A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Temporal Information in Data Science: An Integrated Framework and its Applications

Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems.Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems

How to improve TTS systems for emotional expressivity

Several experiments have been carried out that revealed weaknesses of the current Text-To-Speech (TTS) systems in their emotional expressivity. Although some TTS systems allow XML-based representations of prosodic and/or phonetic variables, few publications considered, as a pre-processing stage, the use of intelligent text processing to detect affective information that can be used to tailor the parameters needed for emotional expressivity. This paper describes a technique for an automatic prosodic parameterization based on affective clues. This technique recognizes the affective information conveyed in a text and, accordingly to its emotional connotation, assigns appropriate pitch accents and other prosodic parameters by XML-tagging. This pre-processing assists the TTS system to generate synthesized speech that contains emotional clues. The experimental results are encouraging and suggest the possibility of suitable emotional expressivity in speech synthesis

Automatic Text Summarization

Writing text was one of the first ever methods used by humans to represent their knowledge. Text can be of different types and have different purposes. Due to the evolution of information systems and the Internet, the amount of textual information available has increased exponentially in a worldwide scale, and many documents tend to have a percentage of unnecessary information. Due to this event, most readers have difficulty in digesting all the extensive information contained in multiple documents, produced on a daily basis. A simple solution to the excessive irrelevant information in texts is to create summaries, in which we keep the subject’s related parts and remove the unnecessary ones. In Natural Language Processing, the goal of automatic text summarization is to create systems that process text and keep only the most important data. Since its creation several approaches have been designed to create better text summaries, which can be divided in two separate groups: extractive approaches and abstractive approaches. In the first group, the summarizers decide what text elements should be in the summary. The criteria by which they are selected is diverse. After they are selected, they are combined into the summary. In the second group, the text elements are generated from scratch. Abstractive summarizers are much more complex so they still need a lot of research, in order to represent good results. During this thesis, we have investigated the state of the art approaches, implemented our own versions and tested them in conventional datasets, like the DUC dataset. Our first approach was a frequency­based approach, since it analyses the frequency in which the text’s words/sentences appear in the text. Higher frequency words/sentences automatically receive higher scores which are then filtered with a compression rate and combined in a summary. Moving on to our second approach, we have improved the original TextRank algorithm by combining it with word embedding vectors. The goal was to represent the text’s sentences as nodes from a graph and with the help of word embeddings, determine how similar are pairs of sentences and rank them by their similarity scores. The highest ranking sentences were filtered with a compression rate and picked for the summary. In the third approach, we combined feature analysis with deep learning. By analysing certain characteristics of the text sentences, one can assign scores that represent the importance of a given sentence for the summary. With these computed values, we have created a dataset for training a deep neural network that is capable of deciding if a certain sentence must be or not in the summary. An abstractive encoder­decoder summarizer was created with the purpose of generating words related to the document subject and combining them into a summary. Finally, every single summarizer was combined into a full system. Each one of our approaches was evaluated with several evaluation metrics, such as ROUGE. We used the DUC dataset for this purpose and the results were fairly similar to the ones in the scientific community. As for our encoder­decode, we got promising results.O texto é um dos utensílios mais importantes de transmissão de ideias entre os seres humanos. Pode ser de vários tipos e o seu conteúdo pode ser mais ou menos fácil de interpretar, conforme a quantidade de informação relevante sobre o assunto principal. De forma a facilitar o processamento pelo leitor existe um mecanismo propositadamente criado para reduzir a informação irrelevante num texto, chamado sumarização de texto. Através da sumarização criam­se versões reduzidas do text original e mantém­se a informação do assunto principal. Devido à criação e evolução da Internet e outros meios de comunicação, surgiu um aumento exponencial de documentos textuais, evento denominado de sobrecarga de informação, que têm na sua maioria informação desnecessária sobre o assunto que retratam. De forma a resolver este problema global, surgiu dentro da área científica de Processamento de Linguagem Natural, a sumarização automática de texto, que permite criar sumários automáticos de qualquer tipo de texto e de qualquer lingua, através de algoritmos computacionais. Desde a sua criação, inúmeras técnicas de sumarização de texto foram idealizadas, podendo ser classificadas em dois tipos diferentes: extractivas e abstractivas. Em técnicas extractivas, são transcritos elementos do texto original, como palavras ou frases inteiras que sejam as mais ilustrativas do assunto do texto e combinadas num documento. Em técnicas abstractivas, os algoritmos geram elementos novos. Nesta dissertação pesquisaram­se, implementaram­se e combinaram­se algumas das técnicas com melhores resultados de modo a criar um sistema completo para criar sumários. Relativamente às técnicas implementadas, as primeiras três são técnicas extractivas enquanto que a ultima é abstractiva. Desta forma, a primeira incide sobre o cálculo das frequências dos elementos do texto, atribuindo­se valores às frases que sejam mais frequentes, que por sua vez são escolhidas para o sumário através de uma taxa de compressão. Outra das técnicas incide na representação dos elementos textuais sob a forma de nodos de um grafo, sendo atribuidos valores de similaridade entre os mesmos e de seguida escolhidas as frases com maiores valores através de uma taxa de compressão. Uma outra abordagem foi criada de forma a combinar um mecanismo de análise das caracteristicas do texto com métodos baseados em inteligência artificial. Nela cada frase possui um conjunto de caracteristicas que são usadas para treinar um modelo de rede neuronal. O modelo avalia e decide quais as frases que devem pertencer ao sumário e filtra as mesmas através deu uma taxa de compressão. Um sumarizador abstractivo foi criado para para gerar palavras sobre o assunto do texto e combinar num sumário. Cada um destes sumarizadores foi combinado num só sistema. Por fim, cada uma das técnicas pode ser avaliada segundo várias métricas de avaliação, como por exemplo a ROUGE. Segundo os resultados de avaliação das técnicas, com o conjunto de dados DUC, os nossos sumarizadores obtiveram resultados relativamente parecidos com os presentes na comunidade cientifica, com especial atenção para o codificador­descodificador que em certos casos apresentou resultados promissores

On relational learning and discovery in social networks: a survey

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements