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检索条件"丛书名=Lecture notes in artificial intelligence,"

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A Novel Approach to Analyzing Defects: Enhancing Knowledge Graph Embedding Models for Main Electrical Equipment 19th

A Novel Approach to Analyzing Defects: Enhancing Knowledge G...

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Chen, Yanyu Huang, Jianye Qian, Jian Yi, Longqiang Li, Jinhu Huang, Jiangsheng Zhang, Zhihong Xiamen Univ Sch Informat Xiamen 361005 Peoples R China State Grid Fujian Elect Power Res Inst Fuzhou 350000 Peoples R China Kehua Data Co Ltd Xiamen 361005 Peoples R China State Grid Infotelecom Great Power Sci & Technol Xiamen 361005 Peoples R China

ISBN: (纸本)9789819947607;9789819947614

The safety of electric power grids can be threatened by defects in main electrical equipment, creating significant risks and pressures for dispatching operations. To analyze defects inmain electrical equipment, we adopt a knowledge graph link prediction approach. We found that using pre-training models, such as BERT, to extract node features and embed initial embeddings significantly improves the effectiveness of knowledge graph embedding models (KGEMs). However, this approach may not always work and could lead to performance degradation. To address this, we propose a transfer learning method that utilizes a small amount of domain-specific electric power corpus to fine-tune the pre-training model. The PCA algorithm is used to reduce the dimensionality of extracted features, thereby lowering the computational cost of KGEMs. Experimental results showthat our model effectively improves link prediction performance in analyzing defects in main electrical equipment.

关键词： Main electrical equipment defects Knowledge graph embedding models Pre-training models

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Triplets Oversampling for Class Imbalanced Federated Datasets

Triplets Oversampling for Class Imbalanced Federated Dataset...

引用

European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Xiao, Chenguang Wang, Shuo Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

ISBN: (纸本)9783031434143;9783031434150

Class imbalance is a pervasive problem in machine learning, leading to poor performance in the minority class that is inadequately represented. Federated learning, which trains a shared model collaboratively among multiple clients with their data locally for privacy protection, is also susceptible to class imbalance. The distributed structure and privacy rules in federated learning introduce extra complexities to the challenge of isolated, small, and highly skewed datasets. While sampling and ensemble learning are state-of-the-art techniques for mitigating class imbalance from the data and algorithm perspectives, they face limitations in the context of federated learning. To address this challenge, we propose a novel oversampling algorithm called "Triplets" that generates synthetic samples for both minority and majority classes based on their shared classification boundary. The proposed algorithm captures new minority samples by leveraging three triplets around the boundary, where two come from the majority class and one from the minority class. This approach offers several advantages over existing oversampling techniques on federated datasets. We evaluate the effectiveness of our proposed algorithm through extensive experiments using various real-world datasets and different models in both centralized and federated learning environments. Our results demonstrate the effectiveness of our proposed algorithm, which outperforms existing oversampling techniques. In conclusion, our proposed algorithm offers a promising solution to the class imbalance problem in federated learning. The source code is released at ***/Xiao-Chenguang/Triplets-Oversampling.

关键词： Class imbalance Oversampling Federated learning

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Enhancing Temporal Planning by Sequential Macro-Actions 18th

Enhancing Temporal Planning by Sequential Macro-Actions

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18th European Conference on Logics in artificial intelligence (JELIA)

作者： De Bortoli, Marco Chrpa, Lukas Gebser, Martin Steinbauer-Wagner, Gerald Graz Univ Technol Graz Austria Czech Tech Univ Prague Czech Republic Univ Klagenfurt Klagenfurt Austria

ISBN: (纸本)9783031436185;9783031436192

Temporal planning is an extension of classical planning involving concurrent execution of actions and alignment with temporal constraints. Unfortunately, the performance of temporal planning engines tends to sharply deteriorate when the number of agents and objects in a domain gets large. A possible remedy is to use macro-actions that are well-studied in the context of classical planning. In temporal planning settings, however, introducing macro-actions is significantly more challenging when the concurrent execution of actions and shared use of resources, provided the compliance to temporal constraints, should not be suppressed entirely. Our work contributes a general concept of sequential temporal macro-actions that guarantees the applicability of obtained plans, i.e., the sequence of original actions encapsulated by a macro-action is always executable. We apply our approach to several temporal planners and domains, stemming from the International Planning Competition and RoboCup Logistics League. Our experiments yield improvements in terms of obtained satisficing plans as well as plan quality for the majority of tested planners and domains.

关键词： Sports

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Reinforcement Learning-Based Recommendation with User Reviews on Knowledge Graphs 1

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16th International Conference on Knowledge Science, Engineering and Management (KSEM)

作者： Zhang, Siyuan Ouyang, Yuanxin Liu, Zhuang He, Weijie Rong, Wenge Xiong, Zhang Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Minist Educ Engn Res Ctr Adv Comp Applicat Technol Beijing Peoples R China

ISBN: (数字)9783031402890

ISBN: (纸本)9783031402883;9783031402890

Introducing knowledge graphs (KGs) into recommendation systems can improve their performance, while reinforcement learning (RL) methods can help utilize graph data for recommendation. We investigate existing RL-based methods for recommendation on KGs, and find that such approaches do not make full use of information from user reviews. Introducing user reviews into a recommendation system can reveal user preferences more deeply and equip a RL agent with a stronger ability to distinguish users' preferences for an item or not, which in turn improves the accuracy of recommendation results. We propose Reinforced Knowledge Graph Reasoning with User Reviews (RKGR-UR) by introducing user reviews into a RL-based recommendation model, which combines a rating prediction task to transform predicted ratings into rewards feedback for the RL agent. Experiments on three real datasets demonstrate the effectiveness of our method.

关键词： Recommendation system Knowledge graph Reinforcement learning User reviews

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Emotion4MIDI: A Lyrics-Based Emotion-Labeled Symbolic Music Dataset 22nd

Emotion4MIDI: A Lyrics-Based Emotion-Labeled Symbolic Music ...

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： Sulun, Serkan Oliveira, Pedro Viana, Paula Technol & Sci INESC TEC Inst Syst & Comp Engn Porto Portugal Univ Porto Fac Engn Porto Portugal Polytech Porto Sch Engn ISEP Porto Portugal

ISBN: (纸本)9783031490101;9783031490118

We present a new large-scale emotion-labeled symbolic music dataset consisting of 12 k MIDI songs. To create this dataset, we first trained emotion classification models on the GoEmotions dataset, achieving state-of-the-art results with a model half the size of the baseline. We then applied these models to lyrics from two large-scale MIDI datasets. Our dataset covers a wide range of fine-grained emotions, providing a valuable resource to explore the connection between music and emotions and, especially, to develop models that can generate music based on specific emotions. Our code for inference, trained models, and datasets are available online.

关键词： Sentiment analysis Symbolic music Emotion classification Music dataset

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Graph-FCA Meets Pattern Structures 17th

Graph-FCA Meets Pattern Structures

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17th International Conference on Formal Concept Analysis

作者： Ferre, Sebastien Univ Rennes CNRS INRIA IRISA F-35000 Rennes France

ISBN: (纸本)9783031359484;9783031359491

A number of extensions have been proposed for Formal Concept Analysis (FCA). Among them, Pattern Structures (PS) bring complex descriptions on objects, as an extension to sets of binary attributes;while Graph-FCA brings n-ary relationships between objects, as well as n-ary concepts. We here introduce a novel extension named GraphPS that combines the benefits of PS and Graph-FCA. In conceptual terms, Graph-PS can be seen as the meet of PS and Graph-FCA, seen as sub-concepts of FCA. We demonstrate how it can be applied to RDFS graphs, handling hierarchies of classes and properties, and patterns on literals such as numbers and dates.

关键词： Formal concept analysis

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An Experimental Overview of Neural-Symbolic Systems 1

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32nd International Conference on Inductive Logic Programming (ILP)

作者： Vermeulen, Arne Manhaeve, Robin Marra, Giuseppe Katholieke Univ Leuven Dept Comp Sci Leuven Belgium Leuven AI Leuven Belgium

ISBN: (数字)9783031492990

ISBN: (纸本)9783031492983;9783031492990

Neural-symbolic AI is the field that seeks to integrate deep learning with symbolic, logic-based methods, as they have complementary strengths. Lately, more and more researchers have encountered the limitations of deep learning, which has led to a rise in the popularity of neural-symbolic AI, with a wide variety of systems being developed. However, many of these systems are either evaluated on different benchmarks, or introduce new benchmarks that other systems have not been tested on. As a result, it is unclear which systems are suited to which tasks, and whether the difference between systems is actually significant. In this paper, we give an overview and classification of the tasks used in state-of-the-art neural-symbolic system. We show that most tasks fall in one of five categories, and that very few systems are compared on the same benchmarks. We also provide a methodological experimental comparison of a variety of systems on two popular tasks: learning with distant supervision and structured prediction. Our results show that a systems based on (probabilistic) logic programming achieve superior performance on these tasks, and that the performance amongst these methods does not differ significantly. Finally, we also discuss how the properties of the (probabilistic) logic programming-based systems are desirable for most neural-symbolic tasks.

关键词： Neural-symbolic AI Probabilistic Logic Programming Experimental Survey

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Object Recognition with Class Conditional Gaussian Mixture Model - A Statistical Learning Approach 25th

Object Recognition with Class Conditional Gaussian Mixture M...

引用

RoboCup Symposium

作者： Lu, Wentao Sheh, Qingbin Li, Liangde Sammut, Claude Univ New South Wales Sydney NSW 2052 Australia Kingsoft Off Software Beijing Peoples R China Univ Calif San Diego La Jolla CA USA

ISBN: (纸本)9783031284687;9783031284694

Object recognition is one of the key tasks in robot vision. In RoboCup SPL, the Nao Robot must identify objects of interest such as the ball, field features et al. These objects are critical for the robot players to successfully play soccer games. We propose a new statistical learning method, Class Conditional Gaussian Mixture Model (ccGMM), that can be used either as an object detector or a false positive discriminator. It is able to achieve a high recall rate and a low false positive rate. The proposed model has low computational cost on a mobile robot and the learning process takes a relatively short time, so that it is suitable for real robot competition play.

关键词： Mixture model Object recognition Statistical learning

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Navigational Strategies for Mobile Robots Using Rough Mereological Potential Fields and Weighted Distance to Goal

Navigational Strategies for Mobile Robots Using Rough Mereol...

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International Joint Conference on Rough Sets (IJCRS)

作者： Szpakowska, Aleksandra Artiemjew, Piotr Cybowski, Wojciech Univ Warmia & Mazury Ul Sloneczna 54 PL-10710 Olsztyn Poland

ISBN: (纸本)9783031509582;9783031509599

The present study examines the path planning methods based on rough mereological potential fields for remote mobile robots, building upon a modification of an originally designed project to prepare a foundation for three-dimensional path planning. For this purpose, we have implemented our own library for robot control and developed relevant algorithms - including AR marker recognition, image-based robot detection, and path planning based on the rough mereological potential field in conjunction with a weighted distance to the goal. These algorithms are also customized to facilitate tests in a laboratory setting. Using a video camera, the study captures real-time imagery, allowing for the continuous updating of the robot's position on the designated map. In this paper, we show how to find the right path that a robot follows while constantly updating its position. Furthermore, the research refines the precision of the optimal path through the application of smoothing techniques, ensuring an optimized trajectory from the robot's starting point to its destination. We demonstrate a special Euclidean distance responsible for path optimization. We present a complete project in the work, with all the elements to reproduce it. We carried out real-world tests using the Smart Element Hub cube with LED screen of the lego robot inventor kit.

关键词： Rough Mereology Mobile Robotics Path planning

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Application of Federated Learning to Prediction of Patient Mortality in Vasculitis Disease

Application of Federated Learning to Prediction of Patient M...

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International Joint Conference on Rough Sets (IJCRS)

作者： Bazan, Jan G. Milan, Pawel Bazan-Socha, Stanislawa Wojcik, Krzysztof Univ Rzeszow Inst Comp Sci Pigonia 1 PL-35310 Rzeszow Poland State Univ Appl Sci Krosno Rynek 1 PL-38400 Krosno Poland Jagiellonian Univ Coll Med Dept Internal Med 2 M Jakubowskiego 2 PL-30688 Krakow Poland

ISBN: (纸本)9783031509582;9783031509599

In this paper, we propose two methods of application of federated learning to the construction of classifiers for the analysis of data related to predicting the death of patients suffering from the vasculitis. The paper contains results of experiments on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland. In order to evaluate the proposed methods, which are trained on data samples, we compared their functionality with the work results of classical classifiers trained on the entire data. It turned out that the quality of classification of federated learning methods is comparable to the quality of classical methods. This means that access to the whole data is not necessary to construct effective classifiers for the considered decision problem.

关键词： rough sets classifiers federated learning

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