An artificial neural network approach for assigning rating judgements to Italian Small Firms

Based on new regulations of Basel II Accord in 2004, banks and financial nstitutions have now the possibility to develop internal rating systems with the aim of correctly udging financial health status of firms. This study analyses the situation of Italian small firms that are difficult to judge because their economic and financial data are often not available. The intend of this work is to propose a simulation framework to give a rating judgements to firms presenting poor financial information. The model assigns a rating judgement that is a simulated counterpart of that done by Bureau van Dijk-K Finance (BvD). Assigning rating score to small firms with problem of poor availability of financial data is really problematic. Nevertheless, in Italy the majority of firms are small and there is not a law that requires to firms to deposit balance-sheet in a detailed form. For this reason the model proposed in this work is a three-layer framework that allows us to assign ating judgements to small enterprises using simple balance-sheet data.rating judgements, artificial neural networks, feature selection

Non-native children speech recognition through transfer learning

This work deals with non-native children's speech and investigates both multi-task and transfer learning approaches to adapt a multi-language Deep Neural Network (DNN) to speakers, specifically children, learning a foreign language. The application scenario is characterized by young students learning English and German and reading sentences in these second-languages, as well as in their mother language. The paper analyzes and discusses techniques for training effective DNN-based acoustic models starting from children native speech and performing adaptation with limited non-native audio material. A multi-lingual model is adopted as baseline, where a common phonetic lexicon, defined in terms of the units of the International Phonetic Alphabet (IPA), is shared across the three languages at hand (Italian, German and English); DNN adaptation methods based on transfer learning are evaluated on significant non-native evaluation sets. Results show that the resulting non-native models allow a significant improvement with respect to a mono-lingual system adapted to speakers of the target language

La responsabilité sociale, est-elle une variable influençant les performances d’entreprise?

In the last decades, Corporate Social Responsibility (CSR) has been deeply studied. Many researchers focused on the best social report form underlining advantages, and they shown that these documents follow more and more often balance-sheets. This work analyses the relation between the writing of social report and both with the profitability and with the technical efficiency. The outcomes suggest that Corporate Social Responsibility improves firm profitability and expands firm market share. Moreover, the relation between the writing of social report and technical efficiency shows that firms interested in Corporate Social Responsibility are also the most efficient, from a technical point of view.Corporate Social Responsibility (CSR), Firm technical efficiency, Firm profitability, Data Envelopment Analysis, Bootstrap

Automatic Quality Estimation for ASR System Combination

Recognizer Output Voting Error Reduction (ROVER) has been widely used for system combination in automatic speech recognition (ASR). In order to select the most appropriate words to insert at each position in the output transcriptions, some ROVER extensions rely on critical information such as confidence scores and other ASR decoder features. This information, which is not always available, highly depends on the decoding process and sometimes tends to over estimate the real quality of the recognized words. In this paper we propose a novel variant of ROVER that takes advantage of ASR quality estimation (QE) for ranking the transcriptions at "segment level" instead of: i) relying on confidence scores, or ii) feeding ROVER with randomly ordered hypotheses. We first introduce an effective set of features to compensate for the absence of ASR decoder information. Then, we apply QE techniques to perform accurate hypothesis ranking at segment-level before starting the fusion process. The evaluation is carried out on two different tasks, in which we respectively combine hypotheses coming from independent ASR systems and multi-microphone recordings. In both tasks, it is assumed that the ASR decoder information is not available. The proposed approach significantly outperforms standard ROVER and it is competitive with two strong oracles that e xploit prior knowledge about the real quality of the hypotheses to be combined. Compared to standard ROVER, the abs olute WER improvements in the two evaluation scenarios range from 0.5% to 7.3%

La responsabilité sociale, est-elle une variable influençant les performances d'entreprise?

WP 10/2008; In the last decades, Corporate Social Responsibility (CSR) has been deeply studied. Many researchers focused on the best social report form underlining advantages, and they shown that these documents follow more and more often balance-sheets. This work analyses the relation between the writing of social report and both with the profitability and with the technical efficiency. The outcomes suggest that Corporate Social Responsibility improves firm profitability and expands firm market share. Moreover, the relation between the writing of social report and technical efficiency shows that firms interested in Corporate Social Responsibility are also the most efficient, from a technical point of view

Analysis and forecasting models for default risk. A survey of applied methodologies

During the last three decades various models have been proposed by the literature to predict the risk of bankruptcy and of firm insolvency, which make use of structural and empirical tools, namely rating system, credit scoring, option pricing and three alternative methods (fuzzy logic, efficient frontier and a forward looking model).In the present paper we focus on experting systems of neural networks, by taking into account theoretical as well as empirical literature on the topic.Adding to this literature, a set of alternative indicators is proposed that can be used in addition to traditional financial ratios.rischio d’insolvenza, default, neural networks, option pricing, sistemi esperti, algoritmi genetici, logica fuzzy Classification JEL: C45, C53, C67, G33

DNN adaptation by automatic quality estimation of ASR hypotheses

In this paper we propose to exploit the automatic Quality Estimation (QE) of ASR hypotheses to perform the unsupervised adaptation of a deep neural network modeling acoustic probabilities. Our hypothesis is that significant improvements can be achieved by: i)automatically transcribing the evaluation data we are currently trying to recognise, and ii) selecting from it a subset of "good quality" instances based on the word error rate (WER) scores predicted by a QE component. To validate this hypothesis, we run several experiments on the evaluation data sets released for the CHiME-3 challenge. First, we operate in oracle conditions in which manual transcriptions of the evaluation data are available, thus allowing us to compute the "true" sentence WER. In this scenario, we perform the adaptation with variable amounts of data, which are characterised by different levels of quality. Then, we move to realistic conditions in which the manual transcriptions of the evaluation data are not available. In this case, the adaptation is performed on data selected according to the WER scores "predicted" by a QE component. Our results indicate that: i) QE predictions allow us to closely approximate the adaptation results obtained in oracle conditions, and ii) the overall ASR performance based on the proposed QE-driven adaptation method is significantly better than the strong, most recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201

Aprendizagem ubíqua na modalidade b-learning: estudo de caso do mestrado de Tecnologia Educativa da UMinho

Atualmente, a tendência já não é oferecer cursos só com presença física, pois a formação via ambientes virtuais de aprendizagem (AVA) tende a aumentar, recaindo a preferência em modalidades mistas (b-learning), integrando ainda o m-learning (mobile learning) e o u-learning (ubiquitous learning). Este texto aborda esta temática, fundamentada em pesquisa sobre o Mestrado de Tecnologia Educativa, área de especialização do Mestrado em Ciências da Educação da Universidade, que funciona na modalidade b-learning. Pretende-se estudar a edição do mestrado do ano letivo de 2013-15 que teve também a particularidade da aprendizagem ubíqua, pois os estudantes estão concentrados em dois grandes polos: Universidade do Minho (Braga, Portugal) e São Francisco de Paula (Rio Grande do Sul), no polo da Universidade Aberta do Brasil-UAB, com apoio da Prefeitura e da Secretaria Municipal de Educação. Para o efeito, utilizamos a investigação qualitativa onde, para além de observação e notas de campo, se recorreu a um questionário para recolher a opinião dos mestrandos sobre aspetos de organização e funcionamento pedagógico do curso.Prefeitura de São Francisco de Paulainfo:eu-repo/semantics/publishedVersio

Trombose de seio dural: relato de caso

We report the case of a 24 year-old pregnant woman, seem at the neurology service by presenting agitation, hallucinations, mental confusion, headache, vision loss, aphasia and seizures. the neurorradiologic exam was compatible with thrombosis in dural sinus and cortical veins. Treatment with abciximab was accomplished and the mechanical lysis of the thrombus was made obtaining restoration of cerebral vein flow. After the procedure, she presented frontal hematoma wich was withdrawn surgically. We discuss this infrequent pathology in clinical picture, pathogenesis, image exams and therapeutics.Univ Caixas Sul, Disciplina Neurol, Caixas Do Sul, RS, BrazilUniversidade Federal de São Paulo, Escola Paulista Med, São Paulo, BrazilUniv Caxias do Sul, Curso Med, Caxias Do Sul, RS, BrazilUniversidade Federal de São Paulo, Escola Paulista Med, São Paulo, BrazilWeb of Scienc