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Heterogeneous software Effort Estimation via Cascaded Adversarial Auto-Encoder 1

21st International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Qi, Fumin Jing, Xiao-Yuan Zhu, Xiaoke Jia, Xiaodong Cheng, Li Dong, Yichuan Fang, Ziseng Ma, Fei Feng, Shengzhong College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Wuhan University Wuhan China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China National Supercomputing Center in Shenzhen Guangzhou Guangdong China School of Computer Science Qufu Normal University Rizhao China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

In software Effort Estimation (SEE) practice, the data drought problem has been plaguing researchers and practitioners. Leveraging heterogeneous SEE data collected by other companies is a feasible solution to relieve the data drought problem. However, how to make full use of the heterogeneous effort data to conduct SEE, which is called as Heterogeneous software Effort Estimation (HSEE), has not been well studied. In this paper, we propose a HSEE model, called Dynamic Heterogeneous software Effort Estimation (i.e., DHSEE), which leverages the adversarial auto-encoder and convolutional neural network techniques. Meanwhile, we have investigated the scenario of conducting HSEE with dynamically increasing effort data. Experiments on ten public datasets indicate that our approach can significantly outperform the state-of-the-art HSEE method and other competing methods on both static and dynamic SEE scenarios. © 2021, Springer Nature Switzerland AG.

关键词： Drought

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Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

arXiv

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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A Blockchain-Based Multi-Cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： Cloud computing data centers Costs Blockchains Behavioral sciences

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Adaptive Divergence-based Non-negative Latent Factor Analysis

arXiv

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arXiv 2022年

作者： Yuan, Ye Yuan, Guangxiao Wang, Renfang Luo, Xin The College of Computer and Information Science Southwest University Chongqing400715 China The Business School University of Technology Sydney PO Box 123 BroadwayNSW2007 Australia The College of Big Data and Software Engineering Zhejiang Wanli University Ningbo315100 China

High-Dimensional and Incomplete (HDI) data are frequently found in various industrial applications with complex interactions among numerous nodes, which are commonly non-negative for representing the inherent non-negativity of node interactions. A Non-negative Latent Factor (NLF) model is able to extract intrinsic features from such data efficiently. However, existing NLF models all adopt a static divergence metric like Euclidean distance or α-β divergence to build its learning objective, which greatly restricts its scalability of accurately representing HDI data from different domains. Aiming at addressing this issue, this study presents an Adaptive Divergence-based Non-negative Latent Factor (ADNLF) model with three-fold ideas: a) generalizing the objective function with the α-β-divergence to expand its potential of representing various HDI data;b) adopting a non-negative bridging function to connect the optimization variables with output latent factors for fulfilling the non-negativity constraints constantly;and c) making the divergence parameters adaptive through particle swarm optimization, thereby facilitating adaptive divergence in the learning objective to achieve high scalability. Empirical studies are conducted on four HDI datasets from real applications, whose results demonstrate that in comparison with state-of-the-art NLF models, an ADNLF model achieves significantly higher estimation accuracy for missing data of an HDI dataset with high computational efficiency. © 2022, CC BY-NC-ND.

关键词： Scalability

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Impact of climate zone migration on geographical distribution of indigenous vegetation in Northeast China 2

Impact of climate zone migration on geographical distributio...

引用

2020 2nd International Conference on Advances in Civil engineering, Energy Resources and Environment engineering, ACCESE 2020

作者： Wu, Yunyang Li, Shengju Wang, Xue Zhang, Yuehua Gu, Ye Li, Lan College of Science Jiamusi University Jiamusi City Heilongjiang Province154007 China College of Big Data and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

Based on the relationship between climate and vegetation, this study uses the time lag of vegetation response to climate change, simulates the dynamic response process of different vegetation types to climate change, retrieves relevant literature and collates data, and deduces the actual occurrence and geographic distribution of vegetation under the current climate potential and future climate change scenarios. This experiment infer the adaptation relationship between plant geographical distribution and climatic conditions. The results show that the plants in Northeast China are generally more adaptable to climate change. The areas with poor adaptability are mainly at the junction of forest shrubs and grasslands. The geographical distribution of plants has changed, accounting for about 5%. The grassland ecosystem in the bush-grassland transition zone in the Greater Xing'an Mountains is also less adaptable to climate, accounting for about 35%. The vegetation in these areas tends to degenerate. The geographical distribution may change. The adjustment of plants in Northeast China to the future climate change is usually reasonable. 84% of the vegetation changes are positive changes. Especially in the eastern regions, the vegetation deduction conditions will be improved in the future, and the vegetation coverage in these regions may be improved. About 79% of the potential changes in vegetation can adapt to the future climate. Nevertheless, the grassland ecosystems in the Greater Xing'an Mountains and parts of Inner Mongolia are less adaptable to future climate change and tend to degrade. It provides new ideas for assessing vegetation adaptability or ecosystem adaptability. Research on the interaction between climate change and terrestrial ecosystems of different scales needs to be strengthened in the context of global change. © Published under licence by IOP Publishing Ltd.

关键词： Vegetation

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Modeling thunderstorm based on paralleled and improved naive bayes 2

Modeling thunderstorm based on paralleled and improved naive...

引用

2020 2nd International Conference on Computer Modeling, Simulation and Algorithm, CMSA 2020

作者： Zhou, Chengdong Li, Leixiao Wang, Hui Liu, Shuang College of Data science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering and Technology Research Center ofBig Data Based Software Service Hohhot China

Aiming at improving the low efficiency and inaccuracy of the traditional thunderstorm prediction model, a paralleled and improved naive Bayesian thunderstorm prediction model SPNBC is proposed. The native Bayesian classifier (NBC) model assumes that the attribute data set is independent, which leads to inaccurate prediction. The principal component analysis algorithm is used to optimize the Bayesian algorithm to build PNBC. First, a new attribute data set is constructed by the principal component analysis (PCA) algorithm to eliminate the dependency between attributes, and a naive Bayesian classifier is constructed on the new attribute data set. Secondly, SPNBC is obtained by parallel design of PNBC based on spark framework to improve the time efficiency. Finally, taking the thunderstorm prediction in Hohhot as the application background, the accuracy rate, false alarm rate, speedup ratio and scalability ratio were used to analyze the experiment. The experimental results show that the SPNBC of this paper is better than the traditional naive Bayes algorithm and neural network algorithm, and the prediction accuracy rate and empty alarm rate are larger when dealing with massive data Performance advantages. © 2020 Institute of Physics Publishing. All rights reserved.

关键词： Thunderstorms

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A Grey Wolf Optimization Algorithm with Triangular Community and Crossover Operator for Community Discovery

A Grey Wolf Optimization Algorithm with Triangular Community...

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International Conference on Systems and Informatics (ICSAI)

作者： Yan Kang Xin Huang Zhongming Xu Xuekun Yang Xinyan Li Kunming Data Science and Computing Lab Software College. Software Engineering of Yunnan University Yunnan China Kunming Data Science and Computing Lab School of Business Travel. School of Business and Tourism Management of Yunnan University Yunnan China

ISBN: (纸本)9781665426251

Community discovery under complex networks is a hot discussion issue in network science research. It is very necessary to find a good community structure to study complex networks. At present, many evolutionary algorithms are used for community discovery. However, prior knowledge is not considered for community detection, and full consideration of the network topology can further improve community discovery performance. Therefore, we propose a new algorithm TM-GWO to optimize community discovery. TM-GWO is based on the gray wolf optimization algorithm. It designs a new initialization method and migration-based crossover operator to realize community discovery. The experimental results show that TM-GWO is better than the current Multi-objective evolutionary algorithm.

关键词： Knowledge engineering Evolutionary computation Complex networks Informatics Optimization

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Screening of rhizosphere growth promoting bacteria and their growth promoting ability of sunflower in cold black soil area 2

Screening of rhizosphere growth promoting bacteria and their...

引用

2020 2nd International Conference on Advances in Civil engineering, Energy Resources and Environment engineering, ACCESE 2020

作者： Zou, Danzhi Zheng, Hongjuan Zhang, Yaosheng Gu, Ye Cao, Yajie Song, Yujun Li, Lan College of Science Jiamusi University Jiamusi City Heilongjiang Province154007 China College of Big Data and Software Engineering Wuzhou University Wuzhou City Guangxi Province543000 China

High efficient growth promoting bacteria were screened from the rhizosphere soil of sunflower in the cold black soil area, and their growth promoting ability was tested to obtain the strains with comprehensive growth promoting ability. The phosphate solubilizing bacteria were screened out by inorganic phosphate bacteria culture medium, the phosphate solubilizing ability of the strain was determined by mo sb anti chromogenic method, the indoleacetic acid producing ability of the strain was determined by salkowski method, the iron carrier producing ability of the strain was determined by CAS method, and the growth promoting ability of the strain was evaluated comprehensively. Eleven strains of phosphate solubilizing bacteria were isolated and screened from soil samples. Among them, strain WP2 had the strongest phosphate solubilizing ability, and the concentration of phosphate radical was 12.75 μg/mL;strain WP1 had the strongest indoleacetic acid producing ability, and the concentration of indoleacetic acid was 25.37μg/mL;strain WP1 had the strongest iron producing ability, with Su = 72.76%, and the strength was 4+. It was found that WP1, WP2 and WP10 had strong ability of promoting growth and had strong potential of development and utilization. The above results can provide excellent strains for the development and promotion of special bio fertilizer suitable for the cold black soil. © Published under licence by IOP Publishing Ltd.

关键词： Iron

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Water Quality Prediction Based on Multi-Indicator Relation Learning and Knowledge Transfer

Water Quality Prediction Based on Multi-Indicator Relation L...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Huan Wu Feng Jiang Zehua Zhao Liang Tao Lin Peng Wenxun Han Min Gao T.Y.Lin International Engineering Consulting (China) Co. Ltd. Chongqing China Tongji University Shanghai China College of Environmental Science and Engineering Chongqing Business Vocational College Chongqing China Faculty of Engineering the University of Sydney Sydney Australia School of Big Data and Software Engineering Chongqing University Chongqing China

Water quality prediction is widely used in many aspects of water pollution. In recent years, researchers have begun using deep learning models for prediction, significantly improving prediction performance. However, these methods usually aim single-prediction task. They are not good at linking multiple prediction indicators of water quality, and are difficult to train at water quality monitoring stations with small samples. For this reason, we explore a model based on multi-indicators relationship learning and knowledge transfer for the prediction in a small sample scenario. Firstly, we propose a water quality multi-indicator gated implicit variable parameter sharing model MGH to extract the common characteristics, individual characteristics, and correlation of water quality multi-indicators at multi-sample sites. Then, we combine two migration methods to carry out the water quality prediction model in the small sample area to migrate the trained model parameters or sample distribution knowledge to the small sample area. The experimental results show that our multi-indicator relationship learning and knowledge transfer model can achieve better prediction accuracy. We also verified the role of the model in ensuring the prediction effect of small sample monitor stations. The proposed model provides data support for water quality managers to understand the changes in water quality indicators in advance and make corresponding water quality management decisions.

关键词：

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Remote Sensing Scene Classification Based on Improved GhostNet

Remote Sensing Scene Classification Based on Improved GhostN...

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2020 International Conference on Computer science and Communication Technology, ICCSCT 2020

作者： Wei, Biyun Shen, Xiaole Yuan, Yule College of Big Data and Internet Shenzhen Technology University Shenzhen China College of Applied Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Nowadays, the design of convolutional neural network (CNN) models is getting deeper and wider. When traditional CNN is used to process limited data of remote sensing images, it will lead to overfitting. We will use lightweight and efficient models to classify remote sensing images. In order to improve the classification accuracy and reduce the intermediate parameters, we improved GhostNet and proposed a smaller CNN named Improved GhostNet. Meanwhile, we use image enhancement methods to enlarge the datasets and dropout, it will reduce the amount of parameters. We experimented on three datasets, such as AID, UC Merced, NWPU-RESISC45. Then, we used MobileNetV3-Small and GhostNet to compare with our CNN model. The classification accuracy of improved GhostNet achieves more than 91%, and the accuracy on the AID is improved by 2.05% compared to the original GhostNet. These results demonstrate the effectiveness and efficiency of improved GhostNet. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Image enhancement

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