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Proceedings of the 37th IEEE/ACM International Conference on Automated software engineering

作者： Jianzhong Su Hong-Ning Dai Lingjun Zhao Zibin Zheng Xiapu Luo School of Software Engineering Sun Yat-sen University China and Ant Group China Department of Computer Science Hong Kong Baptist University China School of Software Engineering Sun Yat-sen University China The Hong Kong Polytechnic University China

ISBN: (纸本)9781450394758

As computer programs run on top of blockchain, smart contracts have proliferated a myriad of decentralized applications while bringing security vulnerabilities, which may cause huge financial losses. Thus, it is crucial and urgent to detect the vulnerabilities of smart contracts. However, existing fuzzers for smart contracts are still inefficient to detect sophisticated vulnerabilities that require specific vulnerable transaction sequences to trigger. To address this challenge, we propose a novel vulnerability-guided fuzzer based on reinforcement learning, namely RLF, for generating vulnerable transaction sequences to detect such sophisticated vulnerabilities in smart contracts. In particular, we firstly model the process of fuzzing smart contracts as a Markov decision process to construct our reinforcement learning framework. We then creatively design an appropriate reward with consideration of both vulnerability and code coverage so that it can effectively guide our fuzzer to generate specific transaction sequences to reveal vulnerabilities, especially for the vulnerabilities related to multiple functions. We conduct extensive experiments to evaluate RLF’s performance. The experimental results demonstrate that our RLF outperforms state-of-the-art vulnerability-detection tools (e.g., detecting 8%-69% more vulnerabilities within 30 minutes).

关键词： Reinforcement learning Fuzzing Smart contract

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Fast Distributed Polynomial Multiplication Algorithm for Lattice-based Cryptographic Decryption In Blockchain Systems

Fast Distributed Polynomial Multiplication Algorithm for Lat...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Hongjian Zhao Yunting Tao Fanyu Kong Guoyan Zhang Hanlin Zhang Jia Yu School of Software Shandong University Jinan China College of Information Engineering Binzhou Polytechnic Binzhou China School of Software Shandong University Shandong Sansec Information Technology Co. Ltd Jinan China School of Cyber Science and Technology Shandong University Qingdao China College of Computer Science and Technology Qingdao University Qingdao China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Lattice-based Post-Quantum Cryptography (PQC) can effectively resist the quantum threat to blockchain's underlying cryptographic algorithms. Blockchain node decryption is one of the most commonly used cryptographic computations in blockchain systems, and polynomial multiplication, a time-consuming operation for decryption, is one of the factors limiting blockchain efficiency. This paper proposes a novel distributed computing algorithm for polynomial multiplication, applicable in blockchain decryption. By splitting polynomials into lower-degree terms and delegating tasks to distributed nodes, our approach reduces computation time. A novel verification strategy based on the Karatsuba algorithm ensures result accuracy. The experimental results demonstrate that our proposed scheme improves the execution efficiency of NTT and INTT operations by approximately 47.8% and 52.4%, and reduces Kyber decryption time by up to 23.5%.

关键词： Distributed processing Limiting Accuracy Resists Approximation algorithms Information leakage Polynomials Blockchains Computational efficiency Distributed computing

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mRS-DBFT: An improved consensus mechanism suitable for end-edge-cloud hybrid trust models

mRS-DBFT: An improved consensus mechanism suitable for end-e...

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Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Xue Zhai Shanchen Pang Sibo Qiao Shihang Yu Qingdao institute of software college computer science and technology China University of Petroleum (East China) Qingdao China School of Mechanical Engineering Tiangong University Tianjin China

As a three-terminal collaborative architecture of terminal, edge and cloud, end-edge-cloud is jointly participated by multiple institutions or organizations, and has the characteristics of a hybrid trust architecture of “partially trustworthy, globally untrustworthy”. Traditional consensus algorithm designs are mostly used in relatively unified trust model environments and do not provide a mechanism for cross-organizational cooperation. Therefore, they cannot effectively solve the trust and consensus issues between organizations. We propose an improved DPoS-PBFT consensus mechanism based on reputation scoring (mRS-DBFT). The core idea of the mRS-DBFT is to add a reputation scoring mechanism based on the DPOS-PBFT consensus algorithm. The reputation score is calculated based on factors such as the node’s historical behavior, contribution, and credibility. Nodes with higher reputation score have greater influence and decision-making power in the consensus process. This mechanism can solve the problem of mutual distrust among nodes of different organizations in the end-edge-cloud architecture, and each node participates in the consensus process based on its reputation score, thus forming a credible and consistent system, achieving cross-organizational consensus, and ensuring node trust and data consistency in an open end-edge-cloud environment. The experimental results demonstrate that the mRS-DBFT consensus algorithm helps select nodes with higher stability to participate in the consensus by evaluating the reputation of computing devices, it can reduce fluctuations in delay and improve the stability of the consensus process.

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Tailin Liang Zhaolin Yuan State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Department of Industrial and Systems Engineering Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications.

关键词： Meters Deep learning Accuracy Ultrasonic variables measurement Transportation Manuals Feature extraction Surface texture Convolutional neural networks Surface treatment

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Discrete Geometric Coded Data Layout for Large-scale Object Storage Systems

Discrete Geometric Coded Data Layout for Large-scale Object ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Yi Tian Guangping Xu Hongzhang Yang Yue Ni JiaXin Cao Lei Yang School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Roycom (Tianjin) Information Technology Co. Ltd.

Regenerating codes are new network codes proposed to reduce the data required for fault repair, which can improve the recovery efficiency of faulty nodes in data storage systems. However, unlike Reed-Solomon code, which repairs at the granularity of bytes, regenerating codes require data stored in large chunks but leads to severe read amplification, which reads out excess data when degraded read objects and increases degraded read time. That reflects a mutual constraint between improving recovery efficiency and degraded read performance, as manifested in the amplification of data reads, a vital issue considered in this *** solve this problem, we propose a new type of data layout — discrete geometric, which splits the object into a series of geometric sequences of data blocks. They are placed discretely into corresponding containers on the disks at different nodes, with containers of the same size made into a strip for encoding. The discrete characteristic ensures lower repair costs for degraded reads. The geometric characteristic ensures the repair performance of regenerating codes by large blocks, and read amplification can be mitigated through small blocks. To reduce IOPS for discrete geometric, we propose Discrete Geometric-Locally Regenerating Codes (DG-LRCs), guaranteeing lower degraded read latency while improving recovery *** results show that the degraded read time of DG-LRCs compared to regenerating codes combined with geometric partitioning is 22.56% lower at 2Gbpsand 60.56% lower at 4Gbps, and the recovery performance is 7.04 times better than that of RS code.

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Fine-grained Spatio-Temporal Distribution Prediction of Mobile Content Delivery in 5G Ultra-Dense Networks

arXiv

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arXiv 2023年

作者： Huang, Shaoyuan Zhang, Heng Wang, Xiaofei Chen, Min Li, Jianxin Leung, Victor C.M. Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin University Tianjin300350 China The School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China The School of Information Technology Deakin University BurwoodVIC3220 Australia The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

The 5G networks have extensively promoted the growth of mobile users and novel applications, and with the skyrocketing user requests for a large amount of popular content, the consequent content delivery services (CDSs) have been bringing a heavy load to mobile service providers. As a key mission in intelligent networks management, understanding and predicting the distribution of CDSs benefits many tasks of modern network services such as resource provisioning and proactive content caching for content delivery networks. However, the revolutions in novel ubiquitous network architectures led by ultra-dense networks (UDNs) make the task extremely challenging. Specifically, conventional methods face the challenges of insufficient spatio precision, lacking generalizability, and complex multi-feature dependencies of user requests, making their effectiveness unreliable in CDSs prediction under 5G UDNs. In this paper, we propose to adopt a series of encoding and sampling methods to model CDSs of known and unknown areas at a tailored fine-grained level. Moreover, we design a spatio-temporal-social multi-feature extraction framework for CDSs hotspots prediction, in which a novel edge-enhanced graph convolution block is proposed to encode dynamic CDSs networks based on the social relationships and the spatio features. Besides, we introduce the Long-Short Term Memory (LSTM) to further capture the temporal dependency. Extensive performance evaluations with real-world measurement data collected in two mobile content applications demonstrate the effectiveness of our proposed solution, which can improve the prediction area under the curve (AUC) by 40.5% compared to the state-of-the-art proposals at a spatio granularity of 76m, with up to 80% of the unknown areas. © 2023, CC BY-NC-ND.

关键词： Forecasting

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An Exploratory Study on Automatic Identification of Assumptions in the Development of Deep Learning Frameworks

SSRN

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SSRN 2024年

作者： Yang, Chen Liang, Peng Ma, Zinan School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen518055 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Chalmers University of Technology GothenburgS-412 96 Sweden

Context: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources).Objective: The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on ***: We constructed a new and largest dataset (i.e., AssuEval) of assumptions collected from the TensorFlow and Keras repositories on GitHub;explored the performance of seven traditional machine learning models (e.g., Support Vector Machine, Classification and Regression Trees), a popular DL model (i.e., ALBERT), and a large language model (i.e., ChatGPT) of identifying assumptions on the ***: The experiment results show that ALBERT achieves the best performance (f1-score: 0.9584) of identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.6211, achieved by ChatGPT). Though ChatGPT is the most popular large language model, we do not recommend using it to identify assumptions in DL framework development because of its low performance on the task. Fine-tuning ChatGPT specifically for assumption identification could improve the ***: This study provides researchers with the largest dataset of assumptions for further research (e.g., assumption classification, evaluation, and

关键词： Large datasets

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An exploratory study on automatic identification of assumptions in the development of deep learning frameworks

arXiv

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arXiv 2024年

Context: Stakeholders constantly make assumptions in the development of deep learning (DL) frameworks. These assumptions are related to various types of software artifacts (e.g., requirements, design decisions, and technical debt) and can turn out to be invalid, leading to system failures. Existing approaches and tools for assumption management usually depend on manual identification of assumptions. However, assumptions are scattered in various sources (e.g., code comments, commits, pull requests, and issues) of DL framework development, and manually identifying assumptions has high costs (e.g., time and resources). Objective: The objective of the study is to evaluate different classification models for the purpose of identification with respect to assumptions from the point of view of developers and users in the context of DL framework projects (i.e., issues, pull requests, and commits) on GitHub. Method: First, we constructed a new and largest dataset (i.e., the AssuEval dataset) of assumptions collected from the TensorFlow and Keras repositories on GitHub. Then we explored the performance of seven non-transformers based models (e.g., Support Vector Machine, Classification and Regression Trees), the ALBERT model, and three decoder-only models (i.e., ChatGPT, Claude, and Gemini) for identifying assumptions on the AssuEval dataset. Results: The study results show that ALBERT achieves the best performance (f1-score: 0.9584) for identifying assumptions on the AssuEval dataset, which is much better than the other models (the 2nd best f1-score is 0.8858, achieved by the Claude 3.5 Sonnet model). Though ChatGPT, Claude, and Gemini are popular models, we do not recommend using them to identify assumptions in DL framework development because of their low performance. Fine-tuning ChatGPT, Claude, Gemini, or other language models (e.g., Llama3, Falcon, and BLOOM) specifically for assumptions might improve their performance for assumption identification. Conclusions: This stu

关键词： Chatbots

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Robust DNA Image Storage Decoding with Residual CNN

Robust DNA Image Storage Decoding with Residual CNN

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Cihan Ruan Liang Yang Rongduo Han Shan Gao Haoyu Wu Nam Ling Department of Computer Science and Engineering Santa Clara University Santa Clara CA USA College of Software Nankai University Tianjin China School of Mathematical Sciences Nankai University Tianjin China College of Life Sciences Nankai University Tianjin China Roku Inc. San Jose CA USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

DNA storage is a promising data storage method with high density, durability, and easy maintenance, ideal for data archiving. However, wide-scale adoption is hindered by challenges like high synthesis costs, data loss, and I/O complexities. Addressing robustness is a primary concern in advancing DNA storage. Traditional strategies for robustness rely on increasing redundancy, replicas, and error-correction codes (ECC) for each DNA sequence strand. Given the unpredictable errors in DNA storage and the associated costs of absolute accuracy, we’ve embraced an error-tolerance approach. This paper introduces an innovative method utilizing the residual Convolutional Neural Network (CNN) during image decoding in DNA storage to combat noise and enhance robustness. We simulated compressed images in DNA sequences and restored them using our network, achieving commendable peak signal-to-noise ratios (PSNR) even with lower-quality images. Our method offers a balance between redundancy and image quality in DNA storage.

关键词： PSNR Image coding Redundancy Noise DNA Robustness Error correction codes

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Algorithm selection for software verification based on adversarial LSTM 7

Algorithm selection for software verification based on adver...

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7th IEEE International Conference on Big Data Security on Cloud, 7th IEEE International Conference on High Performance and Smart Computing, and 6th IEEE International Conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2021

作者： Wang, Qiang Jiang, Jiawei Zhao, Yongxin Cao, Weipeng Wang, Chunjiang Li, Shengdong Institute of System Engineering Chinese Academy of Military Science Beijing China School of Software Engineering East China Normal University Shanghai China School of Computer Science and Engineering Shenzhen University Shenzhen China Technology Tianjin China

ISBN: (纸本)9781665439275

As a prevalent technique for checking the correctness of software, software verification has achieved a significant progress in the past decades, reaching a point where a large number of verification algorithms and tools are available and sophisticated enough to handle the large-scale industrial software. However, it remains a difficult task to select a suitable verification algorithm or tool for the software at hand, given the fact that the underlying algorithms are diverse and the performance tradeoffs are hard to accurately characterize. In this paper, we study the algorithm selection problem for software verification, and propose a novel algorithm selection model based on the Long Short Term Memory network (LSTM). Our solution employs word2vec to obtain the embedding representation of the code, avoiding constructing the software features manually. We also propose a novel approach to construct the adversarial code examples in order to solve the sparsity and data imbalance problem. The experimental evaluations on the latest available dataset show that our solution improves the prediction accuracy by about 7% compared with the state-of-the-art selection algorithm. © 2021 IEEE.

关键词： Long short-term memory

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