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arXiv 2024年

作者： Chen, Chuan Liao, Tianchi Deng, Xiaojun Wu, Zihou Huang, Sheng Zheng, Zibin School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Key Laboratory of Big Data Analysis and Processing China School of Software Engineering Sun Yat-sen University Zhuhai519000 China Zhuhai Key Laboratory of Trusted Large Language Models China School of Systems Science and Engineering Sun Yat-sen University Guangzhou510006 China

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources. This diversity leads to significant heterogeneity, which increases the complexity of model training. In this paper, we first outline the basic concepts of heterogeneous FL and summarize the research challenges in FL in terms of five aspects: data, model, task, device and communication. In addition, we explore how existing state-of-the-art approaches cope with the heterogeneity of FL, and categorize and review these approaches at three different levels: data-level, model-level, and architecture-level. Subsequently, the paper extensively discusses privacy-preserving strategies in heterogeneous FL environments. Finally, the paper discusses current open issues and directions for future research, aiming to promote the further development of heterogeneous FL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Social Media and Stock Market Prediction: A big data Approach

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Computers, Materials & Continua 2021年第5期67卷 2569-2583页

作者： Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz School of Computing Faculty of EngineeringUniversity Teknologi MalaysiaJohorMalaysia Department of Software Engineering University of Management and TechnologyLahorePakistan Collage of Business Abu Dhabi UniversityAbu DhabiUnited Arab Emirates Oxford Center for Islamic Studies The University of OxfordOxfordUK The University of Liverpool Management School The University of LiverpoolLiverpoolUK Department of Computer Engineering National University of TechnologyIslamabadPakistan School of Computing UTM Big Data CentreUniversiti Teknologi MalaysiaJohorMalaysia

big data is the collection of large datasets from traditional and digital sources to identify trends and *** quantity and variety of computer data are growing exponentially for many *** example,retailers are building vast databases of customer sales *** are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and *** of big data include volume and variety in both structured and unstructured *** this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional *** studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision *** implemented naive Bayes and logistic regression classification *** results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

关键词： big data analytics artificial intelligence machine learning stock market social media business analytics

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Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network

arXiv

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arXiv 2023年

作者： Lei, Yan Wen, Tiantian Xie, Huan Fu, Lingfeng Liu, Chunyan Xu, Lei Sun, Hongxia School of Big Data & Software Engineering Chongqing University Chongqing China Peng Cheng Laboratory Shenzhen China Haier Smart Home Co. Ltd. Qingdao China Qingdao Haidacheng Purchasing Service Co. Ltd. Qingdao China

Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL effectiveness. To mitigate the effect of class imbalance in FL, we propose CGAN4FL: a data augmentation approach using Context-aware Generative Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program dependencies to construct a failure-inducing context showing how a failure is caused. Then, CGAN4FL leverages a generative adversarial network to analyze the failure-inducing context and synthesize the minority class of test cases (i.e., failing test cases). Finally, CGAN4FL augments the synthesized data into original test cases to acquire a class-balanced dataset for FL. Our experiments show that CGAN4FL significantly improves FL effectiveness, e.g., promoting MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10 respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Mitigating the Effect of Class Imbalance in Fault Localization Using Context-aware Generative Adversarial Network

Mitigating the Effect of Class Imbalance in Fault Localizati...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Yan Lei Tiantian Wen Huan Xie Lingfeng Fu Chunyan Liu Lei Xu Hongxia Sun School of Big Data & Software Engineering Chongqing University Chongqing China Peng Cheng Laboratory ShenZhen China Haier Smart Home Co. Ltd. Qingdao China Qingdao Haidacheng Purchasing Service Co. Ltd. Qingdao China

Fault localization (FL) analyzes the execution information of a test suite to pinpoint the root cause of a failure. The class imbalance of a test suite, i.e., the imbalanced class proportion between passing test cases (i.e., majority class) and failing ones (i.e., minority class), adversely affects FL *** mitigate the effect of class imbalance in FL, we propose CGAN4FL: a data augmentation approach using Context-aware Generative Adversarial Network for Fault Localization. Specifically, CGAN4FL uses program dependencies to construct a failure-inducing context showing how a failure is caused. Then, CGAN4FL leverages a generative adversarial network to analyze the failure-inducing context and synthesize the minority class of test cases (i.e., failing test cases). Finally, CGAN4FL augments the synthesized data into original test cases to acquire a class-balanced dataset for FL. Our experiments show that CGAN4FL significantly improves FL effectiveness, e.g., promoting MLP-FL by 200.00%, 25.49%, and 17.81% under the Top-1, Top-5, and Top-10 respectively.

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Computer-Automated Design of Self-Aligned Multi-Ball Pitch Artifact with High Precision

SSRN

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SSRN 2024年

作者： Zhu, Hongjun Liu, Binghong Zhou, Sen Zhang, Xiaoyan Xin, Liu Chen, Kuo Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China Chongqing Academy of Metrology and Quality Inspection Chongqing401121 China School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland1010 New Zealand Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

This study presents a computational methodology for developing a multi-ball system that automatically aligns during assembly processes. The contributions of this work are threefold: Firstly, a comprehensive error analysis of the multi-ball system is performed. It is found that temperature and pressure-induced deformations should not be ignored when micrometer-level precision is required. Secondly, a novel process flow for designing the multi-ball pitch artifact is introduced, which enables optimization decision-making. Lastly, a series of experiments are conducted on standard bearing balls (grade G20). The resulting artifact meets the Class 1 standards. It is reasonable to assume that, by using balls of grade G3 precision, the multi-ball artifact can achieve accuracy at the sub-micrometer level. © 2024, The Authors. All rights reserved.

关键词： Decision making

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data Center Microgrid Energy Management Based on Model-Based Reinforcement Learning

Data Center Microgrid Energy Management Based on Model-Based...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： He Zhao Jianxiong Wan Leixiao Li Inner Mongolia University of Technology Hohhot China Inner Mongolia University of Technology Inner Mongolia Autonomous Region Engineering &TechnologyResearch Center of Big Data Based Software Service Hohhot China

With the increasing of energy consumption and price, energy management is becoming increasingly important for data center with larger and larger scale. Microgrid can provide reliable, stable and continuous power services for data centers, and improve energy efficiency and reduce the operating cost. In this paper, we study the energy management problem for data center microgrid (DCMG) integrated with renewable energy. We introduce the energy storage device to mediate the intermittent nature of renewable energy. The energy management problem is formulated as a Markov Decision Process (MDP). Traditional methods greedy choice actions does not consider the impact of the actions on future cumulative operating cost. Model-free reinforcement learning is a candidate solution to this problem. However, model-free reinforcement learning methods converge slowly. Therefore, in this paper, we propose a model- based deep reinforcement learning algorithms to solve the energy management problem (Model-Based Reinforcement Learning Energy Management, MBRL-EM) of DCMG. We use long short-term memory (LSTM) model to design a system transition model, which predicts the future renewable energy production and energy demand. Then we obtain the optimal policy based on model predictive control to minimize the long-term operation cost. The simulation results show that MBRL-EM outperforms the baseline algorithms of model-based PPO and model-free PPO and reduces the average cost by up to 23% and 38% respectively.

关键词： data centers Renewable energy sources Costs Computational modeling Reinforcement learning Microgrids Predictive models

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Comprehensive evaluation method of driving behavior based on neural network 4

Comprehensive evaluation method of driving behavior based on...

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2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021

作者： Li, Jiayao Li, Li Jiao, Ge Zhao, Xianjing He, Yuhong College of Computer Science and Technology Hengyang Normal University Hunan Hengyang China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Chongqing University of Education Chongqing China Artificial Intelligence and Big Data College Chongqing College of Electronic Engineering Chongqing China

In recent years, the number of traffic accidents in the world has increased sharply. Reasonable mining of the OBD data generated in the process of vehicle driving will help to improve traffic safety. However, the existing driving behavior scoring models have the following shortcomings: the indexes are not comprehensive enough, and the selection of weights is not objective. In order to solve these shortcomings, this paper proposes 16 driving behavior indexes and related definitions;the neural network is used to construct the model to distribute the weight reasonably;a new comprehensive evaluation model of driving behavior is constructed. The feasibility and efficiency of the model are verified by experiments on real vehicle OBD data sets. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

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software Vulnerabilities Detection Based on a Pre-trained Language Model

Software Vulnerabilities Detection Based on a Pre-trained La...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Wenlin Xu Tong Li Jinsong Wang Haibo Duan Yahui Tang School of Information Science and Engineering Yunnan University Kunming China School of Big Data Yunnan Agricultural University Kunming China Information Management Center Yunnan University of Finance and Economics Kunming China School of Software Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

software vulnerabilities detection is crucial in cyber security which protects the software systems from malicious attacks. The majority of earlier techniques relied on security professionals to provide software features before training a classification or regression model on the features to find vulnerabilities. However, defining software features and collecting high-quality labeled vulnerabilities for training are both time consuming. To handle these issues, in this paper, we propose an unsupervised and effective method for extracting software features and detecting software vulnerabilities automatically. Firstly, we obtain software features and build a new pre-trained BERT model through constructing C/C++ vocabulary and pre-training on software source code. We then fine-tune the pre-trained BERT model with a deep autoencoder and create low-dimensional embedding from the software features. We finally apply a clustering-based outlier detection method on the embedding to detect vulnerabilities. We evaluate our method on five datasets with programs written in C/C++, experimental results show that our method outperforms state-of-the-art software vulnerability detection methods.

关键词： Training Vocabulary Privacy Computational modeling Source coding Feature extraction software systems

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SMOG: Accelerating Subgraph Matching on GPUs

SMOG: Accelerating Subgraph Matching on GPUs

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Zhibin Wang Ziheng Meng Xue Li Xi Lin Long Zheng Chen Tian Sheng Zhong State Key Laboratory for Novel Software Technology Nanjing University Alibaba Group“ National Engineering Research Center for Big Data Technology and System/ Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Zhejiang Lab

Subgraph matching is a crucial problem in graph theory with diverse applications in fields, such as bioinformatics, social networks and recommendation systems. Accelerating subgraph matching can be greatly facilitated by GPUs, which offer exceptional parallelism and high memory bandwidth. By leveraging the power of multiple GPU cards, subgraph matching can be scaled to achieve unprecedented levels of performance. In this paper, we propose SMOG, an abbreviation for. Subgraph Matching On Multi-Card GPUs. It is a general, high-performance and scalable sub graph matching system that utilizes multi-card GPUs. To address the issue of duplication resulting from subgraph automorphism, SMOG introduces a two-step approach. Firstly, it analyzes the symmetry within the subgraph. Then, it adaptively adjusts the graph preprocessing and generates subgraph-aware GPU codes tailored to the given subgraph. Furthermore, SMOG leverages multi-level parallelism by designing the specific strategy for each level, enabling it to scale from 1 to 1,024 GPU cards, resulting in an extraordinary $553\times$ speedup. We evaluate SMOG on various subgraph queries and datasets. The experimental results demonstrate that SMOG outperforms the triangle-specific system TRUST with an average speedup of $2.94\times$ . And it performs significantly better than the subgraph matching system RPS by $203.55\times$ and the graph processing system Gunrock by 35, $455.52\times$ on average.

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Modality Decoupling is All You Need: A Simple Solution for Unsupervised Hyperspectral Image Fusion

arXiv

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arXiv 2024年

作者： Du, Songcheng Zou, Yang Wang, Zixu Li, Xingyuan Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University China School of Software Technology Dalian University of Technology China Institute of Mathematics Physics and Computer Science Aberystwyth University United Kingdom The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University China

Hyperspectral Image Fusion (HIF) aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and high spectral resolution images. Current methods typically apply direct fusion from the two modalities without valid supervision, failing to fully perceive the deep modality-complementary information and hence, resulting in a superficial understanding of inter-modality connections. To bridge this gap, we propose a simple and effective solution for unsupervised HIF with an assumption that modality decoupling is essential for HIF. We introduce the modality clustering loss that ensures clear guidance of the modality, decoupling towards modality-shared features while steering clear of modality-complementary ones. Also, we propose an end-to-end Modality-Decoupled Spatial-Spectral Fusion (MossFuse) framework that decouples shared and complementary information across modalities and aggregates a concise representation of the LR-HSI and HR-MSI to reduce the modality redundancy. Systematic experiments over multiple datasets demonstrate that our simple and effective approach consistently outperforms the existing HIF methods while requiring considerably fewer parameters with reduced inference time. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectral resolution

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