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Data representation is a fundamental task in machine learning, which affects the performance of the whole machine learning system. In the past few years, with the rapid development of deep learning, the models for word embedding based on neural networks have brought new inspiration to the research of natural language processing. In this paper, two kinds of schemes for improving the Continuous Bag-of-Words...
In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly,...
Educational games have been used as an innovative instructional strategy in order to achieve learning more effectively in Software Engineering (SE) education. However, it is essential to systematically evaluate such games in order to obtain sound evidence on their benefits. And, although, several SE games have been evaluated adopting diverse research designs and measurements, so far no larger scale...
Adding context information into recurrent neural network language models (RNNLMs) have been investigated recently to improve the effectiveness of learning RNNLM. Conventionally, a fast approximate topic representation for a block of words was proposed by using corpus-based topic distribution of word incorporating latent Dirichlet allocation (LDA) model. It is then updated for each subsequent word...
In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat preceding utterances as a sequence of inputs, without considering dialog interactions. We design recurrent neural network (RNN) based contextual language models that specially track the interactions between speakers in a dialog...
Robust appearance model is significantly important to state-of-the-art trackers. However, such trackers highly rely on the reliability of foreground appearance model. When the foreground is seriously occluded or the scene contains multiple objects with similar appearance, such foundation is destroyed. To extend the ability of trackers to handle these difficulties, we propose selective object and context...
Group activity recognition from videos is a very challenging problem that has barely been addressed. We propose an activity recognition method using group context. In order to encode both single-person description and two-person interactions, we learn mappings from highdimensional feature spaces to low-dimensional dictionaries. In particular the proposed two-person descriptor takes into account geometric...
Human-centered Internet-of-Things (IoT) applications utilize computational algorithms such as machine learning and signal processing techniques to infer knowledge about important events such as physical activities and medical complications. The inference is typically based on data collected with wearable sensors or those embedded in the environment. A major obstacle in large-scale utilization of these...
In this paper, we present a heuristic for labeling a given term a taxonomy label. Specifically, for a given term, our goal is to construct a model for determining an "is-a" relationship between the given term and an inferred concept. Such term-labelling problem is not new, but the existing solutions require semi-supervised training processing, e.g., supervised LDA, or rely on lexicographers,...
Human drivers continuously attend to important scene elements in order to safely and smoothly navigate in intricate environments and under uncertainty. This paper develops a human-centric framework for object recognition by analyzing a notion of object importance, as measured in a spatio-temporal context of driving a vehicle. Given a video, a main research question in this paper is - which of the...
Short text classification is a crucial task for information retrieval, social medial text categorization, and many other applications. In reality, due to the inherent sparsity and the limited information available in the short texts, learning and classifying short texts is a significant challenge. In this paper, we propose a new framework, WEFEST, which expands short texts using word embedding for...
In this paper, we present Bengali word embeddings and it's application in the classification of news documents. Word embeddings are multi-dimensional vectors that can be created by exploiting the linguistic context of the words in large corpus. To generate the embeddings, we collected Bengali news document of last five years from the major daily newspapers. Word embeddings are generated using the...
We propose an attention-enabled encoder-decoder model for the problem of grapheme-to-phoneme conversion. Most previous work has tackled the problem via joint sequence models that require explicit alignments for training. In contrast, the attention-enabled encoder-decoder model allows for jointly learning to align and convert characters to phonemes. We explore different types of attention models, including...
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...
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...
This paper presents a data-driven approach towards the modeling of agent behaviors in a full-fledged, commercial off-the-shelf simulation milieu for tactical military training. The modeling approach employs machine learning to identify behavioral rules and patterns in data. Potential advantages of this approach are that it may improve modeling efficiency and, perhaps more importantly, increase the...
Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we propose a simple...
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. While many approaches involve manual behavior specification via code or reliance...
We design a way to model apps as vectors, inspired by the recent deep learning approach to vectorization of words called word2vec. Our method relies on how users use apps. In particular, we visualize the time series of how each user uses mobile apps as a “document”, and apply the recent word2vec modeling on these documents, but the novelty is that the training context is carefully weighted by the...
In the context of medical team leaders training, we present a multiagent communication model that can introduce errors in a team of agents. This model is built from existing work from the literature in multiagents systems and information science, but also from a corpus of dialogues collected during actual field training for medical teams. Our model supports four types of communication errors (misunderstanding,...
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