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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Jiaguo Wang Yan Lei Maojin Li Guanyu Ren Huan Xie Shifeng Jin Junchao Li Jian Hu School of Big Data & Software Engineering Chongqing University Chongqing China Peng Cheng Laboratory Shenzhen China University College London London United Kingdom

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

The ideal principle of software testing is that test results ought to be deterministic: a test failure indicates the presence of a software bug, while a test success suggests the absence of a bug. Nevertheless, flaky tests break the principle. Flaky tests yield inconsistent results when executed repeatedly under the same conditions. The most straightforward approach runs the tests multiple times to predict flaky tests whereas it is highly time-consuming. Many researchers have proposed efficient approaches to reduce the cost, e.g., recent approaches leverage machine learning techniques for the prediction of flaky tests. However, traditional machine learning primarily focuses on predicting specific instances, which is not conducive to identify flaky tests across an entire project. Therefore, we propose Flakyrank, a ranking framework based on augmented learning to rank to predict flaky tests. The insight is that learning to rank, as compared with traditional machine learning, not only concentrates on individual samples but also optimizes the overall ranking. Since flaky tests constitute a small proportion of the dataset (i.e., approximately 3.6% of the total tests), we utilize generative adversarial networks to generate some synthetic flaky tests to augment the dataset. Based on the augmented dataset, FLAKYRANK treats predicting flaky tests as an information retrieval task, where newly detected flaky tests and test cases serve as queries and documents, respectively. For each newly detected flaky test (i.e., query), FLAKYRANK combines multiple relevant features into a learning to rank model to predict flaky tests candidate tests. We conduct large-scale experiments on different learning to rank models, and the results show that FLAKYRANK with the LambdaMART algorithm yields the best performance. In addition, the experimental results on 23 benchmark projects show that FLAKYRANK outperforms the state-of-the-art predictors.

关键词： software testing Computer bugs software algorithms Machine learning Predictive models Prediction algorithms Information retrieval

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A Multi-Granularity Distance with its Application in Pattern Classification

SSRN

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SSRN 2022年

作者： Zhang, Zhanhao Xiao, Fuyuan School of Big Data and Software Engineering Chongqing University Chongqing401331 China

In the decision-making systems, the distance measure is an important concept, which represents the degree of dissimilarity between bodies of evidence to improve decision-making performance. And the Jousselme's distance, as the most commonly used one so far, is used to measure the distance between BBAs in evidence theory. However, the Jousselme's distance was found to have some limitations. In this paper, a BBA refinement method and a novel multi-granularity distance are proposed. Moreover, the methods are verified to be effective in the problems that Jousselme's distance cannot satisfy. At the same time, a hypothetical physical model is used to verify the practicability of the proposed methods with multiple granularity. Furthermore, based on the proposed multiple granularity distance, the improvement of the deng et al's fusion method is proposed and a novel decision-making algorithm is designed to applly pattern classification. The results validate that the proposed decision-making methods are beneficially applicable to different real-world datasets. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Beamforming Design for Semantic-Bit Coexisting Communication System

arXiv

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arXiv 2024年

作者： Zhang, Maojun Zhu, Guangxu Jin, Richeng Chen, Xiaoming Shi, Qingjiang Zhong, Caijun Huang, Kaibin College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai200092 China Shenzhen Research Institute of Big Data Shenzhen518172 China The University of Hong Kong Hong Kong

Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. This method is referred to as the MM-FP algorithm. Additionally, inspired by the semi-closed form solution, we also propose a low-complexity version of the MM-FP algorithm, called the low-complexity MM-FP (LP-MM-FP), which alleviates the need for iterative optimization of beamforming vectors. Extensive simulation results demonstrate that the proposed MM-FP algorithm outperforms convention

关键词： Mean square error

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DGN-MEF:A DenseNet-Based Multi-Exposure Image Fusion Framework using Adaptive Gabor convolution

DGN-MEF:A DenseNet-Based Multi-Exposure Image Fusion Framewo...

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第43届中国控制会议

作者： Mei Zhang Ye Jin Jinhui Zhu School of Automation Science and Engineering South China University of Technology School of Software Engineering South China University of Technology Key Laboratory of Big Data and Intelligent Robot Ministry of Education

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method.

关键词： Multi-exposure Image Fusion Adaptive Gabor Convolution Cross-domain Aggregation Attention

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A Novel and Efficient Risk Minimization-Based Missing Value Imputation Algorithm

SSRN

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SSRN 2023年

作者： He, Yu-Lin Yu, Jia-Yin Fournier-Viger, Philippe Huang, Joshua Zhexue Shenzhen518107 China Big Data Institute College of Computer Science & Software Engineering Shenzhen University Shenzhen518060 China

In contrast to externally driven MVI algorithms, this paper proposes a novel risk minimization-based MVI algorithm (abbreviated as RM-MVI) that considers both the internal characteristics of the missing data and the external performance for specific applications. RM-MVI is applied in two stages: filling with structural risk minimization and refining with empirical risk minimization. In the filling stage, an autoencoder with a single hidden-layer is trained on the original data set without missing values. Missing values are first initialized with random numbers and then imputation values are iteratively adjusted based on a derived updating rule to minimize a structural risk-oriented objective function. After obtaining the preliminarily-optimized imputation values in the filling stage, a neural network-based classifier is trained in the refining stage to further optimize the imputation values to obtain sophisticatedly-optimized imputation values that reduce the minimum empirical risk (i.e., classification error). Exhaustive experiments were conducted on several benchmark data sets to validate the feasibility, rationality, and effectiveness of the RM-MVI algorithm. Results show that (1) the optimization processes of imputation values in the filling and refining stages are convergent with the iterative adjustments to random imputation values;(2) the preliminarily-optimized and sophisticatedly-optimized imputation values preserve a rough distribution consistency with the non-missing values corresponding to the same condition attribute;and (3) statistical analysis results indicate that the RM-MVI algorithm can yield considerably better MVI performance on benchmark data sets than 10 other MVI algorithms. This demonstrates that RM-MVI is a viable approach to deal with the MVI problem. © 2023, The Authors. All rights reserved.

关键词： Refining

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MA-Det: A Discriminative Morphology-Aware Detector for Cervical Lesion Cell Clumps

MA-Det: A Discriminative Morphology-Aware Detector for Cervi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ziyang Yin Qian Huang Yulin Chen Hao Lu College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automated detection of cervical lesion cell clumps is crucial for cervical cancer screening. However, the dense packing and overlap of cells, caused by adhesion molecules, make detection challenging. To address this issue, we propose the Morphology-Aware Detector (MA-Det). Specifically, by innovatively employing Deformable Convolution, we propose the Dynamic Context Aggregation Module (DCAM) which dynamically captures contextual information, improving the feature representation of lesion cells. To enhance discrimination in high-density regions, we design the Distance-Weighted Interaction Module (DWIM) and Adaptive Morphology-Aware (AMA) loss into the detection head. These components improve spatial awareness by utilizing interactions between cell features and proposals. In particular, our method achieves state-of-the-art performance on the CDetector and CRIC datasets while significantly reducing parameters.

关键词： Convolution Adhesives Detectors Medical services Feature extraction Complexity theory Lesions Proposals Speech processing Cervical cancer

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A Robust GLRT Detector Against Missing data in Cooperative Sensing

A Robust GLRT Detector Against Missing Data in Cooperative S...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinghui Guan Rui Zhou Wenqiang Pu Qingjiang Shi Tsung-Hui Chang School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Software Engineering Tongji University Shanghai China

Cooperative sensing, a technique employed in cognitive radio (CR) networks for spectrum sensing, exhibits promising potential in bolstering spectrum utilization and enhancing network performance. This approach leverages the information captured by distributed CR users, which is subsequently aggregated at a fusion center. However, the challenges arise when the data are transmitted with low-quality, resulting in the consequential issue of missing data. These factors introduce complexity in detecting primary signals and undermine the reliability of cooperative sensing. In this study, we present a significant advancement in cooperative sensing methodologies by introducing a novel approach: a generalized likelihood ratio test (GLRT) type detector specifically designed to be robust to missing data. More specifically, our proposed robust GLRT detector modifies the computation of the classical GLRT test statistic to accommodate the inherent incompleteness of the data and effectively estimates the desired unknown parameters. Through numerical experiments, we demonstrate the resilience and robustness of our proposed cooperative signal detection method.

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A Large-Capacity Information Hiding Algorithm Based on the Fractal Carrier

A Large-Capacity Information Hiding Algorithm Based on the F...

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Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Xuejiao Tao Yahong Leng Qian Yang Sen Bai School of Software Chongqing Institute of Engineering Chongqing China Office of Teaching Affairs Chongqing Institute of Engineering Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

Information security is a pressing concern in the era of the Internet. Within the realm of information security, significant research focus has been placed on information hiding technology. Researchers strive to enhance both the information hiding capacity and the anti-attack capability. This paper explores the concept of concealing information within fractal images. Fractal images possess a notable attribute of being affine transformed through simple IFS code parameters, thereby generating unique fractal representations. The simplicity of the transmission parameters facilitates an effortless restoration of the carrier. By employing straightforward affine or iterative parameters, the recipient can successfully extract the originally embedded carrier, subsequently unveiling the concealed information. To further augment the security of the information hiding process, an encryption strategy may be implemented to transmit sensitive data. In this scenario, the secret carrier serves as the public key, while the fractal parameter information functions as the private key, thereby intensifying the complexity of steganography analysis. Consequently, this paper introduces a high-capacity fractal carrier information hiding algorithm, which effectively embeds information within fractal images, amplifies and adapts fractal parameters based on requirements, and enhances the overall capacity of the fractal carrier. Exploiting fractal images for information hiding purposes conveys advantages such as minimized reliance on key information and exceptional concealment abilities, thus bolstering the secrecy of hidden data, expanding the storage capacity, and fortifying the resistance against steganalysis.

关键词： Resistance Steganography Shape Information security Public key Pressing Fractals

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data Center Microgrid Energy Management Based on Model-Based Reinforcement Learning 4

Data Center Microgrid Energy Management Based on Model-Based...

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4th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2022

作者： Zhao, He Wan, Jianxiong Li, Leixiao Inner Mongolia University of Technology Hohhot China Inner Mongolia University of Technology Inner Mongolia Autonomous Region Engineering &technology Research Center of Big Data Based Software Service Hohhot China

ISBN: (纸本)9798350321951

With the increasing of energy consumption and price, energy management is becoming increasingly important for data center with larger and larger scale. Microgrid can provide reliable, stable and continuous power services for data centers, and improve energy efficiency and reduce the operating cost. In this paper, we study the energy management problem for data center microgrid (DCMG) integrated with renewable energy. We introduce the energy storage device to mediate the intermittent nature of renewable energy. The energy management problem is formulated as a Markov Decision Process (MDP). Traditional methods greedy choice actions does not consider the impact of the actions on future cumulative operating cost. Model-free reinforcement learning is a candidate solution to this problem. However, model-free reinforcement learning methods converge slowly. Therefore, in this paper, we propose a model- based deep reinforcement learning algorithms to solve the energy management problem (Model-Based Reinforcement Learning Energy Management, MBRL-EM) of DCMG. We use long short-term memory (LSTM) model to design a system transition model, which predicts the future renewable energy production and energy demand. Then we obtain the optimal policy based on model predictive control to minimize the long-term operation cost. The simulation results show that MBRL-EM outperforms the baseline algorithms of model-based PPO and model-free PPO and reduces the average cost by up to 23% and 38% respectively. © 2022 IEEE.

关键词： Operating costs

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NC-ALG: Graph-Based Active Learning under Noisy Crowd 40

NC-ALG: Graph-Based Active Learning under Noisy Crowd

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Li, Yang Cao, Gang Yang, Zhi Cui, Bin Center for Machine Learning Research Peking University China Key Lab of High Confidence Software Technologies Peking University China Institute of Advanced Algorithms Research Shanghai China Institute of Computational Social Science Peking University Qingdao China National Engineering Labratory for Big Data Analytics and Applications China TEG Tencent Inc. Department of Data Platform China Beijing Academy of Artificial Intelligence China

ISBN: (纸本)9798350317152

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active Learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed. © 2024 IEEE.

关键词： Crowdsourcing

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