Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

SAPO campus: aprendizagem, ensino e pessoas em rede

As escolas portuguesas do ensino não superior estão dotadas com infraestruturas e equipamentos que permitem trazer o mundo para dentro da sala de aula, tornando o processo de ensino e de aprendizagem mais rico e motivador para os alunos. A adoção institucional de uma plataforma que segue os princípios da web social, o SAPO Campus (SC), definida pela abertura, partilha, integração, inovação e personalização, pode ser catalisadora de processos de mudança e inovação. O presente estudo teve como finalidade acompanhar o processo de adoção do SC em cinco escolas, bem como analisar o impacto no processo de ensino e de aprendizagem e a forma como os alunos e professores se relacionam com esta tecnologia. As escolas envolvidas foram divididas em dois grupos: o primeiro grupo, constituído por três escolas onde o acompanhamento teve uma natureza mais interventiva e presente, enquanto que no segundo grupo, composto por duas escolas, foram apenas observadas as dinâmicas que se desenvolveram no processo de adoção e utilização do SC. No presente estudo, que se assume como um estudo longitudinal de multicasos, foram aplicadas técnicas de tratamento de dados como a estatística descritiva, a análise de conteúdo e a Social Network Analysis (SNA), com o objetivo de, através de uma triangulação permanente, proceder a uma análise dos impactos observados pela utilização do SC. Estes impactos podem ser situados em três níveis diferentes: relativos à instituição, aos professores e aos alunos. Ao nível da adoção institucional de uma tecnologia, verificou-se que essa adoção passa uma mensagem a toda a organização e que, no caso do SC, apela à participação coletiva num ambiente aberto onde as hierarquias se dissipam. Verificou-se ainda que deve implicar o envolvimento dos alunos em atividades significativas e a adoção de estratégias dinâmicas, preferencialmente integradas num projeto mobilizador. A adoção do SC foi ainda catalisadora de dinâmicas que provocaram mudanças nos padrões de consumo e de produção de conteúdos bem como de uma atitude diferente perante o papel da web social no processo de ensino e aprendizagem. As conclusões apontam ainda no sentido da identificação de um conjunto de fatores, observados no estudo, que tiveram impacto no processo de adoção como o papel das lideranças, a importância da formação de professores, a cultura das escolas, a integração num projeto pedagógico e, a um nível mais primário, as questões do acesso à tecnologia. Algumas comunidades construídas à volta do SAPO Campus, envolvendo professores, alunos e a comunidade, evoluíram no sentido da autossustentação, num percurso de reflexão sobre as práticas pedagógicas e partilha de experiências.

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.

Gestão do marketing de relacionamento: um estudo em empresas hoteleiras de Natal - RN

The present study it analyzes the Management of the Marketing of strategy Relationship as distinguishing for the host s companies of the city of Natal - RN. To carry through this analysis interviews with managers had been carried through, as well as the direct comment of processes, documents, actions and strategies developed for the hotels, with intention to know the level of perception and valuation of the relationship with customers, to verify resources and technologies used in the Management of the Relationship Marketing, identification, segmentation and differentiation of customers, personalization of products and services, and results of the emphasis in the relationship with customers for the host s companies. The research can be classified as exploratory - descriptive, and its universe is limited to the city of Natal, having enclosed hotels that have carried through tourist activity in 2005 and 2006. Still on the criteria of election of the sample, the study it investigated host s companies who if fit in the category superior luxury, or either, five stars, pertaining the national nets and international. How much to the treatment and analysis of the data the was made to leave of the theoretical support of the authors who work the thematic one and of the analysis of the interviews with managers, documents and processes observed for the researcher in the studied hotels. The research sample that the interviewed ones understand the importance to work the Management of the Marketing of Relationship in the host s companies me intention to get sustainable competitive advantage. One still evidenced that the searched hotels make use of strategies and instruments of Management of the Marketing of Relationship, however without an ample theoretical knowledge and yes only as base in the experience of the managers and spread processes already, generating one moment competitive advantage and not relationships of long duration

Ontology-Based Workflow Design for the Coordination of Homecare Interventions

Part 20: Health and Care Networks

Guidelines for Creating ICT-based Adaptable Learning Resources

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Preventing worker’s musculoskeletal disorders with wearable inertial sensors: ergonomics and modelling of physical human-robot interactions

The most widespread work-related diseases are musculoskeletal disorders (MSD) caused by awkward postures and excessive effort to upper limb muscles during work operations. The use of wearable IMU sensors could monitor the workers constantly to prevent hazardous actions, thus diminishing work injuries. In this thesis, procedures are developed and tested for ergonomic analyses in a working environment, based on a commercial motion capture system (MoCap) made of 17 Inertial Measurement Units (IMUs). An IMU is usually made of a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer that, through sensor fusion algorithms, estimates its attitude. Effective strategies for preventing MSD rely on various aspects: firstly, the accuracy of the IMU, depending on the chosen sensor and its calibration; secondly, the correct identification of the pose of each sensor on the worker’s body; thirdly, the chosen multibody model, which must consider both the accuracy and the computational burden, to provide results in real-time; finally, the model scaling law, which defines the possibility of a fast and accurate personalization of the multibody model geometry. Moreover, the MSD can be diminished using collaborative robots (cobots) as assisted devices for complex or heavy operations to relieve the worker's effort during repetitive tasks. All these aspects are considered to test and show the efficiency and usability of inertial MoCap systems for assessing ergonomics evaluation in real-time and implementing safety control strategies in collaborative robotics. Validation is performed with several experimental tests, both to test the proposed procedures and to compare the results of real-time multibody models developed in this thesis with the results from commercial software. As an additional result, the positive effects of using cobots as assisted devices for reducing human effort in repetitive industrial tasks are also shown, to demonstrate the potential of wearable electronics in on-field ergonomics analyses for industrial applications.

[In search of] Digital biomarkers for objective pain assessment

Pain is a highly complex phenomenon involving intricate neural systems, whose interactions with other physiological mechanisms are not fully understood. Standard pain assessment methods, relying on verbal communication, often fail to provide reliable and accurate information, which poses a critical challenge in the clinical context. In the era of ubiquitous and inexpensive physiological monitoring, coupled with the advancement of artificial intelligence, these new tools appear as the natural candidates to be tested to address such a challenge. This thesis aims to conduct experimental research to develop digital biomarkers for pain assessment. After providing an overview of the state-of-the-art regarding pain neurophysiology and assessment tools, methods for appropriately conditioning physiological signals and controlling confounding factors are presented. The thesis focuses on three different pain conditions: cancer pain, chronic low back pain, and pain experienced by patients undergoing neurorehabilitation. The approach presented in this thesis has shown promise, but further studies are needed to confirm and strengthen these results. Prior to developing any models, a preliminary signal quality check is essential, along with the inclusion of personal and health information in the models to limit their confounding effects. A multimodal approach is preferred for better performance, although unimodal analysis has revealed interesting aspects of the pain experience. This approach can enrich the routine clinical pain assessment procedure by enabling pain to be monitored when and where it is actually experienced, and without the involvement of explicit communication,. This would improve the characterization of the pain experience, aid in antalgic therapy personalization, and bring timely relief, with the ultimate goal of improving the quality of life of patients suffering from pain.

Characterizing “Exposure-PK/PD-Biomarker” relationships in patients with hematologic malignancies

Antimicrobial stewardship programs are gaining more and more relevance in optimizing anti-infective treatment and in preventing the emergence of antimicrobial resistance. Personalization of antimicrobial treatment based on real-time therapeutic drug morning (TDM) and dosing adaptation may represent an important tool in antimicrobial stewardship programs. In this Ph.D project, we aim to focus on differences in pharmacokinetics (PK) for meropenem and piperacillin/tazobactam and host response biomarkers (e.g., C-reactive protein) in severe Gram‐negative related infections occurring in oncohematologic patients. We are interested in identifying optimized model‐based individualized dosing strategies for these antibiotics focusing on biomarkers-guided prediction of PK and pharmacodynamic (PD) parameters using population PK/PD modelling. We expect to identify optimal model‐based dosing targets for these antibiotics for special populations for implementation in TDM routines, and mathematical models characterizing the relationship between biomarkers and outcomes in these populations.