Statistical Dialog Management for Health Interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

The Knowledge of High-Performance Gymnastic Coaches : Competition and Training Considerations

The purpose of this study is to report the knowledge used in training and competition by 17 expert high-performance gymnastic coaches. A qualitative research methodology was used to collect and inductively analyze the data. The knowledge elicited for the competition component was categorized as competition site, competition floor, and trial competitions. These categories indicated that the coaches are minimally involved with the gymnasts in competition. The knowledge of the coaches elicited within the training component were categorized as coach involvement in training, intervention style, technical skills, mental skills, and simulation. Properties of these categories that were extensively discussed by the expert coaches, such as teaching progressions, being supportive, and helping athletes to deal with stress,are consistent with the literature on coaching and on sport psychology. Other aspects considered important in the sport psychology literature, such as developing concentration skills, were not discussed as thoroughly by the expert coaches.

Considering Efficacy of an Outdoor Environmental Education Program: Facilitating Elementary Students’ Environmental Knowledge, Attitudes, and Behaviours

Various sources have sought to consider the educational interventions that foster changes in perception of and attitudes toward nature, with the ultimate intent of understanding how education can be used to encourage environmentally responsible behaviours. With these in mind, the current study identified an outdoor environmental education program incorporating these empirically supported interventions, and assessed its ability to influence environmental knowledge, attitudes, and behaviours. Specifically, this study considered the following research questions: 1) To what degree can participation in this outdoor education program foster environmental knowledge and encourage pro-environmental attitudes and self-reported pro-environmental behaviours? 2) How is this effect different among students of different genders, and those who have different prior experiences in nature? Two motivational frameworks guided inquiry in the current study: the Value-Belief-Norm Model of Environmentalism (VBN) and the Theory of Planned Behaviour (TPB). The study employed a quantitative survey methodology, combining contemporary data measuring knowledge, attitudes, and behaviours with archived data collected by program staff, reflecting frequency of environmentally responsible behaviour. Further, a single qualitative item was included for which students provided “the first three words that [came] to mind when [they] think of the word nature.” Terms provided before and after the program were compared for differences in theme to detect subtle or underlying changes. Quantitative results indicated no significant change in student knowledge or attitudes through the outdoor environmental education program. However, a significant change in self-reported behaviour was identified from both the contemporary and archived data. This agreement in positive findings across the two data sets, collected using different measures and different participants, lends evidence of the program’s ability to encourage self-reported pro-environmental behaviour. Further, qualitative results showed some change in students’ perceptions of nature through the program, providing direction for future research. These findings suggest that this particular outdoor education program was successful in encouraging students’ self-reported environmentally responsible behaviour. This change was achieved without significant change in knowledge or environmental attitudes, suggesting that external factors not measured in this study might have played a role in affecting behaviour.

La enseñanza de las construcciones pasivas e impersonales con "se" en español como L2. ¿Cuántas distinciones son necesarias?

Este artículo sugiere un enfoque nuevo a la enseñanza de las dos estructuras gramaticales la pasiva refleja y el “se” impersonal para las clases universitarias de E/LE. Concretamente, se argumenta que las dos se deberían tratar como construcciones pasivas, basada en un análisis léxico-funcional de ellas que enfoca la lingüística contrastiva. Incluso para la instrucción de E/LE, se recomienda una aproximación contrastiva en la que se enfocan tanto la reflexión metalingüística como la competencia del estudiante en el L2. Específicamente, el uso de córpora lingüísticos en la clase forma una parte integral de la instrucción. El uso de un corpus estimula la curiosidad del estudiante, le expone a material de lengua auténtica, y promulga la reflexión inductiva independiente.

INVISIBLE LABOR FOR DATA: INSTITUTIONS, INFRASTRUCTURE, AND VIRTUAL SPACE

Americans are accustomed to a wide range of data collection in their lives: census, polls, surveys, user registrations, and disclosure forms. When logging onto the Internet, users’ actions are being tracked everywhere: clicking, typing, tapping, swiping, searching, and placing orders. All of this data is stored to create data-driven profiles of each user. Social network sites, furthermore, set the voluntarily sharing of personal data as the default mode of engagement. But people’s time and energy devoted to creating this massive amount of data, on paper and online, are taken for granted. Few people would consider their time and energy spent on data production as labor. Even if some people do acknowledge their labor for data, they believe it is accessory to the activities at hand. In the face of pervasive data collection and the rising time spent on screens, why do people keep ignoring their labor for data? How has labor for data been become invisible, as something that is disregarded by many users? What does invisible labor for data imply for everyday cultural practices in the United States? Invisible Labor for Data addresses these questions. I argue that three intertwined forces contribute to framing data production as being void of labor: data production institutions throughout history, the Internet’s technological infrastructure (especially with the implementation of algorithms), and the multiplication of virtual spaces. There is a common tendency in the framework of human interactions with computers to deprive data and bodies of their materiality. My Introduction and Chapter 1 offer theoretical interventions by reinstating embodied materiality and redefining labor for data as an ongoing process. The middle Chapters present case studies explaining how labor for data is pushed to the margin of the narratives about data production. I focus on a nationwide debate in the 1960s on whether the U.S. should build a databank, contemporary Big Data practices in the data broker and the Internet industries, and the group of people who are hired to produce data for other people’s avatars in the virtual games. I conclude with a discussion on how the new development of crowdsourcing projects may usher in the new chapter in exploiting invisible and discounted labor for data.

The MUSE Machine -- an Architecture for Structured Data Flow Computation

Computers employing some degree of data flow organisation are now well established as providing a possible vehicle for concurrent computation. Although data-driven computation frees the architecture from the constraints of the single program counter, processor and global memory, inherent in the classic von Neumann computer, there can still be problems with the unconstrained generation of fresh result tokens if a pure data flow approach is adopted. The advantages of allowing serial processing for those parts of a program which are inherently serial, and of permitting a demand-driven, as well as data-driven, mode of operation are identified and described. The MUSE machine described here is a structured architecture supporting both serial and parallel processing which allows the abstract structure of a program to be mapped onto the machine in a logical way.

Tempo atmosférico numa perspetiva CTS através do ensino por pesquisa

O ser Humano, desde sempre tem tentado estabelecer relações entre si, o tempo e o clima, de modo a melhorar as suas condições de vida. Atualmente existem questões problema que ameaçam a humanidade, nomeadamente as alterações climáticas e o aquecimento global com vista à promoção de um Desenvolvimento Sustentável. À educação é atribuída extrema importância no desenvolvimento de uma adequada perceção da situação do planeta. Este facto levou as Nações Unidas a proclamarem, no início deste século (dezembro de 2002), a Década da Educação para o Desenvolvimento Sustentável. Um desafio internacional lançado aos países para que recorram à educação como ferramenta essencial na promoção do Desenvolvimento Sustentável. A vida nas sociedades contemporâneas é extremamente influenciada pelos desenvolvimentos científicos e tecnológicos, dependendo dos seus respetivos progressos. Como tal, a Educação Científica assume um papel fundamental na compreensão das problemáticas que o ser Humano enfrenta, assim como na sua própria consciencialização da responsabilidade na situação planetária atual. Devendo promover o desenvolvimento de cidadanias proactivas, fundamentadas e responsáveis, no sentido da mudança, numa perspetiva crítica global que garanta a sustentabilidade do planeta. Estes factos são alvo de reflexão por parte de diversas instâncias da sociedade tais como a UNESCO, comunidades nacionais e internacionais de investigação em Educação Científica, e o poder político que se espelham em propostas de reforma e de revisão curricular em diversos países. Neste contexto, a Escola, como instituição formal de Educação, toma o papel primordial de promover o Desenvolvimento Sustentável através da aquisição de conhecimentos, atitudes, valores e competências que permitam desenvolver nos alunos uma consciencialização ecológica e uma literacia científica. Com este propósito em mente surge a questão investigativa deste estudo “Como Abordar o Tempo Atmosférico numa Perspetiva CTS Através do Ensino Por Pesquisa?”. Assim, usando o laboratório mais acessível e gratuito, a Atmosfera, e recursos facilmente acessíveis para desenvolver atividades simples é apresentada uma proposta de abordagem em sala de aula para a temática “Tempo Atmosférico” em particular a “Previsão e Descrição do Tempo Atmosférico”. A Atmosfera é um fascinante laboratório de ensino, porque nela se podem estudar alguns processos físicos lecionados ao longo dos mais variados níveis de ensino nas disciplinas de Física, Química e Geografia. Na Atmosfera, podem realizar-se diversos estudos simples, que de uma forma fácil respondem a inquietantes questões relacionadas com a Previsão e Descrição do Tempo Atmosférico. Neste estudo foi desenvolvida e usada uma metodologia para construir e interpretar mapas de tempo permitindo a alunos do 3º Ciclo do Ensino Básico fazer a Previsão e Descrição do Tempo Atmosférico. Após a aplicação da estratégia para o desenvolvimento de capacidades, de criatividade, envolvimento, cidadania e de pensamento crítico, os alunos responderam a um questionário. Através do tratamento dos dados obtidos pode-se considerar que em média 97% dos alunos consideram importante ou muito importante o estudo desta temática e que tem influência no seu dia-a-dia. Verificou-se que em média, se passou de 26% de respostas cientificamente corretas ou parcialmente corretas para 83% de respostas cientificamente corretas ou parcialmente corretas, o que demostra que a estratégia proposta atingiu os seus propósitos que passavam por dinamizar e fomentar uma cultura meteorológica nas escolas e para as escolas. Salienta-se a importância da possibilidade do trabalho em rede e das suas potencialidades na motivação dos alunos dada a oportunidade de fazer diagnóstico do tempo atmosférico local e inter-regiões. Os alunos consultaram e interpretaram mapas de tempo atmosférico, usaram a Internet e compreendam a relação e a influência entre diferentes parâmetros meteorológicos. Os resultados obtidos neste estudo permitem afirmar que os alunos desenvolveram competências numa área que é uma preocupação de cada um, o diagnóstico do tempo atmosférico. Cresceu neles uma cultura meteorológica e as aprendizagens nesta temática podem transbordar para colegas, amigos, pais e toda a comunidade escolar. Assim pode considerar-se que a estratégia implementada foi promotora de mudança, de aquisição de conhecimentos e do desenvolvimento de competências numa temática tão aliciante que envolve o Desenvolvimento Sustentável. Considera-se ainda que a estratégia usada neste estudo é motivadora, aliada à dinâmica CTS e ao Ensino Por Pesquisa, com vista a ser utilizada em contexto de sala de aula. Este estudo é uma forte contribuição para o Ensino das Ciências em especial no ensino da temática Tempo Atmosférico e é uma ferramenta importante que pode e deve ser utilizada em contexto escolar pois está escrito de modo a ser consultado por profissionais de ensino, nomeadamente pelos professores de Física, Química e de Geografia de modo a promoverem o desenvolvimento de competências de literacia científica e de cidadania e contribuir para a formação de futuros cidadãos ativos e conscientes defensores da Sustentabilidade da Terra.

Collecting valuable consumer data: Motivating consumers to disclose personal information

Collecting and analyzing consumer data is essential in today’s data-driven business environment. However, consumers are becoming more aware of the value of the information they can provide to companies, thereby being more reluctant to share it for free. Therefore, companies need to find ways to motivate consumers to disclose personal information. The main research question of the study was formed as “How can companies motivate consumers to disclose personal information?” and it was further divided into two subquestions: 1) What types of benefits motivate consumers to disclose personal information? 2) How does the disclosure context affect the consumers’ information disclosure behavior? The conceptual framework consisted of a classification of extrinsic and intrinsic benefits, and moderating factors, which were recognized on the basis of prior research in the field. The study was conducted by using qualitative research methods. The primary data was collected by interviewing ten representatives from eight companies. The data was analyzed and reported according to predetermined themes. The findings of the study confirm that consumers can be motivated to disclose personal information by offering different types of extrinsic (monetary saving, time saving, self-enhancement, and social adjustment) and intrinsic (novelty, pleasure, and altruism) benefits. However, not all the benefits are equally useful ways to convince the customer to disclose information. Moreover, different factors in the disclosure context can either alleviate or increase the effectiveness of the benefits and the consumers’ motivation to disclose personal information. Such factors include the consumer’s privacy concerns, perceived trust towards the company, the relevancy of the requested information, personalization, website elements (especially security, usability, and aesthetics of a website), and the consumer’s shopping motivation. This study has several contributions. It is essential that companies recognize the most attractive benefits regarding their business and their customers, and that they understand how the disclosure context affects the consumer’s information disclosure behavior. The likelihood of information disclosure can be increased, for example, by offering benefits that meet the consumers’ needs and preferences, improving the relevancy of the asked information, stating the reasons for data collection, creating and maintaining a trustworthy image of the company, and enhancing the quality of the company’s website.

Using interaction data for improving the offline and online evaluation of search engines

This thesis investigates how web search evaluation can be improved using historical interaction data. Modern search engines combine offline and online evaluation approaches in a sequence of steps that a tested change needs to pass through to be accepted as an improvement and subsequently deployed. We refer to such a sequence of steps as an evaluation pipeline. In this thesis, we consider the evaluation pipeline to contain three sequential steps: an offline evaluation step, an online evaluation scheduling step, and an online evaluation step. In this thesis we show that historical user interaction data can aid in improving the accuracy or efficiency of each of the steps of the web search evaluation pipeline. As a result of these improvements, the overall efficiency of the entire evaluation pipeline is increased. Firstly, we investigate how user interaction data can be used to build accurate offline evaluation methods for query auto-completion mechanisms. We propose a family of offline evaluation metrics for query auto-completion that represents the effort the user has to spend in order to submit their query. The parameters of our proposed metrics are trained against a set of user interactions recorded in the search engine’s query logs. From our experimental study, we observe that our proposed metrics are significantly more correlated with an online user satisfaction indicator than the metrics proposed in the existing literature. Hence, fewer changes will pass the offline evaluation step to be rejected after the online evaluation step. As a result, this would allow us to achieve a higher efficiency of the entire evaluation pipeline. Secondly, we state the problem of the optimised scheduling of online experiments. We tackle this problem by considering a greedy scheduler that prioritises the evaluation queue according to the predicted likelihood of success of a particular experiment. This predictor is trained on a set of online experiments, and uses a diverse set of features to represent an online experiment. Our study demonstrates that a higher number of successful experiments per unit of time can be achieved by deploying such a scheduler on the second step of the evaluation pipeline. Consequently, we argue that the efficiency of the evaluation pipeline can be increased. Next, to improve the efficiency of the online evaluation step, we propose the Generalised Team Draft interleaving framework. Generalised Team Draft considers both the interleaving policy (how often a particular combination of results is shown) and click scoring (how important each click is) as parameters in a data-driven optimisation of the interleaving sensitivity. Further, Generalised Team Draft is applicable beyond domains with a list-based representation of results, i.e. in domains with a grid-based representation, such as image search. Our study using datasets of interleaving experiments performed both in document and image search domains demonstrates that Generalised Team Draft achieves the highest sensitivity. A higher sensitivity indicates that the interleaving experiments can be deployed for a shorter period of time or use a smaller sample of users. Importantly, Generalised Team Draft optimises the interleaving parameters w.r.t. historical interaction data recorded in the interleaving experiments. Finally, we propose to apply the sequential testing methods to reduce the mean deployment time for the interleaving experiments. We adapt two sequential tests for the interleaving experimentation. We demonstrate that one can achieve a significant decrease in experiment duration by using such sequential testing methods. The highest efficiency is achieved by the sequential tests that adjust their stopping thresholds using historical interaction data recorded in diagnostic experiments. Our further experimental study demonstrates that cumulative gains in the online experimentation efficiency can be achieved by combining the interleaving sensitivity optimisation approaches, including Generalised Team Draft, and the sequential testing approaches. Overall, the central contributions of this thesis are the proposed approaches to improve the accuracy or efficiency of the steps of the evaluation pipeline: the offline evaluation frameworks for the query auto-completion, an approach for the optimised scheduling of online experiments, a general framework for the efficient online interleaving evaluation, and a sequential testing approach for the online search evaluation. The experiments in this thesis are based on massive real-life datasets obtained from Yandex, a leading commercial search engine. These experiments demonstrate the potential of the proposed approaches to improve the efficiency of the evaluation pipeline.

WordPress as a framework for automated data capture, filtering and structuring processes

The Exhibitium Project , awarded by the BBVA Foundation, is a data-driven project developed by an international consortium of research groups . One of its main objectives is to build a prototype that will serve as a base to produce a platform for the recording and exploitation of data about art-exhibitions available on the Internet . Therefore, our proposal aims to expose the methods, procedures and decision-making processes that have governed the technological implementation of this prototype, especially with regard to the reuse of WordPress (WP) as development framework.

Multivalent reuse of web data about temporary art exhibitions: The Exhibitium Project

Our proposal aims to display the analysis techniques, methodologies as well as the most relevant results expected within the Exhibitium project framework (http://www.exhibitium.com). Awarded by the BBVA Foundation, the Exhibitium project is being developed by an international consortium of several research groups . Its main purpose is to build a comprehensive and structured data repository about temporary art exhibitions, captured from the web, to make them useful and reusable in various domains through open and interoperable data systems.

POPLAR GENE EXPRESSION DATA ANALYSIS PIPELINES

Analyzing large-scale gene expression data is a labor-intensive and time-consuming process. To make data analysis easier, we developed a set of pipelines for rapid processing and analysis poplar gene expression data for knowledge discovery. Of all pipelines developed, differentially expressed genes (DEGs) pipeline is the one designed to identify biologically important genes that are differentially expressed in one of multiple time points for conditions. Pathway analysis pipeline was designed to identify the differentially expression metabolic pathways. Protein domain enrichment pipeline can identify the enriched protein domains present in the DEGs. Finally, Gene Ontology (GO) enrichment analysis pipeline was developed to identify the enriched GO terms in the DEGs. Our pipeline tools can analyze both microarray gene data and high-throughput gene data. These two types of data are obtained by two different technologies. A microarray technology is to measure gene expression levels via microarray chips, a collection of microscopic DNA spots attached to a solid (glass) surface, whereas high throughput sequencing, also called as the next-generation sequencing, is a new technology to measure gene expression levels by directly sequencing mRNAs, and obtaining each mRNA’s copy numbers in cells or tissues. We also developed a web portal (http://sys.bio.mtu.edu/) to make all pipelines available to public to facilitate users to analyze their gene expression data. In addition to the analyses mentioned above, it can also perform GO hierarchy analysis, i.e. construct GO trees using a list of GO terms as an input.

Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project

Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining.

Firms’ knowledge search and local knowledge externalities in innovation performance

We use an augmented version of the UK Innovation Surveys 4–7 to explore firm-level and local area openness externalities on firms’ innovation performance. We find strong evidence of the value of external knowledge acquisition both through interactive collaboration and non-interactive contacts such as demonstration effects, copying or reverse engineering. Levels of knowledge search activity remain well below the private optimum, however, due perhaps to informational market failures. We also find strong positive externalities of openness resulting from the intensity of local interactive knowledge search—a knowledge diffusion effect. However, there are strong negative externalities resulting from the intensity of local non-interactive knowledge search—a competition effect. Our results provide support for local initiatives to support innovation partnering and counter illegal copying or counterfeiting. We find no significant relationship between either local labour quality or employment composition and innovative outputs.

How textbook design may influence learning with geography textbooks

This paper investigates how textbook design may influence students’ visual attention to graphics, photos and text in current geography textbooks. Eye tracking, a visual method of data collection and analysis, was utilised to precisely monitor students’ eye movements while observing geography textbook spreads. In an exploratory study utilising random sampling, the eye movements of 20 students (secondary school students 15–17 years of age and university students 20–24 years of age) were recorded. The research entities were double-page spreads of current German geography textbooks covering an identical topic, taken from five separate textbooks. A two-stage test was developed. Each participant was given the task of first looking at the entire textbook spread to determine what was being explained on the pages. In the second stage, participants solved one of the tasks from the exercise section. Overall, each participant studied five different textbook spreads and completed five set tasks. After the eye tracking study, each participant completed a questionnaire. The results may verify textbook design as one crucial factor for successful knowledge acquisition from textbooks. Based on the eye tracking documentation, learning-related challenges posed by images and complex image-text structures in textbooks are elucidated and related to educational psychology insights and findings from visual communication and textbook analysis.