A document management methodology based on similarity contents

The advent of the WWW and distributed information systems have made it possible to share documents between different users and organisations. However, this has created many problems related to the security, accessibility, right and most importantly the consistency of documents. It is important that the people involved in the documents management process have access to the most up-to-date version of documents, retrieve the correct documents and should be able to update the documents repository in such a way that his or her document are known to others. In this paper we propose a method for organising, storing and retrieving documents based on similarity contents. The method uses techniques based on information retrieval, document indexation and term extraction and indexing. This methodology is developed for the E-Cognos project which aims at developing tools for the management and sharing of documents in the construction domain

Recognition of online handwritten music symbols

Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.An effective way of digitizing a new musical composition is to use an e-pen and tablet application in which the user's pen strokes are recognized online and the digital score is created with the sole effort of the composition itself. This work aims to be a starting point for research on the recognition of online handwritten music notation. To this end, different alternatives within the two modalities of recognition resulting from this data are presented: online recognition, which uses the strokes marked by a pen, and offline recognition, which uses the image generated after drawing the symbol. A comparative experiment with common machine learning algorithms over a dataset of 3800 samples and 32 different music symbols is presented. Results show that samples of the actual user are needed if good classification rates are pursued. Moreover, algorithms using the online data, on average, achieve better classification results than the others

Testing the "PRESTo" Early Warning Algorithm in North-Eastern Italy, Austria and Slovenia: update analysis

Since 2002 OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) in Udine (Italy), the Agencija Republike Slovenije za Okolje (ARSO) in Ljubljana (Slovenia) and the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Vienna (Austria), are collecting, analyzing, archiving and exchanging seismic data in real time. The data exchange has proved to be effective and very useful in case of seismic events at the borders between Italy, Austria and Slovenia, where the poor coverage of individual national seismic networks precluded a precise earthquake location, while the usage of common data from the integrated networks improves significantly the overall capability of real time event detection and rapid characterization in this area. In order to extend the seismic monitoring in North-eastern Italy, Slovenia and Southern Austria, towards earthquake early warning applications, at the end of 2013 OGS, ARSO and ZAMG teamed with the RISSCLab group (http://www.rissclab.unina.it) of the Department of Physics at the University of Naples Federico II in Italy. The collaboration focuses on massive testing on OGS, ARSO and ZAMG data of the EW platform PRESTo (Probabilistic and Evolutionary early warning SysTem) developed by RISSC-Lab (http://www.prestoews.org)

Community Service Work-Study Program (CSWSP) Student Handbook

Materials for the Be Proud! · Be Responsible! CONGRESO do Latinos Unidos, Inc. event on June 14 - 15, 2005

Recognition of pen-based music notation with finite-state machines

This work presents a statistical model to recognize pen-based music compositions using stroke recognition algorithms and finite-state machines. The series of strokes received as input is mapped onto a stochastic representation, which is combined with a formal language that describes musical symbols in terms of stroke primitives. Then, a Probabilistic Finite-State Automaton is obtained, which defines probabilities over the set of musical sequences. This model is eventually crossed with a semantic language to avoid sequences that does not make musical sense. Finally, a decoding strategy is applied in order to output a hypothesis about the musical sequence actually written. Comprehensive experimentation with several decoding algorithms, stroke similarity measures and probability density estimators are tested and evaluated following different metrics of interest. Results found have shown the goodness of the proposed model, obtaining competitive performances in all metrics and scenarios considered.This work was supported by the Spanish Ministerio de Educación, Cultura y Deporte through a FPU Fellowship (Ref. AP2012–0939) and the Spanish Ministerio de Economía y Competitividad through the TIMuL Project (No. TIN2013-48152-C2-1-R, supported by UE FEDER funds)

Efficient Data Management and Statistics with Zero-Copy Integration

Statistical analysts have long been struggling with evergrowing data volumes. While specialized data management systems such as relational databases would be able to handle the data, statistical analysis tools are far more convenient to express complex data analyses. An integration of these two classes of systems has the potential to overcome the data management issue while at the same time keeping analysis convenient. However, one must keep a careful eye on implementation overheads such as serialization. In this paper, we propose the in-process integration of data management and analytical tools. Furthermore, we argue that a zero-copy integration is feasible due to the omnipresence of C-style arrays containing native types. We discuss the general concept and present a prototype of this integration based on the columnar relational database MonetDB and the R environment for statistical computing. We evaluate the performance of this prototype in a series of micro-benchmarks of common data management tasks

Intelligent decision support systems for optimised diabetes

Computers now pervade the field of medicine extensively; one recent innovation is the development of intelligent decision support systems for inexperienced or non-specialist pbysicians, or in some cases for use by patients. In this thesis a critical review of computer systems in medicine, with special reference to decision support systems, is followed by a detailed description of the development and evaluation of two new, interacting, intelligent decision support systems in the domain of diabetes. Since the discovery of insulin in 1922, insulin replacement therapy for the treatment of diabetes mellitus bas evolved into a complex process; there are many different formulations of insulin and much more information about the factors which affect patient management (e.g. diet, exercise and progression of complications) are recognised. Physicians have to decide on the most appropriate anti-diabetic therapy to prescribe to their patients. Insulin-treated patients also have to monitor their blood glucose and decide how much insulin to inject and when to inject it. In order to help patients determine the most appropriate dose of insulin to take, a simple-to-use, hand-held decision support system has been developed. Algorithms for insulin adjustment have been elicited and combined with general rules of therapy to offer advice for every dose. The utility of the system has been evaluated by clinical trials and simulation studies. In order to aid physician management, a clinic-based decision support system has also been developed. The system provides wide-ranging advice on all aspects of diabetes care and advises an appropriate therapy regimen according to individual patient circumstances. Decisions advised by the pbysician-related system have been evaluated by a panel of expert physicians and the system has undergone informal primary evaluation within the clinic setting. An interesting aspect of both systems is their ability to provide advice even in cases where information is lacking or uncertain

Paperspace : a novel approach to document management by combining paper and digital documents

Personal document management systems provide good support for storing and organizing digital documents. However, there are no computer tools that support organization of paper documents on our desks. We ran a study of people's organization of their office desk space with respect to their digital workspace. This study resulted in a set of requirements for a media bridging tool. Based on these requirements, we built a prototype media bridging tool called PaperSpace that uses computer vision to link paper and digital documents. The system also tracks piles of paper documents on the real desktop, and links those papers to digital documents stored in the computer. Digital documents can be sorted and grouped according to the physical layout of the corresponding papers on the desk. The system automatically creates digital piles of documents in a simulated desktop that reflect the paper piles on the real desktop. The user can access valuable information through the system, such as printing statistics, location of a printed document on the desk, and past projects and their documents. A two week user evaluation of the system showed interesting usage scenarios and future trends for improving user interaction