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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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Effective Entry-Wise Flow for Molecule Generation 40

Effective Entry-Wise Flow for Molecule Generation

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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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FakeDiffer: Distributional Disparity Learning on Differentiated Reconstruction for Face Forgery Detection 39

FakeDiffer: Distributional Disparity Learning on Differentia...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Bo Zhang, Zhao Zhao, Suiyi Ye, Xianming Zhang, Haijun Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunnan China Department of Electronic and Computer Engineering University of Pretoria South Africa Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (纸本)157735897X

Existing face forgery detection methods achieve promising performance when training and testing forgery data are from identical manipulation types, while they fail to generalize well to unseen samples. In this paper, we experimentally investigate and find that the poor generalization of the methods mainly arises from their overfitting on the known fake patterns. Excessively focused on seen fakes, those detectors fail to effectively learn image-intrinsic information and the distributional disparity between real and fake images. Then, to address this issue, we redefine fake learning as real-fake distributional disparity learning. We propose a novel deepfake detection framework learning distributional disparity based on the differentiated reconstruction on real and fake images for improved generalization. Specifically, distributional disparity learning on differentiated reconstruction of the real and fake images, enforces the model to learn image-invariant intrinsic representations. The reconstruction on real and fake images forces the decoders to learn the distribution of real and fake images, respectively. Moreover, to avoid the influence from the specificalization of the known fake patterns, we further propose the information interaction learning on the encoded intrinsic information and the pixel disparity between the input image and its reconstruction to distinguish face forgeries that are even unknown. Extensive experiments on large-scale benchmark datasets demonstrated the effectiveness of addressing the overfitting issue of the classification network, and verified the superior performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Metric localization for lunar rovers via cross-view image matching

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Visual Intelligence 2024年第1期2卷 1-14页

作者： Chen, Zihao Li, Kunhong Li, Haoran Fu, Zhiheng Zhang, Hanmo Guo, Yulan School of Electronics and Comunication Engineering the Shenzhen Campus of Sun Yat-sen University Sun Yat-sen University Shenzhen 510275 China Peng Cheng Laboratory Guangdong Shenzhen 518055 China Department of Computer Science and Software Engineering The University of Western Australia Perth 6009 Australia Department of Shanghai Key Laboratory of Aerospace Intelligent Control Technology Shanghai Aerospace Control Technology Institute Shanghai 201109 China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha 410073 China

Accurate localization is critical for lunar rovers exploring lunar terrain features. Traditionally, lunar rover localization relies on sensor data from odometers, inertial measurement units and stereo cameras. However, localization errors accumulate over long traverses, limiting the rover’s localization accuracy. This paper presents a metric localization framework based on cross-view images (ground view from a rover and air view from an orbiter) to eliminate accumulated localization errors. First, we employ perspective projection to reduce the geometric differences in cross-view images. Then, we propose an image-based metric localization network to extract image features and generate a location heatmap. This heatmap serves as the basis for accurate estimation of query locations. We also create the first large-area lunar cross-view image (Lunar-CV) dataset to evaluate the localization performance. This dataset consists of 30 digital orthophoto maps (DOMs) with a resolution of 7 m/pix, collected by the Chang’e-2 lunar orbiter, along with 8100 simulated rover panoramas. Experimental results on the Lunar-CV dataset demonstrate the superior performance of our proposed framework. Compared to the second best method, our method significantly reduces the average localization error by 26% and the median localization error by 22%. © The Author(s) 2024.

关键词： Aerial patch Cross-view metric localization (CVML) Lunar rover Panorama Perspective projection

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Infinity-Norm Weighted Low-Order Sparse Iterative DOA Estimation under Impulsive Noise

Infinity-Norm Weighted Low-Order Sparse Iterative DOA Estima...

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2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023

作者： Zhang, Yongqing Liu, Zhiqiang Wang, Weidong Li, Hui Shi, Wentao School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454003 China School of Computer Science and Technology Henan Polytechnic University Jiaozuo454003 China School of Software Henan Polytechnic University Jiaozuo454003 China Ocean Institute Northwestern Polytechnical University Taicang215400 China

ISBN: (纸本)9798350316728

This paper investigates the subject of the direction of arrival (DOA) estimation with acoustic vector sensor array (AVSA) under impulsive noise. A low-order-based variant of the typical sparse reconstruction method is designed to address this challenge. The proposed scheme successively weights the array output snapshots using the infinity norm to remove high outliers. Subsequently, the Frobenius norm based approach is extended to lower order, and a new cost function is created to address the impulse characteristics of the residual. Finally, the expression of the signal power is quantified, and DOA is approximated on the signal power by spectral peak search mechanism. Numerical results demonstrate that the proposed algorithm offers significant performance enhancements over some current techniques in impulsive noise scenarios. © 2023 IEEE.

关键词： Impulse noise

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Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video

arXiv

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arXiv 2024年

作者： Fan, Junkai Wang, Kun Yan, Zhiqiang Chen, Xiang Gao, Shangbing Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Huaiyin Institute of Technology China

In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness consistency constraint (BCC). To tackle these intertwined tasks, we propose a novel depth-centric learning framework that integrates the ASM model with the BCC constraint. Our key idea is that both ASM and BCC rely on a shared depth estimation network. This network simultaneously exploits adjacent dehazed frames to enhance depth estimation via BCC and uses the refined depth cues to more effectively remove haze through ASM. Additionally, we leverage a non-aligned clear video and its estimated depth to independently regularize the dehazing and depth estimation networks. This is achieved by designing two discriminator networks: DMFIR enhances high-frequency details in dehazed videos, and DMDR reduces the occurrence of black holes in low-texture regions. Extensive experiments demonstrate that the proposed method outperforms current state-of-the-art techniques in both video dehazing and depth estimation tasks, especially in real-world hazy scenes. Project page: https://***/projectpage/DCL/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Demulsification

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A Taxonomy for Requirements engineering and software Test Alignment

arXiv

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arXiv 2023年

作者： Unterkalmsteiner, Michael Feldt, Robert Gorschek, Tony Software Engineering Research Lab School of Computing Blekinge Institute of Technology Sweden Department of Computer Science and Engineering Chalmers University of Technology Sweden

Requirements engineering and software Testing are mature areas and have seen a lot of research. Nevertheless, their interactions have been sparsely explored beyond the concept of traceability. To fill this gap we propose a definition of requirements engineering and software test (REST) alignment, a taxonomy that characterizes the methods linking the respective areas, and a process to assess alignment. The taxonomy can support researchers to identify new opportunities for investigation, as well as practitioners to compare alignment methods and evaluate alignment, or lack thereof. We constructed the REST taxonomy by analyzing alignment methods published in literature, iteratively validating the emerging dimensions. The resulting concept of an information dyad characterizes the exchange of information required for any alignment to take place. We demonstrate use of the taxonomy by applying it on five in-depth cases and illustrate angles of analysis on a set of thirteen alignment methods. In addition we developed an assessment framework (REST-bench), applied it in an industrial assessment, and showed that it, with a low effort, can identify opportunities to improve REST alignment. Although we expect that the taxonomy can be further refined, we believe that the information dyad is a valid and useful construct to understand alignment. Categories and Subject Descriptors: D.2.1 [software engineering]: Requirements/Specifications;D.2.4 [software engineering]: software/Program Verification;D.2.9 [software engineering]: Management—software quality assurance © 2023, CC BY-NC-SA.

关键词： Requirements engineering

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Predictive Scheme for Mixed Transmission in Time-Sensitive Networking

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ZTE Communications 2022年第4期20卷 78-88页

作者： LI Zonghui YANG Siqi YU Jinghai HE Fei SHI Qingjiang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China ZTE Corporation Shenzhen 518057China School of Software Tsinghua UniversityBeijing 100084China School of Software Engineering Tongji UniversityShanghai 201804China Shenzhen Research Institute of Big Data Shenzhen 518172China

Time-sensitive networking(TSN)is an important research area for updating the infrastructure of industrial Internet of *** a product of the integration of the operation technology(OT)and the information technology(IT),it meets the real-time and deterministic nature of industrial control and is compatible with Ethernet to support the mixed transmission of industrial control data and Ethernet *** paper systematically summarizes and analyzes the shortcomings of the current mixed transmission technologies of the bursty flows and the periodic *** conquer these shortages,we propose a predictive mixed-transmission scheme of the bursty flows and the periodic *** core idea is to use the predictability of timetriggered transmission of TSN to further reduce bandwidth loss of the previous mixed-transmission *** paper formalizes the probabilistic model of the predictive mixed transmission mechanism and proves that the proposed mecha⁃nism can effectively reduce the loss of ***,based on the formalized probabilistic model,we simulate the bandwidth loss of the proposed *** results demonstrate that compared with the previous mixed-transmission method,the bandwidth loss of the pro⁃posed mechanism achieves a 79.48%reduction on average.

关键词： time-sensitive networking 802.1Qbv 802.1Qbu guard band strategy preemption strategy

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Study on Potability Water Quality Classification Based on Integrated Learning 16

Study on Potability Water Quality Classification Based on In...

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16th IEEE International Conference on Intelligent Systems and Knowledge engineering, ISKE 2021

作者： Fen, Li Fen Lei, Zhou Ting, Chen Huaiyin Institute of Technology Faculty of Computer and Software Engineering Research Center for Logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaian China

ISBN: (纸本)9781665405539

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. The effect of set classifier is higher than that of simple classifier. © 2021 IEEE.

关键词： Water quality

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Advancing Medical Innovation through Blockchain-Secured Federated Learning for Smart Health

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Khan, Salabat Khan, Mansoor Khan, Muhammad Asghar Wang, Lu Wu, Kaishun Qilu Institute of Technology School of Computer and Information Engineering Jinan China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Jinan China Prince Mohammad Bin Fahd University Department of Electrical Engineering P.O. Box 1664 Al-Khobar31952 Saudi Arabia Shenzhen University College of Computer Science and Software Engineering Shenzhen China Thrust Guangzhou China

The rapid digitization of healthcare systems has led to a vast accumulation of electronic medical records (EMRs), offering an invaluable source of patient data that can significantly advance medical research and improve patient care. However, sharing EMRs for research purposes presents challenges, particularly concerning data privacy, security, and the limitations of traditional centralized data-sharing models. This paper introduces a novel approach that leverages blockchain technology to facilitate federated learning with EMRs, thereby addressing these challenges. Federated learning enables multiple institutions to collaboratively train a robust machine learning model without sharing raw data, preserving privacy and security. By integrating blockchain, this framework enhances data integrity, immutability, and trust, all in a decentralized environment. The blockchain serves as a transparent and secure ledger, recording model updates and aggregating them through a consensus-based mechanism. Smart contracts further enforce data usage policies, allowing only authorized access and maintaining control over data ownership and sharing. This approach empowers medical researchers and institutions to collaborate more effectively, accelerating the discovery of treatments, advancements in personalized medicine, and insights into rare diseases. It also enables patients to contribute to medical research while retaining control over their personal data, fostering a patient-centered approach to healthcare innovation. Experimental results confirm the efficacy and efficiency of this blockchain-enabled federated learning framework, highlighting its potential to transform medical research and adhere to stringent privacy and security standards. This study emphasizes the pivotal role of blockchain in enhancing big data analytics within healthcare, paving the way for improved collaboration, innovation, and patient outcomes. © 2025 IEEE.

关键词： Federated learning

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