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SSRN 2018年

作者： Meilicke, Christian García-Castro, Raúl Freitas, Fred van Hage, Willem Robert Montiel-Ponsoda, Elena de Azevedo, Ryan Ribeiro Stuckenschmidt, Heiner Šváb-Zamazal, Ondřej Svátek, Vojtěch Tamilin, Andrei Trojahn, Cássia Wang, Shenghui Universidade Federal de Pernambuco University of Mannheim Universidad Politécnica de Madrid Web & Media Group Ontology Engineering Group Data and Web Science Group Department of Information and Knowledge Engineering Fondazione Bruno Kessler Grenoble - Rhône-Alpes VU University Amsterdam

In this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages – Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish - we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism. © 2018, The Authors. All rights reserved.

关键词： Ontology

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Foundations of digital archæoludology

arXiv

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arXiv 2019年

作者： Browne, Cameron Soemers, Dennis J.N.J. Piette, Éric Stephenson, Matthew Conrad, Michael Crist, Walter Depaulis, Thierry Duggan, Eddie Horn, Fred Kelk, Steven Lucas, Simon M. Neto, João Pedro Parlett, David Saffidine, Abdallah Schädler, Ulrich Silva, Jorge Nuno de Voogt, Alex Winands, Mark H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Kunsthistorisches Institut Universität Zürich Switzerland Department of Anthropology American Museum of Natural History United States Department of Science Technology and Engineering University of Suffolk United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom University of Lisbon Portugal School of Computer Science and Engineering University of New South Wales Australia Swiss Museum of Games Switzerland Economics & Business Department Drew University United States

Digital Archæoludology (DAL) is a new field of study involving the analysis and reconstruction of ancient games from incomplete descriptions and archaeological evidence using modern computational techniques. The aim is to provide digital tools and methods to help game historians and other researchers better understand traditional games, their development throughout recorded human history, and their relationship to the development of human culture and mathematical knowledge. This work is being explored in the ERC-funded Digital Ludeme Project. The aim of this inaugural international research meeting on DAL is to gather together leading experts in relevant disciplines – computer science, artificial intelligence, machine learning, computational phylogenetics, mathematics, history, archaeology, anthropology, etc. – to discuss the key themes and establish the foundations for this new field of research, so that it may continue beyond the lifetime of its initiating project. Copyright © 2019, The Authors. All rights reserved.

关键词： History

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Strategy intervention for the evolution of fairness

arXiv

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arXiv 2017年

作者： Zhang, Yanling Fu, Feng Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Department of Mathematics Dartmouth College HanoverNH United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College HanoverNH United States

Masses of experiments have shown individual preference for fairness which seems irrational. The reason behind it remains a focus for research. The effect of spite (individuals are only concerned with their own relative standing) on the evolution of fairness has attracted increasing attention from experiments, but only has been implicitly studied in one evolutionary model. The model did not involve high-offer rejections, which have been found in the form of non-monotonic rejections (rejecting offers that are too high or too low) in experiments. Here, we introduce a high offer and a non-monotonic rejection in structured populations of finite size, and use strategy intervention to explicitly study how spite influences the evolution of fairness: five strategies are in sequence added into the competition of a fair strategy and a selfish strategy. We find that spite promotes fairness, altruism inhibits fairness, and the non-monotonic rejection can cause fairness to overcome selfishness, which cannot happen without high-offer rejections. Particularly for the group-structured population with seven discrete strategies, we analytically study the effect of population size, mutation, and migration on fairness, selfishness, altruism, and spite. A larger population size cannot change the dominance of fairness, but it promotes altruism and inhibits selfishness and spite. Intermediate mutation maximizes selfishness and fairness, and minimizes spite;intermediate mutation maximizes altruism for intermediate migration and minimizes altruism otherwise. The existence of migration inhibits selfishness and fairness, and promotes altruism;sufficient migration promotes spite. Our study may provide important insights into the evolutionary origin of fairness. Copyright © 2017, The Authors. All rights reserved.

关键词： Population statistics

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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A deep belief network-based method to identify proteomic risk markers for Alzheimer’s disease

arXiv

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arXiv 2020年

作者： An, Ning Jin, Liuqi Ding, Huitong Yang, Jiaoyun Yuan, Jing Key Laboratory of Knowledge Engineering Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Neurology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing China

While a large body of research has formally identified apolipoprotein E (APOE) as a major genetic risk marker for Alzheimer’s disease, accumulating evidence supports the notion that other risk markers may exist. The traditional Alzheimer’s-specific signature analysis methods, however, have not been able to make full use of rich protein expression data, especially the interaction between attributes. This paper develops a novel feature selection method to identify pathogenic factors of Alzheimer’s disease using the proteomic and clinical data. This approach has taken the weights of network nodes as the importance order of signaling protein expression values. After generating and evaluating the candidate subset, the method helps to select an optimal subset of proteins that achieved an accuracy greater than 90%, which is superior to traditional machine learning methods for clinical Alzheimer’s disease diagnosis. Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer’s disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Milling of multi-relational association rules

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Ruan Jian Xue Bao/Journal of Software 2007年第11期18卷 2752-2765页

作者： He, Jun Liu, Hong-Yan Du, Xiao-Yong Department of Computer Science and Technology Renmin University of China Beijing 100872 China Department of Management Science and Engineering Tsinghua University Beijing 100084 China Key Laboratory of Data Engineering and Knowledge Engineering Beijing 100872 China

Association rule mining is one of the most important and basic technique in data mining, which has been studied extensively and has a wide range of applications. However, as traditional data mining algorithms usually only focus on analyzing data organized in single table, applying these algorithms in multi-relational data environment will result in many problems. This paper summarizes these problems, proposes a framework for the mining of multi-relational association rule, and gives a definition of the mining task. After classifying the existing work into two categories, it describes the main techniques used in several typical algorithms, and it also makes comparison and analysis among them. Finally, it points out some issues unsolved and some future further research work in this area.

关键词： data mining

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Sparse Training data-Based Hyperspectral Image Super Resolution Via ANFIS Interpolation

Sparse Training Data-Based Hyperspectral Image Super Resolut...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Changjing Shang Lu Chen Pan Su Qiang Shen School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science Aberystwyth University Aberystwyth UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China Department of Computer Hebei Key Laboratory of Knowledge Computing for Energy & Power Baoding North China Electric Power University China

Hyperspectral image super resolution aims to improve the spatial resolution of given hyperspectral images, which has become a highly attractive topic in the field of image processing. Existing techniques typically focus on super resolution with sufficient training data. However, restricted by data acquisition conditions, certain hyperspectral images or band images are very different to obtain, resulted in insufficient training data. In order to solve this problem, a new hyperspectral image super resolution method is proposed in this paper in an effort to conduct the super resolution task over insufficient (sparse) training data, by applying the recently introduced ANFIS (Adaptive Network-based Fuzzy Inference System) interpolation method. Particularly, the training dataset is divided into several subsets. For the subsets with sufficient training data, the relevant ANFIS models are trained using standard ANFIS learning algorithm, while for the subsets with sparse training data, the corresponding ANFIS models are interpolated through the use of ANFIS interpolation. Experimental results indicate that compared with the methods using sufficient training data, the proposed method can achieve very similar result, showing its effectiveness for situations where only sparse training data is available.

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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

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IEEE International Conference on data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

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LSBert: Lexical Simplification Based on BERT

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IEEE/ACM Transactions on Audio Speech and Language Processing 2021年 29卷 3064-3076页

作者： Qiang, Jipeng Li, Yun Zhu, Yi Yuan, Yunhao Shi, Yang Wu, Xindong Department of Computer Science Jiangsu Yangzhou China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Anhui Hefei China Mininglamp Academy of Sciences Minininglamp Beijing 100864 China

Lexical simplification (LS) aims at replacing complex words with simpler alternatives. LS commonly consists of three main steps: complex word identification, substitute generation, and substitute ranking. Existing LS methods focus on the contextual information of the complex word in the last step (substitute ranking). However, they miss out the following two facts: (1) The word complexity of a polysemous word is very closely related to its context;(2) The step of substitute generation regardless of the context will inevitably produce a large number of spurious candidates. Therefore, we propose a novel LS system LSBert based on pretrained language model BERT to address the aforementioned issues, which is capable of making use of the wider context when both identifying the words in need of simplification and generating substitute candidates for the complex words. Specifically, LSBert consists of a network for complex word identification by fine-tuning BERT and a network for substitute generation based on BERT. Experimental results show that LSBert performs well in both complex word identification and substitute generation, achieving state-of-the-art results in three benchmarks. © 2014 IEEE.

关键词： BERT Complex word identification Lexical simplification Sentence simplification

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Machine Learning Method for Recognizing Human Eye Pupil Region

Machine Learning Method for Recognizing Human Eye Pupil Regi...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： R. Rajkumar Gokulavani D Rajesh K Nagaiah Apuri T. Chandrasekar R Benazir Begam Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of Electronics and Communication Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of CSD (Data Science) Marri Laxman Reddy Institute of Technology and Management Dhundigal Medchal Telangana India Department of Electrical and Electronics Engineering Velammal College of Engineering and Technology Madurai Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamilnadu India

This research study describes how machine learning techniques can be employed to segment human pupils. The objective is to recognize and isolate the pupil region in ocular images. The aspects of a methodology are data set preparation, feature extraction, machine learning model selection, model training, model optimization, model testing, model evaluation, and finally post-processing. During preprocessing, this identifies and designates pupil regions in the dataset to obtain high-quality eye images. This compares two machine learning models, the Support Vector Machine (SVM) and the Random Forest, after identifying the most significant image characteristics. After being trained and fine-tuned using the dataset, models are evaluated based on metrics such as precision, recall, F1 score, and accuracy. In post-processing, the segmented pupil regions are modified after the initial segmentation. Random Forest attained 96.27 accuracy, while Support Vector Machine (SVM) obtained 95.17. This research contributes to the advancement of computer vision and is beneficial for software that employs human vision.

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