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computer Technology and Information science (ISCTIS), International Symposium on

作者： Hong Li Fei Wang Shaoxun Deng Research and Development Department Guizhou Bit Software Co. Ltd. Guiyang China Power China Guiyang Engineering Co. Ltd. Institute of Engineering Safety Science Guiyang China School of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

In the context of ensuring operational safety for reservoir dams and other vital infrastructures, engineering safety monitoring systems play an essential role, functioning as a core component in assessing and guaranteeing structural health. Traditional monitoring systems, however, have been constrained by their reliance on data architectures tailored to specific manufacturers' devices, limiting system scalability and maintainability. This paper introduces an innovative strategy aimed at eliminating differences among instruments, achieving an integrated process from data acquisition, processing, analysis to report *** study adopts JSON format to standardize instrument models, incorporating abstract design principles that define not only fundamental parameters and data fields but also specific computational logic to accommodate diverse monitoring needs. particularly for complex instruments like multipoint displacement meters, the system introduces the concept of virtual instrument types, realizing advanced functionality through combinations of basic instrument models. Furthermore, by employing predefined templates for data visualization, graphics, and analysis, the system can flexibly adapt to various monitoring *** approach has been successfully implemented in the engineering safety monitoring projects of China Power Construction Group Guiyang Institute, covering over 82 dams, foundation pits, and other structures, processing more than 20 million monitoring records, significantly enhancing monitoring efficiency and data analysis quality. While the current system operates effectively with relational databases, future research aims to explore its potential within a Bson and NoSQL environment, further boosting scalability and flexibility.

关键词： Meters Dams Instruments Computational modeling Scalability Relational databases Reservoirs Safety Logic Monitoring

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Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

SSRN

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SSRN 2023年

作者： Li, Ping Zhang, Yu Yuan, Li Xiao, Huaxin Lin, Binbin Xu, Xianghua School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Electrical and Computer Engineering Peking University Beijing China College of Systems Engineering National University of Defense Technology Changsha China College of Computer Science Zhejiang University Hangzhou China

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high © 2023, The Authors. All rights reserved.

关键词： Pixels

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Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models

Aster: Encoding Data Augmentation Relations into Seed Test S...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Haipeng Wang Zhengyuan Wei Qilin Zhou Bo Jiang W. K. Chan City University of Hong Kong Hong Kong China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

Data-augmented deep learning models are widely used in real-world applications. However, many state-of the-art loss-based or coverage-based fuzzing techniques fail to produce fuzzing samples for them from many seeds. This paper proposes Aster, a novel technique to address this problem to enhance their fuzzing effectiveness for deep learning models trained with multi-sample data augmentation methods. Aster formulates a novel reachability-based strategy to encode the insights of every seed’s direct and indirect data augmentation relation instances into the replacement seed of that seed systematically. Our experiment shows that Aster is highly effective. On average, loss-based and coverage-based fuzzing techniques can generate 166% and 110% more fuzzing samples and reduce 31% and 22% unsuccessful seeds, respectively, after adopting the replacement seeds generated by Aster to replace their original seeds. Their improved models also become up to 55% and 40% on average more robust against FGSM and PGD attacks in the experiment.

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An Improved Quantum Private Set Intersection Protocol Based on Hadamard Gates

arXiv

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arXiv 2023年

作者： Liu, Wen-Jie Li, Wen-Bo Wang, Hai-Bin School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China

Recently, Liu and Yin (Int. J. Theor. Phys. 60, 2074-2083 (2021)) proposed a two-party private set intersection protocol based on quantum Fourier transform. We find the participant can deduce the other party’s private information, which violates the security requirement of private set computation. In order to solve this problem, an improved private set intersection protocol based on Hadamard gate is proposed. Firstly, the more feasible Hadamard gates are used to perform on the original n qubits instead of the quantum Fourier transform, which may reduce the difficulty of implementation. In addition, through the exclusive OR calculation, the participant’s private information is randomly chosen and encoded on the additional n qubits, which prevents participants from obtaining the result of the difference set Sdiff, and then avoids the internal leakage of private information. Finally, the correctness and security analysis are conducted to show the proposed protocol can guarantee the correctness of computation result as well as resist outside attacks and participant internal attacks. © 2023, CC BY.

关键词： Quantum cryptography

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Prediction Model of Dow Jones Index Based on LSTM-Adaboost 3

Prediction Model of Dow Jones Index Based on LSTM-Adaboost

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3rd IEEE International Conference on Communications, Information System and computer engineering, CISCE 2021

作者： Ying, RunKai Shou, Yuntao Liu, Chang School of Software Beijing University of Technology Faculty of Information Technology Beijing China School of Computer and Information Engineering Central South University of Forestry and Technology Hunan China School of Computer Science and Technology Huaqiao University Xiamen China

ISBN: (纸本)9780738112152

How to accurately predict the Dow Jones index is an important issue in quantitative finance. This paper designs an LSTM-AdaBoost(Long Short-Term Memory and Adaptive Boosting) prediction model based on the Dow Jones index's characteristics. The AdaBoost algorithm was used to integrate multiple LSTM weak classifiers. By iteratively updating the weights of each version and combining the strategy, the improved robust classifier was formed, and the prediction for the Dow Jones index was finally obtained. Experimental results have shown that the prediction results obtained by the LSTM-AdaBoost model have been significantly higher than those of the traditional classification model, with an average increase of 43.76 percent in R_square. The establishment of this model provides a theoretical basis for predicting the Dow Jones index and provides ideas for the intersection of finance and computer industries. © 2021 IEEE.

关键词： Adaptive boosting

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FlakyFix: Using Large Language Models for Predicting Flaky Test Fix Categories and Test Code Repair

arXiv

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arXiv 2023年

作者： Fatima, Sakina Hemmati, Hadi Briand, Lionel The School of EECS University of Ottawa Canada The Electrical Engineering and Computer Science Department York University Canada The Lero SFI centre for Software Research University of Limerick Ireland

Flaky tests are problematic because they non-deterministically pass or fail for the same software version under test, causing confusion and wasting development effort. While machine learning models have been used to predict flakiness and its root causes, there is much less work on providing support to fix the problem. To address this gap, in this paper, we focus on predicting the type of fix that is required to remove flakiness and then repair the test code on that basis. We do this for a subset of flaky tests where the root cause of flakiness is in the test itself and not in the production code. One key idea is to guide the repair process with additional knowledge about the test’s flakiness in the form of its predicted fix category. Thus, we first propose a framework that automatically generates labeled datasets for 13 fix categories and trains models to predict the fix category of a flaky test by analyzing the test code only. Our experimental results using code models and few-shot learning show that we can correctly predict most of the fix categories. To show the usefulness of such fix category labels for automatically repairing flakiness, we augment the prompts of GPT 3.5 Turbo, a Large Language Model (LLM), with such extra knowledge to request repair suggestions. The results show that our suggested fix category labels, complemented with in-context learning, significantly enhance the capability of GPT 3.5 Turbo in generating fixes for flaky tests. Based on the execution and analysis of a sample of GPT-repaired flaky tests, we estimate that a large percentage of such repairs, (roughly between 51% and 83%) can be expected to pass. For the failing repaired tests, on average, 16% of the test code needs to be further changed for them to pass. © 2023, CC BY.

关键词： software testing

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An Exploratory Study of Deep learning Supply Chain

An Exploratory Study of Deep learning Supply Chain

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International Conference on software engineering (ICSE)

作者： Xin Tan Kai Gao Minghui Zhou Li Zhang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China School of Software &#x0026 Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing China

ISBN: (纸本)9781665495899

Deep learning becomes the driving force behind many contemporary technologies and has been successfully applied in many fields. Through software dependencies, a multi-layer supply chain (SC) with a deep learning framework as the core and substantial down-stream projects as the periphery has gradually formed and is constantly developing. However, basic knowledge about the structure and characteristics of the SC is lacking, which hinders effective support for its sustainable development. Previous studies on software SC usually focus on the packages in different registries without paying attention to the SCs derived from a single project. We present an empirical study on two deep learning SCs: TensorFlow and PyTorch SCs. By constructing and analyzing their SCs, we aim to understand their structure, application domains, and evolutionary factors. We find that both SCs exhibit a short and sparse hierarchy structure. Overall, the relative growth of new projects increases month by month. Projects have a tendency to attract downstream projects shortly after the release of their packages, later the growth becomes faster and tends to stabilize. We propose three criteria to identify vulnerabilities and identify 51 types of packages and 26 types of projects involved in the two SCs. A comparison reveals their similarities and differences, e.g., TensorFlow SC provides a wealth of packages in experiment result analysis, while PyTorch SC contains more specific framework packages. By fitting the GAM model, we find that the number of dependent packages is significantly negatively associated with the number of downstream projects, but the relationship with the number of authors is nonlinear. Our findings can help further open the “black box” of deep learning SCs and provide insights for their healthy and sustainable development.

关键词： Deep learning Industries Supply chains Force Fitting Ecosystems software

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RITNet: A Rotation Invariant Transformer based Network for Point Cloud Registration

RITNet: A Rotation Invariant Transformer based Network for P...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Min Yang Yaochen Li Su Wang Shaohan Yang Hujun Liu School of Software Engineering Xi'an Jiaotong University China Department of Computer Science Xi'an Jiaotong-liverpool University China School of Computer Science and Technology Xi'an Jiaotong University China

Conventional point cloud registration methods usually employ an encoder-decoder architecture, where mid-level features are locally aggregated to extract geometric information. However, the over-reliance on local features may raise the boundary points cannot be adequately matched for two point clouds. To address this issue, we argue that the boundary features can be further enhanced by the rotation information, and propose a rotation invariant representation to replace common 3D Cartesian coordinates as the network inputs that enhances generalization to arbitrary orientations. Based on this technique, we propose rotation invariant Transformer for point cloud registration, which utilizes insensitivity to arrangement and quantity of data in the Transformer module to capture global structural knowledge within local parts for overall comprehension of each point clouds. Extensive quantitative and qualitative experimental on ModelNet40 evaluations show the effectiveness of the proposed method.

关键词： Point cloud compression Three-dimensional displays Fuses Transformers Feature extraction Data mining Artificial intelligence

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Transition Role of Entangled Data in Quantum Machine Learning

arXiv

引用

arXiv 2023年

作者： Wang, Xinbiao Du, Yuxuan Tu, Zhuozhuo Luo, Yong Yuan, Xiao Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei430072 China National Engineering Research Center for Multimedia Software Wuhan University Hubei430072 China JD Explore Academy Beijing101111 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science Faculty of Engineering University of Sydney NSW2008 Australia Center on Frontiers of Computing Studies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Entanglement serves as the resource to empower quantum computing. Recent progress has highlighted its positive impact on learning quantum dynamics, wherein the integration of entanglement into quantum operations or measurements of quantum machine learning (QML) models leads to substantial reductions in training data size, surpassing a specified prediction error threshold. However, an analytical understanding of how the entanglement degree in data affects model performance remains elusive. In this study, we address this knowledge gap by establishing a quantum no-free-lunch (NFL) theorem for learning quantum dynamics using entangled data. Contrary to previous findings, we prove that the impact of entangled data on prediction error exhibits a dual effect, depending on the number of permitted measurements. With a sufficient number of measurements, increasing the entanglement of training data consistently reduces the prediction error or decreases the required size of the training data to achieve the same prediction error. Conversely, when few measurements are allowed, employing highly entangled data could lead to an increased prediction error. The achieved results provide critical guidance for designing advanced QML protocols, especially for those tailored for execution on early-stage quantum computers with limited access to quantum resources. © 2023, CC BY.

关键词： Machine learning

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Multi-dimensional Fair Federated Learning

arXiv

引用

arXiv 2023年

作者： Su, Cong Yu, Guoxian Wang, Jun Li, Hui Li, Qingzhong Yu, Han School of Software Shandong University Jinan China SDU-NTU Joint Centre for AI Research Shandong University Jinan China School of Computer Science and Engineering Nanyang Technological University Singapore

Federated learning (FL) has emerged as a promising collaborative and secure paradigm for training a model from decentralized data without compromising privacy. Group fairness and client fairness are two dimensions of fairness that are important for FL. Standard FL can result in disproportionate disadvantages for certain clients, and it still faces the challenge of treating different groups equitably in a population. The problem of privately training fair FL models without compromising the generalization capability of disadvantaged clients remains open. In this paper, we propose a method, called mFairFL, to address this problem and achieve group fairness and client fairness simultaneously. mFairFL leverages differential multipliers to construct an optimization objective for empirical risk minimization with fairness constraints. Before aggregating locally trained models, it first detects conflicts among their gradients, and then iteratively curates the direction and magnitude of gradients to mitigate these conflicts. Theoretical analysis proves mFairFL facilitates the fairness in model development. The experimental evaluations based on three benchmark datasets show significant advantages of mFairFL compared to seven state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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