Identifying the role of natural resources in knowledge-based strategies of development

Knowledge has adopted a preferential role in the explanation of development while the evidence about the effect of natural resources in countries’ performance is more controversial in the economic literature. This paper tries to demonstrate that natural resources may positively affect growth in countries with a strong natural resources specialization pattern although the magnitude of these effects depend on the type of resources and on other aspects related to the production and innovation systems. The positive trajectory described by a set of national economies mainly specialized in natural resources and low-tech industries invites us to analyze what is the combination of factors that serves as engine for a sustainable development process. With panel data for the period 1996-2008 we estimate an applied growth model where both traditional factors and other more related to innovation and absorptive capabilities are taken into account. Our empirical findings show that according to the postulates of a knowledge-based approach, a framework that combines physical and intangible factors is more suitable for the definition of development strategies in those prosperous economies dominated by natural resources and connected activities, while the internationalization process of activities and technologies become also a very relevant aspect.

Evaluating knowledge-based approaches to the multilingual extension of a temporal expression normalizer

The extension to new languages is a well known bottleneck for rule-based systems. Considerable human effort, which typically consists in re-writing from scratch huge amounts of rules, is in fact required to transfer the knowledge available to the system from one language to a new one. Provided sufficient annotated data, machine learning algorithms allow to minimize the costs of such knowledge transfer but, up to date, proved to be ineffective for some specific tasks. Among these, the recognition and normalization of temporal expressions still remains out of their reach. Focusing on this task, and still adhering to the rule-based framework, this paper presents a bunch of experiments on the automatic porting to Italian of a system originally developed for Spanish. Different automatic rule translation strategies are evaluated and discussed, providing a comprehensive overview of the challenge.

A knowledge-based textual entailment approach applied to the QA answer validation at CLEF 2006

The Answer Validation Exercise (AVE) is a pilot track within the Cross-Language Evaluation Forum (CLEF) 2006. The AVE competition provides an evaluation frame- work for answer validations in Question Answering (QA). In our participation in AVE, we propose a system that has been initially used for other task as Recognising Textual Entailment (RTE). The aim of our participation is to evaluate the improvement our system brings to QA. Moreover, due to the fact that these two task (AVE and RTE) have the same main idea, which is to find semantic implications between two fragments of text, our system has been able to be directly applied to the AVE competition. Our system is based on the representation of the texts by means of logic forms and the computation of semantic comparison between them. This comparison is carried out using two different approaches. The first one managed by a deeper study of the Word- Net relations, and the second uses the measure defined by Lin in order to compute the semantic similarity between the logic form predicates. Moreover, we have also designed a voting strategy between our system and the MLEnt system, also presented by the University of Alicante, with the aim of obtaining a joint execution of the two systems developed at the University of Alicante. Although the results obtained have not been very high, we consider that they are quite promising and this supports the fact that there is still a lot of work on researching in any kind of textual entailment.

eFisioTrack: a telerehabilitation environment based on motion recognition using accelerometry

The growing demand for physical rehabilitation processes can result in the rising of costs and waiting lists, becoming a threat to healthcare services’ sustainability. Telerehabilitation solutions can help in this issue by discharging patients from points of care while improving their adherence to treatment. Sensing devices are used to collect data so that the physiotherapists can monitor and evaluate the patients’ activity in the scheduled sessions. This paper presents a software platform that aims to meet the needs of the rehabilitation experts and the patients along a physical rehabilitation plan, allowing its use in outpatient scenarios. It is meant to be low-cost and easy-to-use, improving patients and experts experience. We show the satisfactory results already obtained from its use, in terms of the accuracy evaluating the exercises, and the degree of users’ acceptance. We conclude that this platform is suitable and technically feasible to carry out rehabilitation plans outside the point of care.

Algorithm for the detection of outliers based on the theory of rough sets

Outliers are objects that show abnormal behavior with respect to their context or that have unexpected values in some of their parameters. In decision-making processes, information quality is of the utmost importance. In specific applications, an outlying data element may represent an important deviation in a production process or a damaged sensor. Therefore, the ability to detect these elements could make the difference between making a correct and an incorrect decision. This task is complicated by the large sizes of typical databases. Due to their importance in search processes in large volumes of data, researchers pay special attention to the development of efficient outlier detection techniques. This article presents a computationally efficient algorithm for the detection of outliers in large volumes of information. This proposal is based on an extension of the mathematical framework upon which the basic theory of detection of outliers, founded on Rough Set Theory, has been constructed. From this starting point, current problems are analyzed; a detection method is proposed, along with a computational algorithm that allows the performance of outlier detection tasks with an almost-linear complexity. To illustrate its viability, the results of the application of the outlier-detection algorithm to the concrete example of a large database are presented.

A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities

Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal.

A Belief System's Organization Based on a Computational Model of the Dynamic Context: First Approximation

In this article we present a model of organization of a belief system based on a set of binary recursive functions that characterize the dynamic context that modifies the beliefs. The initial beliefs are modeled by a set of two-bit words that grow, update, and generate other beliefs as the different experiences of the dynamic context appear. Reason is presented as an emergent effect of the experience on the beliefs. The system presents a layered structure that allows a functional organization of the belief system. Our approach seems suitable to model different ways of thinking and to apply to different realistic scenarios such as ideologies.

Methodology based on registration techniques for representing subjects and their deformations acquired from general purpose 3D sensors

In this thesis a methodology for representing 3D subjects and their deformations in adverse situations is studied. The study is focused in providing methods based on registration techniques to improve the data in situations where the sensor is working in the limit of its sensitivity. In order to do this, it is proposed two methods to overcome the problems which can difficult the process in these conditions. First a rigid registration based on model registration is presented, where the model of 3D planar markers is used. This model is estimated using a proposed method which improves its quality by taking into account prior knowledge of the marker. To study the deformations, it is proposed a framework to combine multiple spaces in a non-rigid registration technique. This proposal improves the quality of the alignment with a more robust matching process that makes use of all available input data. Moreover, this framework allows the registration of multiple spaces simultaneously providing a more general technique. Concretely, it is instantiated using colour and location in the matching process for 3D location registration.

The Importance of Foreign Language Skills in the Labour Markets of Central and Eastern Europe: An assessment based on data from online job portals. CEPS Special Report No. 129/January 2016

In a globalised world, knowledge of foreign languages is an important skill. Especially in Europe, with its 24 official languages and its countless regional and minority languages, foreign language skills are a key asset in the labour market. Earlier research shows that over half of the EU27 population is able to speak at least one foreign language, but there is substantial national variation. This study is devoted to a group of countries known as the Visegrad Four, which comprises the Czech Republic, Hungary, Poland and Slovakia. Although the supply of foreign language skills in these countries appears to be well-documented, less is known about the demand side. In this study, we therefore examine the demand for foreign language skills on the Visegrad labour markets, using information extracted from online job portals. We find that English is the most requested foreign language in the region, and the demand for English language skills appears to go up as occupations become increasingly complex. Despite the cultural, historical and economic ties with their German-speaking neighbours, German is the second-most-in-demand foreign language in the region. Interestingly, in this case there is no clear link with the complexity of an occupation. Other languages, such as French, Spanish and Russian, are hardly requested. These findings have important policy implications with regards to the education and training offered in schools, universities and job centres.

Essays and essay-writing, based on Atlantic monthly models,

"Made up almost entirely of short, familiar essays published anonymously in the 'Contributors' club' of the Atlantic."--Pref.

A Knowledge-based System for Intelligent Support in Pharmacogenomics Evidence Assessment: Ontology-driven Evidence Representation, Retrieval, Classification and Interpretation

Thesis (Ph.D.)--University of Washington, 2016-06

Promoting better use of the PSA test in general practice: Randomized controlled trial of educational strategies based on outreach visits and mailout

Background. Prostate-specific antigen (PSA) testing for prostate cancer is controversial. Demand for PSA testing is likely to rise in the UK, Australia and other western countries. Primary care needs to develop appropriate strategies to respond to this demand. Objectives. Our aim was to compare the effectiveness of educational outreach visits (EOVs) and mailout strategies targeting PSA testing in Australian primary care. Methods. A randomized controlled trial was conducted in general practices in southern Adelaide. The main outcome measures at baseline, 6 months and 12 months post-intervention were PSA testing rates and GP knowledge in key areas relating to prostate cancer and PSA testing. Results. The interventions were able to demonstrate a change in clinical practice. In the 6 months post-intervention, median PSA testing rate in the EOV group was significantly lower than in the postal group, which in turn was significantly lower than the control group (P < 0.001). Statistically significant differences were not, however, maintained in the 6-12 month post-intervention period. The EOV group, at 6 months follow-up, had a significantly greater proportion of 'correct' responses than the control group to questions about prostate cancer treatment effectiveness (P = 0.004) and endorsement of PSA screening by professional bodies (P = 0.041). Conclusions. Primary care has a central role in PSA testing for prostate cancer. Clinical practice in this area is receptive to evidence-based interventions.

Attitudes and management of alcohol problems in general practice: Descriptive analysis based on findings of a world health organization international collaborative survey

Aims: To determine if general practitioners' (GPs) experience of education on alcohol, support in their working environment for intervening with alcohol problems, and their attitudes have an impact on the number of patients they manage with alcohol problems. Methods: 1300 GPs from nine countries were surveyed with a postal questionnaire as part of a World Health Organization (WHO) collaborative study. Results: GPs who received more education on alcohol (OR = 1.5; 95% CI, 1.3-1.7), who perceived that they were working in a supportive environment (OR = 1.6; 95% CI, 1.4-1.9), who expressed higher role security in working with alcohol problems (OR = 2.0; 95% CI, 1.5-2.5) and who reported greater therapeutic commitment to working with alcohol problems (OR = 1.4: 95% CI, 1.1-1.7) were more likely to manage patients with alcohol-related harm. Conclusion: Both education and support in the working environment need to be provided to enhance the involvement of GPs in the management of alcohol problems.

A knowledge-based system for liquid retaining structure design with blackboard architecture

Owing to the high degree of vulnerability of liquid retaining structures to corrosion problems, there are stringent requirements in its design against cracking. In this paper, a prototype knowledge-based system is developed and implemented for the design of liquid retaining structures based on the blackboard architecture. A commercially available expert system shell VISUAL RULE STUDIO working as an ActiveX Designer under the VISUAL BASIC programming environment is employed. Hybrid knowledge representation approach with production rules and procedural methods under object-oriented programming are used to represent the engineering heuristics and design knowledge of this domain. It is demonstrated that the blackboard architecture is capable of integrating different knowledge together in an effective manner. The system is tailored to give advice to users regarding preliminary design, loading specification and optimized configuration selection of this type of structure. An example of application is given to illustrate the capabilities of the prototype system in transferring knowledge on liquid retaining structure to novice engineers. (C) 2004 Elsevier Ltd. All rights reserved.

Transcript annotation in FANTOM3: Mouse gene catalog based on physical cDNAs

T he international FANTOM consortium aims to produce a comprehensive picture of the mammalian transcriptome, based upon an extensive cDNA collection and functional annotation of full-length enriched cDNAs. The previous dataset, FANTOM(2), comprised 60,770 full- length enriched cDNAs. Functional annotation revealed that this cDNA dataset contained only about half of the estimated number of mouse protein- coding genes, indicating that a number of cDNAs still remained to be collected and identified. To pursue the complete gene catalog that covers all predicted mouse genes, cloning and sequencing of full- length enriched cDNAs has been continued since FANTOM2. In FANTOM3, 42,031 newly isolated cDNAs were subjected to functional annotation, and the annotation of 4,347 FANTOM2 cDNAs was updated. To accomplish accurate functional annotation, we improved our automated annotation pipeline by introducing new coding sequence prediction programs and developed a Web- based annotation interface for simplifying the annotation procedures to reduce manual annotation errors. Automated coding sequence and function prediction was followed with manual curation and review by expert curators. A total of 102,801 full- length enriched mouse cDNAs were annotated. Out of 102,801 transcripts, 56,722 were functionally annotated as protein coding ( including partial or truncated transcripts), providing to our knowledge the greatest current coverage of the mouse proteome by full- length cDNAs. The total number of distinct non- protein- coding transcripts increased to 34,030. The FANTOM3 annotation system, consisting of automated computational prediction, manual curation, and. nal expert curation, facilitated the comprehensive characterization of the mouse transcriptome, and could be applied to the transcriptomes of other species.