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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Lyu, Shen-Huan Wu, Jin-Hui Zheng, Qin-Cheng Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9781643685489

Random forests are classical ensemble algorithms that construct multiple randomized decision trees and aggregate their predictions using naive averaging. Zhou and Feng [51] further propose a deep forest algorithm with multi-layer forests, which outperforms random forests in various tasks. The performance of deep forests is related to three hyperparameters in practice: depth, width, and tree size, but little has been known about its theoretical explanation. This work provides the first upper and lower bounds on the approximation complexity of deep forests concerning the three hyperparameters. Our results confirm the distinctive role of depth, which can exponentially enhance the expressiveness of deep forests compared with width and tree size. Experiments validate these theoretical findings. The detailed proof and code are available in the full version [31]. © 2024 The Authors.

关键词： Random forests

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Revisiting and Improving Retrieval-Augmented Deep Assertion Generation

Revisiting and Improving Retrieval-Augmented Deep Assertion ...

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IEEE International Conference on Automated software engineering (ASE)

作者： Weifeng Sun Hongyan Li Meng Yan Yan Lei Hongyu Zhang School of Big Data and Software Engineering Chongqing University Chongqing China

Unit testing validates the correctness of the unit under test and has become an essential activity in software development process. A unit test consists of a test prefix that drives the unit under test into a particular state, and a test oracle (e.g., assertion), which specifies the behavior in that state. To reduce manual efforts in conducting unit testing, Yu et al. proposed an integrated approach (integration for short), combining information retrieval with a deep learning-based approach, to generate assertions for a unit test. Despite being promising, there is still a knowledge gap as to why or where integration works or does not work. In this paper, we describe an in-depth analysis of the effectiveness of integration. Our analysis shows that: ① The overall performance of integration is mainly due to its success in retrieving assertions. ② integration struggles to understand the semantic differences between the retrieved focal-test (focal-test includes a test prefix and a unit under test) and the input focal-test, resulting in many tokens being incorrectly modified; ③ integration is limited to specific types of edit operations (i.e., replacement) and cannot handle token addition or deletion. To improve the effectiveness of assertion generation, this paper proposes a novel retrieve-and-edit approach named EDITAS. Specifically, Editas first retrieves a similar focal-test from a pre-defined corpus and treats its assertion as a prototype. Then, Editas reuses the information in the prototype and edits the prototype automatically. Editas is more generalizable than integration because it can ❶ comprehensively understand the semantic differences between input and similar focal-tests; ❷ apply appropriate assertion edit patterns with greater flexibility; and ❸ generate more diverse edit actions than just replacement operations. We conduct experiments on two large-scale datasets and the experimental results demonstrate that Editas outperforms the state-of-the-art approache

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Confidence-aware Contrastive Learning for Selective Classification 41

Confidence-aware Contrastive Learning for Selective Classifi...

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41st International Conference on Machine Learning, ICML 2024

作者： Wu, Yu-Chang Lyu, Shen-Huan Shang, Haopu Wang, Xiangyu Qian, Chao National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Key Laboratory of Water Big Data Technology Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China

Selective classification enables models to make predictions only when they are sufficiently confident, aiming to enhance safety and reliability, which is important in high-stakes scenarios. Previous methods mainly use deep neural networks and focus on modifying the architecture of classification layers to enable the model to estimate the confidence of its prediction. This work provides a generalization bound for selective classification, disclosing that optimizing feature layers helps improve the performance of selective classification. Inspired by this theory, we propose to explicitly improve the selective classification model at the feature level for the first time, leading to a novel Confidence-aware Contrastive Learning method for Selective Classification, CCL-SC, which similarizes the features of homogeneous instances and differentiates the features of heterogeneous instances, with the strength controlled by the model's confidence. The experimental results on typical datasets, i.e., CIFAR-10, CIFAR-100, CelebA, and ImageNet, show that CCL-SC achieves significantly lower selective risk than state-of-the-art methods, across almost all coverage degrees. Moreover, it can be combined with existing methods to bring further improvement. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Thermal-Aware Approximate Real-Time Task Scheduling for Multicore Embedded Systems

Thermal-Aware Approximate Real-Time Task Scheduling for Mult...

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2023 China Automation Congress, CAC 2023

作者： Peng, Chujun Mo, Lei Liu, Ji Niu, Dan School of Software Engineering Southeast University Suzhou215123 China School of Automation Southeast University Nanjing210096 China Baidu Research Big Data Laboratory Beijing100085 China

ISBN: (纸本)9798350303759

High temperature affects the processing performance of chips and accelerates electronic component aging, leading to reliability issues. This paper proposes a two-stage temperature-aware scheduling scheme for approximate tasks based on homoge-neous multicore platforms, solving approximate task scheduling problem under time, energy, reliability, and temperature constraints. In the first stage, we design a heuristic scheduling algorithm to perform task-to-processor assignment and preliminarily task frequency selection to minimize energy and makespan. In the second stage, we design a temperature optimization algorithm that combines DVFS and slack distribution to ensure that processor temperature is lower than the temperature threshold. Compared with the existing methods, the experimental simulations show that our method achieves 97.6% runtime reduction and 14.9% peak temperature reduction. © 2023 IEEE.

关键词： Scheduling algorithms

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Plausible Heterogeneous Graph k-Anonymization for Social Networks

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Tsinghua Science and Technology 2022年第6期27卷 912-924页

作者： Kaiyang Li Ling Tian Xu Zheng Bei Hui School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu 610000China

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph which is amenable to be adopted in traditional machine learning algorithms in favor of vector *** embedding methods build an important bridge between social network analysis and data analytics as social networks naturally generate an unprecedented volume of graph data *** social network data not only bring benefit for public health,disaster response,commercial promotion,and many other applications,but also give birth to threats that jeopardize each individual’s privacy and ***,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social *** be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network *** this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links while persevering sufficient non-sensitive information such as graph topology and node attributes in graph *** experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

关键词： social network graph embedding privacy preservation adversarial learning

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An Empirical Study of ChatGPT-Related Projects and Their Issues on GitHub

arXiv

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arXiv 2024年

作者： Lin, Zheng Zhang, Neng Liu, Chao Zheng, Zibin School of Software Engineering Sun Yat-Sen University Zhuhai China School of Big Data & Software Engineering Chongqing University Chongqing China

Due to its powerful capabilities in natural language understanding and content generation, ChatGPT has received widespread attention since its launch in 2022. An increasing number of ChatGPT-related projects (that enhance the capabilities of ChatGPT, develop applications by calling ChatGPT APIs, improve the usage of ChatGPT, etc.) are being released on GitHub and have sparked widespread discussions. However, GitHub does not provide a detailed classification of these projects to help users effectively explore interested projects. Additionally, the issues raised by users for these projects cover various aspects, e.g., installation, usage, and updates. It would be valuable to help developers prioritize more urgent issues and improve development efficiency. Unfortunately, there is currently no research focused on understanding the categories and issues of ChatGPT-related projects. To fill this gap, we retrieved 71,244 projects from GitHub using the keyword ‘ChatGPT’ and selected the top 200 representative projects with the highest numbers of stars as our dataset. By analyzing the project descriptions, we identified three primary categories of ChatGPT-related projects, namely ChatGPT Implementation & Training, ChatGPT Application, ChatGPT Improvement & Extension. Next, we applied a topic modeling technique to 23,609 issues of those projects and identified ten issue topics, e.g., model reply and interaction interface. We further analyzed the popularity, difficulty, and evolution of each issue topic within the three project categories. Our main findings are: 1) The increase in the number of projects within the three categories is closely related to the development of ChatGPT;and 2) There are significant differences in the popularity, difficulty, and evolutionary trends of the issue topics across the three project categories. Based on these findings, we finally provided implications for project developers and platform managers on how to better develop and manage ChatGPT-rel

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Model Checking of Service-Oriented Manufacturing Business Process Based on User Feedback 23

Model Checking of Service-Oriented Manufacturing Business Pr...

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9th International Conference on Industrial and Business engineering, ICIBE 2023

作者： Li, Junchao Lei, Yan Li, Maojin Wang, Jiaguo Xu, Lei Sun, Hongxia School of Big Data & Software Engineering Chongqing University China Haier Smart Home Co. Ltd China Qingdao Haidacheng Purchasing Service Co. Ltd China

ISBN: (纸本)9798400708824

Service-oriented manufacturing (SOM) is a strategy pursued by traditional manufacturing industries in recent years for sustainable development. SOM combines services and manufacturing to provide customized products and services for customers, adding value to the products and increasing industrial profits. Business processes are a critical aspect of service-oriented manufacturing, involving the entire process from customer demand to product delivery, and finally to after-sales service. The success of service-oriented manufacturing business processes depends on the correctness and rationality of the process design. However, the relationships between process states in the business process of service-oriented manufacturing are complex, making analysis and validation of the business process challenging. In this paper, we propose a method for verifying service-oriented manufacturing business processes based on user feedback. First, we construct user satisfaction level as threshold criteria in the business process. Then, by defining and formalizing the process and using computing tree logic (CTL) formulas to describe the properties to be verified, we can use the model checking tool UPPAAL to verify the correctness and reliability of the process model. This method helps provide process optimization suggestions to professionals in the early stages of development and ensures the quality of the business process model. © 2023 ACM.

关键词： Model checking

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DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution 43

DGN-MEF: A DenseNet-Based Multi-Exposure Image Fusion Framew...

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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Mei Jin, Ye Zhu, Jinhui South China University of Technology School of Automation Science and Engineering Guangzhou510641 China South China University of Technology School of Software Engineering Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581581

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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A New Complex Deng Entropy with Its Application in Pattern Classification

A New Complex Deng Entropy with Its Application in Pattern C...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Shuoshuo Liu Yangyang Zhao Fuyuan Xiao The school of Big Data and Software Engineering Chongqing University Chongqing China

Complex evidence theory is an effective method for modeling and reasoning uncertain information. But how to handle and measure the uncertainty of complex basic belief assignments is still an open issue. The meaning of this paper is to propose a new complex Deng entropy in complex evidence theory including the information on phase angle and measure of discord and non-specificity. It effectively expresses the uncertainty of complex basic belief assignments and considers more phase information, that is, the physical meaning of the phase angle. When the phase is zero, the number degenerates into a real number, and the new complex Deng entropy degenerates into Deng entropy. And Deng entropy degenerates to Shannon entropy when the space is assumed to be a single subset. We give some numerical examples and discuss them to study the compatibility and effectiveness of the new complex Deng entropy. At last, we apply the proposed new complex Deng entropy to pattern classification. The results further verify that the new complex Deng entropy has better recognition accuracy than the complex-valued Deng entropy on some data sets.

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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