A JESS-enabled context elicitation system for providing context-aware Web services

Providing context-aware Web services is an adaptive process of delivering contextually matched Web services to meet service requesters’ needs. We define the term “context” from two perspectives: one from service requesters; and the other from Web services. From the former perspective, context is defined as the surrounding environment affecting requesters’ services discovery and access, such as requesters’ preferences, locations, activities, and accessible network and devices. From the latter perspective, context is defined as the surrounding environment affecting Web services delivery and execution, such as networks and protocols for service binding, devices and platforms for service execution, and so on. This paper presents a Java Expert System Shell (JESS)-enabled context elicitation system featuring an ontology-based context model that formally describes and acquires contextual information pertaining to service requesters and Web services. Based on the context elicitation system, we present a context-aware services-oriented architecture for providing context-aware Web service request, publication, and discovery. Implementation details of the context elicitation system and the evaluation results of context-aware service provision are also reported.     

Texture and shape information fusion for facial expression and facial action unit recognition

A novel method based on fusion of texture and shape information is proposed for facial expression and Facial Action Unit (FAU) recognition from video sequences. Regarding facial expression recognition, a subspace method based on Discriminant Non-negative Matrix Factorization (DNMF) is applied to the images, thus extracting the texture information. In order to extract the shape information, the system firstly extracts the deformed Candide facial grid that corresponds to the facial expression depicted in the video sequence. A Support Vector Machine (SVM) system designed on an Euclidean space, defined over a novel metric between grids, is used for the classification of the shape information. Regarding FAU recognition, the texture extraction method (DNMF) is applied on the differences images of the video sequence, calculated taking under consideration the neutral and the expressive frame. An SVM system is used for FAU classification from the shape information. This time, the shape information consists of the grid node coordinate displacements between the neutral and the expressed facial expression frame. The fusion of texture and shape information is performed using various approaches, among which are SVMs and Median Radial Basis Functions (MRBFs), in order to detect the facial expression and the set of present FAUs. The accuracy achieved using the Cohn–Kanade database is 92.3% when recognizing the seven basic facial expressions (anger, disgust, fear, happiness, sadness, surprise and neutral), and 92.1% when recognizing the 17 FAUs that are responsible for facial expression development.     

Acquisition of lexical translation relations from MRDS

In this paper we present a methodology for extracting information about lexical translation equivalences from the machine readable versions of conventional dictionaries (MRDs), and describe a series of experiments on semi-automatic construction of a linked multilinguallexical knowledge base for English, Dutch, and Spanish. We discuss the advantages and limitations of using MRDs that this has revealed, and some strategies we have developed to cover gaps where no direct translation can be found.     

Functional data analysis in shape analysis

Mid-level processes on images often return outputs in functional form. In this context the use of functional data analysis (FDA) in image analysis is considered. In particular, attention is focussed on shape analysis, where the use of FDA in the functional approach (contour functions) shows its superiority over other approaches, such as the landmark based approach or the set theory approach, on two different problems (principal component analysis and discriminant analysis) in a well-known database of bone outlines. Furthermore, a problem that has hardly ever been considered in the literature is dealt with: multivariate functional discrimination. A discriminant function based on independent component analysis for indicating where the differences between groups are and what their level of discrimination is, is proposed. The classification results obtained with the methodology are very promising. Finally, an analysis of hippocampal differences in Alzheimer’s disease is carried out.     

Comparing sensitivities of differently oriented multi-walled carbon nanotubes integrated on silicon wafer for electrochemical biosensors

In this study, we report on multi-walled carbon nanotubes fabricated on silicon substrate with four different orientations via chemical vapor deposition. It is well-known that chemical treatments improve the nanotube electrochemical reactivity by creating edge-like defects on their exposed sidewalls. Before use, we performed an acid treatment on carbon nanotubes. To prove the effect of the treatment on these nanostructured electrodes, contact angles were measured. Then, sensitivities and detection limits were evaluated performing cyclic voltammetry. Two target molecules were used: potassium ferricyanide, an inorganic electroactive molecule, and hydrogen peroxide that is a product of reactions catalyzed by many enzymes, such as oxidases and peroxidases. Carbon nanotubes with tilted tips become hydrophilic after the treatment showing a contact angle of 22° ± 2°. This kind of electrode has shown also the best electrochemical performance. Sensitivity and detection limit values are 110.0 ± 0.5 μA/(mM cm²) and 8 μM for potassium ferricyanide solutions and 16.4 ± 0.1 μA/(mM cm²) and 24 μM using hydrogen peroxide as target compound. Considering the results of wettability and voltammetric measurements, nanotubes with tilted tips-based electrodes are found to be the most promising for future biosensing applications.     

Publication behaviour and international impact: Scandinavian clinical and social medicine, 1988–96

The paper presents the results of an empirical study of the Danish and Nordic Publication behaviour and international impact in Clinical and Social Medicine covering the period 1988–96. As indicators are applied the international visibility of Scandinavian research output, the publication activity per capita in SCI journals, the development over time of the national citation impact in an OECD and World context, and the ratio of cited papers relative to the World. Compared toMay's analysis (1997), covering 1981–94, the analysis shows that a certain reshuffle of national positions among the OECD countries in citation impact has occurred. UK and New Zealand as well as Denmark and Sweden have lost in ranking to Finland and Belgium, both countries coming up from behind. The most interesting results concern the opposite research policy strategies displayed by Finland and Denmark which result in similar impact patterns relative to the World impact. The implications are discussed.     

DiSeg 1.0: The first system for Spanish discourse segmentation

Nowadays discourse parsing is a very prominent research topic. However, there is not a discourse parser for Spanish texts. The first stage in order to develop this tool is discourse segmentation. In this work, we present DiSeg, the first discourse segmenter for Spanish, which uses the framework of Rhetorical Structure Theory and is based on lexical and syntactic rules. We describe the system and we evaluate its performance against a gold standard corpus, divided in a medical and a terminological subcorpus. We obtain promising results, which means that discourse segmentation is possible using shallow parsing.