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A Parallelizable Counterfactual Generation Method Based on Gradient Optimization

A Parallelizable Counterfactual Generation Method Based on G...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Haorun Ding Xuesong Jiang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Post-hoc explanations are important for people to understand the predictions of explanation models. One class of methods in post-hoc explanation is the generation of counterfactuals, where a hypothetical example is obtained by perturbing the inputs to show how one could obtain a different prediction from the decision model. Counter-factual explanations should satisfy several properties: One is that counterfactuals generated under specific scenarios and constraints should be feasible for users, i.e., they should accommodate different causal constraints. The other is that it is more desirable for users to have a wider variety of viable examples, i.e., counterfactual diversity. To this end, we propose a parallelizable method based on gradient optimization. We partition the input feasible domains, perform counterfactual generation independently for each feasible domain, and then parallelize the counterfactual generation process for each feasible domain. Experimental results show that our approach effectively improves the diversity, sparsity, and proximity of the generated counterfactual instances on the public datasets Adult-Income, Lending-Club, German-Credit, and COMPAS compared to other models.

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

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Sensitive Data Recognition and Filtering Model of Webpage Content Based on Decision Tree Algorithm 2nd

Sensitive Data Recognition and Filtering Model of Webpage Co...

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2nd International Conference on Big Data and security, ICBDS 2020

作者： Ye, Sheng Cheng, Yong Yang, Yonggang Guo, Qian State Grid ZheJiang Electric Power Company Ltd Hangzhou310000 China State Grid ShanXi Electric Power Company Xi’an710000 China Global Energy Interconnection Research Institute Co. Ltd Nanjing210000 China State Grid Key Laboratory of Information & Network Security Nanjing210003 China

ISBN: (纸本)9789811631498

In recent years, privacy-protecting data mining has attracted widespread concern because it is necessary to provide protection for the privacy level of sensitive and confidential data from unauthorized attacks. The purpose of this study is to develop a privacy-protecting anonymity algorithm using decision tree classification. This paper focuses on k-anonymity technology, which can prevent identity leakage. K-anonymity technology adopts generalization and suppression methods to achieve data anonymity. Then, the privacy level and mining quality of anonymous data sets will be tested by using decision tree classification, and then compared with other data mining technologies (logistic regression and support vector machine). As is shown in the research, compared with other data mining technologies, privacy level and data quality provide better results. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Research and Implementation of User Behavior Simulation Technology Based on power Industry Cyber Range 2

Research and Implementation of User Behavior Simulation Tech...

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2nd IEEE International Conference on information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Yang, Huiting Chen, Tao Bai, Yunxiao Li, Feng Li, Mingxuan Yang, Ruxia State Grid Xinjiang Electric Power Research Institute Urumqi China State Grid Xinjiang Electric Power Co. Ltd Urumqi China State Grid Key Laboratory of Information Network Security Nanjing China Global Energy Interconnection Research Institute Co. Ltd. Jiangsu Nanjing China

ISBN: (纸本)9781665428767

The current cyber range of the electric power industry has background traffic simulation problems, prospect behavior generation problems, and target simulation diversity problems in large-scale network environments. In response to these problems, this paper proposes a user behavior simulation technology suitable for power industry cyber range. We perform large-scale, high-fidelity network user behavior simulation by combining real traffic playback, inject user behavior traffic in the real network into the virtual target network, then model the user behavior in multiple scenarios in the power network space and generate it based on the method of sequence prediction, and finally realize the diversity simulation of user behavior. © 2021 IEEE.

关键词： Behavioral research

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Research on the security Sharing Model of power Grid Data Based on Federated Learning 5

Research on the Security Sharing Model of Power Grid Data Ba...

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5th IEEE information Technology, networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Yang, Huiting Bai, Yunxiao Zou, Zhenwan Shi, Yuanyuan Yang, Ruxia State Grid Xinjiang Electric Power Research Institute Urumqi China State Grid Kuitun Electric Power Supply Company Kuitun China State Grid Key Laboratory of Information Network Security Nanjing210003 China Global Energy Interconnection Research Institute Co. Ltd. Jiangsu Nanjing210003 China

ISBN: (纸本)9781665415989

In order to meet the needs of data availability and practicality in the power grid system to achieve data security and efficient sharing, this paper proposes a power grid data security sharing model based on federated learning, and through the differential privacy, secure multi-party computing, homomorphic encryption, function encryption Four security sharing methods are implemented to ensure the security and feasibility of grid data sharing. © 2021 IEEE.

关键词： Electric power transmission networks

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YOLO-TP: A process detection model of tunnel real scene based on gradient information interaction

YOLO-TP: A process detection model of tunnel real scene base...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiaying Fan Shuping Pan Xinbo Jiang Shengtao Zhang Hongliang Liu Chuan Wang Jianghua Li Chenglin Ke Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China China Railway 14th Bureau Group First Engineering Development Co. Ltd RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Target detection technology is an effective method to detect tunnel operation safety. Although the field has made some progress in detection models, the lack of datasets and detection models has hindered its development. We produce and propose Tunnel Processes data (TP), a challenging dataset of high-precision real-world tunnel process target detection that reduces the limitations of research in this field. TP has a high resolution: 1920×1080, 1734images,9 categories. TP is the first tunnel process data set under construction to date, and its fidelity exceeds that of other tunnel process datasets in the field of target detection. In addition, we also propose a YOLO-TP benchmark model to make up for the lack of a high-precision tunnel process image target detection baseline model. Experiments on TP datasets demonstrate their validity, and experiments on the dataset presented in this paper demonstrate the validity of the benchmark model.

关键词： Accuracy Image resolution Computational modeling Object detection Benchmark testing Big Data Feature extraction Data models Safety Artificial intelligence

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An edge enhancement-based fusion method of infrared and visible images in low light

An edge enhancement-based fusion method of infrared and visi...

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Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： Xiang Gao Xiyan Wang Hanbin Bao Qian Li Guohua Lv Faculty of Comupter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Bresee Technology Co. Ltd. Jinan China Shandong Grain Group Co. Ltd. Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The combination of infrared and visible images can result in the generation of a single image with a high degree of detail and important targets in a variety of scenes, which is beneficial for human recognition. However, in low-light environments, low-intensity visible images tend to blur valuable information, which is often overlooked. This results in the loss of important information. While existing methods address the issue of low illumination and highlight the illumination, a considerable amount of structural information is lost when adjusting illumination, resulting in the omission of texture details. To solve aforementioned problems, we proposes a method for edge enhancement in low-illumination scenes. This approach aims to minimise the loss of edge information while simultaneously enhancing the illumination, thereby producing fusion images with enhanced texture detail. The initial step involves the design of an illumination enhancement module, which is employed to segregate the compromised illumination present from the scene information. Given the fact that image edge information has the capacity for effective structural modeling, a low-light visible image edge extraction network is designed to model structural information, which can accurately highlight important structural information and insert it in fused images. The results produced by this method have better visual perception and minimize structural loss. Extensive experiments demonstrate that the method outperforms SOTA methods in term of visual quality and quantitative metrics.

关键词： Reflectivity Measurement Visualization Target recognition Image edge detection Lighting Data mining Image reconstruction Image fusion Visual perception

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Research on the Role-Based Access Control Model and Data security Method 2nd

Research on the Role-Based Access Control Model and Data Sec...

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2nd International Conference on Big Data and security, ICBDS 2020

作者： Deng, Junhua Zhao, Lei Yuan, Xuechong Tang, Zhu Guo, Qian Nanjing210000 China State Grid Beijing Electric Power Co. Ltd Beijing100000 China Global Energy Interconnection Research Institute Co. Ltd Nanjing210000 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China

ISBN: (纸本)9789811631498

For management of massive data and centralized network, the data security research team has become a central issue. Role-based access control (RBAC) is a common practice to control access to sensitive data. This article uses Flex and C# to develop an RBAC model, which can make data management more flexible. Concentrating on data security, this article presents the layer structure and model design for standardized management of security access. This article also implements detailed approaches for data security by verifying parameters and codes, access control, preventing illegal users and using oracle parameters and stored procedures for database security technology. In addition, an empirical study was conducted to assess effectiveness and usefulness. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Access control

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Adversarial Attacks on Deep Neural networks for Time Series Prediction 2021

Adversarial Attacks on Deep Neural Networks for Time Series ...

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2021 10th International Conference on Internet computing for Science and Engineering

作者： Aidong Xu Xuechun Wang Yunan Zhang Tao Wu Xingping Xian Guangdong Provincial Key Laboratory of Power System Network Security Electric Power Research Institute CSG China School of Computer Science and Technology Chongqing University of Posts and Telecommunications China School of Cybersecurity and Information Law Chongqing University of Posts and Telecommunications China

ISBN: (纸本)9781450384957

Time series data is widespread in real-world scenarios. To recover and infer missing information in practical domains, such as stock price monitoring, electricity load forecasting, traffic flows analysis, climate trend prediction, etc., the problem of time series prediction has been widely studied as a classical research topic in data mining. Over the past decade, deep learning architectures are introduced as a vital part of the next generation of time series prediction models. However, recent studies showed that deep learning models are vulnerable to adversarial attacks. In this paper, we study the adversarial attacks on the time series prediction models prospectively. We propose an attack strategy to generate adversarial samples by adding imperceptible perturbed data to the original time series with the goal of reducing the accuracy of time series prediction models. Specifically, the perturbation-based adversarial example generation algorithm is proposed using gradient information of time series prediction model. Moreover, adversarial examples should be imperceptible to humans. To address the challenge, we craft adversarial samples based on importance measuring to perturb the original data locally. We evaluate our attacks on state-of-the-art time series prediction models using three time series datasets. Our results demonstrate that our attacks can effectively evade the time series prediction models, and the adversarial attacks mechanisms can be used as robustness metric for constructing robust time series prediction models.

关键词： Adversarial samples Time series prediction Adversarial attacks Time series data

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The Database Fine-Grained Access SQL Statement Control Model Based on the Dynamic Query Modification Algorithm 2nd

The Database Fine-Grained Access SQL Statement Control Model...

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2nd International Conference on Big Data and security, ICBDS 2020

作者： Wei, Ya Cai, Rongyan Zhang, Lan Guo, Qian Zhengzhou450000 China State Grid Fujian Electric Power Company Limited Fuzhou350000 China Global Energy Interconnection Research Institute Co. Ltd. Nanjing210000 China State Grid Key Laboratory of Information and Network Security Nanjing210003 China

ISBN: (纸本)9789811631498

The SQL query technology which is the standard computer language for the processing of databases, the existing technology cannot guarantee the integrity of all SQL queries. While the fine-grained access control (FGAC) method in the DBMS shall meet the sanity attribute requirements. It means that the answer to the query obtained by this method under the FGAC shall conform to the answer without the FGAC. In this article, a new query modification algorithm is proposed. And the theory of robustness is extended and perfected, and it is pointed out that for the several SQL queries proposed, the proposed algorithm guarantees the robustness. Finally, the algorithm is implemented by the query modification and performance evaluation is carried out, which is the feasible method. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Access control

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