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The aversion of gaze during dyadic conversations is a social signal that contains information relevant to the detection of interest, turn-taking cues, and conversational engagement. The understanding and modeling of such behavior has implications for the design of embodied conversational agents, as well as computational approaches to conversational analysis. Recent approaches to extracting gaze directions...
Recognition of vehicle types in real life traffic scenarios is a challenging task due to the diversity of vehicles and uncontrolled environments. Efficient methods and feature representations are needed to cope with these challenges. In this paper, we address the vehicle type classification problem in real life traffic scenarios and propose a multimodal method that uses efficient representations of...
Retrieving information from movies is becoming increasingly demanding due to the enormous amount of multimedia data generated each day. Not only it helps in efficient search, archiving and classification of movies, but is also instrumental in content censorship and recommendation systems. Extracting key information from a movie and summarizing it in a few tags which best describe the movie presents...
We address the problem of 3D image registration without pre-registration based on features extracted from point clouds. 3D image registration is currently receiving a great deal of attention as we can get 3D image data easier than before for developing a portable depth camera such as Kinect and Xtion. The Iterative Closest Point (ICP) algorithm is often used to register between current and next frames...
This paper describes the various malware datasets that we have obtained permissions to host at the University of Arizona as part of a National Science Foundation funded project. It also describes some other malware datasets that we are in the process of obtaining permissions to host at the University of Arizona. We have also discussed some preliminary work we have carried out on malware analysis using...
Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
Aspect-based sentiment analysis summarizes what people like and dislike from reviews of products or services. In this paper, we adapt the first rank research at SemEval 2016 to improve the performance of aspect-based sentiment analysis for Indonesian restaurant reviews. We use six steps for aspect-based sentiment analysis i.e.: preprocess the reviews, aspect extraction, aspect categorization, sentiment...
Chronic kidney disease (CKD) is a disease caused by degeneration function of the kidneys. CKD is top ten leading causes of death in the world. There are two leading causes of CKD, i.e. diabetes and hypertension. When the symptoms become more severe, the disease can only be treated with dialysis and kidney transplantation. This disease can be treated if the diagnose is conducted appropriately and quickly...
With the continued growth of the mobile game market, many game companies aim to make money through mobile games. In this situation, knowing the tendency of gamers and predicting the churn in advance can maximize profit through effective game services. For this reason, much study has been conducted for the purpose of gamer analysis and churn prediction. However, the study was mainly conducted using...
Automatic multi-document summarization may help news readers retrieve information from digital news media efficiently. The summarizer create a concise summary containing important information from a collection of articles, enabling readers to read only one text to gain information from multiple text sources. Reflecting on previous researches, we propose an automatic summarization system using sentence...
In Deep Learning, which has become a topic of intense study in recent years, an auto encoder that learns an identity map by a neural network plays an important role to extract features of data. One disadvantage of this method is that it is not always possible to extract appropriate features in the middle layer. In this study, a five-layer auto-encoder based on a sensory integration model proposed...
Indonesian text data from social media is one of large text data that interesting to be mined. Mining the insight knowledge from large text data need more effort and time to processed. Moreover, Indonesian text data from social media contains natural language, including slang that require special treatment. We propose incremental technique for more efficient mining process of large text data with...
A stroke occurs when the blood supply to a person's brain is interrupted or reduced. The stroke deprives person's brain of oxygen and nutrients, which can cause brain cells to die. Numerous works have been carried out for predicting various diseases by comparing the performance of predictive data mining technologies. In this work, we compare different methods with our approach for stroke prediction...
Recently, deep neural network based methods have been widely used in relation extraction, which is an important task for knowledge base population, question answering and other natural language applications, to learn proper features from entities pairs and other sentence parts to extract relations from text. As a kind of important information, the value of position is always been underestimated, which...
In this paper, automatic speaker verification using normal and whispered speech is explored. Typically, for speaker verification systems, varying vocal effort inputs during the testing stage significantly degrades system performance. Solutions such as feature mapping or addition of multi-style data during training and enrollment stages have been proposed but do not show similar advantages for the...
Evolutionary computation is widely used to solve dynamic problems such as association rule mining(ARM) by adapting the solutions to changes of the data pattern. The summarization in the ARM for a wireless sensor network(WSN) is still an issue when its applied to big number of sensor input from multiple location. This paper proposes a parallel processing of ARM for efficient WSN processing using genetic...
We proposed a novel method of feature extraction for multi-modal images called modality-convolution. It extracts both the intra- and inter-modality information. Whats more, it completes the data fusion at pixel-level so that the complementarity of information contained in multi-modal data is fully utilized. Based on the modality-convolution, we describe a modality-CNN for multi-modal gesture recognition...
Word of mouth communication plays an influential role in propelling or sinking movies. Proper analysis of movie reviews published in social media helps in efficient decision making. Sentiment analysis aids in automating this time consuming task. Identifying the relevant data with the elimination of plot in movie reviews enhance the performance of any sentiment analysis algorithm. We propose an entity...
With the rapid development of Internet, how to obtain valuable information from massive messages has become a major problem we need to be solved in the information explosive era. This paper introduces the development route of information extraction technology, and discusses four categories of Chinese entity relation extraction technologies in depth. Finally, the advantages and disadvantages of different...
This study presents a new fault diagnosis method based on a two-stage manifold learning framework to further improve fault diagnosis accuracy. First of all, nonlinear de-noising method with unsupervised manifold learning is presented, by combining advantages of manifold learning in mining of nonlinear structure and phase space reconstruction in representation of signal and noise spatial distribution...
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