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In this paper, we present a feasibility study for using a single Microsoft Kinect sensor to assess the quality of rehabilitation exercises. Unlike competing studies that have focused on the validation of the accuracy of Kinect motion sensing data at the level of joint positions, joint angles, and displacement of joints, we take a rule based approach. The advantage of our approach is that it provides...
In this paper, we analyse the emotion of children's stories in sentence level by considering the context information. We demonstrate that the emotion of a sentence is not only dependent on its content, but also affected by its neighbours in a story. A Hidden Markov Model (HMM) based method is proposed to model the emotion sequence and to detect whether a sentence is neutral or not. We show the important...
In the context-awareness problems, classification models are used to recognize the user scenes based on the sensor signal data. To improve the classification accuracy, a Dynamic Key Context Information Based scenes recognition method is proposed in this paper. Since the location information can be easily obtained by the sensors, the data set is divided into groups by their location information. An...
Electronic detection of linguistic negation in free text is a challenging need for many text handling applications including sentiment analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu to predict the scope of negation in the text. In this paper, our main target was on determining the scope of negation in news articles for two political parties namely...
We propose a novel rule-based model to incorporate contextual information and effect of negation that enhances the performance of sentiment classification performed using bag-of-words models. We employed morphological analysis in feature extraction to ensure feature vector contains only opinionated words in a textual review. Also it reduces the dimensionality of feature vector and, eventually improves...
Sentic Net is a popular resource for concept-level sentiment analysis. Because Sentic Net was created specifically for opinion mining in English language, however, its localization can be very laborious. In this work, a toolkit for creating non-English versions of Sentic Net in a time- and cost-effective way is proposed. This is achieved by exploiting online facilities such as Web dictionaries and...
With the availability of traffic sensors data, various techniques have been proposed to make congestion prediction by utilizing those datasets. One key challenge in predicting traffic congestion is how much to rely on the historical data v.s. The real-time data. To better utilize both the historical and real-time data, in this paper we propose a novel online framework that could learn the current...
As we are moving towards pervasive, ubiquitous and computing paradigm, the interest and research for context-aware systems have substantially taken interest over the past decade and has become the new era of anytime, anywhere and anything computing. Delivering acceptable services for the users requires services to be aware of their contexts and able to adapt automatically to their changing contexts...
This work explores Deep Belief Networks (DBN) for the task of detecting Vowel-like regions (VLRs). Vowels and semivowels are considered as VLRs. By using vocal tract features at the input layer of DBN, we extract an evidence for VLRs by transforming the vocal tract features through multiple non-linear hidden layers. The linear classifier is used to predict the class of evidence, i.e.,whether it is...
Classification approaches, e.g. Decision trees or Naive Bayesian classifiers, are often tightly coupled to learning strategies, special data structures, the type of information captured, and to how common problems, e.g. Over fitting, are addressed. This prevents a simple combination of classifiers of different classification approaches learned over different data sets. Many different methods of combining...
Prior research in neutrally-inspired perceptron predictors and Geometric History Length-based TAGE predictors has shown significant improvements in branch prediction accuracy by exploiting correlations in long branch histories. However, not all branches in the long branch history provide useful context. Biased branches resolve as either taken or not-taken virtually every time. Including them in the...
Data reduction as a critical step in the process of data pre-processing presents a central point of interest across a wide variety of fields. Data pre-processing has a significant impact on the performance of any machine learning algorithm. In this context, we focus our research paper on investigating the data pre-processing phase of a recent evolutionary algorithm named the Dendritic Cell Algorithm...
Labelling maximization (F-max) is an unbiased metric for estimation of the quality of non-supervised classification (clustering) that promotes the clusters with a maximum value of feature F-measure. In this paper, we show that an adaptation of this metric within the supervised classification allows to perform a selection of features and to calculate for each of them a function of contrast. The method...
Nowadays mobile applications demand higher context awareness. The applications aim to understand the user's context (e.g., home or at work) and provide services tailored to the users. The algorithms responsible for inferring the user's context are the so-called context inference algorithms, the place detection being a particular case. Our hypothesis is that people use mobile phones differently when...
Chunking or shallow syntactic parsing is proving to be a task of interest to many natural language processing applications. The problem gets worse for the Arabic language because of its specific features that make it quite different and even more ambiguous than other natural languages when processed. In this paper, we present a method for chunking Arabic texts based on supervised learning. We use...
With the rapid development of the Internet, SNS services and 3G commercial mobile applications which brings tremendous opportunity, although the time on the development of SNS is very short in China, social web game is in the early stage of development, because of massive user, the potential commercial value of Chinese SNS is still a great mining space. A relatively large defects is the precipitation...
Query expansion methods based on search logs could improve the quality of search results to some extends. But when the search logs are sparse, this kind of query expansion methods will have poor quality of search results and are unable to meet the user's search request, etc. This paper presents the search log sparseness oriented query extension method. By introducing the determination rule of data...
Along with the wide use of web application, XSS vulnerability has become one of the most common security problems and caused many serious losses. In this paper, on the basis of database query language technique, we put forward a static analysis method of XSS defect detection of java web application by analyzing data flow reversely. This method first converts the JSP file to a Servlet file, and then...
Scene text recognition has attracted much attention in the research community. Many proposed scene text recognition methods adopt a step-by-step procedure, which includes a text extraction phase and a recognition phase. In this study, in order to eliminate the risk of text extraction error, we try to build a scene text recognition system that does not involve the text extraction phase. In our proposed...
Traditional recommender systems use collaborative filtering or content-based methods to recommend new items for users. New users and items are continuously updated to the system bringing changes in user's preferences, as well as the additional context in form of temporal information. The continuous system updates change not just individual user's preferences, but also group user's preferences affecting...
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