Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

COSPO/CENDI Industry Day Conference

The conference's objective was to provide a forum where government information managers and industry information technology experts could have an open exchange and discuss their respective needs and compare them to the available, or soon to be available, solutions. Technical summaries and points of contact are provided for the following sessions: secure products, protocols, and encryption; information providers; electronic document management and publishing; information indexing, discovery, and retrieval (IIDR); automated language translators; IIDR - natural language capabilities; IIDR - advanced technologies; IIDR - distributed heterogeneous and large database support; and communications - speed, bandwidth, and wireless

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

A DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS

La Sentiment Analysis si riferisce alla analisi qualitativa volta ad identificare e classificare opinioni contenute in frasi e testi, allo scopo di stabilire lo \u201cstato d\u2019animo\u201d dell\u2019autore rispetto ad un particolare argomento o prodotto, e di determinare se tale stato \ue8 di fatto positivo, negativo oppure neutrale. Le opinioni espresse in un testo, come ad esempio giudizi, sentimenti ed emozioni, sono di recente diventate oggetto di studio e di ricerca sia in ambito accademico che industriale. Sfortunatamente la comprensione del linguaggio, applicata a commenti di utenti, \ue8 un attivit\ue0 estremamente complessa per una macchina, specialmente se ci si riferisce ai contesti dei moderni social network. Le modalit\ue0 in cui le persone si esprimono in linguaggio naturale, sono molteplici, e l\u2019utilizzo \u201cinformale\u201d della lingua adottato tipicamente nei social netowrks, genera frasi spesso dense di errori, modi di dire (slang), costrutti sintattici \u201dpersonalizzati\u201d, o anche frasi arricchite da caratteri speciali (come l\u2019hashtag in Twitter), il che complica notevolmente l\u2019analisi. Recentemente, le tecniche di Deep Learning, stanno emergendo nel panorama del machine learning, come un modello computazionale che pu\uf2 essere adoperato con efficacia per scoprire relazioni semantiche complesse, all\u2019interno di un testo, anche senza la necessit\ue0 di dover individuare a priori caratteristiche (features) di tali relazioni. Questi approcci hanno migliorato l\u2019attuale stato dell\u2019arte in diversi settori della Sentiment Analysis, come ad esempio la classificazione di frasi o di documenti, l\u2019apprendimento basato su lexicon, fino ad arrivare alla analisi di fenomeni complessi come il cyber bullismo. I contributi di questa tesi sono di due tipi. Il primo contributo fornito, relativo ad aspetti generali di Sentiment Analysis, riguarda la proposta di un modello di rete neurale semi supervisionata, basato sulle reti di tipo Deep Belief, in grado di affrontare l\u2019incertezza dei dati insita nelle frasi testuali, con particolare riferimento alla lingua italiana. Il modello proposto \ue8 stato testato rispetto a diversi datasets presi dalla letteratura di riferimento, composti da testi relativi a critiche cinematografiche, adottando una rappresentazione dell\u2019informazione basata su vettori (Word2Vec) ed introducendo anche metodi derivati dal campo del Natural Language Processing (NLP). Il secondo contributo fornito in questa tesi, partendo dall\u2019assunto che il cyber bullismo pu\uf2 essere considerato come un caso particolare di Sentiment Analysis, propone un approccio non supervisionato alla rilevazione automatica di tracce di cyber bullismo all\u2019interno di social networks, basato sia su di una rete neurale di tipo GHSOM (Growing Hierarchical Self Organizing Map), sia su di un modello di caratteristiche (features) predefinito. Il modello non supervisionato proposto dimostra di raggiungere comunque risultati interessanti rispetto ai tipici modelli supervisionati, applicati solitamente in questo ambito.Sentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer\u2019s attitude towards a particular topic or product is positive, negative, or even neutral. The views expressed and its related concepts, such as feelings, judgments, and emotions have become recently a subject of study and research in both academic and industrial areas. Unfortunately language comprehension of user comments, especially in social networks, is inherently complex to computers. The ways in which humans express themselves with natural language are nearly unlimited and informal texts is riddled with typos, misspellings, badly set up syntactic constructions and also specific symbols (e.g. hashtags in Twitter) which exponentially complicate this task. Recently, deep learning approaches are emerging as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents, sentiment lexicon learning and also in more complex problems as cyber bullying detection. The contributions of this work are twofold. First, related to the general Sentiment Analysis problem, we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences in Italian language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text (Word2Vec) and exploiting methods from Natural Language Processing (NLP) pre-processing. Second, assuming that the cyber bullying phenomenon can be treated as a particular Sentiment Analysis problem, we propose an unsupervised approach to automatic cyber bullying detection in social networks, based both on Growing Hierarchical Self Organizing Map (GHSOM) and on a new specific features model, showing that our solution can achieve interesting results, respect to classical supervised approaches