The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures...
A machine translation system converts text from a natural language to other while abiding to the syntax and semantics of the latter. The area of interest here is a Rule Based machine translation system that translates text from English to Malayalam using transfer approach. The system is designed to translate sentences from cricket domain related articles. The purpose behind making the system domain...
Collecting user's current location(s) and place-to-place transitions, predicting future destinations, equipping users with location sensitive information, and handling relevant communication requests are core ingredients of new generation of service provider applications on mobile devices. Periodic place-to-place transitions are inherent in human movements. Next place predictions are the atomic units...
We present synthesized findings from a systematic study of user mobility based on a well grounded data set through mining attributes of place-to-place transitions. Next place predictions are the atomic units in constructing end-to-end user mobility trajectories based on historical trace data. These trajectories in turn form models for opportunistic networks to be utilized for providing location and...
In this paper, we propose an expert search scheme in social networks. The proposed scheme updates a profile by analyzing recent activities, and considers the reliability scores of users and users' ratings that are computed by the updated profile. A user's profile is created by extracting a keyword from the recent activity information and calculating similarity with the keyword. To verify the performance...
This paper presents an efficient image exploration scheme for the unshaped object using semantic modelling. The local regions of an image have been classified with respect to the frequency of occurrences. The semantic concept is evaluated using RGB histogram dissimilarity factor, overall dissimilarity factor and regional dissimilarity factor. The dissimilarities determine the local concept with accuracy...
Recognizing textual entailment (RTE) is a task that predict whether a text fragment can be inferred from another text fragment. In this paper, we tackle RTE problem using sentence extraction to cover semantic variation and then extracting subject, predicate and object from each sentence without using external resources like Wordnet. Finally, similarity function is used to predict entailment relation...
Effective in-vehicle icons are used to convey quick and complete understanding of information to drivers for safe driving; however, misunderstood icons may result to worse. Former studies were able to point out the ineffectiveness (in terms of matching accuracy) of non-ISO vehicle icons. This study was conducted to fill in the gap in literature that addresses the need for redesigning ineffective non-ISO...
As the use of Electronic Medical Records (EMRs) becomes widespread, the amount of data in an EMR becomes a challenge for its comprehension. We developed problem-oriented EMR summarization to address this issue, as a part of a larger effort of adapting IBM Watson to the medical domain. The problem-orientation refers to the central role of a patient's medical problems in the summary. The summarization...
Most of the mobile platforms provide a keyword based full text search (FTS) for users to find what they want. However, FTS has difficulties in dealing with the cases where a user cannot remember the exact keywords about target data or the number of search results is too many. To overcome these limitations of FTS, we propose a semantically enhanced method of searching for data on mobile devices along...
Mobile Social Network in Proximity (MSNP) is a new form of social network in which users are capable of interacting with their surroundings via their mobile devices in public mobile peer-to-peer (MP2P) environments. However establishing such an MSNP faces several trust issues. A classic MP2P trust scheme usually requires high amount of data transaction in order to identify the trustworthiness of service/content...
Word Sense Disambiguation is the task of automatically identifying the correct sense of an ambiguous word. Biomedical documents, similar to other narrative documents, suffer from ambiguity, which impacts the ability to automatically extract knowledge contained in the document text. In this study, we propose a graph-based word sense disambiguation algorithm focused on biomedical text. The proposed...
Traditional text categorization methods only deal with the content of the documents and use some statistic based metrics to represent the documents. The representation is then used by a machine learning approach to determine the document class. In this picture, the meaning of the document is missing. In order to add meaning into the text categorization process, we start with using part-of-speech tagging...
Non-negative matrix factorization (NMF) learns the latent semantic space more direct and reliable than the latent semantic indexing (LSI) and the spectral clustering methods, thus performs well in document clustering. Recently, semi-supervised NMF such as N2S2L, CNMF and unsupervised method such as GNMF significantly improve the face recognition performance, but they are designed for classification...
Low information quality is one of the reasons why information extraction initiatives fail. Incomplete information has a pervasive negative impact on downstream processing steps. This work addresses this problem with a novel information extraction approach, which integrates data mining and information extraction methods into a single complementary approach in order to benefit from their respective...
Maintaining trace ability links between application code and unit test cases plays an important role for effectively managing the development and evolution of software systems. Unfortunately, the support in the contemporary development environment to identify such links is still inadequate. This research presents an automated solution to recover trace ability links between test cases and classes under...
Hashing methods have attracted much attention in large scale image research in recent years, because they are not only fast, but also needing a little memory. This paper proposed a balanced semi-supervised hashing method by dividing image into several blocks. With the help of improved semi-supervised hashing, we obtain a short hash code of each block, which jointed together forms a hash code of an...
Among the approaches for solving the semantic image segmentation problem that has proven successful is in formulating an energy minimization expressed on top of a conditional random field (CRF) over image pixels. Recently, high order potentials (cliques of size greater than 2) over superpixels have been incorporated in the CRF energy function yielding promising results. These potentials encourage...
Social tagging systems such as Facebook, YouTube, del.icio.us, Flickr become popular recent years and have achieved widespread success. State-of-art user modeling approaches in tagging systems usually use a vector of weighted tags. Unfortunately, typical user modeling methods using a vector of weighted tags which are based on personal view only and ignore the social view, have some inherent drawbacks...
In this paper, we propose a technique for classifying shots of playfield-based sports video into their respective view classes. Based on common broadcasting style, a shot can be classified as a far-view or a closeup-view. The technique considers the frame-wise color values of each pixel in the HSV color space, while at the same time calculating the assumed object size within the segmented playfield...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.