classification inference of ...

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7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023

作者： Wang, Zhishan Mei, Qiang Wang, Peng Li, Yong Yang, Yang Li, Zhaoxuan Xie, Wenxin Beijing University of Technology School of Software Engineering Beijing China Jimei University Navigation College Xiamen China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9798350308532

In the process of monitoring a myriad of vessels, maritime administrators recurrently confront situations where crucial static fields, such as vessel type, are frequently missing from automatic identification system (AIS) data. This omission presents a significant obstacle to effective safety management, prompting the need for a method to rectify incomplete data. In terrestrial traffic, constraints dictated by road widths do not apply to marine traffic, thus allowing vessels of an identical type to opt for routes that diverge by numerous nautical miles. This condition renders trajectory clustering insufficient. To tackle this issue, this paper proposes a division of the sea area into spatiotemporal grids and a transformation of vessel trajectories into a coded sequence of navigational grids. Word2vec embedding is merged with deep neural networks predicated on convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms through the utilization of natural language processing techniques. The goal is to facilitate efficient vessel classification via deep learning. Applying the Taiwan Strait as a case study, it is shown that under the conditions of a five-dimensional word embedding, the F1 score for vessel classification using RNN models can attain 0.94. This investigation provides a unique method for processing vessel trajectory data, and it also assists in the effective completion of missing vessel types. © 2023 IEEE.

关键词： Embeddings

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Blockchain Scalability Technologies 7th

Blockchain Scalability Technologies

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7th International Conference on Smart computing and Communication, SmartCom 2022

作者： Xu, Nengxiang Cai, Jiahong Gong, Yinyan Zhang, Huan Huang, Weihong Li, Kuan-ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9783031281235

As the underlying implementation technology of the current main-stream digital currency, blockchain can establish a trusted distributed system without relying on third-party trusted institutions or a privacy-protect system. The decentralized characteristics of blockchain have broad application scenarios, such as the Internet of Things, financial technology and other fields. However, the scalability of the current blockchain is seriously insufficient, such as limited throughput in performance, small storage capacity, and difficulty in scaling functions. This paper introduces the definition and technical classification of scalability, analyzes the current problems of scalability and briefly introduces the current main-stream expandable technologies such as sharding, on-chain and off-chain storage, off-chain payment channel and cross-chain technology from two aspects of performance and function, as well as the principles and ideas of these technologies. Finally, the research progress of current blockchain extension technology is summarized, and the problems faced by the current extension scheme are pointed out, which provides a direction for future research work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Research on key technologies of edge cache in virtual data space across WAN

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Frontiers of Computer Science 2023年第1期17卷 15-34页

作者： Jiantong HUO Yaowen XU Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China College of Software Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China College of Computer Science and Technology Zhejiang UniversityHangZhou 310013China

The authors of this paper have previously proposed the global virtual data space system (GVDS) to aggregate the scattered and autonomous storage resources in China’s national supercomputer grid (National Supercomputing Center in Guangzhou, National Supercomputing Center in Jinan, National Supercomputing Center in Changsha, Shanghai Supercomputing Center, and Computer Network Information Center in Chinese Academy of Sciences) into a storage system that spans the wide area network (WAN), which realizes the unified management of global storage resources in China. At present, the GVDS has been successfully deployed in the China National Grid environment. However, when accessing and sharing remote data in the WAN, the GVDS will cause redundant transmission of data and waste a lot of network bandwidth resources. In this paper, we propose an edge cache system as a supplementary system of the GVDS to improve the performance of upper-level applications accessing and sharing remote data. Specifically, we first designs the architecture of the edge cache system, and then study the key technologies of this architecture: the edge cache index mechanism based on double-layer hashing, the edge cache replacement strategy based on the GDSF algorithm, the request routing based on consistent hashing method, and the cluster member maintenance method based on the SWIM protocol. The experimental results show that the edge cache system has successfully implemented the relevant operation functions (read, write, deletion, modification, etc.) and is compatible with the POSIX interface in terms of function. Further, it can greatly reduce the amount of data transmission and increase the data access bandwidth when the accessed file is located at the edge cache system in terms of performance, i.e., its performance is close to the performance of the network file system in the local area network (LAN).

关键词： virtual data space system wide area network edge cache redundant data transmission

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