Análise das condições organizadas em uma escola para promover a inclusão de alunos surdos

O processo de inclusão de alunos surdos é um tema polêmico, em vista da falta de condições da maioria das escolas para atender as necessidades educacionais especiais de tais alunos e a importância da convivência entre surdos para favorecer a construção de sua identidade. O presente artigo tem como objetivo analisar as condições organizadas por uma escola para promover a inclusão de alunos surdos nos anos iniciais do ensino fundamental. A escola pesquisada continha 11 alunos surdos matriculados no ensino fundamental do 1º ao 5º ano. Os dados foram coletados por meio de pesquisa documental e participação da pesquisadora em diversos contextos da escola. Os resultados evidenciaram que, desde a entrada dos alunos surdos, a escola se preocupou em propiciar um ambiente bilíngue para alunos surdos e ouvintes, em adquirir e confeccionar materiais pedagógicos adequados ao processo de aprendizagem deles e em oferecer formação a seus professores e equipe gestora sobre o processo de inclusão de alunos surdos e a Libras. Além disso, foi implantada uma disciplina de Libras no currículo para todos os alunos e disponibilizados professores de apoio com domínio em Libras para auxiliar os alunos surdos em seu processo de aprendizagem em sala de aula. Verificamos que a escola pesquisada, buscou, de forma coletiva por meio de estudos científicos baseados no bilinguismo e no movimento de inclusão e em análises das experiências vivenciadas, construir um ambiente inclusivo.

La comunicazione interculturale

The chapter is about Intercultural communication in Italy (seen from both a sociolinguistic and a pragmatic perspective), and it is structured into four different sections: “Key words/concept”, “Theoretical background”, “Good practice” and “Exercises”. Although based on the most recent research on Intercultural communication, it is addressed to both specialists/scholars and non specialists, as it is meant not only to outline new approaches and interpretations, but also to be regarded as a possible resource by civil servants and operators working with migrants. It is included in a book sponsored by the “Forum Internazionale ed Europeo di Ricerche sull’Immigrazione” (International and European Forum on Migration Research), which gathers contributions on policy, housing, education, health, employment and communication, and it has been widely adopted as a text book in Italian universities and by public institutions.

Piazza Vittorio : la transformazione urbanistica : quale identità per i nuovi cittadini? Identity and meaningful place making

Il caso della riqualificazione di Piazza Vittorio, ormai divenuta luogo storico dell'immigrazione a Roma, è un esempio delle sfide che si pongono oggi alle metropoli multietniche ed un'ottima occasione per riflettere sul significato che questo tipo di interventi assume per l'identità culturale della città e dei suoi abitanti, vecchi e nuovi. Lo studio proposto in questo volume chiama in causa la nozione di cultural built heritage, cioè il retaggio culturale di cui è testimone l'architettura, per mostrare quanto e come l'ambiente costruito dagli interventi architettonici ed urbanistici contribuisca a rappresentare, dunque a raccontare ed organizzare, sia lo spazio, sia gli scambi che in esso hanno luogo, sia le identità di coloro che lo abitano. È il primo volume della collana "Squarci. Mobilità dell'uomo, del suo pensiero e delle sue opere", presentata dal curatore Claudio Rossi in un'ampia introduzione. All'interno di "Squarci" si vuole proporre un percorso di studi e ricerche corredati da un dvd. L'occhio della telecamera - attraverso interviste o la realizzazione di docu-film - arricchisce la ricerca con nuove dimensioni d'analisi, che aggiungono elementi di comprensione e talvolta spingono a riconsiderare i risultati stessi delle indagini svolte. È quanto accade per "Mostafa, il mercato trasferito ed altre storie. La trasformazione di Piazza Vittorio a Roma", scritto e diretto da Emanuele Svezia

Decision support in interest based negotiation support systems: the AssetDivider system

Negotiation Support Systems (NSS) model the process of negotiation from basic template support to more sophisticated decision making support. The authors attempt to develop systems capable of decision support by suggesting possible solutions for the given dispute. Current Negotiation Support Systems primarily rely upon mathematical optimisation techniques and often ignore heuristics and other methods derived from practice. This chapter discusses the technology of several negotiation support systems in family law developed in their laboratory based on data collected and methods derived from practise. The chapter explores similarities and differences between systems the authors have created and demonstrates their latest development, AssetDivider.

Zero-day malware detection based on supervised learning algorithms of API call signatures

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of accuracy and efficiency based on the frequency of Windows API calls. This paper describes the methodology employed for the collection of large data sets to train the classifiers, and analyses the performance results of the various data mining algorithms adopted for the study using a fully automated tool developed in this research to conduct the various experimental investigations and evaluation. Through the performance results of these algorithms from our experimental analysis, we are able to evaluate and discuss the advantages of one data mining algorithm over the other for accurately detecting zero-day malware successfully. The data mining framework employed in this research learns through analysing the behavior of existing malicious and benign codes in large datasets. We have employed robust classifiers, namely Naïve Bayes (NB) Algorithm, k−Nearest Neighbor (kNN) Algorithm, Sequential Minimal Optimization (SMO) Algorithm with 4 differents kernels (SMO - Normalized PolyKernel, SMO – PolyKernel, SMO – Puk, and SMO- Radial Basis Function (RBF)), Backpropagation Neural Networks Algorithm, and J48 decision tree and have evaluated their performance. Overall, the automated data mining system implemented for this study has achieved high true positive (TP) rate of more than 98.5%, and low false positive (FP) rate of less than 0.025, which has not been achieved in literature so far. This is much higher than the required commercial acceptance level indicating that our novel technique is a major leap forward in detecting zero-day malware. This paper also offers future directions for researchers in exploring different aspects of obfuscations that are affecting the IT world today.

The role of emotional intelligence on the resolution of disputes involving the electronic health record

Numerous authors have expressed concerns that the introduction of the Personally Controlled Electronic Health Record (PCEHR) will lead to an escalation of disputes. Some disputes will concern the accuracy of the record whereas others will arise simply due to greater access to health care records. Online dispute resolution (ODR) programs have been successfully applied to cost-effectively help disputants resolve commercial, insurance and other legal disputes, and can also facilitate the resolution of health care related disputes. However, we expect that health differs from other application domains in ODR because of the emotional engagement patients have with their health and those of loved ones. In this study we will be looking at whether the success of an online negotiation is related to how people recognise and manage emotions, and in particular, their Emotional intelligence score.

Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature.

Patient admission prediction using a pruned fuzzy min–max neural network with rule extraction

A useful patient admission prediction model that helps the emergency department of a hospital admit patients efficiently is of great importance. It not only improves the care quality provided by the emergency department but also reduces waiting time of patients. This paper proposes an automatic prediction method for patient admission based on a fuzzy min–max neural network (FMM) with rules extraction. The FMM neural network forms a set of hyperboxes by learning through data samples, and the learned knowledge is used for prediction. In addition to providing predictions, decision rules are extracted from the FMM hyperboxes to provide an explanation for each prediction. In order to simplify the structure of FMM and the decision rules, an optimization method that simultaneously maximizes prediction accuracy and minimizes the number of FMM hyperboxes is proposed. Specifically, a genetic algorithm is formulated to find the optimal configuration of the decision rules. The experimental results using a large data set consisting of 450740 real patient records reveal that the proposed method achieves comparable or even better prediction accuracy than state-of-the-art classifiers with the additional ability to extract a set of explanatory rules to justify its predictions.

Educação e formação de professores surdos: contextos, inserções, dilemas e desafios

Os estudos sobre diversidade tem despertado interesse de pesquisadores em estudos organizacionais, partindo do princípio que a inclusão de pessoas com características diversas nas organizações torna a sociedade mais justa e os meios laborais mais produtivos. No que se refere à inclusão de deficientes, percebe-se em pesquisas que a principal dificuldade do processo é a baixa escolaridade das pessoas. Também há grande movimentação nas instituições de ensino em busca de melhores estratégias para atender suas necessidades. A educação das pessoas surdas tem sido um grande desafio, visto a diferença linguística entre surdos e ouvintes. No cenário de atuação profissional encontramos duas realidades: a dos professores surdos e a dos professores ouvintes. A análise da produção acadêmica sobre a educação de surdos aponta grande quantidade de trabalhos desenvolvidos por professores ouvintes, enquanto as narrativas de profissionais surdos testemunham que, embora possuam formação docente, são solicitados apenas na qualidade de colaboradores em pesquisas, tradutores e auxiliares no processo de criação de vocabulários para a construção de contextos de fala da língua de sinais (REIS, 2006). Para que se possa entender as causas da dificuldade de inclusão de professores surdos para atuação profissional há necessidade de se questionar: “Qual é o preparo e como se realiza o trabalho educacional de professores surdos e ouvintes para atuação com alunos surdos nas escolas estaduais e municipais de SP?" A pesquisa é de natureza qualitativa e do tipo exploratória descritiva. Foram realizadas, além de observação participante, entrevistas semiestruturadas com a participação de professores surdos e não surdos, coordenadores e diretores de escola. Os dados foram analisados pelo método de análise de conteúdo e sob as técnicas de reflexividade, que tem como parte significativa da ciência a liberdade e a necessidade de rever conceitos e propor novos conceitos (SPINK, 2004). Espera-se que os resultados gerem novos conhecimentos, que subsidiem ações afirmativas que oportunizam a atuação conjunta e igualitária de professores surdos e ouvintes