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CherryPicker: A Parallel Solving and State Sharing Hybrid Fuzzing System

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Qingyu Lin, Jiayi Sun, Chenxin Qian, Chenxiong Luo, Xiapu School of Information and Software Engineering University of Electronic Science and Technology of China China Beijing University of Posts and Telecommunications China University of Hong Kong Hong Kong Computer Science Department HKU Hong Kong Hong Kong Polytechnic University Hong Kong

Hybrid testing, combining fuzz testing and concolic execution, has emerged as an effective technique for bug discovery. However, concolic execution becomes the performance bottleneck when applied to real-world software. Despite numerous approaches to optimize seed scheduling, symbolic simulation, and constraint solving, concolic execution remains inefficient and ineffective due to two limitations. First, the concolic executor and fuzzer do not synchronize the testing state in real time, leading to the generation of numerous duplicate inputs in both concolic execution and the fuzzer. Second, the concolic executor overlooks the independence of constraint solving and solves constraints sequentially, which introduces significant slowdown. In this paper, we first conduct a study to identify these limitations in existing hybrid testing systems. We then propose a novel design for hybrid fuzzing, CherryPicker, where the fuzzer and concolic executor share testing states, and concolic execution runs in parallel mode. Finally, we evaluate our system using the LAVA-M benchmark and real-world software and compare it to state-of-the-art systems. The results demonstrate that CherryPicker outperforms current systems in terms of efficiency and effectiveness, delivering improved runtime performance, generating more intriguing inputs, and activating more code. Notably, CherryPicker exclusively uncovers six previously unknown bugs during the evaluation, which have been reported to developers, all of which have been confirmed with three CVEs assigned. © 2004-2012 IEEE.

关键词： software testing

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning

arXiv

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arXiv 2023年

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, The Authors. All rights reserved.

关键词： Students

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LWS: A Framework for Log-based Workload Simulation in Session-based SUT

arXiv

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arXiv 2023年

作者： Han, Yongqi Du, Qingfeng Xu, Jincheng Zhao, Shengjie Chen, Zhekang Cao, Li Yin, Kanglin Pei, Dan School of Software Engineering Tongji University Shanghai201804 China Bizseer Beijing100083 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Artificial intelligence for IT Operations (AIOps) plays a critical role in operating and managing cloud-native systems and microservice-based applications but is limited by the lack of high-quality datasets with diverse scenarios. Realistic workloads are the premise and basis of generating such AIOps datasets, with the session-based workload being one of the most typical examples. Due to privacy concerns, complexity, variety, and requirements for reasonable intervention, it is difficult to copy or generate such workloads directly, showing the importance of effective and intervenable workload simulation. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based systems. LWS extracts the workload specification including the user behavior abstraction based on agglomerative clustering as well as relational models and the intervenable workload intensity from session logs. Then LWS combines the user behavior abstraction with the workload intensity to generate simulated workloads. The experimental evaluation is performed on an open-source cloud-native application with both well-designed and public real-world workloads, showing that the simulated workload generated by LWS is effective and intervenable, which provides the foundation of generating high-quality AIOps datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Abstracting

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The Devil is in the Statistics: Mitigating and Exploiting Statistics Difference for Generalizable Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Qiu, Muyang Zhang, Jian Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China School of Computer Science and Engineering Southeast University School of Data and Computer Science Shandong Women’s University China

Despite the recent success of domain generalization in medical image segmentation, voxel-wise annotation for all source domains remains a huge burden. Semi-supervised domain generalization has been proposed very recently to combat this challenge by leveraging limited labeled data along with abundant unlabeled data collected from multiple medical institutions, depending on precisely harnessing unlabeled data while improving generalization simultaneously. In this work, we observe that domain shifts between medical institutions cause disparate feature statistics, which significantly deteriorates pseudo-label quality due to an unexpected normalization process. Nevertheless, this phenomenon could be exploited to facilitate unseen domain generalization. Therefore, we propose 1) multiple statistics-individual branches to mitigate the impact of domain shifts for reliable pseudo-labels and 2) one statistics-aggregated branch for domain-invariant feature learning. Furthermore, to simulate unseen domains with statistics difference, we approach this from two aspects, i.e., a perturbation with histogram matching at image level and a random batch normalization selection strategy at feature level, producing diverse statistics to expand the training distribution. Evaluation results on three medical image datasets demonstrate the effectiveness of our method compared with recent SOTA methods. The code is available at https://***/qiumuyang/SIAB. © 2024, CC BY.

关键词： Image segmentation

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Macula Swelling Detection using Retinal Fundus Images

Macula Swelling Detection using Retinal Fundus Images

引用

IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Rukham Mumtaz Khan Fazal Saadat Ali Khan Fauzia Khan Rafia Hassan Dept. of Computer Engineering National University of Sciences and Technology Islamabad Pakistan Dept. of Information Security National University of Sciences and Technology Islamabad Pakistan Dept. of Computer Science University of Tartu Tartu Estonia Dept. of Software Engineering Bahria University Islamabad Pakistan

Diabetic Macular Edema (DME) is an advanced symptom of Diabetic Retinopathy (DR) and can lead to irreversible vision loss. Macular Edema (ME) is the swelling of the macula, the part of the eye accountable for thorough central vision. The utilization of image processing in the detection of diabetic macular edema (DME) plays a crucial role in the field of medical imaging, effectively reducing the time and manual effort required by medical specialists to identify this disease. In this paper, we proposed a method that uses digital fundus images to analyze the swelling of the macula. The proposed algorithm consists of four steps, i.e., (i) retinal image dataset collection, (ii) retinal image pre-processing, (iii) feature extraction, and (iv) classification to categorize the disease into three different stages, i.e., normal, stage 1 (mild) and stage 2 (severe). The proposed method is tested on three databases, i.e., MESSIDOR, DMED, and the local dataset AFIO obtained from the local hospital in Pakistan. We use three parameters to check the validity of the proposed algorithm, i.e., visual inspection, accuracy, and area under calculation time. Our method achieves an average accuracy of 96.07 % with a sensitivity of 93.74 % and specificity of 97.32%. We also compare our results with other proposed methods, which shows our method performs better.

关键词：

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Deep Multimodal Architecture for Detection of Long Parameter List and Switch Statements using DistilBERT

Deep Multimodal Architecture for Detection of Long Parameter...

引用

IEEE International Workshop on Source Code Analysis and Manipulation

作者： Anushka Bhave Roopak Sinha Computer Engineering Vishwakarma Institute of Technology Pune (Savitribai Phule Pune University) Pune India IT & Software Engineering Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9781665496100

Code smell detection and refactoring are crucial to sustain quality, reduce complexity and increase the efficiency of a software application. Code smells are observable patterns in the source code of a program that indicate deeper structural issues. Most traditional methods for code smell classification rely exclusively on structural object-oriented metrics and manually-designed heuristics. We propose a novel multimodal deep learning approach that combines structural and semantic information to detect two commonly-encountered code smells: Long Parameter Lists and Switch Statements. The presented architecture applies transfer learning on DistilBERT to generate vector embeddings representing classes and methods concatenated with numerical metrics for joint feature extraction using CNN, to build a complex mapping between the features and predict the output as smelly or non-smelly. Subsequently, to perform a holistic comparative analysis we also implement two multimodal machine learning pipelines, the first employs a sci-kit learn TF-IDF Vectorizer with Random Forest Classifier, and the second merges CNN with Bi-LSTM. Our approach achieves an accuracy of 91.2% as corroborated by experimental evaluation, outperforming the state-of-the-art techniques.

关键词： Measurement Codes Source coding Transfer learning Semantics Switches computer architecture

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Small sets of genuinely nonlocal Greenberger-Horne-Zeilinger states in multipartite systems

引用

Physical Review A 2024年第2期109卷 022428-022428页

作者： Zong-Xing Xiong Yongli Zhang Mao-Sheng Li Lvzhou Li Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-Sen University Guangzhou 510006 China School of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou 510665 China School of Mathematics South China University of Technology Guangzhou 510641 China

A set of orthogonal multipartite quantum states are called (distinguishability-based) genuinely nonlocal if they are locally indistinguishable across any bipartition of the subsystems. In this work, we consider the problem of constructing small genuinely nonlocal sets consisting of generalized Greenberger-Horne-Zeilinger (GHZ) states in multipartite systems. For system (C2)⊗N where N is large, using the language of group theory, we show that a tiny proportion Θ(1/2N) of the states among the N-qubit GHZ basis suffice to exhibit genuine nonlocality. Similar arguments also hold for the canonical generalized GHZ bases in systems (Cd)⊗N, wherever d is even and N is large. What is more, moving to the condition that any fixed N is given, we show that d+1 genuinely nonlocal generalized GHZ states exist in (Cd)⊗N, provided the local dimension d is sufficiently large. As an additional merit, within and beyond an asymptotic sense, the latter result also indicates some evident limitations of the “trivial othogonality-preserving local measurements” (TOPLM) technique that has been utilized frequently for detecting genuine nonlocality.

关键词： Nonlocality Quantum correlations in quantum information

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A Method for Person Re-Identification and Trajectory Tracking in Cross-Device Edge Environments

A Method for Person Re-Identification and Trajectory Trackin...

引用

International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jian An Yunxiang Zhao Feifei Wang Xin He Xiaolin Gui School of Computer Science and Technology Shaanxi Provincial Key Laboratory of Computer Networks Xi’an Jiaotong University Xi’an China Henan Jiaoyuan Engineering Technology Group Co. LTD Henan China School of Software Henan University Henan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

Addressing expanded surveillance scopes, cross-device person re-identification emerges as a crucial issue. Traditional approaches to re-identification and tracking confront challenges in feature matching challenges, and fail to harness the potential of edge computing environments, which offer viable solutions to bandwidth and latency constraints. The paper introduces a novel, edge computing and shared cache-based method for person re-identification and tracking. In this framework, edge nodes exploit deep learning method to execute image recognition for person re-identification and trajectory tracking. Edge nodes are deployed within a tri-layered architecture, encompassing Hierarchical Edge Cloud (HEC), Mobile Edge Computing (MEC), and Central Cloud (CC). Emphasizing part feature significance for person re-identification, the approach employs a refined Part-based Convolutional Baseline model guided by the triplet loss function. Experimental outcomes validate the high re-identification precision and efficacious load balancing capability of this method in person re-identification and tracking operations. Achieving a harmonized trade-off between task accuracy and response time, the proposed framework offers a robust solution for person re-identification and tracking within edge computing contexts.

关键词： Accuracy Trajectory tracking Image edge detection Computational modeling computer architecture Trajectory Time factors

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Generalizable Decision Boundaries: Dualistic Meta-Learning for Open Set Domain Generalization

arXiv

引用

arXiv 2023年

作者： Wang, Xiran Zhang, Jian Qi, Lei Shi, Yinghuan The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University China The School of Computer Science and Engineering Southeast University China

Domain generalization (DG) is proposed to deal with the issue of domain shift, which occurs when statistical differences exist between source and target domains. However, most current methods do not account for a common realistic scenario where the source and target domains have different classes. To overcome this deficiency, open set domain generalization (OSDG) then emerges as a more practical setting to recognize unseen classes in unseen domains. An intuitive approach is to use multiple one-vs-all classifiers to define decision boundaries for each class and reject the outliers as unknown. However, the significant class imbalance between positive and negative samples often causes the boundaries biased towards positive ones, resulting in misclassification for known samples in the unseen target domain. In this paper, we propose a novel meta-learning-based framework called dualistic MEta-learning with joint DomaIn-Class matching (MEDIC), which considers gradient matching towards inter-domain and inter-class splits simultaneously to find a generalizable boundary balanced for all tasks. Experimental results demonstrate that MEDIC not only outperforms previous methods in open set scenarios, but also maintains competitive close set generalization ability at the same time. Our code is available at https://***/zzwdx/MEDIC. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Robust Imagined Speech Production Using AI-Generated Content Network for Patients with Language Impairments

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China

Imagined speech production is critical for brain-computer interface systems. It is able to provide the communication ability for patients with language impairments. Nowadays, many studies have developed algorithms for this purpose. However, they often struggle with an issue that is the noise in physiological signals from various devices. This makes current methods have low robustness when performing speech generation. To this end, we propose a framework to robustly producing imagined speech for patients with language impairments. It is based on artificial intelligent-generated content (AIGC). In specific, this work develops a diffusion model to remove noise from brain signals, which are then converted and normalized into log-mel spectrograms. Next, the product feeds into a transformer-based model to extract local and global features. Finally, these enhanced features are inputted into full connected layers to generate the imagined speech. The effectiveness of our model is validated using the high-quality, i.e., single-word-production-Dutch-iBIDS dataset. It achieves Pearson correlation scores above 0.8 and standard deviations below 0.2 across different volunteers. Experimental results well demonstrate that our model is robust and has advantages over peer methods. © 2024 IEEE.

关键词： Deep learning

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