检索结果-内蒙古大学图书馆

18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Sun, Qiuhong Wang, Jiaqi Zhou, Xiaokang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China The Faculty of Data Science Shiga University Hikone522-8522 Japan Department of Computer Science North China Electric Power University Baoding071003 China The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan

ISBN: (纸本)9789819998920

With the development of science and technology, function fitting has penetrated into various fields of scientific research, scientific and technological innovation. For the function of fitting analysis of a given function image, there is no public software on the market that uses the idea of genetic algorithm to solve the problem of function fitting. In order to make up for the insufficiency of the existing software and seize the opportunity of function fitting in various industries, a function fitting system based on genetic algorithm was proposed. It is a low-threshold software with a wide range of applications. When designing the function of fitting and analyzing a given function image, the genetic algorithm is used. With the application of ray detection in Unity, the closest expression of function fitting is obtained. At the same time, the line graph of fitness reduction during genetic algorithm iteration is given. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

DIFFERENTIABLE CAUSAL DISCOVERY OF LINEAR NON-GAUSSIAN ACYCLIC MODELS UNDER UNMEASURED CONFOUNDING

arXiv

引用

arXiv 2025年

作者： Morinishi, Yoshimitsu Shimizu, Shohei Graduate School of Data Science Shiga University Accenture Co. Ltd Japan Faculty of Data Science Shiga University RIKEN Center for Advanced Intelligence Project Japan

We propose a novel score-based causal discovery method, named ABIC LiNGAM, which extends the linear non-Gaussian acyclic model (LiNGAM) framework to address the challenges of causal structure estimation in scenarios involving unmeasured confounders. By introducing the assumption that error terms follow a multivariate generalized normal distribution, our method leverages continuous optimization techniques to recover acyclic directed mixed graphs (ADMGs), including causal directions rather than just equivalence classes. We provide theoretical guarantees on the identifiability of causal parameters and demonstrate the effectiveness of our approach through extensive simulations and applications to real-world datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Normal distribution

来源：评论

学校读者我要写书评

暂无评论

IMPROVED STATISTICAL AND COMPUTATIONAL COMPLEXITY OF THE MEAN-FIELD LANGEVIN DYNAMICS UNDER STRUCTURED data 12

IMPROVED STATISTICAL AND COMPUTATIONAL COMPLEXITY OF THE MEA...

引用

12th International Conference on Learning Representations, ICLR 2024

作者： Nitanda, Atsushi Oko, Kazusato Suzuki, Taiji Wu, Denny IHPC Agency for Science Technology and Research Singapore CFAR Agency for Science Technology and Research Singapore Department of Mathematical Informatics The University of Tokyo Japan Center for Advanced Intelligence Project RIKEN Japan Center for Data Science New York University United States Center for Computational Mathematics Flatiron Institute United States

Recent works have shown that neural networks optimized by gradient-based methods can adapt to sparse or low-dimensional target functions through feature learning;an often studied target is the sparse parity function on the unit hypercube. However, such isotropic data setting does not capture the anisotropy and low intrinsic dimensionality exhibited in realistic datasets. In this work, we address this shortcoming by studying how gradient-based feature learning interacts with structured (anisotropic) input data: we consider the classification of k-sparse parity on high-dimensional orthotope where the feature coordinates have varying magnitudes, and analyze the learning complexity of the mean-field Langevin dynamics (MFLD), which describes the noisy gradient descent update on two-layer neural network. We show that the statistical complexity (i.e. sample size) and computational complexity (i.e. network width) of MFLD can both be improved when prominent directions of the anisotropic input data align with the support of the target function. Moreover, by employing a coordinate transform determined by the gradient covariance, the width can be made independent of the target degree k. Lastly, we demonstrate the benefit of feature learning by establishing a kernel lower bound on the classification error, which applies to neural networks in the lazy regime. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Rugby Scene Classification Enhanced by Vision Language Model

Rugby Scene Classification Enhanced by Vision Language Model

引用

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Naoki Nonaka Ryo Fujihira Toshiki Koshiba Akira Maeda Jun Seita Advanced Data Science Project RIKEN Information R&D and Strategy Headquarters Hakata Knee & Sports Clinic

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This study investigates the integration of vision language models (VLM) to enhance the classification of situations within rugby match broadcasts. The importance of accurately identifying situations in sports videos is emphasized for understanding game dynamics and facilitating downstream tasks like performance evaluation and injury prevention. Utilizing a dataset comprising 18, 000 labeled images extracted at 0.2-second intervals from 100 minutes of rugby match broadcasts, scene classification tasks including contact plays (scrums, mauls, rucks, tackles, lineouts), rucks, tackles, lineouts, and multiclass classification were performed. The study aims to validate the utility of VLM outputs in improving classification performance compared to using solely image data. Experimental results demonstrate substantial performance improvements across all tasks with the incorporation of VLM outputs. Our analysis of prompts suggests that, when provided with appropriate contextual information through natural language, VLMs can effectively capture the context of a given image. The findings of our study indicate that leveraging VLMs in the domain of sports analysis holds promise for developing image processing models capable of incorpolating the tacit knowledge encoded within language models, as well as information conveyed through natural language descriptions.

关键词： Performance evaluation Scene classification Analytical models Computational modeling Prevention and mitigation Natural languages Pattern recognition

来源：评论

学校读者我要写书评

暂无评论

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating

arXiv

引用

arXiv 2024年

作者： Takahashi, Daisuke Shimizu, Shohei Tanaka, Takuma Graduate School of Data Science Shiga University Hikone Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

Explainable artificial intelligence (XAI) has helped elucidate the internal mechanisms of machine learning algorithms, bolstering their reliability by demonstrating the basis of their predictions. Several XAI models consider causal relationships to explain models by examining the input-output relationships of prediction models and the dependencies between features. The majority of these models have been based their explanations on counterfactual probabilities, assuming that the causal graph is known. However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases. Thus, this study proposed a novel XAI framework that relaxed the constraint that the causal graph is known. This framework leveraged counterfactual probabilities and additional prior information on causal structure, facilitating the integration of a causal graph estimated through causal discovery methods and a black-box classification model. Furthermore, explanatory scores were estimated based on counterfactual probabilities. Numerical experiments conducted employing artificial data confirmed the possibility of estimating the explanatory score more accurately than in the absence of a causal graph. Finally, as an application to real data, we constructed a classification model of credit ratings assigned by Shiga Bank, Shiga prefecture, Japan. We demonstrated the effectiveness of the proposed method in cases where the causal graph is unknown. © 2024, CC BY.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating

Counterfactual Explanations of Black-box Machine Learning Mo...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Daisuke Takahashi Shohei Shimizu Takuma Tanaka Graduate School of Data Science Shiga University Hikone Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

关键词： Machine learning algorithms Explainable AI Neural networks Closed box Companies Predictive models data models

来源：评论

学校读者我要写书评

暂无评论

A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

引用

25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

Software developers create mashups of their interest by searching, selecting, and combining web Application Programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A survey on blockchain-based integrity auditing for cloud data

引用

Digital Communications and Networks 2022年第5期8卷 591-603页

作者： Haoxiang Han Shufan Fei Zheng Yan Xiaokang Zhou The State Key Lab of ISN School of Cyber EngineeringXidian University266 Xinglong Section of Xifeng RoadXi'anShaanxi710126China The Faculty of Data Science Shiga UniversityHikone522-8522Japan The RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

With the rapid advancement of cloud computing,cloud storage services have developed *** issue that has attracted particular attention in such remote storage services is that cloud storage servers are not enough to reliably save and maintain data,which greatly affects users’confidence in purchasing and consuming cloud storage *** data integrity auditing techniques for cloud data storage are centralized,which faces huge security risks due to single-point-of-failure and vulnerabilities of central auditing *** technology offers a new approach to this *** researchers have endeavored to employ the blockchain for data integrity *** on the search of relevant papers,we found that existing literature lacks a thorough survey of blockchain-based integrity auditing for cloud *** this paper,we make an in-depth survey on cloud data integrity auditing based on ***,we cover essential basic knowledge of integrity auditing for cloud data and blockchain ***,we propose a series of requirements for evaluating existing Blockchain-based data Integrity Auditing(BDIA)***,we provide a comprehensive review of existing BDIA schemes and evaluate them based on our proposed ***,according to our completed review and analysis,we explore some open issues and suggest research directions worthy of further efforts in the future.

关键词： Blockchain Cloud storage Integrity auditing Decentralization

来源：评论

学校读者我要写书评

暂无评论

Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States

arXiv

引用

arXiv 2023年

作者： Jiang, Yi Shimizu, Shohei Graduate School of Data Science Shiga University Japan Center for Advanced Intelligence Project RIKEN Japan

While economic theory explains the linkages among the financial markets of different countries, empirical studies mainly verify the linkages through Granger causality, without considering latent variables or instantaneous effects. Their findings are inconsistent regarding the existence of causal linkages among financial markets, which might be attributed to differences in the focused markets, data periods, and methods applied. Our study adopts causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge to explore the linkages among financial markets in Japan and the United States (US) for the post Covid-19 pandemic period under divergent monetary policy directions. The VAR-LiNGAM results reveal that the previous day’s US market influences the following day’s Japanese market for both stocks and bonds, and the bond markets of the previous day impact the following day’s foreign exchange (FX) market directly and the following day’s Japanese stock market indirectly. The LPCMCI results indicate the existence of potential latent confounders. Our results demonstrate that VAR-LiNGAM uniquely identifies the directed acyclic graph (DAG), and thus provides informative insight into the causal relationship when the assumptions are considered valid. Our study contributes to a better understanding of the linkages among financial markets in the analyzed data period by supporting the existence of linkages between Japan and the US for the same financial markets and among FX, stock, and bond markets, thus highlighting the importance of leveraging causal discovery methods in the financial domain. Copyright © 2023, The Authors. All rights reserved.

关键词： Financial markets

来源：评论

学校读者我要写书评

暂无评论

A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

引用

Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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：