Abstract: In the globalized world, good products may not be enough to reach potential clients if creative marketing strategies are not well delineated. Public relations are also important when it comes to capture clients attention, making the first contact between them and companies products while being persuasive enough to gain the of the client that the company has the right products to fit their needs. A virtual public relations is purposed, combining technology and a human like public relations capable of interacting with potential clients placed 180 degrees in front of the installation, by using gestures and sound. Four 4 Microsoft Kinects were used to develop de 180 degrees model for interaction, which allows recognition of gestures, sound sources, words, extract the face and body of the user and track users positions (including an heat map).

Abstract: The conventional way of identifying possible motif sequences in a DNA strand is to use representative scalar weight matrix for searching good match substring alignments. However, this approach, solely based on match alignment information, is susceptible to a high number of ambiguous sites or false positives if the motif sequences are not well conserved. A significant amount of time is then required to verify these sites for the suggested motifs. Hence in this paper, the use of mismatch alignment information in addition to match alignment information for DNA motif searching is proposed. The objective is to reduce the number of ambiguous false positives encountered in the DNA motif searching, thereby making the process more efficient for biologists to use.

Abstract: Isolated left ventricular non-compaction (LVNC) is a myocardial disorder characterised by prominent ventricular trabeculations and deep recesses extending from the LV cavity to the subendocardial surface of the LV. Up to now, there is no common and stable solution in the medical community for quantifying and valuing the non-compacted cardiomyopathy. A software tool for the automatic quantification of the exact hyper-trabeculation degree in the left ventricle myocardium is designed, developed and tested. This tool is based on medical experience, but the possibility of the human appreciation error has been eliminated. The input data for this software are the cardiac images of the patients obtained by means of magnetic resonance. The output results are the percentage quantification of the trabecular zone with respect to the compacted area. This output is compared with human processing performed by medical specialists. The software proves to be a valuable tool to help diagnosis, so saving valuable diagnosis time.

Abstract: This paper evaluates the performance of two unsupervised approaches, Maximum Marginal Relevance (MMR) and concept-based global optimization framework for speech summarization. Automatic summarization is very useful techniques that can help the users browse a large amount of data. This study focuses on automatic extractive summarization on multi-dialogue speech corpus. We propose improved methods by blending each unsupervised approach at sentence level. Sentence level information is leveraged to improve the linguistic quality of selected summaries. First, these scores are used to filter sentences for concept extraction and concept weight computation. Second, we pre-select a subset of candidate summary sentences according to their sentence weights. Last, we extend the optimization function to a joint optimization of concept and sentence weights to cover both important concepts and sentences. Our experimental results show that these methods can improve the system performance comparing to the concept-based optimization baseline for both human transcripts and ASR output. The best scores are achieved by combining all three approaches, which are significantly better than the baseline system.

Abstract: The CardioRisk project addresses the coronary artery disease (CAD), namely, the management of myocardial infarction (MI) patients. The main goal is the development of personalized clinical models for cardiovascular (CV) risk assessment of acute events (e.g. death and new hospitalization), in order to stratify patients according to their care needs. This paper presents an overview of the scientific and technological issues that are under research and development. Three major scientific challenges can be identified: i) the development of fusion approaches to merge CV risk assessment tools; ii) strategies for the grouping (clustering) of patients; iii) biosignal processing techniques to achieve personalized diagnosis. At the end of the project, a set of algorithms/models must properly address these three challenges. Additionally, a clinical platform was implemented, integrating the developed models and algorithms. This platform supports a clinical observational study (100 patients) that is being carried out in Leiria Hospital Centre to validate the developed approach. Inputs from the hospital information system (demographics, biomarkers, clinical exams) are considered as well as an ECG signal acquired based on a Holter device. A real patient dataset provided by Santa Cruz Hospital, Portugal, comprising N=460 ACS-NSTEMI patients is also applied to perform initial validations (individual algorithms). The CardioRisk team is composed by two research institutions, the University of Coimbra (Portugal), Politecnico di Milano (Italy) and Leiria Hospital Centre (a Portuguese public hospital).