Querido diário: a construção identitária de alunos da EJA em diários pessoais

This study aims to present analyses of our research, documentary type, which investigates the construction of cultural identities of the Youth and Adults Education (Educação de Jovens e Adultos – EJA), level III, in a public school in the city of Natal/RN, through personal diaries produced in the school environment. In a qualitative-interpretive approach, we anchor our identity studies (BAUMAN, 2001, 2005, 2006; HALL, 1987, 1997, 2011, 2012) that bring us the idea that identities are built and rebuilt by social relations that we do. To this end, we start from a conception of language that does not require pre-conceptions because they are based on the utterance itself. Therefore, we have analyzed the utterances produced by these students from the perspective of Bakhtin Circle (BAKHTIN, 1988, 1993, 1998, 2002, 2010, 2012), which deals with the discursive construction emerging from intersubjective processes of verbal interaction, in a dialogical relationship of the self to the other, by the otherness and the heteroglossia. Moreover, our study is also guided by the guidance on speech genres (BAKHTIN, 2010) and personal journal (LEJEUNE, 2008; MACHADO, 1998, 2009). We join to Applied Linguistics (MOITA LOPES, 2006, 2009) because we believe that this research focuses on a social practice in which language plays a central role and seeks to demonstrate how the speeches of the subject students of EJA, in personal journals, are building tools not only of their identities, but also knowledge and social life of the position that this subject student takes. We conclude this work in a perception of cultural identities that are built by the subject students of EJA, because the results suggest that the identities of these students are fluidly constructed by the representation that the student makes of his or her school, of being student of EJA and how he or she is a student of this educational modality. Thus, through our work, we plan to present another look about the identity or identities of a student of EJA, pointing an insight of this subject.

Multilingualism and conceptual modelling

One of the leading motivations behind the multilingual semantic web is to make resources accessible digitally in an online global multilingual context. Consequently, it is fundamental for knowledge bases to find a way to manage multilingualism and thus be equipped with those procedures for its conceptual modelling. In this context, the goal of this paper is to discuss how common-sense knowledge and cultural knowledge are modelled in a multilingual framework. More particularly, multilingualism and conceptual modelling are dealt with from the perspective of FunGramKB, a lexico-conceptual knowledge base for natural language understanding. This project argues for a clear division between the lexical and the conceptual dimensions of knowledge. Moreover, the conceptual layer is organized into three modules, which result from a strong commitment towards capturing semantic knowledge (Ontology), procedural knowledge (Cognicon) and episodic knowledge (Onomasticon). Cultural mismatches are discussed and formally represented at the three conceptual levels of FunGramKB.

Hierarchical Task Network Planning with Common-Sense Reasoning for Multiple-People Behaviour Analysis

Safety on public transport is a major concern for the relevant authorities. We
address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone.

Issues and strategies in the parallelisation of unstructured multiphysics code

Unstructured mesh based codes for the modelling of continuum physics phenomena have evolved to provide the facility to model complex interacting systems. Such codes have the potential to provide a high performance on parallel platforms for a small investment in programming. The critical parameters for success are to minimise changes to the code to allow for maintenance while providing high parallel efficiency, scalability to large numbers of processors and portability to a wide range of platforms. The paradigm of domain decomposition with message passing has for some time been demonstrated to provide a high level of efficiency, scalability and portability across shared and distributed memory systems without the need to re-author the code into a new language. This paper addresses these issues in the parallelisation of a complex three dimensional unstructured mesh Finite Volume multiphysics code and discusses the implications of automating the parallelisation process.

Projeto, implementação e análise de um conversor CC-CC de alto ganho e alto rendimento para aplicações fotovoltaicas

In recent years the photovoltaic generation has had greater insertion in the energy mix of the most developed countries, growing at annual rates of over 30%. The pressure for the reduction of pollutant emissions, diversification of the energy mix and the drop in prices are the main factors driving this growth. Grid tied systems plays an important role in alleviating the energy crisis and diversification of energy sources. Among the grid tied systems, building integrated photovoltaic systems suffers from partial shading of the photovoltaic modules and consequently the energy yield is reduced. In such cases, classical forms of modules connection do not produce good results and new techniques have been developed to increase the amount of energy produced by a set of modules. In the parallel connection technique of photovoltaic modules, a high voltage gain DC-DC converter is required, which is relatively complex to build with high efficiency. The current-fed isolated converters explored in this work have some desirable characteristics for this type of application, such as: low input current ripple and input voltage ripple, high voltage gain, galvanic isolation, feature high power capacity and it achieve soft switching in a wide operating range. This study presents contributions to the study of a high gain and high efficiency DC-DC converter for use in a parallel system of photovoltaic generation, being possible the use in a microinverter or with central inverter. The main contributions of this work are: analysis of the active clamping circuit operation proposing that the clamp capacitor connection must be done on the negative node of the power supply to reduce the input current ripple and thus reduce the filter requirements; use of a voltage doubler in the output rectifier to reduce the number of components and to extend the gain of the converter; detailed study of the converter components in order to raise the efficiency; obtaining the AC equivalent model and control system design. As a result, a DC-DC converter with high gain, high efficiency and without electrolytic capacitors in the power stage was developed. In the final part of this work the DC-DC converter operation connected to an inverter is presented. Besides, the DC bus controller is designed and are implemented two maximum power point tracking algorithms. Experimental results of full system operation connected to an emulator and subsequently to a real photovoltaic module are also given.

Oil spill environmental risk assessment of stationary sources in offshore zones (Innovating a mathematical model)

Nowadays, risks arising from the rapid development of oil and gas industries are significantly increasing. As a result, one of the main concerns of either industrial or environmental managers is the identification and assessment of such risks in order to develop and maintain appropriate proactive measures. Oil spill from stationary sources in offshore zones is one of the accidents resulting in several adverse impacts on marine ecosystems. Considering a site's current situation and relevant requirements and standards, risk assessment process is not only capable of recognizing the probable causes of accidents but also of estimating the probability of occurrence and the severity of consequences. In this way, results of risk assessment would help managers and decision makers create and employ proper control methods. Most of the represented models for risk assessment of oil spills are achieved on the basis of accurate data bases and analysis of historical data, but unfortunately such data bases are not accessible in most of the zones, especially in developing countries, or else they are newly established and not applicable yet. This issue reveals the necessity of using Expert Systems and Fuzzy Set Theory. By using such systems it will be possible to formulize the specialty and experience of several experts and specialists who have been working in petroliferous areas for several years. On the other hand, in developing countries often the damages to environment and environmental resources are not considered as risk assessment priorities and they are approximately under-estimated. For this reason, the proposed model in this research is specially addressing the environmental risk of oil spills from stationary sources in offshore zones.

Strategiaprosessi monikansallisessa yrityksessä

Tämän kandidaatintutkielman tavoitteena on tunnistaa strategiaprosessin eri vaiheet sekä kuvailla, miten prosessi toteutettiin tapausyritys Suominen Oyj:llä. Tavoitteina on myös analysoida, miten Suominen Oyj:n henkilöstö kokee oman suhteensa organisaatioon, sen strategiaan, tavoitteisiin ja arvoihin. Analyysin pohjalta pyritään muodostamaan kuva siitä, kuinka strategian implementoinnissa on onnistuttu. Lisäksi tarkastellaan, eroavatko nämä kokemukset eri maissa tapausyrityksessä. Tutkimuksessa käytetään sekä kvalitatiivisia että kvantitatiivisia tutkimusmenetelmiä. Suominen Oyj:n strategiaprosessista hankitaan informaatiota kahdella laadullisella puolistrukturoidulla teemahaastattelulla. Kvantitatiivisen analyysin kohteena puolestaan on tapausyrityksen yhdeksältäkymmeneltä ylimmässä asemassa työskentelevältä henkilöltä kerätty tilastollinen kyselydata, jota analysoidaan muun muassa Kruskal-Wallisin testillä sekä Spearmanin korrelaatioanalyysillä. Tutkielmassa muodostetaan kuva prosessista, jossa hyvin muotoiltu strategia sekä selkeät tavoitteet luovat pohjan strategian implementoinnille. Onnistuneen implementoinnin keinoja ovat muun muassa kehitysohjelmat, tarkka rekrytointi, tavoitteiden ja palkkioiden sitominen strategiaan, strategian kanssa yhteensopiva organisaatiokulttuuri, useamman eri hierarkiatason osallistuminen ja erilaisten kontrollijärjestelmien sekä implementointityökalujen hyödyntäminen. Havaitaan, että tapausyritys käyttää näistä keinoista useampaa. Yrityksen monikansallisuus vaikuttaa erityisesti organisaatiokulttuuriin sekä sisäiseen viestintään. Tutkielman kvantitatiivisen analyysin tulokset näyttävät, ettei maiden välillä ole tilastollisesti merkittäviä eroja siinä, miten henkilöstö kokee oman suhteensa tapausyritykseen, sen strategiaan, tavoitteisiin ja arvoihin. Kvantitatiivisen analyysin perusteella Suominen Oyj:n strategiaprosessi on onnistunut hyvin. Tutkimuksessa esitellään vielä muutamia kehitysajatuksia yritystä sekä mahdollisia jatkotutkimuksia varten.

Path relinking for the fixed spectrum frequency assignment problem

International audience

A novel concept-level approach for ultra-concise opinion summarization

The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users communicate, exchange, and share opinions. The exploitation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed. Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of state-of-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN

Different types of sentences express sentiment in very different ways. Traditional sentence-level sentiment classification research focuses on one-technique-fits-all solution or only centers on one special type of sentences. In this paper, we propose a divide-and-conquer approach which first classifies sentences into different types, then performs sentiment analysis separately on sentences from each type. Specifically, we find that sentences tend to be more complex if they contain more sentiment targets. Thus, we propose to first apply a neural network based sequence model to classify opinionated sentences into three types according to the number of targets appeared in a sentence. Each group of sentences is then fed into a one-dimensional convolutional neural network separately for sentiment classification. Our approach has been evaluated on four sentiment classification datasets and compared with a wide range of baselines. Experimental results show that: (1) sentence type classification can improve the performance of sentence-level sentiment analysis; (2) the proposed approach achieves state-of-the-art results on several benchmarking datasets.

Aplicaciones de los modelos de predicción bio-inspirados en la administración

Las organizaciones y sus entornos son sistemas complejos. Tales sistemas son difíciles de comprender y predecir. Pese a ello, la predicción es una tarea fundamental para la gestión empresarial y para la toma de decisiones que implica siempre un riesgo. Los métodos clásicos de predicción (entre los cuales están: la regresión lineal, la Autoregresive Moving Average y el exponential smoothing) establecen supuestos como la linealidad, la estabilidad para ser matemática y computacionalmente tratables. Por diferentes medios, sin embargo, se han demostrado las limitaciones de tales métodos. Pues bien, en las últimas décadas nuevos métodos de predicción han surgido con el fin de abarcar la complejidad de los sistemas organizacionales y sus entornos, antes que evitarla. Entre ellos, los más promisorios son los métodos de predicción bio-inspirados (ej. redes neuronales, algoritmos genéticos /evolutivos y sistemas inmunes artificiales). Este artículo pretende establecer un estado situacional de las aplicaciones actuales y potenciales de los métodos bio-inspirados de predicción en la administración.

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·

A system's model for maintenance support and decision making in building services systems

Modern buildings are designed to enhance the match between environment, spaces and the people carrying out work, so that the well-being and the performance of the occupants are all in harmony. Building services are systems that facilitate a healthy working environment within which workers productivity can be optimised in the buildings. However, the maintenance of these services is fraught with problems that may contribute to up to 50% of the total life cycle cost of the building. Maintenance support is one area which is not usually designed into the system as this is not common practice in the services industry. The other areas of shortfall for future designs are; client requirements, commissioning, facilities management data and post occupancy evaluation feedback which needs to be adequately planned to capture and document this information for use in future designs. At the University of Reading an integrated approach has been developed to assemble the multitude of aspects inherent in this field. The means records required and measured achievements for the benefit of both building owners and practitioners. This integrated approach can be represented in a Through Life Business Model (TLBM) format using the concept of Integrated Logistic Support (ILS). The prototype TLBM developed utilises the tailored tools and techniques of ILS for building services. This TLBM approach will facilitate the successful development of a databank that would be invaluable in capturing essential data (e.g. reliability of components) for enhancing future building services designs, life cycle costing and decision making by practitioners, in particular facilities managers.