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5th Asia Pacific Information Technology Conference, APIT 2023

作者： Nguyen, Ngoc-Khuong Nguyen, Viet-Ha Le, Anh-Cuong Hai Phong University Faculty of Information and Technology Viet Nam Ai Institute Vnu University of Engineering and Technology Faculty of Information Technology Viet Nam Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh Viet Nam

ISBN: (纸本)9781450399500

Text summarization is a process of generating a concise summary of a given text. It is a popular research topic, and many studies have used sequence-to-sequence deep neural network models such as Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) to tackle it. These models consist of two phases: encoding the input text and generating the summary. However, these models may lose information during the encoding phase, particularly when using deep layers, leading to inaccurate summaries. In this paper, we propose a bi-directional LSTM model with a Recurrent Residual Attention mechanism to address this issue. We tested our model on the Amazon Reviews dataset from the Stanford Network Analysis Project and found that it performed better than standard LSTM models and outperformed previous studies. © 2023 ACM.

关键词： Long short-term memory

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A framework for a practical nurse scheduling approach: A case of operating room of a hospital in thailand 1

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AHFE Virtual Conference on the Human Side of Service engineering, 2020

作者： Rerkjirattikal, Pavinee Huynh, Van-Nam Olapiriyakul, Sun Supnithi, Thepchai School of Knowledge Science Japan Advanced Institute of Science and Technology Ishikawa923-1211 Japan School of Manufacturing Systems and Mechanical Engineering Sirindhorn International Institute of Technology Thammasat University Pathumthani12120 Thailand Language and Semantic Research Team National Electronics and Computer Technology Center Pathumthani12120 Thailand

ISBN: (数字)9783030510572

ISBN: (纸本)9783030510565

Nurse Schedule Problem (NSP) is the assignment of nurses to fulfill hospitals operational goal and regulations. The major aim of NSP is to minimize the total cost, maximizing nurse’s satisfaction, or maintain balanced distribution among preferable assignments and workloads. This paper is the preliminary research in development of NSP for a case of an operating room at a private hospital in Pathumthani, Thailand. An interview with the head nurse was conducted to capture insights and emerging issues that the head nurse wants to resolve. Then, a framework is proposed as a guideline for the development stochastic optimization model to tackle uncertainties in patients’ arrival and surgery duration which are the main concerns. This paper aims to serve as a fundamental stage for a further case based NSP development research. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.

关键词： Job satisfaction

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Learning Spatio-temporal features to detect manipulated facial videos created by the Deepfake techniques

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Forensic Science International: Digital Investigation 2021年 36卷

作者： Nguyen, Xuan Hau Tran, Thai Son Le, Van Thinh Nguyen, Kim Duy Truong, Dinh-Tu Mientrung University of Civil Engineering Viet Nam Faculty of Information Technology Central Industrial and Commercial College Viet Nam Institute of Engineering-Technology Thu Dau Mot University Viet Nam Natural Language Processing and Knowledge Discovery Laboratory Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam

In the last years, the face synthetic video generation has been rapid, and hyper-realistic forged videos are based on Deepfake techniques. It leads to a loss of trust in videos' content and makes it malicious by spreading forged videos on the internet. Until now, there are a few algorithms that have been suggested for detecting forged videos created by Deepfake techniques, but most of them based on analyzing or learning features on frames separately in a video. Those algorithms often pay less attention to Spatio-temporal features, so these algorithms' accuracy is usually not good. This paper proposes a 3-dimensional (3-D) convolutional neural network model that can learn Spatio-temporal features from an adjacent frame sequence in a video. Our proposed network's binary detection accuracy reached over 99% on the two largest benchmark datasets as Deepfake of FaceForensics++ and VidTIMIT datasets. The experimental results of the proposed method outperform state-of-the-art methods. © 2021 Elsevier Ltd

关键词： Generative adversarial networks

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BanFEL: A Blockchain Based Smart Contract for Fair and Efficient Lottery Scheme

BanFEL: A Blockchain Based Smart Contract for Fair and Effic...

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IEEE Conference on Dependable and Secure Computing

作者： Jiasheng Li Zijian Zhang Meng Li Beijing Engineering Research Center of Massive Language Information Beijing Institute of Technology Beijing China School of Computer Science University of Auckland Aukland New Zealand Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Hefei University of Technology Anhui China

Lottery is a game with many people's dreams. But corruptions of lottery centers make the lottery unfair. To address this unfair issue, fair lottery schemes have been studied for several years. In these schemes, delay functions or aggregation protocols can be used to generate the winning numbers fairly. However, to the best of our knowledge, none of the existing schemes can achieve the winning number generated randomly, while the randomness can be verified fairly. In this article, we first propose a [B]lockchain based sm[a]rt co[n]tract for [F]air and [E]fficient [L]ottery (BanFEL) scheme. We further present a winning number random generation smart contract, by which players submit the purchased numbers and the non-tampering property is protected. Security analysis and experiments show that the fairness of BanFEL is protected better than the existing works. Meanwhile, the verification cost of this scheme are at least 0.03s lower than the traditional Grumbach's scheme between 1000 and 10000 players.

关键词： Blockchain Smart contracts Security Mathematical model Privacy Information processing

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A study of lexical ambiguity in large forum discussions for multidisciplinary knowledge engineering 13

A study of lexical ambiguity in large forum discussions for ...

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13th International Conference on knowledge, Information and Creativity Support Systems, KICSS 2018

作者： Takhom, Akkharawoot Boonkwan, Prachya Ulrich Hoppe, H. Ikeda, Mitsuru Usanavasin, Sasiporn Supnithi, Thepchai Japan Advanced Institute of Science and Technology School of Knowledge Science Nomi Japan School of Information Computer and Communication Technology Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Language and Semantic Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Computer Science and Applied Cognitive Science Faculty of Engineering University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781728101613

Lexical ambiguity is a challenging issue in multidisciplinary knowledge engineering due to the tendency that lexical terms can be used among different domains with different specific meanings. Particularly in large forum discussions, such ambiguous cross-disciplinary terms hard to be identified and detected by the discussion participants because domain expertise from several relevant fields is required to detect those terms and discover the actual divergence of interpretation. Having many ambiguous terms in the discussion context will result in gradual misunderstanding and delayed knowledge construction. We studied the effects of data sizes and morphological analysis in discovering ambiguous cross-disciplinary terms in large forum discussions. Our findings are twofold. First, it is more likely to discover cross-disciplinary terms as forum discussions deepen. This correlates with domain experts' tendency to use general terms in metaphorically describing domain-specific concepts, therefore causing lexical ambiguity. Second, we found that lemmatization outperforms stemming in forming more understandable key terms. This is because lemmatization eliminates only inflectional affixes and keeps derivational affixes. On the other hand, stemming eliminates both types of affixes, causing semantic bleaching. © 2018 IEEE.

关键词： knowledge engineering

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Initializing convolutional filters with semantic features for text classification

Initializing convolutional filters with semantic features fo...

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2017 Conference on Empirical Methods in Natural language Processing, EMNLP 2017

作者： Li, Shen Zhao, Zhe Liu, Tao Hu, Renfen Du, Xiaoyong Institute of Chinese Information Processing Beijing Normal University China UltraPower-BNU Joint Laboratory for Artificial Intelligence Beijing Normal University China College of Chinese Language and Culture Beijing Normal University China School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China

ISBN: (纸本)9781945626838

Convolutional Neural Networks (CNNs) are widely used in NLP tasks. This paper presents a novel weight initialization method to improve the CNNs for text classification. Instead of randomly initializing the convolutional filters, we encode semantic features into them, which helps the model focus on learning useful features at the beginning of the training. Experiments demonstrate the effectiveness of the initialization technique on seven text classification tasks, including sentiment analysis and topic classification. © 2017 Association for Computational Linguistics.

关键词： Sentiment analysis

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Author Correction: Altered structural brain asymmetry in autism spectrum disorder in a study of 54 datasets

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Nature communications 2021年第1期12卷 7260页

作者： Merel C Postema Daan van Rooij Evdokia Anagnostou Celso Arango Guillaume Auzias Marlene Behrmann Geraldo Busatto Filho Sara Calderoni Rosa Calvo Eileen Daly Christine Deruelle Adriana Di Martino Ilan Dinstein Fabio Luis S Duran Sarah Durston Christine Ecker Stefan Ehrlich Damien Fair Jennifer Fedor Xin Feng Jackie Fitzgerald Dorothea L Floris Christine M Freitag Louise Gallagher David C Glahn Ilaria Gori Shlomi Haar Liesbeth Hoekstra Neda Jahanshad Maria Jalbrzikowski Joost Janssen Joseph A King Xiang Zhen Kong Luisa Lazaro Jason P Lerch Beatriz Luna Mauricio M Martinho Jane McGrath Sarah E Medland Filippo Muratori Clodagh M Murphy Declan G M Murphy Kirsten O'Hearn Bob Oranje Mara Parellada Olga Puig Alessandra Retico Pedro Rosa Katya Rubia Devon Shook Margot J Taylor Michela Tosetti Gregory L Wallace Fengfeng Zhou Paul M Thompson Simon E Fisher Jan K Buitelaar Clyde Francks Department of Language & Genetics Max Planck Institute for Psycholinguistics Nijmegen The Netherlands. Department of Cognitive Neuroscience Donders Institute for Brain Cognition and Behaviour Donders Centre for Cognitive Neuroimaging Radboud University Medical Centre Nijmegen The Netherlands. Bloorview Research Institute Holland Bloorview Kids Rehabilitation Hospital and Department of Pediatrics University of Toronto Toronto ON USA. Child and Adolescent Psychiatry Department Gregorio Marañón General University Hospital School of Medicine Universidad Complutense IiSGM CIBERSAM Madrid Spain. Institut de Neurosciences de la Timone UMR 7289 Aix Marseille Université CNRS Marseille France. Department of Psychology Carnegie Mellon University Pittsburgh PA USA. Laboratory of Psychiatric Neuroimaging (LIM-21) Departamento e Instituto de Psiquiatria Hospital das Clinicas HCFMUSP Faculdade de Medicina Universidade de Sao Paulo Sao Paulo SP Brazil. IRCCS Stella Maris Foundation viale del Tirreno 331 56128 Pisa Italy. Department of Clinical and Experimental Medicine University of Pisa Pisa Italy. Department of Child and Adolescent Psychiatry and Psychology Hospital Clinic Psychiatry Unit Department of Medicine 2017SGR881 University of Barcelona IDIBAPS CIBERSAM Barcelona Spain. Department of Forensic and Neurodevelopmental Sciences Institute of Psychiatry Psychology & Neuroscience King's College London London UK. Institute for Pediatric Neuroscience NYU Child Study Center New York NY USA. Department of Psychology Ben-Gurion University of the Negev Beer Sheva Israel. Brain Center Rudolf Magnus Department of Psychiatry University Medical Center Utrecht Utrecht The Netherlands. Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University Hospital Goethe University Frankfurt am Main Frankfurt Germany. Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University o

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Using dominance for hidden and degenerated pareto-fronts

Using dominance for hidden and degenerated pareto-fronts

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IEEE Symposium Series on Computational Intelligence, SSCI 2015

作者： Zille, Heiner Mostaghim, Sanaz Institute of Knowledge and Language Engineering University of Magdeburg Germany

ISBN: (纸本)9781479975600

Scalable multi-objective test problems are known to be useful in testing and analyzing the abilities of algorithms. In this paper we focus on test problems with degenerated Pareto-fronts and provide an in-depth insight into the properties of some problems which show these characteristics. In some of the problems with degenerated fronts such as Distance Minimization Problem (DMP) with the Manhattan metric, it is very difficult to dominate some of the non-optimal solutions as the optimal solutions are hidden within a set of so called pseudo-optimal solutions. Hence the algorithms based on Pareto-domination criterion are shown to be inefficient. In this paper, we explore the pseudo-optimal solutions and examine how and why the use of ?-dominance can help to achieve a better approximation of the hidden Pareto-fronts or of degenerated fronts in general. We compare the performance of the ?-MOEA with 3 other algorithms (NSGA-II, NSGA-III and MOEA/D) and show that ?-dominance performs better when dealing with pseudo-optimal kind of solutions. Furthermore, we analyze the performance on the WFG3 test problem and illustrate the advantages and disadvantages of ?-dominance for this degenerated problem. © 2015 IEEE.

关键词： Optimal systems

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Discourse particles in human-human and human-computer interaction – analysis and evaluation 18th

Discourse particles in human-human and human-computer intera...

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18th International Conference on Human-Computer Interaction, HCI International 2016

作者： Siegert, Ingo Krüger, Julia Haase, Matthias Lotz, Alicia Flores Günther, Stephan Frommer, Jörg Rösner, Dietmar Wendemuth, Andreas Institute of Information and Communication Engineering Otto von Guericke University Magdeburg39016 Germany Department of Psychosomatic Medicine and Psychotherapy Otto von Guericke University Magdeburg39016 Germany Institute of Knowledge and Language Engineering Otto von Guericke University Magdeburg39016 Germany Center for Behavioral Brain Sciences Otto von Guericke University Magdeburg39016 Germany

ISBN: (纸本)9783319395098

Discourse particles are verifiably used in both human-human interaction (HHI) and human-computer interaction (HCI). In both types of interaction form-function-relations could be confirmed. Also correlations with specific subject characteristics, personality traits and the use of these particles could be uncovered. But these investigations are performed on separated datasets containing either HHI or HCI. Moreover, the subjects analyzed in both interaction types are not the same and thus, direct connections could not be made. In our contribution, we report about analyses of discourse particles in both HHI and HCI with the same subjects. This enables us to draw conclusions of the communication partner’s influence in relation to subject characteristics and personality traits. This will prospectively help to better understand the use of discourse particles. By using this knowledge, future technical systems can react to known subjects more individually. © Springer International Publishing Switzerland 2016.

关键词： Human computer interaction

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Dealing with data sparseness in SMT with factored models and morphological expansion: A case study on Croatian 19

Dealing with data sparseness in SMT with factored models and...

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19th Annual Conference of the European Association for Machine Translation, EAMT 2016

作者： Sánchez-Cartagena, Víctor M. Ljubešić, Nikola Klubička, Filip Prompsit Language Engineering Av. Universitat s/n. Edifici Quorum III ElxE-03202 Spain Dept. of Knowledge Technologies Jožef Stefan Institute Jamova cesta 32 LjubljanaSI-1000 Slovenia Dept. of Information and Communication Sciences University of Zagreb Croatia

This paper describes our experience using available linguistic resources for Croatian in order to address data sparseness when building an English-to-Croatian general domain phrase-based statistical machine translation system. We report the results obtained with factored translation models and morphological expansion, highlight the impact of the algorithm used for tagging the corpora, and show that the improvement brought by these methods is compatible with the application of data selection on out-of-domain parallel corpora. © 2016, European Association for Machine Translation. All rights reserved.

关键词： Computer aided language translation

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