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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Qifan Yao, Junjie Yang, Yuquan Shi, Yizhou Gao, Wei Wang, Xiaoling School of Computer Science and Technology East China Normal University China Software Engineering Institute East China Normal University China Shanghai Key Laboratory of Trustworthy Computing China University of Massachusetts Department of Chemistry Boston United States University of Michigan Ann Arbor United States

ISBN: (纸本)9798350317152

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts. © 2024 IEEE.

关键词： Complex networks

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A Lightweight CNN-Based Pothole Detection Model for Embedded Systems Using Knowledge Distillation 21

A Lightweight CNN-Based Pothole Detection Model for Embedded...

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21st International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2022

作者： Musa, Aminu Hassan, Mohammed Hamada, Mohamed Kakudi, Habeebah Adamu Amin, Md. Faizul Ibne Watanobe, Yutaka Department of Computer Science Federal University Dutse Nigeria Faculty of Computer Sci. & Info. Technology Bayero University Kano Nigeria Software Engineering Lab University of Aizu Japan Graduate School of Computer Science University of Aizu Japan Division of Information System University of Aizu Japan

ISBN: (纸本)9781643683164

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks;this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive;they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model;the model can be trained and deployed on embedded devices such as smartphones efficiently. © 2022 The authors and IOS Press. All rights reserved.

关键词： Autonomous vehicles

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To cool or not to cool? temperature network meets large foundation models via DRO 24

To cool or not to cool? temperature network meets large foun...

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Proceedings of the 41st International Conference on Machine Learning

作者： Zi-Hao Qiu Siqi Guo Mao Xu Tuo Zhao Lijun Zhang Tianbao Yang National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Science and Engineering Texas A&M University College Station Computer Science and Engineering Georgia Institute of Technology Atlanta National Key Laboratory for Novel Software Technology Nanjing University Nanjing China and Pazhou Laboratory (Huangpu) Guangzhou China

The temperature parameter plays a profound role during training and/or inference with large foundation models (LFMs) such as large language models (LLMs) and CLIP models. Particularly, it adjusts the logits in the softmax function in LLMs, which is crucial for next token generation, and it scales the similarities in the contrastive loss for training CLIP models. A significant question remains: "Is it viable to learn a neural network to predict a personalized temperature of any input data for enhancing LFMs?" In this paper, we present a principled framework for learning a small yet generalizable temperature prediction network (TempNet) to improve LFMs. Our solution is composed of a novel learning framework with a robust loss underpinned by constrained distributionally robust optimization (DRO), and a properly designed TempNet with theoretical inspiration. TempNet can be trained together with a large foundation model from scratch or learned separately given a pretrained foundation model. It is not only useful for predicting personalized temperature to promote the training of LFMs but also generalizable and transferable to new tasks. Our experiments on LLMs and CLIP models demonstrate that TempNet greatly improves the performance of existing solutions or models, e.g. Table 1. The code to reproduce the experimental results in this paper can be found at https://***/zhqiu/TempNet.

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Robust UAV Policy Learning for Urban Infrastructure Surface Screening*

Robust UAV Policy Learning for Urban Infrastructure Surface ...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Bingqing Du Uddin Md. Borhan Tianxing Chen Jianyong Chen Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Unmanned Aerial Vehicles (UAVs) have emerged as pivotal tools for infrastructure inspection, mitigating risks to human life and enhancing operational efficiency. Despite advancements, achieving autonomous surface coverage inspection in GPS-denied environments remains a significant challenge. This paper presents a novel approach to enhance UAV policy learning for surface screening of urban infrastructure in environments lacking GPS. Utilizing Deep Reinforcement Learning (DRL), the methodology enables UAVs to autonomously conduct surface coverage inspections more robustly and efficiently. A key innovation is the Optimal Speed Decision (OSD) model, which allows UAVs to adjust flight speed dynamically based on scene perception, enhancing efficiency and safety. Additionally, an Off-Policy Critic-Based Reward Shaping (OPCBRS) method is introduced to improve learning efficiency and performance by addressing the issue of sparse rewards using offline data. Simulation results confirm the effectiveness of these methods, with the OSD model demonstrating significant improvements in navigating complex scenarios and maintaining a balance between efficiency and robustness over traditional uniform speed policies. This research offers a robust framework for UAV-based infrastructure inspection in GPS-denied environments, advancing autonomous UAV applications.

关键词： Technological innovation Navigation Simulation Inspection Autonomous aerial vehicles Deep reinforcement learning Robustness Real-time systems Safety Robots

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Dual Dynamic Attention Network for Flexible Job Scheduling with Reinforcement Learning

Dual Dynamic Attention Network for Flexible Job Scheduling w...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuting Zhao Yuepeng Chen Ji Wu Chenyi Guo Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The flexible job shop problem (FJSP) is a classic combinatorial optimization problem that is strongly NP-hard. Recent studies have utilized deep reinforcement learning (DRL) methods for scheduling operations in FJSP problems, achieving results comparable to accurate methods such as OR tools. However, there are still limitations in extracting global representations of machines and operations. This paper proposes the Dual Dynamic Attention Network (DDAN), which addresses these limitations. The proposed method utilizes an in-channel dynamic attention mechanism to capture the global representation of machines and operations. This allows for accurate and efficient representation of complex dependencies between operations and machines, providing effective support for subsequent dispatching model. Assessments using synthetic datasets as well as public benchmarks corroborate the proposed approach’s superiority over traditional priority dispatching rules (PDRs) and state-of-the-art DRL algorithms. In certain cases, it even surpasses deterministic algorithms. Additionally, this method demonstrates superior performance and stronger generalization capabilities compared to current state-of-the-art DRL methods on large-scale FJSP problems that have not been previously encountered.

关键词： Job shop scheduling Accuracy Heuristic algorithms Computational modeling Feature extraction Dynamic scheduling Deep reinforcement learning

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A Privacy Leakage Detection Method for Personalized Course Recommendation Based on Pi-Calculus 24

A Privacy Leakage Detection Method for Personalized Course R...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： Wu, Zixin Zhou, Xiaoyun Zhu, Yi Wang, Chenyu Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Jiangsu Engineering Technology Research Center of Ict in Education Jiangsu Xuzhou China Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

Privacy protection is one of the main approaches to improve the security and reliability of personalized course recommendation systems. However, the current privacy protection methods for personalized course recommendation mainly focus on data protection, but ignore the potential privacy leakage that may be caused by the system behavior. In addition, privacy protection is not fully considered in the initial phases of system development and design. To address this issue, this paper presents a methodology for detecting privacy leakage in personalized course recommendation systems using the process algebra Pi-Calculus. Firstly, the basic semantics and evolution rules of Pi-Calculuss as well as the definition of mutual simulation techniques are introduced. Then the personalized course recommendation system is mapped to a process algebra Pi-Calculus, a formal description and analysis of the interaction behavior of the components of the personalized course recommendation system is given, and finally a framework for privacy leakage detection is introduced and applied to a case study to detect whether there is privacy leakage behavior in the recommendation system by the inter-simulation technique. Experiments demonstrate that the method can effectively detect potential privacy leakage problems in the early stages of system development. © 2024 IEEE.

关键词： Differential privacy

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Multi-subspace RBFNN driven by features correlation learning 4

Multi-subspace RBFNN driven by features correlation learning

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Zheng, Siming Zhao, Yang Pei, Jihong College of Electronics and Information Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781450385053

For the radial basis function neural network (RBFNN), the centers of the kernels and the network weight are critical to the network performance. The expectation maximization (EM) algorithm can learn the network parameters in RBFNN adaptively. However, high-dimensional samples often have complex distributions in the feature space. In this case, the kernels learned by the EM algorithm may be inaccurate or the algorithm cannot converge. To address these problems, this paper proposes a multi-subspace RBFNN (MS-RBFNN) based on features correlation learning. Inspired by the two-dimensional convolutional neural network sliding window operation to extract local information, this paper uses the correlated features to construct feature subsets with local characteristics. In each subspace, a multi-layer RBFNN is used to extract the local distribution response characteristics of samples. Finally, the distributed response features in multi subspaces are combined to perform classification tasks. This method can use more potential local characteristics of vector features to learn more information about the data. At the same time, The constructed low-dimensional subspaces make the network easier to train and use. Experiment results show that compared with some existing algorithms, the model proposed in this paper has a higher classification accuracy than the state-of-the-art method. © 2021 ACM.

关键词： Vectors

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SolSearch: An LLM-Driven Framework for Efficient SAT-Solving Code Generation

arXiv

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arXiv 2025年

作者： Sheng, Junjie Lin, Yanqiu Wu, Jiehao Huang, Yanhong Shi, Jianqi Zhang, Min Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China

The Satisfiability (SAT) problem is a core challenge with significant applications in software engineering, including automated testing, configuration management, and program verification. This paper presents SolSearch, a novel framework that harnesses large language models (LLMs) to discover and optimize SAT-solving strategies automatically. Leveraging a curriculum-based, trial-and-error process, SolSearch enables the LLM to iteratively modify and generate SAT solver code, thereby improving solving efficiency and performance. This automated SAT-solving paradigm has the advantage of being plug-and-play, allowing integration with any SAT solver and accelerating the development or design process of new SAT solvers (new methods). Our preliminary experimental results are encouraging by demonstrating that the LLM-powered paradigm improves state-of-the-art SAT solvers on general SAT benchmarks and significantly enhances the performance of the widely used Z3 solver (11% on PAR-2 score). These results highlight the potential for using LLM-driven methods to advance solver adaptability and effectiveness in real-world software engineering challenges. Future research directions are discussed to further refine and validate this approach, offering a promising avenue for integrating AI with traditional software engineering tasks. © 2025, CC BY.

关键词： Application programs

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Optimal scheduling of graph states via path decompositions

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Physical Review A 2025年第1期111卷 012627-012627页

作者： Samuel J. Elman Jason Gavriel Ryan L. Mann Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Centre for Quantum Computation and Communication Technology Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia

We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path decomposition of minimal width. Our analysis shows that approximating the spatial cost of a graph is NP-hard, while for graphs with bounded spatial cost, we establish an efficient algorithm for computing an optimal measurement schedule.

关键词： Quantum computers

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Random Forest and Sobol Method Based Data Center PUE Prediction 12

Random Forest and Sobol Method Based Data Center PUE Predict...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Yan, Longchuan Luan, Zhaofang He, Ling Jiang, Congfeng Liu, Junming Xu, Chuanqi Zhang, Liangbin State Grid Corporation of China Information & Telecommunication Branch Beijing China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Information Technology Center Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (纸本)9798350355802

Data centers are critical infrastructures that support a multitude of business loads such as big data, artificial intelligence, and cloud computing. While providing the necessary computing power, the problem of high energy consumption in data centers has become a major obstacle limiting their scale-up, reliability and quality of service. Improving the power usage effectiveness (PUE) of data centers is an important way to improve the energy efficiency of data centers. With the development of theories and methods of machine learning, modeling, analysis and prediction of data center energy consumption using machine learning techniques has become a feasible option. In this study, several accurate PUE prediction models are constructed using neural networks and machine learning techniques for the PUE of a small air-cooled data center in actual operation, among which the mean absolute error of the optimal model reaches 0.29%. In addition, this paper also uses Random Forest and Sobol methods to model and conduct sensitivity analyses of key factors affecting data center PUE, revealing the relationship between data center operating parameters and PUE, and finding that the cooling system exhibits different energy consumption patterns under different IT equipment power conditions, which has a decisive impact on PUE. Based on these sensitivity analyses, we argue that for small air-cooled data centers, improving internal insulation and humidity is an important strategy for optimizing PUE. © 2024 IEEE.

关键词： Sensitivity analysis

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