Behavioral Task Modeling for Entity Recommendation

Our everyday tasks involve interactions with a wide range of information. The information that we manage is often associated with a task context. However, current computer systems do not organize information in this way, do not help the user find information in task context, but require explicit user actions such as searching and information seeking. We explore the use of task context to guide the delivery of information to the user proactively, that is, to have the right information easily available at the right time. In this thesis, we used two types of novel contextual information: 24/7 behavioral recordings and spoken conversations for task modeling. The task context is created by monitoring the user's information behavior from temporal, social, and topical aspects; that can be contextualized by several entities such as applications, documents, people, time, and various keywords determining the task. By tracking the association amongst the entities, we can infer the user's task context, predict future information access, and proactively retrieve relevant information for the task at hand. The approach is validated with a series of field studies, in which altogether 47 participants voluntarily installed a screen monitoring system on their laptops 24/7 to collect available digital activities, and their spoken conversations were recorded. Different aspects of the data were considered to train the models. In the evaluation, we treated information sourced from several applications, spoken conversations, and various aspects of the data as different kinds of influence on the prediction performance. The combined influences of multiple data sources and aspects were also considered in the models. Our findings revealed that task information could be found in a variety of applications and spoken conversations. In addition, we found that task context models that consider behavioral information captured from the computer screen and spoken conversations could yield a promising improvement in recommendation quality compared to the conventional modeling approach that considered only pre-determined interaction logs, such as query logs or Web browsing history. We also showed how a task context model could support the users' work performance, reducing their effort in searching by ranking and suggesting relevant information. Our results and findings have direct implications for information personalization and recommendation systems that leverage contextual information to predict and proactively present personalized information to the user to improve the interaction experience with the computer systems.Jokapäiväisiin tehtäviimme kuuluu vuorovaikutusta monenlaisten tietojen kanssa. Hallitsemamme tiedot liittyvät usein johonkin tehtäväkontekstiin. Nykyiset tietokonejärjestelmät eivät kuitenkaan järjestä tietoja tällä tavalla tai auta käyttäjää löytämään tietoja tehtäväkontekstista, vaan vaativat käyttäjältä eksplisiittisiä toimia, kuten tietojen hakua ja etsimistä. Tutkimme, kuinka tehtäväkontekstia voidaan käyttää ohjaamaan tietojen toimittamista käyttäjälle ennakoivasti, eli siten, että oikeat tiedot olisivat helposti saatavilla oikeaan aikaan. Tässä väitöskirjassa käytimme kahdenlaisia uusia kontekstuaalisia tietoja: 24/7-käyttäytymistallenteita ja tehtävän mallintamiseen liittyviä puhuttuja keskusteluja. Tehtäväkonteksti luodaan seuraamalla käyttäjän tietokäyttäytymistä ajallisista, sosiaalisista ja ajankohtaisista näkökulmista katsoen; sitä voidaan kuvata useilla entiteeteillä, kuten sovelluksilla, asiakirjoilla, henkilöillä, ajalla ja erilaisilla tehtävää määrittävillä avainsanoilla. Tarkastelemalla näiden entiteettien välisiä yhteyksiä voimme päätellä käyttäjän tehtäväkontekstin, ennustaa tulevaa tiedon käyttöä ja hakea ennakoivasti käsillä olevaan tehtävään liittyviä asiaankuuluvia tietoja. Tätä lähestymistapaa arvioitiin kenttätutkimuksilla, joissa yhteensä 47 osallistujaa asensi vapaaehtoisesti kannettaviin tietokoneisiinsa näytönvalvontajärjestelmän, jolla voitiin 24/7 kerätä heidän saatavilla oleva digitaalinen toimintansa, ja joissa tallennettiin myös heidän puhutut keskustelunsa. Mallien kouluttamisessa otettiin huomioon datan eri piirteet. Arvioinnissa käsittelimme useista sovelluksista, puhutuista keskusteluista ja datan eri piirteistä saatuja tietoja erilaisina vaikutuksina ennusteiden toimivuuteen. Malleissa otettiin huomioon myös useiden tietolähteiden ja näkökohtien yhteisvaikutukset. Havaintomme paljastivat, että tehtävätietoja löytyi useista sovelluksista ja puhutuista keskusteluista. Lisäksi havaitsimme, että tehtäväkontekstimallit, joissa otetaan huomioon tietokoneen näytöltä ja puhutuista keskusteluista saadut käyttäytymistiedot, voivat parantaa suositusten laatua verrattuna tavanomaiseen mallinnustapaan, jossa tarkastellaan vain ennalta määritettyjä vuorovaikutuslokeja, kuten kyselylokeja tai verkonselaushistoriaa. Osoitimme myös, miten tehtäväkontekstimalli pystyi tukemaan käyttäjien suoritusta ja vähentämään heidän hakuihin tarvitsemaansa työpanosta järjestämällä hakutuloksia ja ehdottamalla heille asiaankuuluvia tietoja. Tuloksillamme ja havainnoillamme on suoria vaikutuksia tietojen personointi- ja suositusjärjestelmiin, jotka hyödyntävät kontekstuaalista tietoa ennustaakseen ja esittääkseen ennakoivasti personoituja tietoja käyttäjälle ja näin parantaakseen vuorovaikutuskokemusta tietokonejärjestelmien kanssa

Design and evaluation of adaptive multimoldal systems

Tese de doutoramento em Informática (Engenharia Informática), presentada à Universidade de Lisboa através da Faculdade de Ciências, 2008This thesis focuses on the design and evaluation of adaptive multi-modal systems. The design of such systems is approached from an integrated perspective, with the goal of obtaining a solution where aspects related to both adaptive and multimodal systems are considered. The result is FAME, a model based framework for the design and development of adaptive multimodal systems, where adaptive capabilities impact directly over the process of multimodal fusion and fission operations. FAME over views the design of systems capable of adapting to a diversified context, including variations in users,execution platform, and environment. FAME represents an evolution from previous frameworks by incorporating aspects specific to multimodal interfaces directly in the development of an adaptive platform. One of FAME's components is the Behavioral Matrix, a multi purpose instrument, used during the design phase to represent the adaptation rules. In addition, the Behavioral Matrix is also the component responsible for bridging the gap between design and evaluation stages. Departing from an analogy between transitionnet works for representing interaction with a system, and behavioral spaces, the Behavioral Matrix makes possible the application of behavioral complexity metrics to general adaptive systems. Moreover,this evaluation is possible during the design stages,which translates into a reduction of there sources required for evaluation of adaptive systems.The Behavior al Matrix allows a designer to emulate the behavior of anon-adaptiveversionoftheadaptivesystem,allowing for comparison of the versions, one of the most used approaches to adaptive systems evaluation. In addition, the designer may also emulate the behavior of different user profiles and compare their complexity measures. The feasibility of FAME was demonstrated with the development of an adaptive multimodal Digital Book Player. The process was successful, as demonstrated by usability evaluations. Besides these evaluations, behavioral complexity metrics, computed in accordance with the proposed methodology, were able to discern between adaptive and non-adaptive versions of the player. When applied to user profiles of different perceived complexity, the metrics were also able to detect the different interaction complexity.FCT - IPSOM (POSI/PLP/34252/2000) e RiCoBA (POSC/EIA/61042/2004

Reframing the Picturesque in Contemporary Australian and Canadian Nature Writing

This thesis explores aesthetic representation in Australian and Canadian nature writing from the turn of the twenty-first century to the present day. I analyse nine representative texts to explore the relationship between aesthetic representation of the so-called natural environment and the texts’ central themes, which I identify as (i) belonging (in place) (ii) digging (uncovering colonial history), (iii) walking (pilgrimage), and (iv) working (ecological rehabilitation). In connection with each theme, I examine how the environment is perceived, how notions of aesthetic value are constructed around it, and how aesthetic language¬¬ contributes to the narrative and argument of the text. In so doing, I seek insight from contemporary environmental aesthetics as developed by philosophers including Allan Carlson, Yuriko Saito, and Arnold Berleant. I argue that recent nature writing from both Australia and Canada shows an increasingly self-conscious engagement with the politics of representation that is often characterised by anxiety on the part of the narrator about representation and the possibility of the ‘truthful framing’ of place. This leads recent writers to enquire (albeit with different levels of success) into the discourses that drive beliefs about the natural environment. Some writers put pressure on popular modes of perception such as the picturesque by disrupting conventional representational styles, while others use those popular modes as the basis for a normative model of aesthetics and a spur to action. I suggest that one of the distinctive features of recent Australian and Canadian nature writing is its critical engagement with ways of seeing and describing nature that were developed during the colonial period, in particular in debates surrounding picturesque aesthetics, which in turn influenced travel and nature writing. In this way, much of contemporary Australian and Canadian nature writing can be seen as engaging, either explicitly or implicitly, in a critical project of reframing the picturesque

Reframing the Picturesque in Contemporary Australian and Canadian Nature Writing

This thesis explores aesthetic representation in Australian and Canadian nature writing from the turn of the twenty-first century to the present day. I analyse nine representative texts to explore the relationship between aesthetic representation of the so-called natural environment and the texts’ central themes, which I identify as (i) belonging (in place) (ii) digging (uncovering colonial history), (iii) walking (pilgrimage), and (iv) working (ecological rehabilitation). In connection with each theme, I examine how the environment is perceived, how notions of aesthetic value are constructed around it, and how aesthetic language¬¬ contributes to the narrative and argument of the text. In so doing, I seek insight from contemporary environmental aesthetics as developed by philosophers including Allan Carlson, Yuriko Saito, and Arnold Berleant. I argue that recent nature writing from both Australia and Canada shows an increasingly self-conscious engagement with the politics of representation that is often characterised by anxiety on the part of the narrator about representation and the possibility of the ‘truthful framing’ of place. This leads recent writers to enquire (albeit with different levels of success) into the discourses that drive beliefs about the natural environment. Some writers put pressure on popular modes of perception such as the picturesque by disrupting conventional representational styles, while others use those popular modes as the basis for a normative model of aesthetics and a spur to action. I suggest that one of the distinctive features of recent Australian and Canadian nature writing is its critical engagement with ways of seeing and describing nature that were developed during the colonial period, in particular in debates surrounding picturesque aesthetics, which in turn influenced travel and nature writing. In this way, much of contemporary Australian and Canadian nature writing can be seen as engaging, either explicitly or implicitly, in a critical project of reframing the picturesque

Advanced techniques for personalized, interactive question answering

Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with naturallangilage to enable the computer to understand what its user asks. The discipline that studies the conD:ection between natural language and the represen~ tation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language, aims at finding one or more concise answers in the form of sentences or phrases. Question Answering can be interpreted as a sub-discipline of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text. Although it is widely accepted that Question Answering represents a step beyond standard infomiation retrieval, allowing a more sophisticated and satisfactory response to the user's information needs, it still shares a series of unsolved issues with the latter. First, in most state-of-the-art Question Answering systems, the results are created independently of the questioner's characteristics, goals and needs. This is a serious limitation in several cases: for instance, a primary school child and a History student may need different answers to the questlon: When did, the Middle Ages begin? Moreover, users often issue queries not as standalone but in the context of a wider information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces would encourage and accommodate the submission of multiple related questions and handle the user's requests for clarification, interactive Question Answering is still at its early stages: Furthermore, an i~sue which still remains open in current Question Answering is that of efficiently answering complex questions, such as those invoking definitions and descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess the correctness of answers to such complex questions. .. These are the central research problems addressed by this thesis, and are solved as follows. An in-depth study on complex Question Answering led to the development of classifiers for complex answers. These exploit a variety of lexical, syntactic and shallow semantic features to perform textual classification using tree-~ernel functions for Support Vector Machines. The issue of personalization is solved by the integration of a User Modelling corn': ponent within the the Question Answering model. The User Model is able to filter and fe-rank results based on the user's reading level and interests. The issue ofinteractivity is approached by the development of a dialogue model and a dialogue manager suitable for open-domain interactive Question Answering. The utility of such model is corroborated by the integration of an interactive interface to allow reference resolution and follow-up conversation into the core Question Answerin,g system and by its evaluation. Finally, the models of personalized and interactive Question Answering are integrated in a comprehensive framework forming a unified model for future Question Answering research

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity