检索结果-内蒙古大学图书馆

The probabilistic Harsanyi power solutions for probabilistic graph games

RAIRO-OPERATIONS RESEARCH 2023年第5期57卷 2873-2887页

作者： Li, Zijun Meng, Fanyong North China Univ Water Resources & Elect Power Sch Management & Econ Zhengzhou 450000 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China

This paper analyzes the probabilistic Harsanyi power solutions (PHPSs) for probabilistic graph games (PGGs), which distribute the Harsanyi dividends proportional to weights determined by a probabilistic power measure for probabilistic graph structure. The probabilistic power measure considers the role of players in all possible deterministic graphs, which can reflect the powers of players more effectively. Three axiomatic systems of the PHPSs on PGGs and cycle-free probabilistic graph games (CFPGGs) are provided to show the rationality of the PHPSs, and their independence is analyzed.

关键词： Cooperative game probabilistic graph probabilistic power measure

来源：评论

学校读者我要写书评

暂无评论

probabilistic graph Modeling based Safety Classifier Algorithm for Smart Transportation

引用

Procedia Computer Science 2024年 236卷 502-507页

作者： Najib El Karkouri Smail Tigani Rachid Saadane Abdelah Chehri Samuel Pierre Noureddine Neya Mathematics and Informatics Lab Faculty of Sciences University Ibno Toufial Kenitra Morocco Research and Development Unit Accsellium LLC Fez Morocco SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada Department of Computer and Software Engineering Polytechnique Montreal Canada

This research introduces a novel probabilistic graph Modeling-based Safety Classifier Algorithm designed for the purpose of classifying road safety in smart transportation systems. Leveraging a combination of numerical integration methods and Gaussian kernel density estimation models, the proposed algorithm offers an effective approach to assess and categorizing the safety levels of roads. The integration of these techniques enables a comprehensive analysis, allowing for a more nuanced understanding of the complex interactions within transportation networks. The algorithm's efficacy in road safety classification holds promising implications for enhancing traffic management and promoting safer urban environments.

关键词： probabilistic graph Numerical Computation Smart Transportation Classification Road Safety

来源：评论

学校读者我要写书评

暂无评论

A value for cooperative games with coalition and probabilistic graph structures

引用

JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第3期43卷 646-671页

作者： Shi, Jilei Cai, Lei Shan, Erfang Lyu, Wenrong Shanghai Univ Sch Management Shanghai 200444 Peoples R China Ningbo Univ Finance & Econ Ningbo 315175 Peoples R China

In this paper we generalize TU-games with coalition and graph structures to TU-games with coalition and probabilistic graph structures. We introduce the probabilistic graph-partition value and we show that the value is uniquely determined by the axioms of probabilistic graph efficiency, probabilistic balanced contributions and probabilistic collective balanced contributions and the axioms of probabilistic graph efficiency, probabilistic balanced contributions, probabilistic balanced per capita contributions and either probabilistic fairness for joining the grand coalition or probabilistic population solidarity within unions, respectively. Also, we apply this value to China's railway network and compare it with other values.

关键词： TU-game probabilistic graph Coalition structure graph-partition value C71 D60

来源：评论

学校读者我要写书评

暂无评论

probabilistic graph Model Based Recommendation Algorithm for Material Selection in Self-directed Learning

引用

SAGE OPEN 2024年第2期14卷

作者： Qiu, Zhiyong Cui, Yingjin Harbin Normal Univ Harbin Peoples R China Harbin Univ Sci & Technol Harbin Peoples R China Harbin Normal Univ Sch Educ Sci 1Shida Rd Harbin 150025 Peoples R China

Faced the vast amount of information, choosing the appropriate materials is a prerequisite for effective self-directed learning. The recommendation algorithm is a kind of intelligent technology that can accurately locate the required information which the users care about most. However, many recommendation techniques experience can not be trained adequately in scenarios with small sample data and extremely sparse ratings. Moreover, DLRAs (Deep learning based Recommendation Algorithms) require high hardware support. The probabilistic graph (PG) can effectively represent the implicit complex relations among nodes, but it still has the problem of sparse data sensitivity. Therefore, we propose a Matrix-Factorization-based probabilistic graph Model for Recommendation Algorithm (MF-PGMRA): By matrix-factorizing the sparse rating matrix, the users and items are mapped to the user/item spaces, respectively;We employ the inner product to data-enhance and overcome the problems of sparse data and cold start;Then, we build probabilistic graph to construct the "user-item" latent spaces and estimate the probability distribution based on expectation maximization (EM), so as to predict the ratings;Finally, we built a library management system with the recommendation module to highlight the benefits of MF-PGMRA for students' subject learning. According to a questionnaire, we confirmed that the students are satisfied with the system from four aspects of speed, accuracy, usability and convenience, which can confirm that the library management system based on MF-PGMRA can efficiently and accurately recommend suitable materials for students from the huge amount of learning materials to improve students' self-directed learning efficiency. We designed an intelligent recommendation method for material selection of self-directed learning based on matrix factorization and probabilistic graph model, and built a library management system with the recommendation module with our method for pract

关键词： self-directed learning intelligent recommendation e-learning materials matrix factorization probabilistic graph

来源：评论

学校读者我要写书评

暂无评论

Optimized Adversarial Example Generating Algorithm Based on probabilistic graph 16th

Optimized Adversarial Example Generating Algorithm Based on ...

引用

16th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Li, Chaochao Yang, Yu Man, Yufei Di, Junyu Zhan, Rui Beijing Univ Posts & Telecommun Sch Cyberspace Secur Beijing 100876 Peoples R China

ISBN: (纸本)9783030859282;9783030859275

Deep learning technology is widely used in various fields. Once the deep model is attacked, it will cause huge economic losses and security problems. Therefore, the security of deep model has become a research hotspot. For attacking the depth model and detecting the robustness of the depth model, the adversarial examples is the core technology. And stAdv is one of the most advanced adversarial examples generation technologies. It has the advantages of a high success rate of attack and small visual distortion of adversarial examples, but it also has the problems of long generation time of adversarial examples and low efficiency. Aiming at the above-mentioned shortcomings, this paper improves the efficiency of adversarial examples generation algorithm, and proposes an optimized adversarial examples generation algorithm P&stAdv based on probabilistic graph. This method combines the steganography algorithm and CAM technique. P&stAdv perform attack evaluation on each pixel of the image, and introduces a method of obtaining the "appropriate point" of the image based on the steganography algorithm to generate the adversarial examples. Then the cost matrix of image modification is obtained and the image probabilistic graph is generated. Finally, use adversarial examples to attack images according to probabilistic graph. The experimental results show that the proposed algorithm has high efficiency. Compared with stAdv, the generation time of adversarial examples is reduced by 35%. This algorithm can modify the minimum number of pixels while ensuring a high success rate of attack. Moreover, it can make more effective attacks on complex texture images.

关键词： Adversarial examples Spatial transformation Deep neural network probabilistic graph Color channels

来源：评论

学校读者我要写书评

暂无评论

Prefix-graph: A Versatile Log Parsing Approach Merging Prefix Tree with probabilistic graph 37

Prefix-Graph: A Versatile Log Parsing Approach Merging Prefi...

引用

37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Chu, Guojun Wang, Jingyu Qi, Qi Sun, Haifeng Tao, Shimin Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Huawei Beijing Peoples R China

ISBN: (纸本)9781728191843

Logs play an important part in analyzing system behavior and diagnosing system failures. As the basic step of log analysis, log parsing converts raw log messages into structured log templates. However, existing log parsing approaches are not adaptive and versatile enough to ensure their high accuracy on all types of datasets. In particular, it is required to design regular expressions or fine-tune the hyper-parameters manually for the best performance. In this paper, we propose Prefix-graph, an online versatile log parsing approach. Prefix-graph is a probabilistic graph structure extended from prefix tree. It iteratively merges together two branches which have high similarity in probability distribution, and represents log templates as the combination of cut-edges in root-to-leaf paths of the graph. Since no domain knowledge is used and all the parameters are fixed, Prefix-graph can be easily applied to different log datasets without any additional manual work. We evaluate our approach on 10 real-world datasets and 117GB log messages obtained from Huawei. The experimental results demonstrate that Prefix-graph achieves the highest average accuracy of 0.975 and the smallest standard deviation of 0.037. Our approach is superior to baseline methods in terms of adaptability and versatility.

关键词： log parsing template extraction prefix tree probabilistic graph

来源：评论

学校读者我要写书评

暂无评论

Multi-BERT-wwm Model Based on probabilistic graph Strategy for Relation Extraction 1

引用

10th International Conference on Health Information Science (HIS)

作者： Zhang, Yingxiang Liao, Xiangyun Chen, Li Kang, Hongjun Cai, Yunpeng Wang, Qiong Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Sch Chinese PLA Gen Med Dept Beijing Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Sch Chinese PLA Dept Crit Care Med Beijing Peoples R China

ISBN: (数字)9783030908850

ISBN: (纸本)9783030908850;9783030908843

As the core work of information extraction, relation extraction aims to find medical entity pairs with relations from the medical records. The current methods of relation extraction either ignore the relevance of entity extraction and relation classification, or fail to solve the problem of multiple relations or entities in one sentence. To handle those problems, this paper proposes a cascading pointer Multi-BERT-wwm model based on the probabilistic graph strategy. The model selects an entity randomly from all the predicted entities each time, then predicts other entities and their relations according to that entity. Meanwhile, the pointer labeling network helps to solve the problem of overlapping entities. The Multi-BERT-wwm model is improved based on BERT, which connects a layer of self-attention to the multi-head attention layer in the first six Encoder modules to strengthen the feature extraction ability. In addition, we add the adversarial training to improve the robustness and generalization ability of the model. The experimental results tested on the CMeIE dataset show that compared with the traditional CNN+Attention and BERT methods, our method improves the F1-score by 4.42% and 1.91% respectively in relation extraction task.

关键词： Relation extraction probabilistic graph Multi-BERT-wwm Pointer labeling Adversarial training

来源：评论

学校读者我要写书评

暂无评论

probabilistic graph Attention for Relation Extraction for Domain of Geography

Probabilistic Graph Attention for Relation Extraction for Do...

引用

作者： Jiaorou Jr Yin Pengfei Pf Duan Weitao Wt Huang Shengwu Sw Xiong School of Computer Science and Technology Wuhan University of Technology

In view of the lack of labeled corpus and the difficulty of extracting multiple relations in the extraction of entity relation in the geographic domain, a method based on probabilistic graph is proposed for extracting the entity relation in the geographic domain. This method uses the semantic information in the knowledge base to enhance the representation of the geographic domain corpus to alleviate the problem of insufficient labeled corpus. It uses character-word hybrid vectors that can be effectively integrated into the semantic information as the feature vectors. The vectors are transmitted to BiLSTM and self-attention for global deep feature extraction. Finally drawing on the idea of probabilistic graph, the "semi pointer-semi annotation" method is utilized to extract the head entities, traverse the head entity, and then uses the same method to extract tail entities and relations. By comparing the experimental results on the geographic domain corpus and ACE05 corpus with other advanced methods, the probabilistic graph-based extraction method effectively improves the geographic domain entity relation extraction effect.

关键词： Geographic Domain Entity Relation Extraction Bi-LSTM probabilistic graph

来源：评论

学校读者我要写书评

暂无评论

A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on probabilistic graph Model

引用

IEEE TRANSACTIONS ON SMART GRID 2018年第2期9卷 695-702页

作者： Sheng, Gehao Hou, Huijuan Jiang, Xiuchen Chen, Yufeng Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China State Grid Shandong Elect Power Res Inst Jinan 250002 Shandong Peoples R China

The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.

关键词： Power transformers state parameters association rules big data data-driven method Apriori algorithm probabilistic graph state prediction

来源：评论

学校读者我要写书评

暂无评论

ChiSeL: graph Similarity Search using Chi-Squared Statistics in Large probabilistic graphs

引用

PROCEEDINGS OF THE VLDB ENDOWMENT 2020年第10期13卷 1654-1668页

作者： Agarwal, Shubhangi Dutta, Sourav Bhattacharya, Arnab Indian Inst Technol Comp Sc & Engn Kanpur Uttar Pradesh India Huawei Res Ctr Dublin Ireland

Subgraph querying is one of the most important primitives in many applications. Although the field is well studied for deterministic graphs, in many situations, the graphs are probabilistic in nature. In this paper, we address the problem of subgraph querying in large probabilistic labeled graphs. We employ a novel algorithmic framework, called CHISEL, that uses the idea of statistical significance for approximate subgraph matching on uncertain graphs that have uncertainty in edges. For each candidate matching vertex in the target graph that matches a query vertex, we compute its statistical significance using the chi-squared statistic. The search algorithm then proceeds in a greedy manner by exploring the vertex neighbors having the largest chi-square score. In addition to edge uncertainty, we also show how CHISEL can handle uncertainty in labels and/or vertices. Experiments on large real-life graphs show the efficiency and effectiveness of our algorithm.

关键词： probabilistic graph Subgraph Query Chi-Square Statistic Statistical Significance Greedy Neighborhood Search

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：