Using Cross-Lingual Explicit Semantic Analysis for Improving Ontology Translation

Semantic Web aims to allow machines to make inferences using the explicit conceptualisations contained in ontologies. By pointing to ontologies, Semantic Web-based applications are able to inter-operate and share common information easily. Nevertheless, multilingual semantic applications are still rare, owing to the fact that most online ontologies are monolingual in English. In order to solve this issue, techniques for ontology localisation and translation are needed. However, traditional machine translation is difficult to apply to ontologies, owing to the fact that ontology labels tend to be quite short in length and linguistically different from the free text paradigm. In this paper, we propose an approach to enhance machine translation of ontologies based on exploiting the well-structured concept descriptions contained in the ontology. In particular, our approach leverages the semantics contained in the ontology by using Cross Lingual Explicit Semantic Analysis (CLESA) for context-based disambiguation in phrase-based Statistical Machine Translation (SMT). The presented work is novel in the sense that application of CLESA in SMT has not been performed earlier to the best of our knowledge.

A Satisfaction-based Model for Affect Recognition from Conversational Features in Spoken Dialog Systems

Detecting user affect automatically during real-time conversation is the main challenge towards our greater aim of infusing social intelligence into a natural-language mixed-initiative High-Fidelity (Hi-Fi) audio control spoken dialog agent. In recent years, studies on affect detection from voice have moved on to using realistic, non-acted data, which is subtler. However, it is more challenging to perceive subtler emotions and this is demonstrated in tasks such as labelling and machine prediction. This paper attempts to address part of this challenge by considering the role of user satisfaction ratings and also conversational/dialog features in discriminating contentment and frustration, two types of emotions that are known to be prevalent within spoken human-computer interaction. However, given the laboratory constraints, users might be positively biased when rating the system, indirectly making the reliability of the satisfaction data questionable. Machine learning experiments were conducted on two datasets, users and annotators, which were then compared in order to assess the reliability of these datasets. Our results indicated that standard classifiers were significantly more successful in discriminating the abovementioned emotions and their intensities (reflected by user satisfaction ratings) from annotator data than from user data. These results corroborated that: first, satisfaction data could be used directly as an alternative target variable to model affect, and that they could be predicted exclusively by dialog features. Second, these were only true when trying to predict the abovementioned emotions using annotator?s data, suggesting that user bias does exist in a laboratory-led evaluation.

A Novel Method for Radio Propagation Simulation Based on Automatic 3D Environment Reconstruction

In this paper, a novel method to simulate radio propagation is presented. The method consists of two steps: automatic 3D scenario reconstruction and propagation modeling. For 3D reconstruction, a machine learning algorithm is adopted and improved to automatically recognize objects in pictures taken from target regions, and 3D models are generated based on the recognized objects. The propagation model employs a ray tracing algorithm to compute signal strength for each point on the constructed 3D map. Our proposition reduces, or even eliminates, infrastructure cost and human efforts during the construction of realistic 3D scenes used in radio propagation modeling. In addition, the results obtained from our propagation model proves to be both accurate and efficient

Detecting unknown attacks in wireless sensor networks that contain mobile nodes

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.