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

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A Contextual Information-Augmented Probabilistic Case-Based Reasoning Model for Knowledge Graph Reasoning 31st

A Contextual Information-Augmented Probabilistic Case-Based ...

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31st International Conference on Case-Based Reasoning Research and Development (ICCBR)

作者： Wu, Yuejia Zhou, Jian-tao Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Minist Educ Engn Res Ctr Ecol Big Data Hohhot Peoples R China Inner Mongolia Engn Lab Cloud Comp & Serv Softwar Hohhot Peoples R China Inner Mongolia Key Lab Social Comp & Data Proc Hohhot Peoples R China Inner Mongolia Key Lab Discipline Inspect & Super Hohhot Peoples R China Inner Mongolia Engn Lab Big Data Anal Technol Hohhot Peoples R China

ISBN: (纸本)9783031401763;9783031401770

Knowledge Graph Reasoning (KGR) is one effective method to improve incompleteness and sparsity problems, which infers new knowledge based on existing knowledge. Although the probabilistic case-based reasoning (CBR) model can predict attributes for an entity and outperform other rule-based and embedding-based methods by gathering reasoning paths from similar entities in KG, it still suffers from some problems such as insufficient graph feature acquisition and omission of contextual relation information. This paper proposes a contextual information-augmented probabilistic CBR model for KGR, namely CICBR. The proposed model frame the reasoning task as the query answering and evaluates the likelihood that a path is valuable at answering a query about the given entity and relation by designing a joint contextual information-obtaining algorithm with entity and relation features. What's more, to obtain a more fine-grained representation of entity features and relation features, the CICBR introduces Graph Transformer for KG's representation and learning. Extensive experimental results on various benchmarks prominently demonstrate that the proposed CICBR model can obtain the state-of-the-art results of current CBR-based methods.

关键词： Knowledge Graph Reasoning Case-based Reasoning Graph Neural Network Graph Transformer Query Answering

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MISC: A Multimodal Approach for Sentiment Classification of Classical Chinese Poetry 1

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

作者： Su, Chang Liu, Shupin Luo, Chalian Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

ISBN: (数字)9789819947522

ISBN: (纸本)9789819947515;9789819947522

Classical Chinese poetry is a literary genre that is rich in emotion and lyricism. However, sentiment classification on such poetry poses a challenge as they are typically short texts in classical Chinese, which are characterized by sparse features and non-standard information. To address these challenges, we propose a novel approach called MISC (the Multimodal Imagery based Sentiment Classification), which takes the multimodal information of imagery into account. Imagery is widely used in classical Chinese poetry and has a strong positive correlation with the poet's emotional expression. Considering that imagery words are closely related to visual modality, we have introduced visual modality information from imagery words in addition to the text modality. Our approach utilizes imagery words in poems and applies multi-head attention to the poem text, imagery words, and imagery pictures, respectively. Finally, we integrate multimodal features of the poem, and use it on sentiment classification taskwhich output ternary classification results of negative, neutral and positive. Our results demonstrate an improvement in sentiment analysis of classical Chinese poetry through the incorporation of visual modalities.

关键词： Sentiment classification Chinese poetry Multimodality

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Advancing Air Combat Tactics with Improved Neural Fictitious Self-play Reinforcement Learning 19th

Advancing Air Combat Tactics with Improved Neural Fictitious...

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

作者： He, Shaoqin Gao, Yang Zhang, Baofeng Chang, Hui Zhang, Xinchen Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9789819947607;9789819947614

We study the problem of utilizing reinforcement learning for action control in 1v1 Beyond-Visual-Range (BVR) air combat. In contrast to most reinforcement learning problems, 1v1 BVR air combat belongs to the class of two-player zero-sum games with long decision-making periods and sparse rewards. The complexity of action and state space in this game makes it difficult to learn high-level air combat strategies from scratch. To address this problem, we propose a reinforcement learning self-play training framework to solve it from two aspects: the decision model and the training algorithm. Our decision-making model uses the Soft actor-critic (SAC) algorithm, amethod based onmaximum entropy, as the action control of the reinforcement learning part, and introduces an action mask to achieve efficient exploration. Our training algorithm improves Neural Fictitious Self-Play (NFSP) and proposes the best response history correction (BRHC) version of NFSP. These two components helped our algorithm to achieve efficient training in the high-fidelity simulation environment. The result of the 1v1 BVR air combat problem shows that the improved NFSP-BRHC algorithm outperforms both the NFSP and the Self-Play (SP) algorithms.

关键词： Air Combat Reinforcement Learning Neural Fictitious Self-Play

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A Comparison of Automated Machine Learning Tools for Predicting Energy Building Consumption in Smart Cities 22nd

A Comparison of Automated Machine Learning Tools for Predict...

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22nd EPIA Conference on artificial intelligence (EPIA)

作者： Soares, Daniela Pereira, Pedro Jose Cortez, Paulo Goncalves, Carlos CCG ZGDV Inst EPMQ IT Engn Matur & Qual Lab Guimaraes Portugal Univ Minho Dept Informat Syst ALGORITMI Ctr LASI Braga Portugal Polytech Porto ISEP Rua Dr Antonio Bernardino Almeida Porto Portugal

ISBN: (纸本)9783031490071;9783031490088

In this paper, we explore and compare three recently proposed Automated Machine Learning (AutoML) tools (AutoGluon, H2O, Oracle AutoMLx) to create a single regression model that is capable of predicting smart city energy building consumption values. Using a recently collected one year hourly energy consumption dataset, related with 29 buildings from a Portuguese city, we perform several Machine Learning (ML) computational experiments, assuming two sets of input features (with and without lagged data) and a realistic rolling window evaluation. Furthermore, the obtained results are compared with a univariate Time Series Forecasting (TSF) approach, based on the automated FEDOT tool, which requires generating a predictive model for each building. Overall, competitive results, in terms of both predictive and computational effort performances, were obtained by the input lagged AutoGluon single regression modeling approach.

关键词： Automated machine learning Smart cities Regression

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Alignment-Aware Word Distance 27th

Alignment-Aware Word Distance

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Zhao, Guoliang Zhang, Jinglei Du, DongDong Gao, Qing Lin, Shujun Xiao, Xiongfeng Wu, Yupeng Peking Univ Natl Engn Res Ctr Software Engn Beijing Peoples R China China Acad Ind Internet Beijing Peoples R China Peking Univ Inst Global Hlth & Dev Beijing Peoples R China Aegis Informat Technol Nanjing Peoples R China

ISBN: (纸本)9783031333736;9783031333743

Recent approaches [6,20] formulated the task of unsupervised Semantic Textual Similarity (STS) as Earth Mover's Distance problem, demonstrating superior interpretability and competitive performance. The main idea behind is using various word distances (or word dissimilarity) as word transportation cost, and then measure text dissimilarity by optimizing the accumulative cost of transporting all words of texts. However, these approaches use static word distance without considering the context of text pairs. Intuitively, the distance of two words tends to contribute more to text dissimilarity if they are well-aligned between texts. Inspired by this observation, we propose Alignment-aware Word Distance (AWD), which leverages prior word alignment information of sentence pairs to refine word transportation cost. Specifically, we design two simple and effective mechanisms to capture prior alignment knowledge via exploiting word position and syntactic dependency, respectively. By incorporating AWD, our method remarkably outperforms current state-of-the-art models on STS tasks.

关键词： Earth Mover's Distance Word Distance Semantic Textual Similarity

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Reduction from Complementary-Label Learning to Probability Estimates 27th

Reduction from Complementary-Label Learning to Probability E...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Lin, Wei-, I Lin, Hsuan-Tien Natl Taiwan Univ Taipei Taiwan

ISBN: (纸本)9783031333767;9783031333774

Complementary-Label Learning (CLL) is a weakly-supervised learning problem that aims to learn a multi-class classifier from only complementary labels, which indicate a class to which an instance does not belong. Existing approaches mainly adopt the paradigm of reduction to ordinary classification, which applies specific transformations and surrogate losses to connect CLL back to ordinary classification. Those approaches, however, face several limitations, such as the tendency to over-fit. In this paper, we sidestep those limitations with a novel perspective-reduction to probability estimates of complementary classes. We prove that accurate probability estimates of complementary labels lead to good classifiers through a simple decoding step. The proof establishes a reduction framework from CLL to probability estimates. The framework offers explanations of several key CLL approaches as its special cases and allows us to design an improved algorithm that is more robust in noisy environments. The framework also suggests a validation procedure based on the quality of probability estimates, offering away to validate models with only CLs. The flexible framework opens a wide range of unexplored opportunities in using deep and non-deep models for probability estimates to solve CLL. Empirical experiments further verified the framework's efficacy and robustness in various settings. The full paper can be accessed at https://***/abs/2209.09500.

关键词： complementary-label learning weakly-supervised learning

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TCR: Short Video Title Generation and Cover Selection with Attention Refinement 27th

TCR: Short Video Title Generation and Cover Selection with A...

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27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Yu, Yakun Yang, Jiuding Guo, Weidong Liu, Hui Xu, Yu Niu, Di Univ Alberta Edmonton AB Canada Tencent Shenzhen Peoples R China

ISBN: (纸本)9783031333798;9783031333804

With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab viewers' attention. Existing studies on video captioning mostly focus on generating factual descriptions of actions, which do not conform to video titles intended for catching viewer attention. Furthermore, research for cover selection based on multimodal information is sparse. These problems motivate the need for tailored methods to specifically support the joint task of short video title generation and cover selection (TG-CS) as well as the demand for creating corresponding datasets to support the studies. In this paper, we first collect and present a real-world dataset named Short Video Title Generation (SVTG) that contains videos with appealing titles and covers. We then propose a Title generation and Cover selection with attention Refinement (TCR) method for TG-CS. The refinement procedure progressively selects high-quality samples and highly relevant frames and text tokens within each sample to refine model training. Extensive experiments show that our TCR method is superior to various existing video captioning methods in generating titles and is able to select better covers for noisy real-world short videos.

关键词： Title generation Cover selection Multimodal learning

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Distance Correlation GAN: Fair Tabular Data Generation with Generative Adversarial Networks 4th

Distance Correlation GAN: Fair Tabular Data Generation with ...

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4th International Conference on artificial intelligence in HCI (AI-HCI) Held as Part of the 25th International Conference on Human-Computer Interaction (HCII)

作者： Rajabi, Amirarsalan Garibay, Ozlem Ozmen Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Dept Ind Engn & Management Syst Orlando FL 32816 USA

ISBN: (纸本)9783031358906;9783031358913

With the growing impact of artificial intelligence, the topic of fairness in AI has received increasing attention for valid reasons. In this paper, we propose a generative adversarial network for fair tabular data generation. The model is a WGAN, where the generator is enforcing fairness by penalizing distance correlation between protected attribute and target attribute. We compare our results with another state-of-the-art generative adversarial network for fair tabular data generation and a preprocessing repairment method on four datasets, and show that our model is able to produce synthetic data, such that training a classifier on it results in a fair classifier, beating the other two methods. This makes the model suitable for applications that concern with fairness and preserving privacy.

关键词： Fairness in AI Human-centered AI Generative Adversarial Networks

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Improving Detection Efficiency: Optimizing Block Size in the Local Outlier Factor (LOF) Algorithm

Improving Detection Efficiency: Optimizing Block Size in the...

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International Joint Conference on Rough Sets (IJCRS)

作者： Horyn, Czeslaw Nowak-Brzezinska, Agnieszka Univ Silesia Inst Comp Sci Bankowa 12 Katowice Poland

ISBN: (纸本)9783031509582;9783031509599

Detecting outliers in data is essential in various fields, such as finance, healthcare, and many other domains with anomalies. Among well-known outlier detection algorithms, Local Outlier Factor (LOF) is widely used for identifying unusual data points. However, the computational time of LOF significantly increases when dealing with large datasets containing numerical and categorical features. We propose an innovative approach using block size optimisation to speed up the outlier detection process while maintaining high accuracy. By optimizing the block size, we achieve a significant improvement in LOF's performance without compromising its effectiveness. Experiment results on diverse datasets containing mixed categorical and numerical features demonstrate the effectiveness of our method in accelerating outlier detection while retaining high detection accuracy. This advancement in outlier detection has the potential to improve decision-making processes. It empowers the timely identification of anomalous events, which is significant in critical applications, including cybersecurity.

关键词： Outlier detection Local Outlier Factor Efficient data processing Cybersecurity

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Intent Recognition Using Recurrent Neural Networks on Vital Sign Data: A Machine Learning Approach 18th

Intent Recognition Using Recurrent Neural Networks on Vital ...

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18th International Conference on Hybrid artificial intelligence Systems

作者： Mihirette, Samson Tang, Qing De la Cal Martin, Enrique Antonio Univ Oviedo Oviedo Spain Athabasca Univ Athabasca AB Canada

ISBN: (纸本)9783031407246;9783031407253

The growing importance of technology in daily life has led to a focus on making robots think like humans to enhance the integration of humans and robots in Cyber-Physical Systems (CPS). Cognitive science and psychology offer important knowledge and tools for integrating human-like learning processes into robots. The challenge is to enhance robots with prior knowledge and information, rather than starting the learning process from scratch. The goal of this research is to enable efficient interaction and co-existence of humans, robots, and other agents in CPS. This paper presents a review of the current academic literature on identifying human intentions and feeding robots for their effectiveness when interacting with humans. As a new contribution, this paper also proposes a state-of-the-art solution for human intent recognition studies and focuses our research roadmap on emotion recognition using Vital Signs including electroencephalography (EEG) data (signals) to understand the intent of human action using deep learning techniques. The research also compares the prediction performance of recurrent neural networks (RNN) with other algorithms. Understanding humans' intent using vital signs for effective co-existence of humans in the cyber physical system and how to identify the intent of the agent and ensure that it aligns with the context of the given task or goal based on immediate perceptible visual attributes and dynamic properties (the perception of movement, gaze, vocalization, and emotional state.)

关键词： Intent Recognition Vital Signs EEG Signal Context Aware

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