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Vulnerable Region-Aware Greybox Fuzzing

Journal of computer science & technology 2021年第5期36卷 1212-1228页

作者： Ling-Yun Situ Zhi-Qiang Zuo Le Guan Lin-Zhang Wang Xuan-Dong Li Jin Shi Peng Liu State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China School of Information Management Nanjing UniversityNanjing 210023China Department of Computer Science University of GeorgiaAthensGA 30602U.S.A. College of Information Sciences and Technology Pennsylvania State UniversityState CollegePA 16802U.S.A

Fuzzing is known to be one of the most effective techniques to uncover security vulnerabilities of large-scale software *** fuzzing,it is crucial to distribute the fuzzing resource appropriately so as to achieve the best fuzzing performance under a limited *** distribution strategies of American Fuzzy Lop(AFL)based greybox fuzzing focus on increasing coverage blindly without considering the metrics of code regions,thus lacking the insight regarding which region is more likely to be vulnerable and deserves more fuzzing *** tackle the above drawback by proposing a vulnerable region-aware greybox fuzzing ***,we distribute more fuzzing resources towards regions that are more likely to be vulnerable based on four kinds of code *** implemented the approach as an extension to AFL named ***-scale experimental evaluations validate the effectiveness and efficiency of RegionFuzz-11 new bugs including three new CVEs are successfully uncovered by RegionFuzz.

关键词： vulnerability detection greybox fuzzing code metrics resource distribution

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Vehicle Check-In Data-driven POI Recommendation Based on Improved SVD and Graph Convolutional Network 19

Vehicle Check-In Data-driven POI Recommendation Based on Imp...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yuwen Zhang, Jie Dou, Ruihan Zhou, Xiaokang Xu, Xiaolong Wang, Shoujin Qi, Lianyong College of Computer Science and Technology Qingdao China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology China Shanghai Meteorological Information and Technology Support Center China University of Waterloo Faculty of Mathematics Waterloo Canada Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Riken Japan Nanjing University of Information Science and Technology School of Computer and Software China Macquarie University School of Computing Australia Qufu Normal University School of Computer Science China

ISBN: (纸本)9798350346558

The application of automobile tool software is becoming increasingly widespread, and its functions are becoming increasingly diverse. Using vehicle traffic data to give users with recommendations for Points-of-interest (POI) is becoming an intriguing research challenge. However, the current POI recommendation is either a sequence-based method, ignoring the interaction information between users and POIs. Or it needs a lot of model training time to use graph correlation method to mine the interaction between users or vehicles and POIs. In order to address these issues, we propose a method for vehicle check-in data-driven POI recommendation based on improved Singular Value Decomposition (SVD) and Graph Convolutional Network (GCN). First, we apply SVD to replace the neighborhood aggregation update method in traditional GCN, update the node embedding with fewer parameters and faster speed. Second, we propose an information enhancement strategy to make up for the information loss caused by the SVD. Experiments on large vehicle check-in datasets demonstrate that our method is both faster and more effective when making POI recommendations. © 2022 IEEE.

关键词： Singular value decomposition

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Camera Seismocardiogram Based Monitoring of Left Ventricular Ejection Time

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Zhou, Zhiqin Huang, Jia Li, Haozhe Liu, Lin Zhu, Yingen Shan, Caifeng Wang, Wenjin Nanjing University State Key Laboratory for Novel Software Technology School of Intelligence Science and Technology China Southern University of Science and Technology Department of Biomedical Engineering China The Third People's Hospital of Shenzhen Intensive Care Unit China Shandong University of Science and Technology College of Electrical Engineering and Automation China

Left Ventricular Ejection Time (LVET), reflecting the duration from the onset to the end of blood ejection by the left ventricle during each heartbeat, is a critical parameter for measuring cardiac pumping efficiency. Continuous and regular monitoring of LVET is particularly crucial in assessing cardiac health, valvular function, and myocardial contractility. Seismocardiogram (SCG) signals can be utilized for LVET monitoring, as the temporal distance between the aortic valve opening (AO) and aortic valve closure (AC) in SCG signals can accurately depict LVET. This study proposes a novel way to extract LVET from laser speckle videos recorded by a remote camera based on the principle of defocused speckle imaging, thereby enabling non-contact monitoring of LVET. We extract both the low-frequency components of laser speckle motion (LSM-LF), regarded as SCG signals, and the high-frequency components of laser speckle motion (LSM-HF) from recorded videos. We utilize LSM-HF to assist the detection of AO and AC markers in LSM-LF. We validated the effectiveness of our AO and AC detection algorithm on a self-made dataset comprising 21 participants with 9616 SCG cycles. The benchmark shows that the detection accuracy for AO and AC reached 98.16% and 97.94%, respectively, with an mean absolute error of 0.5571 ms for LVET estimation. The results demonstrate that camera-SCG has strong potential for cardiac health monitoring. © 1964-2012 IEEE.

关键词： Benchmarking

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Duplicate Data Elimination Optimization Strategy Based on Edge Service Management

Duplicate Data Elimination Optimization Strategy Based on Ed...

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Cyber science and technology Congress (CyberSciTech), IEEE

作者： Yonglin Pu Xiaolong Xu Lianyong Qi Wanchun Dou Jiong Yu Ziyang Li School of Software Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) Dongying China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Software Xinjiang University Urumqi China

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Due to the absence of duplicate data elimination technologies in edge architectures, the interaction experience with mobile users suffers. In response to this issue, an optimized strategy for duplicate data elimination, based on edge service management (DDE-ESM), is proposed. Initially, a model for the placement of edge devices and a model for the collection of cache data are established. Subsequently, a caching device placement algorithm is designed; this not only undertakes comprehensive data collection and cache storage but also forms the basis for querying duplicate data within the architecture. Following this, a model for querying duplicate data in the cache is created, identifying duplicates by segmenting data blocks and generating data fingerprints via hash functions. Ultimately, a model for eliminating duplicate data at the edge is developed, which leads to the creation of an algorithm that rationally deletes duplicate data in accordance with device resource constraints. Experimental results indicate that this optimization strategy for duplicate data elimination not only saves resources on edge devices but also significantly enhances data management efficiency and the utilization of architectural resources.

关键词： Hash functions Fingerprint recognition Data collection Data models Cache storage Optimization

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Cops: An Improved Information Retrieval-Based Bug Localization Technique Using Context-Aware Program Simplification

SSRN

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SSRN 2023年

作者： Yang, Yilin Wang, Ziyuan Chen, Zhenyu Xu, Baowen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China

Information Retrieval-based Bug Localization (IRBL) techniques have fewer external dependencies and lower execution costs. However, existing IRBL techniques have many challenges regarding localization granularity and applicability. First, existing techniques have not yet achieved statement-level bug localization. Second, almost all studies are limited to Java-based projects, and the effectiveness of these techniques for other widely used programming languages (e.g., Python) is still unknown. The reason for these deficiencies is that existing IRBL techniques mainly employ conventional NLP techniques to analyze the bug reports and have not yet fully exploited the stack traces. In this paper, we propose a context-aware program simplification technique—COPS, which enables statement-level bug localization for Python-based projects. Our experiment is based on 948 bug reports, and the results show that COPS can effectively localize faulty statements. Compared with the original stack traces, Top@10 is improved by 102.6%, MAP@10 by 56.2%, and MRR@10 by 95.6%. Compared with the statement-level bug localization technique, COPS can improve by 19.1% in MAP@10 and achieve 92% buggy statement coverage with a full-scope search. Experimental results show that COPS has higher bug localization effectiveness than existing IRBL techniques;and that COPS achieves the same effectiveness with higher execution efficiency than the most advanced techniques. © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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CACam: Consecutive Angular Measurements with Camera on Smartphone for Distance Estimation

CACam: Consecutive Angular Measurements with Camera on Smart...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yifan Zhu Xiaojun Zhu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Novel Software Technology Nanjing University Nanjing China

To estimate the distance between a user and the target object for personal interest or for subsequent actions, the commonly used method is to search the target in the map application. However this is limited by GPS signal status under different environments and incomplete information on the map application of smartphones. This paper introduces a ranging method, CACam, which utilizes consecutive angle measurement based on camera of smartphone. Geometric model and ranging process are proposed to calculate the distance. When the user aligns the target from different position points, we record readings from the gyroscope sensor during every rotation in the procedure. The camera is used for aligning the target to build geometric model. Vector mapping of angular readings and data processing of intermediate points are used to get more accurate angles, which is suitable for the situation that the smartphone could not be strictly rotated in perpendicular to the plane of the gravity. The effectiveness of our ranging method is verified through experiments in outdoor environments.

关键词： Geometric modeling Cameras Search problems Distance measurement Hardware Gyroscopes Smart phones

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Adaptive Collaborative Air-Ground Edge Caching in Dynamic Vehicular Network Scenarios

Adaptive Collaborative Air-Ground Edge Caching in Dynamic Ve...

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International Conference on Information, Communication and Networks (ICICN)

作者： Fei Luo Junhua Wang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Mobile edge caching is a promising technique for supporting the applications of Intelligent Transportation Systems (ITS), which involve huge volumes of image and video contents. Specifically, critical challenges arise in meeting the contents requirements of moving vehicles. In this work, we investigate the collaborative contents delivery in air-ground integrated vehicular networks, where unmanned aerial vehicles (UAVs) and roadside units (RSUs) cache popular contents and further deliver it to nearby vehicles. Efficient contents placement is crucial because of the high mobility of vehicles and UAVs, and the limited caching capacity of edge nodes. To address this, we formulate the heterogeneous cache optimization problem as a generalized multi-dimensional multiple knapsack problem (MMKP), which is NP-hard. In particular, we propose an adaptive collaborative caching optimization (ACCO) algorithm that employs the Kernel Search (KS) framework to find a near-optimal caching scheme, with dynamically adjustable dimensions and solution time for subproblems. Furthermore, we use the k-means clustering algorithm to categorize contents based on service latency, caching cost, and contents size, adding a greedy-based load balancing strategy to achieve cost-effective optimal matching of contents and edge nodes. Comprehensive simulations verify the effectiveness of the proposed cooperative edge caching architecture and the ACCO algorithm in reducing service delay and caching costs for contents delivery.

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arXiv

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arXiv 2024年

作者： Song, Jianqing Huang, Jianguo Jiang, Wenyu Zhang, Baoming Li, Shuangjie Wang, Chongjun State Key Laboratory of Novel Software Technology Nanjing University China Department of Statistics and Data Science Southern University of Science and Technology China College of Computing and Data Science Nanyang Technological University Singapore

Graph Neural Networks have achieved remarkable accuracy in semi-supervised node classification tasks. However, these results lack reliable uncertainty estimates. Conformal prediction methods provide a theoretical guarantee for node classification tasks, ensuring that the conformal prediction set contains the ground-truth label with a desired probability (e.g., 95%). In this paper, we empirically show that for each node, aggregating the non-conformity scores of nodes with the same label can improve the efficiency of conformal prediction sets while maintaining valid marginal coverage. This observation motivates us to propose a novel algorithm named Similarity-Navigated Adaptive Prediction Sets (SNAPS), which aggregates the non-conformity scores based on feature similarity and structural neighborhood. The key idea behind SNAPS is that nodes with high feature similarity or direct connections tend to have the same label. By incorporating adaptive similar nodes information, SNAPS can generate compact prediction sets and increase the singleton hit ratio (correct prediction sets of size one). Moreover, we theoretically provide a finite-sample coverage guarantee of SNAPS. Extensive experiments demonstrate the superiority of SNAPS, improving the efficiency of prediction sets and singleton hit ratio while maintaining valid coverage. © 2024, CC BY.

关键词： Prediction models

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Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation

arXiv

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arXiv 2025年

作者： Lyu, Shen-Huan He, Yi-Xiao Wang, Yanyan Qu, Zhihao Tang, Bin Ye, Baoliu Key Laboratory of Water Big Data Technology of Ministry of Water Resources College of Computer Science and Software Engineering Hohai University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China

Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been proven to have a stronger representation ability than DT, as it provides a way to create shallower tree structures while still approximating complex decision boundaries. However, its learning efficiency is still insufficient, since the linear projections cannot be transmitted to the child nodes, resulting in a waste of model parameters. In this work, we propose an enhanced ODT method with Feature Concatenation (FC-ODT), which enables in-model feature transformation to transmit the projections along the decision paths. Theoretically, we prove that our method enjoys a faster consistency rate w.r.t. the tree depth, indicating that our method possesses a significant advantage in generalization performance, especially for shallow trees. Experiments show that FC-ODT can outperform the other state-of-the-art decision trees with a limited tree depth. Copyright © 2025, The Authors. All rights reserved.

关键词： Efficiency

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Grid4D: 4D Decomposed Hash Encoding for High-fidelity Dynamic Gaussian Splatting

arXiv

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arXiv 2024年

作者： Xu, Jiawei Fan, Zexin Yang, Jian Xie, Jin PCA Lab VCIP College of Computer Science Nankai University China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Intelligence Science and Technology Nanjing University Suzhou China

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the inappropriate low-rank assumption and excessively decompose the space-time 4D encoding, resulting in overmuch feature overlap and unsatisfactory rendering quality. To tackle these problems, we propose Grid4D, a dynamic scene rendering model based on Gaussian splatting and employing a novel explicit encoding method for the 4D input through the hash encoding. Different from plane-based explicit representations, we decompose the 4D encoding into one spatial and three temporal 3D hash encodings without the low-rank assumption. Additionally, we design a novel attention module that generates the attention scores in a directional range to aggregate the spatial and temporal features. The directional attention enables Grid4D to more accurately fit the diverse deformations across distinct scene components based on the spatial encoded features. Moreover, to mitigate the inherent lack of smoothness in explicit representation methods, we introduce a smooth regularization term that keeps our model from the chaos of deformation prediction. Our experiments demonstrate that Grid4D significantly outperforms the state-of-the-art models in visual quality and rendering speed. Project page: https://***/Grid4D-web/. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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