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A Reverse Auction-Based Incentive Mechanism for Cost-Effective Data Collection in Mobile Crowdsensing 29

A Reverse Auction-Based Incentive Mechanism for Cost-Effecti...

引用

29th IEEE International conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, MingHe Wang, BoTao Liu, Gang Huang, Jing Guilin University of Electronic Technology School of Computer Science and Information Security Guilin China Shanghai University School of Computer Engineering and Science Shanghai China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350330717

Incentive mechanisms play a crucial role in mobile crowdsensing between mobile users, third-party platforms, and clients. However, existing mechanisms often do not sufficiently consider users' future potential and past contributions to data sharing when recruiting or retaining them. Moreover, few people consider that the client in this mechanism requires high-quality data collection for training artificial intelligence models. To solve these problems, this paper proposes an innovative mobile crowdsensing incentive mechanism based on reverse auctions. This mechanism not only attracts users who provide high-quality data but also helps the client collect an appropriate amount of cost-effective data for model training. This is achieved by setting prerequisites for the release of tasks by the platform, that is, only after the client's model training effect reaches a certain level of improvement, the platform can release new tasks for data collection. Additionally, when recruiting users to participate in auctions, the platform will selectively recruit based on their potential value to ensure high-quality data collection. In cases where users might consider leaving the platform, measures for retention will be implemented. This will involve offering rewards adjusted proportionally according to their potential and contribution value to encourage them to stay. The comprehensive simulation results demonstrate the effectiveness of this new mechanism, highlighting its superior performance over existing mechanisms. © 2023 IEEE.

关键词： data collection incentive mechanism Mobile crowdsensing reverse auction

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OpenVenus: An Open Service Interface for HPC Environment Based on SLURM 1

引用

7th International conference on Smart Computing and Communication, SmartCom 2022

作者： Wan, Meng Cao, Rongqiang Wang, Yangang Wang, Jue Li, Kai Wang, Xiaoguang Yang, Qinmeng Computer Network Information Center Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the emergence of more and more "AI + Field + HPC" applications, it is urgent to solve the problem of scheduling and management of high-performance Computing (HPC) resources, as well as the fast and efficient "cloud service" of HPC applications. This engineering problem is particularly critical because it affects the progress of scientific research, the development period of the research platform, and the learning cost of scientists. To solve the problem, a set of reusable life cycle processes for HPC resources are designed. Based on the life cycle, we propose an open service interface based on HPC, which reduces the startup time under multiple refreshes and abnormal retries by using the mode of contention lock. The active interruption of users is a typical scenario in the startup phase. Furthermore, a read-write strategy with an overlay based on Singularity is implemented to save storage space and improve running speed. In order to evaluate the serviceability and performance of the proposed interface, we deploy the service on the Venus platform and make a startup comparison experiment. In addition, the reduction of storage for 100 users is also tested. The experimental results show that under the HPC environment with SLURM, the proposed open-service interface can effectively shorten 46% startup time of applications and services and reduce 25% storage at least for each user of the Venus platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer software reusability

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Research on Chinese Elementary Geometry Text Matching Based on Self-Attention and Bi-LSTM Network 20

Research on Chinese Elementary Geometry Text Matching Based ...

引用

20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chen, Guoqing School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Text matching can assist computers in understanding and processing large amounts of textual data, thereby providing more intelligent and personalized services. Addressing issues such as missing traditional machine learning mathematical pre- training data and incomplete semantic extraction of mathematical formula features, a Chinese mathematical plane geometry text matching model based on self-attention mechanism and bidirectional long short-term memory networks (SABi-LSTM) is proposed. The model constructs a parsing tree for mathematical expressions, divides semantics, and establishes a Bi-LSTM network for feature extraction. The performance and timeliness of the model's network structure are evaluated, optimizing the Bi-LSTM layers and hidden layer nodes of the proposed network to determine the optimal network structure. A self-attention mechanism is introduced for optimizing weight parameters to reduce the impact of noise data. Experimental results indicate that compared to traditional machine learning algorithms, the Bi-LSTM network with the introduced self-attention mechanism improves the accuracy of Chinese mathematical text matching by an average of 0.55%. After further optimizing the construction of word vectors, the recognition accuracy increases by an average of 0.34%, resulting in a final accuracy of 89.49%. The proposed model effectively determines the matching degree of Chinese elementary geometry text. © 2023 IEEE.

关键词： Semantics

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Masking Modelling for Underwater Acoustic Target Recognition with Self-supervised Learning 5

Masking Modelling for Underwater Acoustic Target Recognition...

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5th International conference on Frontiers Technology of Information and computer, ICFTIC 2023

作者： Feng, Sheng Zhu, Xiaoqian Ma, Shuqing Lan, Qiang National University of Defense Technology College of Computer Science Changsha Hunan410073 China National University of Defense Technology College of Meteorology & Oceanography Changsha Hunan410073 China

ISBN: (纸本)9798350309034

Underwater acoustic target recognition (UATR) involves classifying targets in a marine environment using sonar signals. This task has been tackled using supervised learning techniques, including CNNs, RNNs, and Transformer models. However, these models often face challenges with generalization and performance due to limited labelled underwater signals and annotation difficulties. Simulated datasets for deep learning-based UATR algorithms are not practical. To overcome these issues, we propose a frame and patch masking Transformer (FPMT) combined with self-supervised learning (SSL) to exploit the high-level knowledge present in the underwater signals themselves. FPMT employs a hierarchical masking procedure on both frame and patch domains with acoustic Mel-spectrograms, allowing the model to learn supervision from joint time-frequency representations. Experimental results based on real-world underwater dataset demonstrate that our method outperforms state-of-the-art SSL methods in recognizing underwater acoustic targets. © 2023 IEEE.

关键词： mask modelling mel-spectrogram self-supervised learning underwater acoustic target recognition

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Research on internal surface finishing of multi-cro b channels 1

Research on internal surface finishing of multi-cro b channe...

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1st International conference on Digital Sovereignty, ICDS 2024

作者： Guo, Jiang Li, Qikai Wang, Liang Qin, Pu Lu, Mingyang Wang, Chao State Key Laboratory of High-performance Precision Manufacturing Dalian University of Technology Dalian116024 China Institute of Machinery Manufacturing Technology China Academy of Engineering Physics Mianyang Sichuan621900 China

The quality of the internal surface plays a crucial role in the performance of parts with complex structures, which are widely used in aerospace, biomedical, and other fields. However, due to poor acce bibility, the uniformity of the internal surface of multi-cro b channels are low after finishing with existing methods. To addre b this i bue, this paper proposes a step-by-step abrasive flow machining method according to the characteristics of multi-cro b channel structures. By flexibly setting the positions of inlets and outlets and adjusting finishing parameters in each step, the material removal rate in different sections of the multi-cro b channels can be regulated. Guided by simulation results, the material removal rate in each section becomes consistent after succe bive steps, achieving uniform finishing of the entire channel. To verify this method, a separatable stainle b-steel part with six cro bed channels was finished under an inlet pre bure of 10 MPa for 40 minutes by using SiC abrasive with a size of 75 μm. The internal surface roughne b of the workpiece was reduced from 1.919 μm to le b than 1 μm, with variation in each section controlled to le b than 12%. © 2025 The Author(s).

关键词： Abrasive cutting

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A Joint Relational Triple Extraction Model Based on Relational Bidirectional Encoding 20

A Joint Relational Triple Extraction Model Based on Relation...

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20th International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Jiahao, Wang Luding, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350318982

Tagging based relational triple extraction methods have recently gained significant research attention. However, most of these methods suffer from the limitation of a unidirectional extraction framework. This framework is overly reliant on the subjects identified by the model in the first stage, leading to an Error Propagation Problem. To conquer this limitation, we devise a relational bidirectional encoding framework that identifying triples and its inverse based on relation and its inverse, then combining the scores of bidirectional triples to obtain factual triples. We use a shared DAE (Denosing Autoencoder) to recognize bidirectional entity pairs, so that the bidirectional features can flow during their respective recognition processes, thereby enhancing and complementing each other. Then we employ a bidirectional biaffine score function to assign every relation to every forward and inverse entity pair. During training, we address the problem of convergence rate inconsistency stemming from shared DAE through introducing a multi-task learning scheme. Our model is experimented on several typical benchmark datasets, and the results demonstrate its effectiveness. It achieves SOTA (state-of-the-art) performance on almost all datasets. Furthermore, our relational bidirectional encoding framework and multi-task learning scheme exhibit excellent adaptability and can improve the precision of other Tagging based factual triple recognition models. © 2023 IEEE.

关键词： Extraction

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Accelerating high-Precision Vulnerability Detection in C programs with Parallel Graph Summarization 6

Accelerating High-Precision Vulnerability Detection in C Pro...

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6th International conference on Software Engineering and computer Science, CSECS 2023

作者： Xu, Rulin Mao, Xiaoguang Xiao, Wei School of Computer NUDT Changsha China

ISBN: (纸本)9798350306378

C language programs are often subject to memory vulnerabilities, posing substantial security risks to software systems. Conventional detection techniques, rooted in static value-flow analysis, necessitate exhaustive searches across the entirety of value-flow graphs. This approach results in inefficient analyses of large-scale codes and presents difficulties in parallelization due to interdependent steps. In this study, we propose an innovative approach based on parallel graph summarization. This technique effectively transforms the computational bottleneck into a task that can be expedited in a parallel manner, leveraging multicore computation to significantly enhance the performance and scalability of vulnerability detection within $C$ programs. We segment the value-flow graph of the program into multiple subgraphs, extracting summaries of three pivotal types of information from each subgraph: path summaries, guard summaries, and behaviour summaries. These summaries significantly stream-line subsequent vulnerability detection analyses. Moreover, we implement a task-level parallel technique to accelerate the graph summary process in a multicore environment. Notably, empirical results reveal that our method, while ensuring accuracy, achieves 2.7X-6.1X speedup compared to serial algorithms. When assessed against prevalent open-source detection tools, our approach demonstrates superior trade-offs between accuracy and efficiency. In conclusion, this research presents an efficient and effective strategy for vulnerability detection in larze-scale C programs. © 2023 IEEE.

关键词： Static analysis

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Collaborative Estimating Multiple Gaussian Graphical Models on Resource Constrained Devices in IoT Networks

Collaborative Estimating Multiple Gaussian Graphical Models ...

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhan, Ying Wang, Beilun School of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9798350337020

In recent years, the rapid development of the Internet of Things (IoT) has attracted significant interest in smart healthcare. However, such collaborative IoT applications still face three major challenges: multi-task data heterogeneity, high communication cost, decentralized computation and privacy preservation. As a result, efficiently integrating and processing such complex data has become a crucial problem to solve. In this paper, we propose COFFE, a collaborative federated learning framework that allows edge devices to participate in the model training. Our framework leverages the local computing power of edge devices and utilizes the communication resources of the IoT network to enable distributed learning. We apply a compression algorithm which optimizes the communication cost by minimizing the amount of exchanged data between the edge devices and the cloud server. We demonstrate the effectiveness of our approach through a set of experiments on a simulated IoT network. Our results show that our proposed COFFE approach achieves significant performance gains compared to traditional centralized learning approaches. Overall, our work provides a promising direction in smart healthcare for applying federated learning to IoT setting and enabling edge device collaboration. © 2023 IEEE.

关键词： Environmental Sensing Medical Informatics Systems Safety and Security

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A Survey on Vehicular Edge Computing Architectures 13

A Survey on Vehicular Edge Computing Architectures

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13th International conference on Information and Communication Technology Convergence, ICTC 2022

作者： Lee, Jaehwan Na, Woongsoo Kongju National University Department of Computer Science and Engineering Korea Republic of Kongju National University Department of Software Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the recent development of intelligent transportation system (ITS), vehicular applications are being actively studied including autonomous driving, vehicle platooning, that require real-time and high-performance processing resources. Accordingly, various system architectures are being studied to satisfy the high-performance computing requirements. Although the mobile cloud computing architecture provides large-scale computing resources, it is not suitable for vehicular environment that requires real-time processing due to the structure in which the edge node and the computing resources are geographically distant. The mobile edge computing (MEC) architecture places computing resources around edge devices. However, it does not consider edge devices moving at a high speed in a limited service area. Vehicular edge computing (VEC) and vehicular fog computing paradigm have emerged to address the latency requirement with the development of vehicular communications. In this paper, such a computation offloading architecture is presented and analyzed. © 2022 IEEE.

关键词： computer architecture

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Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients 41

Beyond the Federation: Topology-aware Federated Learning for...

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41st International conference on Machine Learning, ICML 2024

作者： Ma, Mengmeng Li, Tang Peng, Xi DeepREAL Lab Department of Computer & Information Sciences University of Delaware United States

Federated Learning is widely employed to tackle distributed sensitive data. Existing methods primarily focus on addressing in-federation data heterogeneity. However, we observed that they suffer from significant performance degradation when applied to unseen clients for out-of-federation (OOF) generalization. The recent attempts to address generalization to unseen clients generally struggle to scale up to large-scale distributed settings due to high communication or computation costs. Moreover, methods that scale well often demonstrate poor generalization capability. To achieve OOF-resiliency in a scalable manner, we propose Topology-aware Federated Learning (TFL) that leverages client topology - a graph representing client relationships - to effectively train robust models against OOF data. We formulate a novel optimization problem for TFL, consisting of two key modules: Client Topology Learning, which infers the client relationships in a privacy-preserving manner, and Learning on Client Topology, which leverages the learned topology to identify influential clients and harness this information into the FL optimization process to efficiently build robust models. Empirical evaluation on a variety of real-world datasets verifies TFL's superior OOF robustness and scalability. Copyright 2024 by the author(s)

关键词： Federated learning

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