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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...
This paper describes a localization method for an IR-UWB (Impulse Radio Ultra Wideband) two-way ranging system developed for precise time-of-arrival measurements. The ranging system provides a time resolution of 275 ps, which allows precise indoor distance estimation with the accuracy of 4 cm. In this work, a two-way ranging algorithm has been extended into a localization algorithm without the need...
Stress affects people's health and well-being of the world's economies. Despite the progress in physiological stress recognition, there are problems that require solutions in the creation of automated systems of stress determination in prolonged real-life situations. These tasks are analysis of stress in daily life, during physical activity and personalization of this analysis. We described these...
Decision makers are often required to make decisions with incomplete information. In order to design decision support systems (DSSs) to assist decision makers in these situations, it is essential to understand why and how decision makers select their strategies. This paper presents a simulation which examines the impact of incomplete information on the effort and accuracy of decision strategies. The...
'Hubness' is a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. It is related to the concentration of distances which impairs the contrast of distances in high dimensional spaces. Computation of secondary distances inspired by...
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...
In this paper, we present a method of recognizing hand gestures in the form of point clouds recorded by Kinect sensor. Firstly, through Laplacian-based contraction and further processing, we extract skeleton points from point clouds of hands. Then, we apply a novel partition-based descriptor and corresponded algorithm to classify these skeletons and, taking one step further, to recognize gestures...
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...
Word Sense Disambiguation (WSD) is the task of choosing the most appropriate sense of a word having multiple senses in a given context. Collocational features acquired from the words in neighborship with the ambiguous word are one of the important knowledge sources in this area. This paper explores the effective sets of collocational features in Turkish in order to obtain better Turkish WSD systems...
This paper proposes a new information fusion approach that employs two information components for mobile landmark recognition, which includes: content analysis and context analysis. Existing landmark recognition works are mainly based on PC platform, which uses content analysis alone for recognition, and thus has a large computation cost and cannot satisfy mobile users' fast response time requirements...
The volume of email that help-desks receive every day is very high and often queries are repeated. Any kind of automation in processing of emails requires good understanding of the emails. In the current work we propose a schema for tagging author composed sentences in help-desk emails by the intent of the author. We have created a corpus taking email data from two help-desks and annotated them at...
Word Sense Disambiguation (WSD) is the task of selecting the meaning of a word based on the context in which the word occurs. The principal statistical WSD approaches are supervised and unsupervised learning. The Lesk method is an example of unsupervised disambiguation. We present a measure for sense assignment useful for the simple Lesk algorithm. We use word co-occurrences of the gloss and the context,...
Global and distributed software development increases the need to find and connect developers with relevant expertise. Existing recommendation systems typically model expertise based on file changes (implementation expertise). While these approaches have shown success, they require a substantial recorded history of development for a project. Previously, we have proposed the concept of usage expertise,...
Current research of natural language steganographic algorithms based on synonymy substitution mostly focused on invisibility, but ignored robustness. However, automatic disambiguation of Chinese word senses (WSD) achieves high accuracy, the adversary could destroyed the watermark easily if he disambiguated the stego-text and did the synonym substitution again. In this paper, against the high accuracy...
In smart living spaces, a reliable context-aware application must adapt to the variable environment and should function to complete the user's requirements. Therefore, if a context-aware application is to achieve the user's requirements smoothly, it must understand the environment status according to trustworthy context information. However, the context information influenced by the environment status...
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