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IEEE International Conference on Automated software engineering (ASE)

作者： Van-Hoang Le Hongyu Zhang School of Information and Physical Sciences University of Newcastle Australia School of Big Data and Software Engineering Chongqing University China

software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks. However, its performance in automated log parsing remains unclear. In this paper, we evaluate ChatGPT's ability to undertake log parsing by addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) How does ChatGPT perform with different prompting methods? Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting. Based on our findings, we outline several challenges and opportunities for ChatGPT-based log parsing.

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What Really Matters for Graph Contrastive Learning-Based Recommendations? A Unified Learning Strategy

What Really Matters for Graph Contrastive Learning-Based Rec...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Hongwei Zhou Min Gao Zongwei Wang Linxin Guo Yinghui Tao Wentao Li School of Big Data and Software Engineering Chongqing University Chongqing China Hong Kong University of Science and Technology China

Graph contrastive learning (GCL) has become increasingly popular in recommendation due to its remarkable ability to reduce reliance on labels. Typically, GCL employs data augmentation methods, e.g., structure perturbation and representation perturbation, and CL loss to enhance performance. Recent studies have shown that structural perturbation plays a minor role in GCL, but there is still a lack of exploration on the representation perturbation. Therefore, we compare the two data perturbations in detail and reveal that both of them have a limited impact on performance. Simply combining recommendation loss and CL loss can produce comparable improvements. Besides, we identify a shared principle between the designs of recommendation loss and CL loss: both aim to optimize representation by increasing similarity between a target node and its positive samples while decreasing similarity with negative samples. Based on these findings, we propose Compact Graph Contrastive Learning (CGCL), a streamlined strategy that eliminates the data augmentation and deeply unifies recommendation loss and CL loss by elegantly incorporating their respective contributions. Leveraging the benefits of the unified strategy, we discover that our model can learn a concentrated mode length distribution of representation, which can enhance the ability to debias and thus improve the performance of the recommendation. This is a novel perspective on representation learning, and we also validate its rationality through rigorous experiments. Our comprehensive study on multiple benchmark datasets demonstrates that CGCL outperforms existing GCL methods.

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Log Parsing with Prompt-based Few-shot Learning

Log Parsing with Prompt-based Few-shot Learning

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International Conference on software engineering (ICSE)

作者： Van-Hoang Le Hongyu Zhang School of Information and Physical Sciences The University of Newcastle Australia School of Big Data and Software Engineering Chongqing University China

Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.

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Visual Explanations: Activation-Based Acute Lymphoblastic Leukemia Cell Classification

Visual Explanations: Activation-Based Acute Lymphoblastic Le...

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IEEE International Conference on Development and Learning, ICDL

作者： Muhammad Umair Raza Adil Nawaz Xintong Liu Zhuangzhuang Chen Victor C.M. Leung Jie Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

The saliency methods are widely used for generating heatmaps that emphasize the important portions of an input image for deep networks on a specific classification task. Interpretability is crucial for deploying deep neural networks in real-world applications. However, the heatmaps produced by current visual explainable methods may contain or visualize particulars differently. To analyze and compare the visualization of different methods, such as Gradient-based, Activation-based, Perturbation-based, and Region-based methods, we empirically evaluated them on the acute lymphoblastic leukemia (cancer cell) classification task using state-of-the-art convolutional neural networks. We also visualized the essential pathological features (salient parts) that are the reasons for the classification results on the classification of normal versus malignant cell (CNMC) dataset.

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Key Factor Analysis of Computer Applications and Technologies Curriculum Based on Professional Certificate Program in Higher Vocational Education

Key Factor Analysis of Computer Applications and Technologie...

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IEEE International Conference on Orange Technologies (ICOT)

作者： Kai Yang Wenbo Cai Zhanglong Nie Dawei Tao Shih-Pang Tseng School of Electronic Engineering Changzhou College of Information Technology Changzhou China School of Software and Big Data Changzhou College of Information Technology Changzhou China

ISBN: (数字)9798331523602

ISBN: (纸本)9798331523619

With the advancement of professional certification in higher vocational education, the formulation of talent training programs has become particularly important. Taking the Computer Application Technology major at Changzhou College of Information Technology as a case study, this paper explores several key elements in writing talent training programs under the background of professional certification: career orientation, training objectives, and graduation requirements. Firstly, it analyzes the sources of content in the career orientation table, especially the focus on main positions. Secondly, it clarifies the method for setting training objectives, providing a general template and an example for the Computer Application Technology major. Finally, it elaborates on the formulation of graduation requirements, particularly the decomposition of indicators to ensure they sufficiently support the graduation requirements. The research aims to provide references and insights for higher vocational colleges to optimize their talent training programs in the context of professional certification.

关键词： Context engineering profession Computer applications Writing Planning Microelectronics Social implications of technology Information technology Certification Standards

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Cost-effective company response policy for product co-creation in company-sponsored online community

arXiv

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arXiv 2024年

作者： Hu, Jiamin Yang, Lu-Xing Yang, Xiaofan Huang, Kaifan Li, Gang Xiang, Yong The School of Big Data & Software Engineering Chongqing University Chongqing400044 China The School of Information Technology Deakin University MelbourneVIC3125 Australia

Product co-creation based on company-sponsored online community has come to be a paradigm of developing new products collaboratively with customers. In such a product co-creation campaign, the sponsoring company needs to interact intensively with active community members about the design scheme of the product. We call the collection of the rates of the company's response to active community members at all time in the co-creation campaign as a company response policy (CRP). This paper addresses the problem of finding a cost-effective CRP (the CRP problem). First, we introduce a novel community state evolutionary model and, thereby, establish an optimal control model for the CRP problem (the CRP model). Second, based on the optimality system for the CRP model, we present an iterative algorithm for solving the CRP model (the CRP algorithm). Thirdly, through extensive numerical experiments, we conclude that the CRP algorithm converges and the resulting CRP exhibits excellent cost benefit. Consequently, we recommend the resulting CRP to companies that embrace product co-creation. Next, we discuss how to implement the resulting CRP. Finally, we investigate the effect of some factors on the cost benefit of the resulting CRP. To our knowledge, this work is the first attempt to study value co-creation through optimal control theoretic approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Iterative methods

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data Hybrid Attacks Based on Sensitive Users in Recommender Systems

Data Hybrid Attacks Based on Sensitive Users in Recommender ...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Hongwei Zhou Min Gao Meiling Chao Yuqi Song Qingyu Xiong Junhao Wen Yi Zhang Big Data and Software Engineering Chongqing University Chongqing China Computer Science and Engineering University of South Carolina Columbia SC USA

ISBN: (数字)9798350359145

ISBN: (纸本)9798350359152

Recommender systems, which make precise recommendations through historical interaction data of users and items, have been widely used in real life. But at the same time, because of the open nature of the systems, they are vulnerable to malicious attacks. To explore the robustness of recommender systems, researchers have proposed various attack methods and analyzed their adverse effects. Although the current attack methods can make good results on rating-based recommender systems, they lack thinking about an attack for a social recommendation. Social relationships have a non-negligible role in recommender systems, but there is still little related research, which only constructs fake rating profiles and fake relationship profiles with simple rules, without considering the mutual influence of rating and social information. To address the above problem, we study the attack method with deeply hidden features and propose a hybrid attack method based on sensitive users. It can further enhance the hidden features of fake users, and then use social attacks to assist in attacking social recommendation methods. Empirical results on three public datasets show that our method outperforms traditional methods in terms of attack effectiveness and has excellent anti-detection capability. Ultimately, we hope that our method could enable more analysis for such a hybrid attack and guides for investigating effective prevention measures.

关键词： Deep learning Robustness Artificial intelligence Recommender systems

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Characterizing and Detecting Gas-Inefficient Patterns in Smart Contracts

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Journal of Computer Science & Technology 2022年第1期37卷 67-82页

作者： Que-Ping Kong Zi-Yan Wang Yuan Huang Xiang-Ping Chen Xiao-Cong Zhou Zi-Bin Zheng Gang Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China School of Software Engineering Sun Yat-sen UniversityZhuhai 519082China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Communication and DesignSun Yat-sen UniversityGuangzhou 510006China Peking University Shenzhen Graduate School Shenzhen 518000China

Ethereum blockchain is a new internetware with tens of millions of smart contracts running on *** from general programs,smart contracts are decentralized,tamper-resistant and permanently ***,to avoid resource abuse,Ethereum charges users for deploying and invoking smart contracts according to the size of contract and the operations executed by *** is necessary to optimize smart contracts to save ***,since developers are not familiar with the operating environment of smart contracts(i.e.,Ethereum virtual machine)or do not pay attention to resource consumption during development,there are many optimization opportunities for smart *** fill this gap,this paper defines six gas-inefficient patterns from more than 25,000 posts and proposes an optimization approach at the source code level to let users know clearly where the contract is *** evaluate the prevalence and economic benefits of gas-inefficient patterns,this paper conducts an empirical study on more than 160,000 real smart *** promising experimental results demonstrate that 52.75%of contracts contain at least one gas-inefficient pattern proposed in this *** these patterns are removed from the contract,at least 0.30 can be saved per contract.

关键词： smart contract anti-pattern detection optimization empirical study

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A Novel Design of Fatigue Driving Detector with mmWave Sensor AWR1642boost 23

A Novel Design of Fatigue Driving Detector with mmWave Senso...

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Proceedings of the 2023 International Conference on Intelligent Computing and Its Emerging Applications

作者： Shih-Pang Tseng I-Hui Hsiao Jhing-Fa Wang School of Software and Big Data Changzhou College of Information Technology Changzhou China Department of Electrical Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9798400709050

According to statistics from the Ministry of Transportation, distracted driving is one of the main causes of traffic accidents. In Taiwan, approximately 20% of total accidents each year are attributed to distracted or fatigued driving, ranking it as the second leading cause of accidents. Typically, drivers start to feel fatigued and experience decreased reaction and decision-making abilities after driving continuously for 2-3 hours. Post-meal and midnight driving also tend to induce drowsiness more than other times. Additionally, alcohol consumption, drug use, and the use of hypnotic medication can lead to loss of consciousness. Fatigued driving poses a serious public safety issue, endangering both drivers and other road users. Therefore, developing effective methods for detecting driver fatigue is crucial. Based on existing research, employing facial monitoring systems can reduce accident rates by 10% to 20%. However, these systems are limited by lighting conditions. Thus, this paper proposes the use of millimeter-wave technology to realize a fatigue detection system, effectively addressing the high reliance of facial monitoring systems on lighting conditions. In this study, data collection was performed using the AWR1642boost, a highly integrated millimeter-wave radar development board developed by Texas Instruments. Subsequently, a Multi-experts system was developed to integrate and analyze four features: nodding, yawning, breathing, and heart rate. The system employed a Support Vector Machine (SVM) model to improve the accuracy of fatigue detection in driving scenarios. Experimental results demonstrated that the system achieved an accuracy rate of 90%, effectively discerning whether the driver was experiencing fatigue. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Highway accidents

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Research on Multi-AGVs dynamic scheduling based on deep reinforcement learning 19

Research on Multi-AGVs dynamic scheduling based on deep rein...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Qin, Zhuorui Wu, Xiaoqian Zheng, Yimin Wu, Qingyao South China University of Technology School of Software Engineering Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Shenzhen Bangqi Technology Intelligent Development Co. Ltd Shenzhen China Pazhou Lab Guangzhou China

ISBN: (纸本)9798350325553

Driven by the latest advances in industrial artificial intelligence, automatic guided vehicles (AGVs) have been widely used for material handling in flexible workshops. In the pursuit of agile and flexible production transportation planning, the ability to handle unpredictable and dynamic events is becoming increasingly important. In order to achieve this real-time dynamic control, this study proposes a distributed multi-agent architecture to solve the dynamic Multi-AGVs scheduling problem. Double Deep Q-Network algorithm is used to train the scheduling agent, capture the relationship between production transportation operation information and scheduling goals, and make real-time scheduling decisions for flexible jobs with constant arrival intervals. Specialized state, action, and reward representations are proposed to handle variable specifications of dynamic scheduling problems. The performance of this method under different scene scales was verified through simulation studies. Numerical results show that compared with existing traditional heuristic scheduling strategies, this method not only has superior performance, but also its advantages persist even if the manufacturing system job scale changes. © 2023 IEEE.

关键词： Reinforcement learning

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