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arXiv 2017年

作者： Uiterwijk, Jos W.H.M. Department of Data Science and Knowledge Engineering Maastricht University Netherlands

When solving k-in-a-Row games, the Hales-Jewett pairing strategy [4] is a well-known strategy to prove that specific positions are (at most) a draw. It requires two empty squares per possible winning line (group) to be marked, i.e., with a coverage ratio of 2.0. In this paper we present a new strategy, called Set Matching. A matching set consists of a set of nodes (the markers), a set of possible winning lines (the groups), and a coverage set indicating how all groups are covered after every first initial move. This strategy needs less than two markers per group. As such it is able to prove positions in k-in-a-Row games to be draws, which cannot be proven using the Hales-Jewett pairing strategy. We show several efficient configurations with their matching sets. These include Cycle Configurations, BiCycle Configurations, and PolyCycle Configurations involving more than two cycles. Depending on configuration, the coverage ratio can be reduced to 1.14. Many examples in the domain of solving k-in-a-Row games are given, including the direct proof (without further investigation) that the empty 4 × 4 board is a draw for 4-in-a-Row. Copyright © 2017, The Authors. All rights reserved.

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Visual Feature Combination Approach for Zero-Shot Learning

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Ruan Jian Xue Bao/Journal of Software 2018年 29卷 16-29页

作者： Yang, Gang Liu, Jin-Lu Li, Xi-Rong Xu, Jie-Ping School of Information Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China

Zero-Shot learning is an important research in the field of machine learning and image recognition. Zero-Shot learning methods normally use the semantic information among unseen classes and seen classes to transfer the knowledge which is learned from examples of seen classes to unseen classes, so as to recognize and classify the examples of unseen classes. In this study, a zero-shot learning approach based on construction of visual feature combination is proposed. The approach generates many examples of unseen class on visual feature level by the way of feature combination, which is first proposed, and thus transforms zero-shot learning problem to be a traditional classification problem solved by supervised learning. The approach mimics human cognition process of associative memory, and includes four steps: feature-attribute relation extraction, example construction, example screening, and domain adaption. On training examples of seen classes, the relationship between class attributes and dimensions of feature is extracted;on visual feature level, examples of unseen classes are generated by visual feature combination;dissimilarity representation is introduced to filter the generated examples of unseen classes;semi-supervised and unsupervised feature domain adaption are proposed to linearly transform the generated examples of unseen classes to be more effective. The proposed approach shows superior performance on three benchmark datasets (AwA, AwA2, and SUN), especially on dataset AwA, it obtains 82.6% top-1 accuracy which is the best result as far as we know. Experiment results demonstrate the effectiveness and superiority of the proposed approach. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Image classification

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MultiFarm: A Benchmark for Multilingual Ontology Matching

SSRN

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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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High-performance and lightweight real-time deep face emotion recognition

High-performance and lightweight real-time deep face emotion...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Justus Schwan Esam Ghaleb Enrique Hortal Stylianos Asteriadis Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538607572

Deep learning is used for all kinds of tasks which require human-like performance, such as voice and image recognition in smartphones, smart home technology, and self-driving cars. While great advances have been made in the field, results are often not satisfactory when compared to human performance. In the field of facial emotion recognition, especially in the wild, Convolutional Neural Networks (CNN) are employed because of their excellent generalization properties. However, while CNNs can learn a representation for certain object classes, an amount of (annotated) training data roughly proportional to the class's complexity is needed and seldom available. This work describes an advanced pre-processing algorithm for facial images and a transfer learning mechanism, two potential candidates for relaxing this requirement. Using these algorithms, a lightweight face emotion recognition application for Human-Computer Interaction with TurtleBot units was developed.

关键词： Face Training Feature extraction Real-time systems Emotion recognition Training data Face detection

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Image schemas and conceptual dependency primitives: A comparison

Image schemas and conceptual dependency primitives: A compar...

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2017 Joint Ontology Workshops Episode 3: The Tyrolean Autumn of Ontology, JOWO 2017

作者： Macbeth, Jamie C. Gromann, Dagmar Hedblom, Maria M. Department of Electrical and Computer Systems Engineering Fairfield University FairfieldCT06824 United States Bellaterra Spain Research Center for Knowledge and Data Free University of Bozen-Bolzano Italy

A major challenge in natural language understanding research in artificial intelligence (AI) has been and still is the grounding of symbols in a representation that allows for rich semantic interpretation, inference, and deduction. Across cognitive linguistics and other disciplines, a number of principled methods for meaning representation of natural language have been proposed that aim to emulate capacities of human cognition. However, little cross-fertilization among those methods has taken place. A joint effort of human-level meaning representation from AI research and from cognitive linguistics holds the potential of contributing new insights to this profound challenge. To this end, this paper presents a first comparison of image schemas to an AI meaning representation system called Conceptual Dependency (CD). Restricting our study to the domain of physical and spatial conceptual primitives, we find connections and mappings from a set of action primitives in CD to a remarkably similar set of image schemas. We also discuss important implications of this connection, from formalizing image schemas to improving meaning representation systems in AI. © 2017 CEUR-WS. All rights reserved.

关键词： Image enhancement

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A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings

A Retrieval-Based Dialogue System Utilizing Utterance and Co...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Alexander Bartl Gerasimos Spanakis Department of Data Science and Knowledge Engineering Maastricht University MD Netherlands

Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the distributed vector representation (embedding) of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.

关键词： Decoding Logic gates Context modeling Encoding Pipelines Training databases

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Social emotion mining techniques for facebook posts reaction prediction

arXiv

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arXiv 2017年

作者： Krebs, Florian Lubascher, Bruno Moers, Tobias Schaap, Pieter Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called 'reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135. Copyright © 2017, The Authors. All rights reserved.

关键词： Social networking (online)

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Factors influencing teenagers behavior on posting and sharing messages via Facebook

Factors influencing teenagers behavior on posting and sharin...

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International Conference on Information Technology (INCIT)

作者： Kanokporn Cheevasuntorn Bunthit Watanapa Suree Funilkul Worarat Krathu Data and Knowledge Engineering Laboratory (D-Lab) KMUTT Bangkok Thailand

ISBN: (纸本)9781538614327

This paper proposes a hypothetical model of factors influencing the behavior of teenagers posting or sharing messages on Facebook. Personality (based on the Big five model), Social influence and perceived ability to control user interfaces and devices for posting are presumed as primary factors in the study. The statistical ANCOVA analysis was conducted to evaluate the model based on a questionnaire responded by 180 teenagers aged between 13 and 15 living in Thailand. Key experimental results are that the trait of extraversion and perceived control over the device and Facebook functions are vital influences and disconfirmation of the expected results of posting can aggravate the posting direction.

关键词： Facebook Reactive power Information technology Internet Testing Computers

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Dual encoding for zero-example video retrieval

arXiv

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arXiv 2018年

作者： Dong, Jianfeng Li, Xirong Xu, Chaoxi Ji, Shouling He, Yuan Yang, Gang Wang, Xun College of Computer and Information Engineering Zhejiang Gongshang University Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China College of Computer Science Zhejiang University Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Alibaba Group

This paper attacks the challenging problem of zero-example video retrieval. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described in natural language text with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is required. The majority of existing methods are concept based, extracting relevant concepts from queries and videos and accordingly establishing associations between the two modalities. In contrast, this paper takes a concept-free approach, proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Dual encoding is conceptually simple, practically effective and end-to-end. As experiments on three benchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the proposed solution establishes a new state-of-the-art for zero-example video retrieval. Copyright © 2018, The Authors. All rights reserved.

关键词： Signal encoding

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Towards customised visualisation of ontologies: State of the art and future applications for online polls analysis 17

Towards customised visualisation of ontologies: State of the...

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2017 Australasian Computer Science Week Multiconference, ACSW 2017

作者： Burgstaller, Felix Stabauer, Martin Morgan, Rebecca Department of Business Informatics - Data and Knowledge Engineering Johannes Kepler University Linz Austria Department of Data Processing in Social Sciences Economics and Business Johannes Kepler University Linz Austria Georg Grossmann School of IT and MS University of South Australia Adelaide Australia

ISBN: (纸本)9781450347686

Online polling is a popular tool to increase user involvement on all kinds of websites. Consumers are interested in sharing their opinion and so contribute to the website's content. Aggregated opinions, attitudes, and preferences convey a great deal of knowledge which is often unutilised as there exists no efficient method to explore them. An ongoing research project suggests methods, technologies, and processes to extract the knowledge that lies within the questions posed by website publishers and the answers given by users. This knowledge is saved in a triple store and enhanced by reasoning and other methods of the semantic web. One important technique is the visualisation of both the structure (entities and relations) and aggregated information of consumers in the knowledge base. Existing techniques often focus on only one of them although integration of both is required to explore the nature of the content and information about user groups such as size and intersections among them. The research described in this paper surveys current visualisation tools and libraries for the support of identified requirements in a case study. Based on the findings, an implementation of Agile Visualization for a specific polling system in specific and ontologies in general is proposed, which allows for a more customisable and exible visualisation. A reusable transformation process for the ontology's data is discussed, which makes it possible to use the aforementioned knowledge base as input for the agile visualization approach. © 2017 ACM.

关键词： data visualization

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