THE USE OF RECOMMENDER SYSTEMS IN WEB APPLICATIONS – THE TROI CASE

Avoiding digital marketing, surveys, reviews and online users behavior approaches on digital age are the key elements for a powerful businesses to fail, there are some systems that should preceded some artificial intelligence techniques. In this direction, the use of data mining for recommending relevant items as a new state of the art technique is increasing user satisfaction as well as the business revenues. And other related information gathering approaches in order to our systems thing and acts like humans. To do so there is a Recommender System that will be elaborated in this thesis. How people interact, how to calculate accurately and identify what people like or dislike based on their online previous behaviors. The thesis includes also the methodologies recommender system uses, how math equations helps Recommender Systems to calculate user’s behavior and similarities. The filters are important on Recommender System, explaining if similar users like the same product or item, which is the probability of neighbor user to like also. Here comes collaborative filters, neighborhood filters, hybrid recommender system with the use of various algorithms the Recommender Systems has the ability to predict whether a particular user would prefer an item or not, based on the user’s profile and their activities. The use of Recommender Systems are beneficial to both service providers and users. Thesis cover also the strength and weaknesses of Recommender Systems and how involving Ontology can improve it. Ontology-based methods can be used to reduce problems that content-based recommender systems are known to suffer from. Based on Kosovar’s GDP and youngsters job perspectives are desirable for improvements, the demand is greater than the offer. I thought of building an intelligence system that will be making easier for Kosovars to find the appropriate job that suits their profile, skills, knowledge, character and locations. And that system is called TROI Search engine that indexes and merge all local operating job seeking websites in one platform with intelligence features. Thesis will present the design, implementation, testing and evaluation of a TROI search engine. Testing is done by getting user experiments while using running environment of TROI search engine. Results show that the functionality of the recommender system is satisfactory and helpful

Event Detection and Tracking Detection of Dangerous Events on Social Media

Online social media platforms have become essential tools for communication and information exchange in our lives. It is used for connecting with people and sharing information. This phenomenon has been intensively studied in the past decade to investigate users’ sentiments for different scenarios and purposes. As the technology advanced and popularity increased, it led to the use of different terms referring to similar topics which often result in confusion. We study such trends and intend to propose a uniform solution that deals with the subject clearly. We gather all these ambiguous terms under the umbrella of the most recent and popular terms to reach a concise verdict. Many events have been addressed in recent works that cover only specific types and domains of events. For the sake of keeping things simple and practical, the events that are extreme, negative, and dangerous are grouped under the name Dangerous Events (DE). These dangerous events are further divided into three main categories of action-based, scenario-based, and sentiments-based dangerous events to specify their characteristics. We then propose deep-learning-based models to detect events that are dangerous in nature. The deep-learning models that include BERT, RoBERTa, and XLNet provide valuable results that can effectively help solve the issue of detecting dangerous events using various dimensions. Even though the models perform well, the main constraint of fewer available event datasets and lower quality of certain events data affects the performance of these models can be tackled by handling the issue accordingly.As plataformas online de redes sociais tornaram-se ferramentas essenciais para a comunicação, conexão com outros, e troca de informação nas nossas vidas. Este fenómeno tem sido intensamente estudado na última década para investigar os sentimentos dos utilizadores em diferentes cenários e para vários propósitos. Contudo, a utilização dos meios de comunicação social tornou-se mais complexa e num fenómeno mais vasto devido ao envolvimento de múltiplos intervenientes, tais como empresas, grupos e outras organizações. À medida que a tecnologia avançou e a popularidade aumentou, a utilização de termos diferentes referentes a tópicos semelhantes gerou confusão. Por outras palavras, os modelos são treinados segundo a informação de termos e âmbitos específicos. Portanto, a padronização é imperativa. O objetivo deste trabalho é unir os diferentes termos utilizados em termos mais abrangentes e padronizados. O perigo pode ser uma ameaça como violência social, desastres naturais, danos intelectuais ou comunitários, contágio, agitação social, perda económica, ou apenas a difusão de ideologias odiosas e violentas. Estudamos estes diferentes eventos e classificamos-los em tópicos para que a ténica de deteção baseada em tópicos possa ser concebida e integrada sob o termo Evento Perigosos (DE). Consequentemente, definimos o termo proposto “Eventos Perigosos” (Dangerous Events) e dividimo-lo em três categorias principais de modo a especificar as suas características. Sendo estes denominados Eventos Perigosos, Eventos Perigosos de nível superior, e Eventos Perigosos de nível inferior. O conjunto de dados MAVEN foi utilizado para a obtenção de conjuntos de dados para realizar a experiência. Estes conjuntos de dados são filtrados manualmente com base no tipo de eventos para separar eventos perigosos de eventos gerais. Os modelos de transformação BERT, RoBERTa, e XLNet foram utilizados para classificar dados de texto consoante a respetiva categoria de Eventos Perigosos. Os resultados demonstraram que o desempenho do BERT é superior a outros modelos e pode ser eficazmente utilizado para a tarefa de deteção de Eventos Perigosos. Salienta-se que a abordagem de divisão dos conjuntos de dados aumentou significativamente o desempenho dos modelos. Existem diversos métodos propostos para a deteção de eventos. A deteção destes eventos (ED) são maioritariamente classificados na categoria de supervisonado e não supervisionados, como demonstrado nos metódos supervisionados, estão incluidos support vector machine (SVM), Conditional random field (CRF), Decision tree (DT), Naive Bayes (NB), entre outros. Enquanto a categoria de não supervisionados inclui Query-based, Statisticalbased, Probabilistic-based, Clustering-based e Graph-based. Estas são as duas abordagens em uso na deteção de eventos e são denonimados de document-pivot and feature-pivot. A diferença entre estas abordagens é na sua maioria a clustering approach, a forma como os documentos são utilizados para caracterizar vetores, e a similaridade métrica utilizada para identificar se dois documentos correspondem ao mesmo evento ou não. Além da deteção de eventos, a previsão de eventos é um problema importante mas complicado que engloba diversas dimensões. Muitos destes eventos são difíceis de prever antes de se tornarem visíveis e ocorrerem. Como um exemplo, é impossível antecipar catástrofes naturais, sendo apenas detetáveis após o seu acontecimento. Existe um número limitado de recursos em ternos de conjuntos de dados de eventos. ACE 2005, MAVEN, EVIN são alguns dos exemplos de conjuntos de dados disponíveis para a deteção de evnetos. Os trabalhos recentes demonstraram que os Transformer-based pre-trained models (PTMs) são capazes de alcançar desempenho de última geração em várias tarefas de NLP. Estes modelos são pré-treinados em grandes quantidades de texto. Aprendem incorporações para as palavras da língua ou representações de vetores de modo a que as palavras que se relacionem se agrupen no espaço vectorial. Um total de três transformadores diferentes, nomeadamente BERT, RoBERTa, e XLNet, será utilizado para conduzir a experiência e tirar a conclusão através da comparação destes modelos. Os modelos baseados em transformação (Transformer-based) estão em total sintonia utilizando uma divisão de 70,30 dos conjuntos de dados para fins de formação e teste/validação. A sintonização do hiperparâmetro inclui 10 epochs, 16 batch size, e o optimizador AdamW com taxa de aprendizagem 2e-5 para BERT e RoBERTa e 3e-5 para XLNet. Para eventos perigosos, o BERT fornece 60%, o RoBERTa 59 enquanto a XLNet fornece apenas 54% de precisão geral. Para as outras experiências de configuração de eventos de alto nível, o BERT e a XLNet dão 71% e 70% de desempenho com RoBERTa em relação aos outros modelos com 74% de precisão. Enquanto para o DE baseado em acções, DE baseado em cenários, e DE baseado em sentimentos, o BERT dá 62%, 85%, e 81% respetivamente; RoBERTa com 61%, 83%, e 71%; a XLNet com 52%, 81%, e 77% de precisão. Existe a necessidade de clarificar a ambiguidade entre os diferentes trabalhos que abordam problemas similares utilizando termos diferentes. A ideia proposta de referir acontecimentos especifícos como eventos perigosos torna mais fácil a abordagem do problema em questão. No entanto, a escassez de conjunto de dados de eventos limita o desempenho dos modelos e o progresso na deteção das tarefas. A disponibilidade de uma maior quantidade de informação relacionada com eventos perigosos pode melhorar o desempenho do modelo existente. É evidente que o uso de modelos de aprendizagem profunda, tais como como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Tem sido evidente que a utilização de modelos de aprendizagem profunda, tais como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Em geral, o BERT tem um desempenho superior ao do RoBERTa e XLNet na detecção de eventos perigosos. É igualmente importante rastrear os eventos após a sua detecção. Por conseguinte, para trabalhos futuros, propõe-se a implementação das técnicas que lidam com o espaço e o tempo, a fim de monitorizar a sua emergência com o tempo

Doctor of Philosophy

dissertationMarketers invest significantly in generating consumer action, with curiosity one of many ways to pique interest. This is the topic of our first essay, in which we discuss how discounted price displays arouse curiosity, thus affecting information search behavior. This essay moves beyond the assumption that any prediscounted price will elicit the same consumer response and considers four moderating factors, including i) absolute price, ii) dispositional curiosity, iii) expected price and iv) drive states such as hunger. In a series of examinations, we propose that higher (lower) prices generate greater (less) curiosity. Findings inform psychology-based accounts of curiosity and provide implications for marketers in understanding pricing`s effect on information seeking. Essays 2 and 3 explore the long-term impact of a referral on sender and receiver behavior. Marketers have long sought to harness the influence of existing customers, with much literature focusing on a referral`s worth. While prior research has extensively examined referral value, less is known about how the specific information within the referral itself differentially influences behavior. Thus, Essay 2 focuses on the degree of customization within the referral, examining for both senders and receivers the influence of custom (sender-generated) versus standard (company-generated on behalf of sender) referrals. To test our predictions, we utilize email referrals from retail customers and compare purchase behavior between these referral types, testing the underlying theories of spotlight effect and reciprocity. In our third essay, we ask whether the act of referring changes long term purchase behavior of referrers. Extensive literature has proved the value of customers acquired through referral efforts of existing customers. However, while much is known about the incremental value of referrals, less is known about the intervening role of the referral itself. Therefore, in our research we seek to understand how a referral influences future sender behavior and ask whether the act of referring results in an increase, decrease, or consistency in purchases for senders. We explore opposing predictions based on i) dissonance and ii) market mavens and explore these predictions through an empirical examination of transaction data, offering implications for marketers and theorists alike

Jolie Microservices: An Experiment

Os microsserviços estão cada vez mais presentes no mundo das tecnologias de informação, por providenciarem uma nova forma construir sistemas mais escaláveis, ágeis e flexíveis. Apesar disto, estes trazem consigo o problema da complexidade de comunicação entre microsserviços, fazendo com que o sistema seja difícil de manter e de se perceber. Linguagens de programação específicas a microsserviços como Jolie entram em cena para tentar resolver este problema e simplificar a construção de sistemas com arquiteturas de microsserviços. Este trabalho fornece uma visão ampla do estado da arte da linguagem de programação Jolie onde é primeiramente detalhado o porquê de surgirem linguagens específicas a microsserviços e como a linguagem Jolie está construída de maneira a coincidir com as arquiteturas de microsserviços através de recursos nativos. Para demonstrar todas as vantagens de usar esta linguagem em comparação com as abordagens mais mainstream é pensado um experimento de desenvolvimento de um sistema de microsserviços no âmbito de uma aplicação de e-commerce. Este sistema é construído de forma igual usando duas bases tecnológicas – Jolie e Spring Boot. O Spring Boot é considerado a tecnologia mais usada para desenvolver sistemas de microsserviços sendo o candidato ideal para comparação. É pensada toda a análise e design deste experimento. Em seguida, a implementação da solução é detalhada a partir das configurações do sistema, escolhas arquitetónicas e como elas são implementadas. Componentes como API gateway, mediadores de mensagens, bases de dados, orquestração de microsserviços, e conteinerização para cada microsserviço e outros componentes do sistema. Pol último as soluções são comparadas e analisadas com base na abordagem Goals, Questions, Metrics (GQM). São analisadas relativamente a atributos de qualidade como manutenção, escalabilidade, desempenho e testabilidade. Após esta análise pode-se concluir que a solução construída com Jolie apresenta diferenças na manutenção sendo significativamente superior à solução baseada em Spring Boot e apresenta diferenças em termos de performance sendo ligeiramente inferior à solução construída com Spring Boot. O trabalho termina com a indicação das conquistas, dificuldades, ameaças à validade, possíveis trabalhos futuros e observações finais.Microservices are increasingly present in the world of information technologies, as they provide a new way to build more scalable, agile, and flexible systems. Despite this, they bring with them the problem of communication complexity between microservices, making the system difficult to maintain and understand. Microservices-specific programming languages like Jolie come into play to try to solve this problem and simplify the construction of systems with microservices architectures. This work provides a broad view of the State of Art of the Jolie programming language, where it is first detailed why microservices-specific languages emerge and how the Jolie language is built to match microservices architectures through native resources. To demonstrate all the advantages of using this language compared to more mainstream approaches, an experiment is designed to develop a microservices system within an e-commerce application. This system is built equally using two technological foundations – Jolie and Spring Boot. Spring Boot is considered the most used technology to develop microservices systems and is an ideal candidate for comparison. The entire analysis and design of this experiment are thought through. Then the implementation of the solution is detailed from system configurations, architectural choices, and how they are implemented. Components such as API gateway, message brokers, databases, microservices orchestration, and containerization for each microservice and other components of the system. Finally, the solutions are compared and analyzed based on the Goals, Questions, Metrics (GQM) approach. They are analyzed for quality attributes such as maintainability, scalability, performance, and testability. After this analysis, it can be concluded that the solution built with Jolie presents differences in maintenance being significant superior to the solution based on Spring Boot, and it presents differences in terms of performance being slightly inferior to the solution built with Spring Boot. The work ends with an indication of the achievements, difficulties, threats to validity, possible future work, and final observations

Causal schema induction for knowledge discovery

Making sense of familiar yet new situations typically involves making generalizations about causal schemas, stories that help humans reason about event sequences. Reasoning about events includes identifying cause and effect relations shared across event instances, a process we refer to as causal schema induction. Statistical schema induction systems may leverage structural knowledge encoded in discourse or the causal graphs associated with event meaning, however resources to study such causal structure are few in number and limited in size. In this work, we investigate how to apply schema induction models to the task of knowledge discovery for enhanced search of English-language news texts. To tackle the problem of data scarcity, we present Torquestra, a manually curated dataset of text-graph-schema units integrating temporal, event, and causal structures. We benchmark our dataset on three knowledge discovery tasks, building and evaluating models for each. Results show that systems that harness causal structure are effective at identifying texts sharing similar causal meaning components rather than relying on lexical cues alone. We make our dataset and models available for research purposes.Comment: 8 pages, appendi

Empirically-Grounded Construction of Bug Prediction and Detection Tools

There is an increasing demand on high-quality software as software bugs have an economic impact not only on software projects, but also on national economies in general. Software quality is achieved via the main quality assurance activities of testing and code reviewing. However, these activities are expensive, thus they need to be carried out efficiently. Auxiliary software quality tools such as bug detection and bug prediction tools help developers focus their testing and reviewing activities on the parts of software that more likely contain bugs. However, these tools are far from adoption as mainstream development tools. Previous research points to their inability to adapt to the peculiarities of projects and their high rate of false positives as the main obstacles of their adoption. We propose empirically-grounded analysis to improve the adaptability and efficiency of bug detection and prediction tools. For a bug detector to be efficient, it needs to detect bugs that are conspicuous, frequent, and specific to a software project. We empirically show that the null-related bugs fulfill these criteria and are worth building detectors for. We analyze the null dereferencing problem and find that its root cause lies in methods that return null. We propose an empirical solution to this problem that depends on the wisdom of the crowd. For each API method, we extract the nullability measure that expresses how often the return value of this method is checked against null in the ecosystem of the API. We use nullability to annotate API methods with nullness annotation and warn developers about missing and excessive null checks. For a bug predictor to be efficient, it needs to be optimized as both a machine learning model and a software quality tool. We empirically show how feature selection and hyperparameter optimizations improve prediction accuracy. Then we optimize bug prediction to locate the maximum number of bugs in the minimum amount of code by finding the most cost-effective combination of bug prediction configurations, i.e., dependent variables, machine learning model, and response variable. We show that using both source code and change metrics as dependent variables, applying feature selection on them, then using an optimized Random Forest to predict the number of bugs results in the most cost-effective bug predictor. Throughout this thesis, we show how empirically-grounded analysis helps us achieve efficient bug prediction and detection tools and adapt them to the characteristics of each software project

Impact of Space Weather on Climate and Habitability of Terrestrial Type Exoplanets

The current progress in the detection of terrestrial type exoplanets has opened a new avenue in the characterization of exoplanetary atmospheres and in the search for biosignatures of life with the upcoming ground-based and space missions. To specify the conditions favorable for the origin, development and sustainment of life as we know it in other worlds, we need to understand the nature of astrospheric, atmospheric and surface environments of exoplanets in habitable zones around G-K-M dwarfs including our young Sun. Global environment is formed by propagated disturbances from the planet-hosting stars in the form of stellar flares, coronal mass ejections, energetic particles, and winds collectively known as astrospheric space weather. Its characterization will help in understanding how an exoplanetary ecosystem interacts with its host star, as well as in the specification of the physical, chemical and biochemical conditions that can create favorable and/or detrimental conditions for planetary climate and habitability along with evolution of planetary internal dynamics over geological timescales. A key linkage of (astro) physical, chemical, and geological processes can only be understood in the framework of interdisciplinary studies with the incorporation of progress in heliophysics, astrophysics, planetary and Earth sciences. The assessment of the impacts of host stars on the climate and habitability of terrestrial (exo)planets will significantly expand the current definition of the habitable zone to the biogenic zone and provide new observational strategies for searching for signatures of life. The major goal of this paper is to describe and discuss the current status and recent progress in this interdisciplinary field and to provide a new roadmap for the future development of the emerging field of exoplanetary science and astrobiology.Comment: 206 pages, 24 figures, 1 table; Review paper. International Journal of Astrobiology (2019

Towards the formation and measurement of ethnic price perception

This research is the outcome of a preeminent interest in the topic of price perception. Pointedly, the perception of prices is part of the purchasing process, the same willingness to pay and the actual purchase behaviour, and is indubitably a perceptual construct. As such, perception is problematic to measure as it does not relate to an observable behaviour. On the other hand, pricing is regarded as an important variable in the marketing mix. This research contributes to theory by augmenting the current knowledge on the perception of prices including the methods used in the measurement of such perception. Moreover, this research addresses a gap in the understanding of how diverse ethnic groups perceive prices. The relationship set in this study between ethnicity and price perception is thought-provoking as it contributes to the current discussion around diversity in the marketplace. For example, the literature shows advances in areas such as multicultural and ethnic marketing and this research makes a significant contribution to these areas from price perception. Accordingly, this study involved a systematic review of the literature and presented a framework that suggested that the formation of price perception is affected by external factors such as culture and ethnicity. Furthermore, a qualitative study examined the formation of price perception around ethnic groups. Next, this research used a quantitative study that sought differences in price perception among ethnic groups. Thus, the quantitative study used a price perception scale (Lichtenstein et al., 1993) and a choice-based conjoint analysis. Also, the study adopted structural equation modelling (SEM) to measure differences among scales and the multinomial logit model to analyse the choice-based conjoint analysis. The findings of both the quantitative and the qualitative studies link to the systematic review and support the framework for the formation and measurement of price perception originally proposed