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The Journal of Machine Learning Research 2023年第1期24卷 483-490页

作者： Takashi Ikeuchi Mayumi Ide Yan Zeng Takashi Nicholas Maeda Shohei Shimizu SCREEN Advanced System Solutions Co. Ltd. Japan Department of Computer Science and Technology Tsinghua University China School of System Design and Technology Tokyo Denki University Japan and Center for Advanced Intelligence Project RIKEN Japan Faculty of Data Science Shiga University Japan and Center for Advanced Intelligence Project RIKEN Japan

Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a well-known model for causal discovery. This paper describes an open-source Python package for causal discovery based on LiNGAM. The package implements various LiNGAM methods under different settings like time series cases, multiple-group cases, mixed data cases, and hidden common cause cases, in addition to evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://***/cdt15/lingam.

关键词： causal structure learning statistical reliability model evaluation

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A four-operator splitting algorithm for nonconvex and nonsmooth optimization

arXiv

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arXiv 2024年

作者： Alcantara, Jan Harold Lee, Ching-Pei Takeda, Akiko Center for Advanced Intelligence Project RIKEN Tokyo Japan Department of Advanced Data Science Institute of Statistical Mathematics Tokyo Japan Department of Mathematical Informatics Graduate School of Information Science and Technology University of Tokyo Tokyo Japan

In this work, we address a class of nonconvex nonsmooth optimization problems where the objective function is the sum of two smooth functions (one of which is proximable) and two nonsmooth functions (one proper, closed and proximable, and the other continuous and weakly concave). We introduce a new splitting algorithm that extends the Davis-Yin splitting (DYS) algorithm to handle such four-term nonconvex nonsmooth problems. We prove that with appropriately chosen step sizes, our algorithm exhibits global subsequential convergence to stationary points with a stationarity measure converging at a rate of 1/k. When specialized to the setting of the DYS algorithm, our results allow for larger stepsizes compared to existing bounds in the literature. Experimental results demonstrate the practical applicability and effectiveness of our proposed *** Codes 90C26, 90C30 © 2024, CC BY.

关键词： Optimization algorithms

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What Machine Learning Tells Us About the Mathematical Structure of Concepts

arXiv

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arXiv 2024年

作者： Otsuka, Jun The Department of Philosophy Kyoto University Japan Data Science and AI Innovation Research Promotion Center Shiga University Japan Center for Advanced Intelligence Project RIKEN Japan

This paper examines the connections among various approaches to understanding concepts in philosophy, cognitive science, and machine learning, with a particular focus on their mathematical nature. By categorizing these approaches into Abstractionism, the Similarity Approach, the Functional Approach, and the Invariance Approach, the study highlights how each framework provides a distinct mathematical perspective for modeling concepts. The synthesis of these approaches bridges philosophical theories and contemporary machine learning models, providing a comprehensive framework for future research. This work emphasizes the importance of interdisciplinary dialogue, aiming to enrich our understanding of the complex relationship between human cognition and artificial intelligence. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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The Design and Implementation of Dust Monitoring System for Photovoltaic Power Generation 23

The Design and Implementation of Dust Monitoring System for ...

引用

23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Yuliang Ma, Jiali Ma, Yanhong Zhang, Yingli Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

In recent years, photovoltaic power generation, as one of the most important modes of clean energy generation, has gradually become the focus of China's energy development efforts. The Jiuquan area in Gansu, with good solar radiation conditions, high light intensity and high solar power generation potential, is an ideal area for building solar photovoltaic power generation farms. However, as the photovoltaic panels(PV panels) are exposed to the outdoors for a long time, the surface of the panels tend to accumulate a layer of dust, which makes it difficult for the sunlight to shine directly on the power generation area, seriously reducing the actual power generation efficiency of the panels, and at the same time, the 'heat island effect' caused by the dust may lead to damage of the panels. In this paper, a PV dust monitoring system is designed to solve the problem of low efficiency of PV power generation system caused by dust. The first part introduces the background, purpose and significance of the research, introduces the current status of domestic and international research, and states the main research work of this topic;the second part introduces the basic theory of PV power generation system and the effect of dust on PV power generation system;the third part introduces the design scheme of the system from the perspective of hardware and software;the fourth part introduces the specific implementation scheme of the system. This paper provides a solution to monitor the dust accumulation on the surface of PV panels, and provides support for the prediction of power generation and the recommendation of the cleaning cycle of PV panels. © 2021 IEEE.

关键词： Photovoltaic systems Smart cities Buildings Software Hardware Cleaning Thermal pollution

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A Comparative Study of Time Series data Forecasting Using Machine Learning Based on Improved Grey Model 23

A Comparative Study of Time Series Data Forecasting Using Ma...

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作者： Li, Yan Zhang, Jiaming Ma, Yanhong Li, Xiaomin Liu, Ziang Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Company Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

This paper proposes a time-series data prediction hybrid model suitable for groundwater level to further study its impact on new energy power generation through the close relationship between groundwater level and climate. The model is based on the improved grey model (IGM) and combined with other models such as backpropagation neural network (BP) and general vector machine (GVM), which are used to predict time series data. Based on the time series data of groundwater level, the prediction ability of IGM BP, IGM RBF, IGM support vector machine, and IGM-GVM hybrid model is studied, which proved the feasibility of the model. The statistical parameter values finally conclude that the IGM-GVM model outperforms other selected models in the dataset utilized in the paper. In addition, the mixed model has a better effect on the dataset than the corresponding single model. © 2021 IEEE.

关键词： Support vector machines Temperature distribution Smart cities Time series analysis Machine learning Predictive models data models

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Lymphoma Ultrasound Image Classification with Causal Attention and Feature Fusion 19

Lymphoma Ultrasound Image Classification with Causal Attenti...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Dehua Han, Yingkang Dong, Yijie Zhou, Xiaokang Donghua University School of Computer Science and Technology Shanghai China Shanghai Jiaotong University School of Medicine Shanghai China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Riken Tokyo Japan

ISBN: (纸本)9798350346558

Medical image classification aids in diagnosis eases the work of physicians. In this paper, we propose an image classification method for lymphoma ultrasound image classification. We make the model more focused on the lymphoma lesion region by fusing the features of the original image and the image of the lesion region obtained through semantic segmentation in parallel, which is used to solve the problem that the grayscale values of the lymphoma lesion region and the background region are similar and difficult to distinguish. The problem of generalization of different lymphoma ultrasound images obtained from different instrument scans is handled by adding a causal attention mechanism to the model. Finally, we use counterfactual interpretation to perturb both the original image and the lesion image to obtain the instance-level interpretation. It is demonstrated experimentally that the method effectively improves the performance of classification. © 2022 IEEE.

关键词： Image classification

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Privacy-aware data Fusion and Prediction for Smart City Services in Edge Computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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FlowADGAN: Adversarial Learning for Deep Anomaly Network Intrusion Detection 18th

FlowADGAN: Adversarial Learning for Deep Anomaly Network In...

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18th International Workshop on Security and Trust Management, STM 2022, co-located with the 27th European Symposium on Research in Computer Security, ESORICS 2022

作者： Wang, Pan Li, Zeyi Zhou, Xiaokang Su, Chunhua Wang, Weizheng School of Modern Posts Nanjing University of Posts and Telecommunications Nanjing China Faculty of Data Science Shiga University Hikone522-8522 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Division of Computer Science University of Aizu Fukushima965–8580 Japan Department of Computer Science City University of Hong Kong Hong Kong

ISBN: (纸本)9783031295034

Due to the increasingly evolved attacks on the Internet, especially IoT, 5G, and vehicle networking, a robust Network Intrusion Detection System (NIDS) has gained increasing attention from academic and industrial communities. Anomaly-based intrusion detection algorithms aim to detect unexpected deviations in the expected network behaviour, thus detecting unknown or novel attacks compared to signature-based methods. Deep Anomaly Detection (DAD) technologies have attracted much attention for their ability to detect unknown attacks without manually building the traffic behaviours profile. However, low recall rates and high dependencies on data labels still hinder the development of DAD technologies. Inspired by the successes of Generative Adversarial Networks (GANs) for detecting anomalies in the area of Computer Vision and Images, we have proposed a deep end-to-end architecture called FlowADGAN for detecting anomalies in NIDS. Unlike traditional GAN-based NIDS methods that usually construct Generator (G) and Discriminator (D) based on vanilla GAN, the proposed architecture is composed of a flow encoder-decoder-encoder for G, and a flow encoder for D. FlowADGAN can learn a latent flow feature space of G so that the latent space better captures the normality underlying the network traffic data. We conduct several experimental comparisons with existing machine learning algorithms like One-Class SVM, LOF, and PCA and existing deep learning methods, including AutoEncoder and VAE, on three public datasets, NSL-KDD CICIDS2017 and UNSW-NB15. The evaluation results show that FlowADGAN can significantly improve the performance of the anomaly-based NIDS. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Design and Implementation of Monitoring System for New Energy data Call Based on Blockchain 23

Design and Implementation of Monitoring System for New Energ...

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作者： Liu, Ziang Huang, Yuanyuan Zhou, Qiang Zhang, Yingli Zhou, Xiaokang Zhou, Rui School of Information Science and Engineering Lanzhou University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China Shiga University Faculty of Data Science Hikone Japan Riken Center for Advanced Intelligence Project Tokyo Japan

ISBN: (纸本)9781665494571

With the energy revolution starting the third transformation to new energy, new energy has become essential for human energy. In order to better organize and utilize new energy, more and more research is committed to predicting the power generation capacity of new energy through the power generation data collected by intelligent systems to make better use of new energy. However, with the increase of new energy digital platforms and supporting infrastructure improvement, the distribution and management of relevant data also begin to face significant challenges. New energy data faces a substantial potential threat in distributing and using data to various digital platforms. Based on this, this paper designs and implements a new energy data call monitoring system based on blockchain to ensure the security of new energy data by tracking data operation information. © 2021 IEEE.

关键词： Smart cities Consensus algorithm Machine learning Blockchains Safety Recording Intelligent systems

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INADMISSIBILITY AND TRANSIENCE

arXiv

引用

arXiv 2023年

作者： Takanashi, Kōsaku McAlinn, Kenichiro RIKEN Center for Advanced Intelligence Project Japan Department of Statistics Operations and Data Science Fox School of Business Temple University United States

We discuss the relation between the statistical question of inadmissibility and the probabilistic question of transience. [2] proved the mathematical link between the admissibility of the mean of a Gaussian distribution and the recurrence of a Brownian motion, which holds for R2 but not for R3 in Euclidean space. We extend this result to symmetric, non-Gaussian distributions, without assuming the existence of moments. As an application, we prove that the relation between the inadmissibility of the predictive density of a Cauchy distribution under a uniform prior and the transience of the Cauchy process differs from dimensions R1 to R2. We also show that there exists an extreme model that is inadmissible in R1MSC Codes 62C15 © 2023, CC BY.

关键词： Gaussian distribution

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