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Nowadays, developing effective techniques able to deal with data coming from structured domains is becoming crucial. In this context kernel methods are the state-of-the-art tool widely adopted in real-world applications that involve learning on structured data. Contrarily, when one has to deal with unstructured domains, deep learning methods represent a competitive, or even better, choice. In this...
The abstraction tasks are challenging for multi-modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain...
Word embeddings are a low-dimensional vector representation of words that incorporates context. TWo popular methods are word2vec and global vectors (GloVe). Word2vec is a single-hidden layer feedforward neural network (SLFN) that has an auto-encoder influence for computing a word context matrix using backpropagation for training. GloVe computes the word context matrix first then performs matrix factorization...
The software architecture is usually the first design artifact that addresses quality issues (e.g., performance, security). Also, the architecture is reference point for other development activities, e.g., coding and maintenance. Based on our experience teaching software engineering and architecture at different institutions and levels, we discuss what makes teaching software architecture difficult,...
Headline generation for spoken content is important since spoken content is difficult to be shown on the screen and browsed by the user. It is a special type of abstractive summarization, for which the summaries are generated word by word from scratch without using any part of the original content. Many deep learning approaches for headline generation from text document have been proposed recently,...
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...
Internet and organizational network security is still threatened by devastating malicious activities. Given the continuous escalation of such attacks in terms of their frequency, sophistication and stealthiness, it is of paramount importance to generate effective cyber threat intelligence that aim at inferring, attributing, characterizing and mitigating such misdemeanors. Nevertheless, such imperative...
Recently, several fast speaker adaptation methods have been proposed for the hybrid DNN-HMM models based on the so-called discriminative speaker codes (SC) [1-3] and applied to unsupervised speaker adaptation in speech recognition [4]. It has been demonstrated that the SC based methods are quite effective in adapting DNNs even when only a very small amount of adaptation data is available. However,...
In this paper, we discuss a classification method of nursing-care texts using the word2vec [1]. The word2vec is a tool which provides the continuous bag-of-words and skip-gram implementations for realizing word vectors. We have tackled to classify nursing-care texts, which are freestyle Japanese texts, for improving nursing quality in several years. Several machine learning methods have been used...
In our data driven world, categorization is of major importance to help end-users and decision makers understanding information structures. Supervised learning techniques rely on annotated samples that are often difficult to obtain and training often overfits. On the other hand, unsupervised clustering techniques study the structure of the data without disposing of any training data. Given the difficulty...
Tourism has become a major sector for economic development on Lombok. Tourist expenditure in Lombok give a good implications on public revenue. Tourist expenditures are not only distributed to the tourism sector, but also to other sectors. Prediction of tourists visit is very important as an information and planning for the future. Prediction is one of very important element in decision, because the...
We proposed neural network architecture based on Convolution Neural Network(CNN) for temporal relation classification in sentence. First, we transformed word into vector by using word embedding. In Feature Extraction, we extracted two type of features. Lexical level feature considered meaning of marked entity and Sentence level feature considered context of the sentence. Window processing was used...
Event recognition from still images is of great importance for image understanding. However, compared with event recognition in videos, there are much fewer research works on event recognition in images. This paper addresses the issue of event recognition from images and proposes an effective method with deep neural networks. Specifically, we design a new architecture, called Object-Scene Convolutional...
Deep learning subsumes algorithms that automatically learn compositional representations. The ability of these models to generalize well has ushered in tremendous advances in many fields such as natural language processing (NLP). Recent research in the software engineering (SE) community has demonstrated the usefulness of applying NLP techniques to software corpora. Hence, we motivate deep learning...
Pedestrian detection is of crucial importance to autonomous driving applications. Methods based on deep learning have shown significant improvements in accuracy, which makes them particularly suitable for applications, such as pedestrian detection, where reducing the miss rate is very important. Although they are accurate, their runtime has been at best in seconds per image, which makes them not practical...
In this paper, we propose a new method for singing voice detection based on a Bidirectional Long Short-Term Memory (BLSTM) Recurrent Neural Network (RNN). This classifier is able to take a past and future temporal context into account to decide on the presence/absence of singing voice, thus using the inherent sequential aspect of a short-term feature extraction in a piece of music. The BLSTM-RNN contains...
The explosive growth of data, images in the World Wide Web makes it critical to the information retrievals. Image retrieval has been recognized as an elementary problem in the retrieval tasks and this exercise has got a wide attention based on the underlying domain characteristics. For instance, in social media data encompasses of noisy, diverse, heterogeneous, interconnected data. To confront these...
This poster paper presents a high-level description of the Metalogue project that is developing a multi-modal dialogue system that is able to implement interactive behaviors that seem natural to users and is flexible enough to exploit the full potential of multimodal interaction. We provide an outline of the initial work undertaken to define a an open architecture for the integrated Metalogue system...
In this paper we present the «Architecture for Representations, Games, Interactions, and Learning among Experts (ARGILE) suitable for «participatory and knowledge-intensive» serious games. It proposes solutions within the context of targeted learning, concerning scenarios, in which conception of serious games, and their utilization through a better share by designers is considered. In this work, we...
Pervasive computing and social computing are two major computing paradigms of this decade, which have evolved more or less in isolation from each other. Integrating pervasive systems with social media can enhance the users' experience and enable them to form pervasive communities with others that share similar interests, habits, profile, behaviour, to communicate and interact with them, to socialise...
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