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Exponential growth of media generated in online social networks demands effective recommendation to improve the efficiency of media access especially for mobile users. In particular, content, objective quality or general popularity are less decisive for the prediction of user-click behavior than friendship-conditioned patterns. Existing recommender systems however, rarely consider user behavior in...
This paper investigates neural character-based morphological tagging for languages with complex morphology and large tag sets. Character-based approaches are attractive as they can handle rarely- and unseen words gracefully. More specifically, beside a rich morphology, non-canonical language, change of language or other linguistic variability can heavily degrade the accuracy of natural language processing...
In the current paper we describe how Watson Experience Manager (WEM), an industrial Question Answering (QA) tool developed by IBM, has been used in an educational context at the National Technical University of Athens (NTUA). During the postgraduate course on Data Science, three student teams experimented with WEM's QA capabilities on three different topics, namely, Nutrition, Autism and New York...
This paper describes the automatic speech recognition (ASR) systems developed by LIUM in the framework of the 2016 Multi-Genre Broadcast (MGB-2) Challenge in the Arabic language. LIUM participated in the first of the two proposed tasks, namely the speech-to-text transcription of Aljazeera recordings. We present the approaches and details found in our systems, as well as our results in the evaluation...
Outlier detection is the task of automatic identification of unknown data not covered by training data (e.g. a previously unknown class in classification). We explore outlier detection in the presence of hubs and anti-hubs, i.e. data objects which appear to be either very close or very far from most other data due to a problem of measuring distances in high dimensions. We compare a classic distance...
Human communicative behavior is both dynamic and bidirectional. This study aims to analyze such behavior by conducting imitative interactions between human subjects and a humanoid robot that has a neuro-dynamical system. For this purpose, we take a robot-centered approach in which the change in robot performance according to difference in human partner is analyzed, rather than adopting the typical...
A tool that automatically identifies subject domains of examination questions is useful in at least three ways: (1) it can help learners hone their ability to perform this subject identification task, which is an important skill in several highstakes examinations, (2) in the context of educational content repositories, it can assist both maintainers in organizing and learners in querying content,...
In this paper, we investigate a DNN tone-based extended recognition network (ERN) approach to Mandarin tone recognition and tone mispronunciation detection. Given a toneless syllable sequence, a tone-based ERN is constructed by assigning five different tones to each toneless syllable, obtaining a fully expanded tonal syllable network. Next, Viterbi decoding is carried out on the tone-based ERN to...
The study of compound-target binding profiles has been a central theme in cheminformatics. For data repositories that only provide positive binding profiles, a popular assumption is all unreported profiles are negative. In this paper, we caution audience not to take such assumption for granted. Under a problem setting where binding profiles are used as features to train predictive models, we present...
This advanced tutorial introduces the engineering principles of combat modeling and distributed simulation. It starts with the historical context and introduces terms and definitions as well as guidelines of interest in this domain. The combat modeling section introduces the main concepts for modeling of the environment, movement, effects, sensing, communications, and decision making. The distributed...
Hough Forest is a framework combining Hough Transform and Random Forest for object detection. The purpose of the present paper is to improve the efficiency and reliability of the original framework by the mean of two contributions. First, instead of generating the image samples by drawing patches randomly from the training set, we bias this step toward the most relevant image content by selecting...
In this paper we examine the use of deep convolutional neural networks for semantic image segmentation, which separates an input image into multiple regions corresponding to predefined object classes. We use an encoder-decoder structure and aim to improve it in convergence speed and segmentation accuracy by adding shortcuts between network layers. Besides, we investigate how to extend an already trained...
The aim of this paper is to analyze available maturity models in the context of assessment of the maturity of IT systems that support communication processes in HCM. The paper presents theoretical issues connected with the evolution of information systems in context of support Human Capital Management (HCM) in a modern organization. Selected problems connected with assessment of maturity were presented,...
Recognizing textual entailment is typically considered as a binary decision task - whether a text T entails a hypothesis H. Thus, in case of a negative answer, it is not possible to express that H is “almost entailed” by T. Partial textual entailment provides one possible approach to this issue. This paper presents an attempt to use word2vec model for recognizing partial (faceted) textual entailment...
Bag of visual words (BoVW) remains a very competitive representation in the domain of scene classification. In this framework, extracting SIFT descriptors on a dense grid of pixels has shown to lead to a better performance. However, due to the nature of SIFT as an edge-based descriptor, computing SIFT on homogeneous regions might result in non-stable region descriptors. The suggested solution in the...
Radio frequency fingerprinting, based on Wi-Fi signals is a popular approach for indoor localization. Recently a few works have explored applicability of machine learning techniques to this problem. However, the challenging task of accurately finding the position depends on prior efforts of fingerprinting. Another challenge is that, distance sensitivity of signal strength depends on proximity to the...
Serious games have been used for several years in order to offer continuous and professional training to the companies employees. These games have unevenly affected different fields, they are less present in certain areas (industry) than others (medical, service, …) because of inherent specificity. In this article, we propose an approach to facilitate the implementation of a playful scenario dedicated...
Serious games are becoming an increasingly used alternative in technical/professional/academic fields. However, scenario development poses a challenging problem since it is an expensive task, only devoted to computer specialists (game developers, programmers…). The ultimate goal of our work is to propose a new scenario-building approach capable of ensuring a high degree of deployment and reusability...
The MOOCs (Massive Open Online Courses) represent a category in the frame of TEL (Technology Enhanced Learning) particularly fashionable today since they allow the largest number of learners to access specific teachings. However, the principle of proposing very sequential and linear pedagogical paths is not attractive enough. In fact, the low success rate shows that it is necessary to maintain the...
When learning a new word in language learning, there are two problems. One is how difficult the word itself is. The second is, in what kind of situation, it will be used. There is a research that defined quantitative ambiguity of words based on the structure of WordNet, then investigated the relationship between the ambiguity and the difficulty level of words. In this paper, we re-define ambiguity...
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