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knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain knowledge embedding knowledge attention Mutual attention

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High-Dimensional Multi-Label data Stream Classification With Concept Drifting Detection

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IEEE Transactions on knowledge and data engineering 2023年第8期35卷 8085-8099页

作者： Li, Peipei Zhang, Haixiang Hu, Xuegang Wu, Xindong Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Second People's Hospital of Hefei Anhui Hefei230011 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China School of Computer Science and Information Engineering Anhui Hefei230002 China

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts. In order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Second, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency. © 1989-2012 IEEE.

关键词： Classification (of information)

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Frontiers of Artificial Intelligent and Quantitative Management: Preface for ITQM 2024

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Procedia Computer science 2024年 242卷 1-8页

作者： Filip, Florin Gheorghe Shi, Yong Pocatilu, Paul Ciurea, Cristian-Eugen Li, Jianping Tien, James M. Berg, Daniel Romanian Academy Information Science and Technology Section Bucharest Romania Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Bucharest University of Economic Studies Bucharest Romania College of Engineering University of Miami Coral GablesFL33124 United States

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‘Thy Algorithm Shalt Not Bear False Witness’: An Evaluation of Multiclass Debiasing Methods on Word Embeddings 1

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32nd Benelux Conference on Artiﬁcial Intelligence and Belgian-Dutch Conference on Machine Learning, BNAIC/Benelearn 2020

作者： Schlender, Thalea Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (数字)9783030766405

ISBN: (纸本)9783030766399

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural language processing domain, it has been shown that social biases persist in word embeddings and are thus in danger of amplifying these biases when used. As an example of social bias, religious biases are shown to persist in word embeddings and the need for its removal is highlighted. This paper investigates the state-of-the-art multiclass debiasing techniques: Hard debiasing, SoftWEAT debiasing and Conceptor debiasing. It evaluates their performance when removing religious bias on a common basis by quantifying bias removal via the Word Embedding Association Test (WEAT), Mean Average Cosine Similarity (MAC) and the Relative Negative Sentiment Bias (RNSB). By investigating the religious bias removal on three widely used word embeddings, namely: Word2Vec, GloVe, and ConceptNet, it is shown that the preferred method is ConceptorDebiasing. Specifically, this technique manages to decrease the measured religious bias on average by 82.42%, 96.78% and 54.76% for the three word embedding sets respectively. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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Densely Annotated Photorealistic Virtual dataset Generation for Abnormal Event Detection 25th

Densely Annotated Photorealistic Virtual Dataset Generation ...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Montulet, Rico Briassouli, Alexia Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9783030687984

Many timely computer vision problems, such as crowd event detection, individual or crowd activity recognition, person detection and re-identification, tracking, pose estimation, segmentation, require pixel-level annotations. This involves significant manual effort, and is likely to face challenges related to the privacy of individuals, due to the intrinsic nature of these problems, requiring in-depth identifying information. To cover the gap in the field and address these issues, we introduce and make publicly available a photorealistic, synthetically generated dataset, with detailed dense annotations. We also publish the tool we developed to generate it, that will allow users to not only use our dataset, but expand upon it by building their own densely annotated videos for many other computer vision problems. We demonstrate the usefulness of the dataset with experiments on unsupervised crowd anomaly detection in various scenarios, environments, lighting, weather conditions. Our dataset and the annotations provided with it allow its use in numerous other computer vision problems, such as pose estimation, person detection, segmentation, re-identification and tracking, individual and crowd activity recognition, and abnormal event detection. We present the dataset as is, along with the source code and tool to generate it, so any modification can be made and new data can be created. To our knowledge, there is currently no other photorealistic, densely annotated, realistic, synthetically generated dataset for abnormal crowd event detection, nor one that allows for flexibility of use by allowing the creation of new data with annotations for many other computer vision problems. dataset and source code available: https://***/RicoMontulet/GTA5Event. © 2021, Springer Nature Switzerland AG.

关键词： Anomaly detection

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Using Machine Learning with Pyspark for Solving a Big data Problem: Searching for Particles in High Energy Physics with Big Mass (HEPMASS) 7th

Using Machine Learning with Pyspark for Solving a Big Dat...

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7th International Conference on Smart City Applications, SCA 2022

作者： Azhari, Mourad Ahaji, Khalid Abarda, Abdallah Ettaki, Badia Zerouaoui, Jamal Center of Guidance and Planning Rabat Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and Knowledge Engineering School of Information Sciences Rabat Morocco Laboratory of Engineering Sciences and Modeling Faculty of Sciences Ibn Tofail University Kenitra Morocco

ISBN: (纸本)9783031268519

The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib library. For this purpose, we analyze and discuss the performance of the following methods: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Tree (GBT). The results are very encouraging and prove that these proposed methods run efficiently with an Accuracy exceeding (83%). While the Gradient Boosted Tree (GBT) reaches a high AUC score 85%, better than Decision Tree (DT) and Random Forest. In this manuscript, we attempt to solve a big data problem using the "HEPMASS" dataset. Our data includes 10.5 million examples collected from the UCI website for the experimental phase. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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ECGI with a Deep Neural Network and 2D Normalized Body Surface Potential Maps

ECGI with a Deep Neural Network and 2D Normalized Body Surfa...

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2021 Computing in Cardiology, CinC 2021

作者： Wang, Tiantian Bonizzi, Pietro Karel, Joel Peeters, Ralf Maastricht University Department of Data Science and Knowledge Engineering Maastricht Netherlands

ISBN: (纸本)9781665479165

Electrocardiographic Imaging (ECGI) reconstructs heart surface potentials (HSPs) from body surface potentials (BSPs) using a patient-specific torso-heart geometry derived from CT or MRI. Potential inaccuracies in the estimate of the torso-heart geometry, and of the electrode positions on the body surface may limit the accuracy of the reconstructed HSPs. In this study, we aim at providing a proof-of-principle Deep Neural Network (DNN) which directly maps BSPs to HSPs, without the need to estimate a transfer matrix for the forward problem. In particular, we propose a torso normalization method that can turn the original torso geometry into a 2D image, and transform it into a normalized body surface potential map (nBSPM). In this paper, we did this for four dog geometries. A 4-layer back propagation (BP) neural network is built and trained with the nBSPMs as the input, and the pixel values of corresponding heart surface potential maps (HSPMs) as the output. The experiments show that the mean-squared error (MSE) on the validation data set decreases and converges to around 3.0. The value of the Pearson correlation coefficient between the original and reconstructed HSPMs on the test data set is larger than 0.92. This indicates that the proposed model is suitable for reconstructing HSPs from BSPs. © 2021 Creative Commons.

关键词： Correlation methods

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Automatic Fusion for Multimodal Entity Alignment: A New Perspective from Automatic Architecture Search

Automatic Fusion for Multimodal Entity Alignment: A New Pers...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chenyang Bu Yunpeng Hong Shiji Zang Guojie Chang Xindong Wu Key Laboratory of Knowledge Engineering With Big Data (the Ministry of Education of China) School of Computer Science and Engineering HeFei University of Technology

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Integrating multimodal data from diverse sources is crucial for enhancing various applications. Multimodal entity alignment (MMEA), which discovers equivalent entities across different sources and modalities, aims to eliminate data silos for comprehensive integration. A key challenge in MMEA is effectively fusing vector representations from different modalities of the same entity for optimal entity matching. Existing fusion methods involve individual fusion operators (e.g., concatenation and summation) or the manual design of complex network structures, incurring significant human resource costs. In this paper, for the first time, we introduce the research question of automatic fusion for MMEA and propose an efficient approach from the perspective of automated architecture search. Experimental comparisons with state-of-the-art methods on real-world datasets demonstrate the effectiveness of the proposed approach.

关键词： Costs Search methods Complex networks Vectors

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P-TECS: An Energy Balance Algorithm for Opportunistic Networks Integrating Multiple Node Attributes 5th

P-TECS: An Energy Balance Algorithm for Opportunistic Netwo...

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5th International Conference on Security and Privacy in New Computing Environments, SPNCE 2022

作者： Xu, Gang Yang, Xiaoying Chen, Jingjian Huang, Baoqi College of Computer Science Inner Mongolia University Hohhot010021 China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia University Hohhot010021 China

ISBN: (纸本)9783031306228

This paper proposes an energy balance opportunistic networks routing algorithm P-TECS. The P-TECS solves the problems of energy consumption of key nodes in existing opportunistic routing algorithms. This paper defines the relay degree of communication between nodes, and designs a P-TECS opportunistic routing algorithm integrating dynamic attributes of multiple nodes. Simulation results show that in resource-constrained opportunistic networks, P-TECS significantly improves the message delivery rate and average remaining energy of nodes, and significantly reduces the routing overhead rate. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Routing algorithms

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HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API 4

HardnessTesterV: A Web Machine Learning application for Vick...

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4th International Conference on Electrical, Communication and Computer engineering, ICECCE 2023

作者： Olanipekun, Ayorinde Tayo Mashao, Daniel Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park South Africa Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park South Africa

ISBN: (纸本)9798350369694

This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design. © 2023 IEEE.

关键词： Machine learning

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