Tangible user interfaces : past, present and future directions

In the last two decades, Tangible User Interfaces (TUIs) have emerged as a new interface type that interlinks the digital and physical worlds. Drawing upon users' knowledge and skills of interaction with the real non-digital world, TUIs show a potential to enhance the way in which people interact with and leverage digital information. However, TUI research is still in its infancy and extensive research is required in or- der to fully understand the implications of tangible user interfaces, to develop technologies that further bridge the digital and the physical, and to guide TUI design with empirical knowledge. This paper examines the existing body of work on Tangible User In- terfaces. We start by sketching the history of tangible user interfaces, examining the intellectual origins of this field. We then present TUIs in a broader context, survey application domains, and review frame- works and taxonomies. We also discuss conceptual foundations of TUIs including perspectives from cognitive sciences, phycology, and philoso- phy. Methods and technologies for designing, building, and evaluating TUIs are also addressed. Finally, we discuss the strengths and limita- tions of TUIs and chart directions for future research

A Framework For Abstracting, Designing And Building Tangible Gesture Interactive Systems

This thesis discusses tangible gesture interaction, a novel paradigm for interacting with computer that blends concepts from the more popular fields of tangible interaction and gesture interaction. Taking advantage of the human innate abilities to manipulate physical objects and to communicate through gestures, tangible gesture interaction is particularly interesting for interacting in smart environments, bringing the interaction with computer beyond the screen, back to the real world. Since tangible gesture interaction is a relatively new field of research, this thesis presents a conceptual framework that aims at supporting future work in this field. The Tangible Gesture Interaction Framework provides support on three levels. First, it helps reflecting from a theoretical point of view on the different types of tangible gestures that can be designed, physically, through a taxonomy based on three components (move, hold and touch) and additional attributes, and semantically, through a taxonomy of the semantic constructs that can be used to associate meaning to tangible gestures. Second, it helps conceiving new tangible gesture interactive systems and designing new interactions based on gestures with objects, through dedicated guidelines for tangible gesture definition and common practices for different application domains. Third, it helps building new tangible gesture interactive systems supporting the choice between four different technological approaches (embedded and embodied, wearable, environmental or hybrid) and providing general guidance for the different approaches. As an application of this framework, this thesis presents also seven tangible gesture interactive systems for three different application domains, i.e., interacting with the In-Vehicle Infotainment System (IVIS) of the car, the emotional and interpersonal communication, and the interaction in a smart home. For the first application domain, four different systems that use gestures on the steering wheel as interaction means with the IVIS have been designed, developed and evaluated. For the second application domain, an anthropomorphic lamp able to recognize gestures that humans typically perform for interpersonal communication has been conceived and developed. A second system, based on smart t-shirts, recognizes when two people hug and reward the gesture with an exchange of digital information. Finally, a smart watch for recognizing gestures performed with objects held in the hand in the context of the smart home has been investigated. The analysis of existing systems found in literature and of the system developed during this thesis shows that the framework has a good descriptive and evaluative power. The applications developed during this thesis show that the proposed framework has also a good generative power.Questa tesi discute l’interazione gestuale tangibile, un nuovo paradigma per interagire con il computer che unisce i principi dei più comuni campi di studio dell’interazione tangibile e dell’interazione gestuale. Sfruttando le abilità innate dell’uomo di manipolare oggetti fisici e di comunicare con i gesti, l’interazione gestuale tangibile si rivela particolarmente interessante per interagire negli ambienti intelligenti, riportando l’attenzione sul nostro mondo reale, al di là dello schermo dei computer o degli smartphone. Poiché l’interazione gestuale tangibile è un campo di studio relativamente recente, questa tesi presenta un framework (quadro teorico) che ha lo scopo di assistere lavori futuri in questo campo. Il Framework per l’Interazione Gestuale Tangibile fornisce supporto su tre livelli. Per prima cosa, aiuta a riflettere da un punto di vista teorico sui diversi tipi di gesti tangibili che possono essere eseguiti fisicamente, grazie a una tassonomia basata su tre componenti (muovere, tenere, toccare) e attributi addizionali, e che possono essere concepiti semanticamente, grazie a una tassonomia di tutti i costrutti semantici che permettono di associare dei significati ai gesti tangibili. In secondo luogo, il framework proposto aiuta a concepire nuovi sistemi interattivi basati su gesti tangibili e a ideare nuove interazioni basate su gesti con gli oggetti, attraverso linee guida per la definizione di gesti tangibili e una selezione delle migliore pratiche per i differenti campi di applicazione. Infine, il framework aiuta a implementare nuovi sistemi interattivi basati su gesti tangibili, permettendo di scegliere tra quattro differenti approcci tecnologici (incarnato e integrato negli oggetti, indossabile, distribuito nell’ambiente, o ibrido) e fornendo una guida generale per la scelta tra questi differenti approcci. Come applicazione di questo framework, questa tesi presenta anche sette sistemi interattivi basati su gesti tangibili, realizzati per tre differenti campi di applicazione: l’interazione con i sistemi di infotainment degli autoveicoli, la comunicazione interpersonale delle emozioni, e l’interazione nella casa intelligente. Per il primo campo di applicazione, sono stati progettati, sviluppati e testati quattro differenti sistemi che usano gesti tangibili effettuati sul volante come modalità di interazione con il sistema di infotainment. Per il secondo campo di applicazione, è stata concepita e sviluppata una lampada antropomorfica in grado di riconoscere i gesti tipici dell’interazione interpersonale. Per lo stesso campo di applicazione, un secondo sistema, basato su una maglietta intelligente, riconosce quando due persone si abbracciano e ricompensa questo gesto con uno scambio di informazioni digitali. Infine, per l’interazione nella casa intelligente, è stata investigata la realizzazione di uno smart watch per il riconoscimento di gesti eseguiti con oggetti tenuti nella mano. L’analisi dei sistemi interattivi esistenti basati su gesti tangibili permette di dimostrare che il framework ha un buon potere descrittivo e valutativo. Le applicazioni sviluppate durante la tesi mostrano che il framework proposto ha anche un valido potere generativo

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research

A Hierarchical Core Reference Ontology for New Technology Insertion Design in Long Life Cycle, Complex Mission Critical Systems

Organizations, including government, commercial and others, face numerous challenges in maintaining and upgrading long life-cycle, complex, mission critical systems. Maintaining and upgrading these systems requires the insertion and integration of new technology to avoid obsolescence of hardware software, and human skills, to improve performance, to maintain and improve security, and to extend useful life. This is particularly true of information technology (IT) intensive systems. The lack of a coherent body of knowledge to organize new technology insertion theory and practice is a significant contributor to this difficulty. This research organized the existing design, technology road mapping, obsolescence, and sustainability literature into an ontology of theory and application as the foundation for a technology design and technology insertion design hierarchical core reference ontology and laid the foundation for body of knowledge that better integrates the new technology insertion problem into the technology design architecture

Legal Knowledge and Information Systems - JURIX 2017: The Thirtieth Annual Conference

The proceedings of the 30th International Conference on Legal Knowledge and Information Systems – JURIX 2017. For three decades, the JURIX conferences have been held under the auspices of the Dutch Foundation for Legal Knowledge Based Systems (www.jurix.nl). In the time, it has become a European conference in terms of the diverse venues throughout Europe and the nationalities of participants

Learning Biosignals with Deep Learning

The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas

Head-Driven Phrase Structure Grammar

Head-Driven Phrase Structure Grammar (HPSG) is a constraint-based or declarative approach to linguistic knowledge, which analyses all descriptive levels (phonology, morphology, syntax, semantics, pragmatics) with feature value pairs, structure sharing, and relational constraints. In syntax it assumes that expressions have a single relatively simple constituent structure. This volume provides a state-of-the-art introduction to the framework. Various chapters discuss basic assumptions and formal foundations, describe the evolution of the framework, and go into the details of the main syntactic phenomena. Further chapters are devoted to non-syntactic levels of description. The book also considers related fields and research areas (gesture, sign languages, computational linguistics) and includes chapters comparing HPSG with other frameworks (Lexical Functional Grammar, Categorial Grammar, Construction Grammar, Dependency Grammar, and Minimalism)

Adapting by copying. Towards a sustainable machine learning

[eng] Despite the rapid growth of machine learning in the past decades, deploying automated decision making systems in practice remains a challenge for most companies. On an average day, data scientists face substantial barriers to serving models into production. Production environments are complex ecosystems, still largely based on on-premise technology, where modifications are timely and costly. Given the rapid pace with which the machine learning environment changes these days, companies struggle to stay up-to-date with the latest software releases, the changes in regulation and the newest market trends. As a result, machine learning often fails to deliver according to expectations. And more worryingly, this can result in unwanted risks for users, for the company itself and even for the society as a whole, insofar the negative impact of these risks is perpetuated in time. In this context, adaptation is an instrument that is both necessary and crucial for ensuring a sustainable deployment of industrial machine learning. This dissertation is devoted to developing theoretical and practical tools to enable adaptation of machine learning models in company production environments. More precisely, we focus on devising mechanisms to exploit the knowledge acquired by models to train future generations that are better fit to meet the stringent demands of a changing ecosystem. We introduce copying as a mechanism to replicate the decision behaviour of a model using another that presents differential characteristics, in cases where access to both the models and their training data are restricted. We discuss the theoretical implications of this methodology and show how it can be performed and evaluated in practice. Under the conceptual framework of actionable accountability we also explore how copying can be used to ensure risk mitigation in circumstances where deployment of a machine learning solution results in a negative impact to individuals or organizations.[spa] A pesar del rápido crecimiento del aprendizaje automático en últimas décadas, la implementación de sistemas automatizados para la toma de decisiones sigue siendo un reto para muchas empresas. Los científicos de datos se enfrentan a diario a numerosas barreras a la hora de desplegar los modelos en producción. Los entornos de producción son ecosistemas complejos, mayoritariamente basados en tecnologías on- premise, donde los cambios son costosos. Es por eso que las empresas tienen serias dificultades para mantenerse al día con las últimas versiones de software, los cambios en la regulación vigente o las nuevas tendencias del mercado. Como consecuencia, el rendimiento del aprendizaje automático está a menudo muy por debajo de las expectativas. Y lo que es más preocupante, esto puede derivar en riesgos para los usuarios, para las propias empresas e incluso para la sociedad en su conjunto, en la medida en que el impacto negativo de dichos riesgos se perpetúe en el tiempo. En este contexto, la adaptación se revela como un elemento necesario e imprescindible para asegurar la sostenibilidad del desarrollo industrial del aprendizaje automático. Este trabajo está dedicado a desarrollar las herramientas teóricas y prácticas necesarias para posibilitar la adaptación de los modelos de aprendizaje automático en entornos de producción. En concreto, nos centramos en concebir mecanismos que permitan reutilizar el conocimiento adquirido por los modelos para entrenar futuras generaciones que estén mejor preparadas para satisfacer las demandas de un entorno altamente cambiante. Introducimos la idea de copiar, como un mecanismo que permite replicar el comportamiento decisorio de un modelo utilizando un segundo que presenta características diferenciales, en escenarios donde el acceso tanto a los datos como al propio modelo está restringido. Es en este contexto donde discutimos las implicaciones teóricas de esta metodología y demostramos como las copias pueden ser entrenadas y evaluadas en la práctica. Bajo el marco de la responsabilidad accionable, exploramos también cómo las copias pueden explotarse como herramienta para la mitigación de riesgos en circunstancias en que el despliegue de una solución basada en el aprendizaje automático pueda tener un impacto negativo sobre las personas o las organizaciones

Behave 2018 : Book of Abstracts

// Openings and Keynotes // In September 2018, more than 300 participants debated the perspectives of energy policy transition and implementation of sustainable energy technologies. At Behave 2018 (Conference on Behaviour and Energy Efficiency) in Zurich, the internationality and the interdisciplinarity of scholarship in the field were surprising and inspiring. The President of the Zurich University of Applied Sciences, Jean-Marc Piveteau, opened the conference by referring to the development of the ZHAW into one of the leading competence centres of applied sciences in the field of energy research in Europe. Benjamin Sovacool, Editor of “Energy Research & Social Sciences”, made a strong claim for research designs that refuse “discliplinary chauvinism” and “theoretical monogamy”. From his point of view, energy research can and should be more oriented towards real world problems and thus have to deal not only with theory, but also with policy relevance and application. The other keynote speakers were Marianne Zünd (Swiss Federal Office of Energy) and Marylin Mehlman (Co-Founder of Legacy 17). They outlined the complex and sometimes curious paths of social change towards more sustainable energy behaviours. Those paths lead us into the value systems of concerned citizens (Mehlman) and into the complex processes of democratic systems in the western world (Zünd). // On the Built Environment // Sustainability and environmental policies that increasingly target the building sector may explain why a lot of studies presented at Behave 2018 were devoted to understanding and changing behaviour related to energy use and CO2-emissions of buildings. While most research still focusses on the individual energy-relevant behaviour of private citizens, there is a small but growing interest in the decisions and actions of institutional building-owners and building professionals. Energy feedback, gamification, nudging, promoting acceptance and adoption of new technologies, science-city collaboration, alternative policies and business models were among the solutions discussed to support the energy transition. // On the Public Sphere // Within the framework of the sessions that were dedicated to communication sciences and discourse studies, scholars from all over Europe presented their investigations of media coverage, stakeholder expectations and goals, and systemic effects of focussed communication efforts. These sessions clearly showed that the challenge of changing behaviours cannot be faced by marketing measures or other isolated strategic measures. It’s rather about curating the common sense, deliberating citizens’ value systems and letting society participate in the huge project of policy transition. // On Interventions and Feedback // Much of the conference dealt with interventions concerning how to change humans’ behaviour towards more environmentally friendly actions in different sectors. A total of three different parallel sessions addressed humans’ behaviour and households. The importance of feedback was also considered in many talks during the conference. Last but not least, a session of several talks was dedicated to the transportation sector, which accounts for a large quantity of CO2-emissions. // Acknowledgement // With more than 30 sessions on these and other subjects and numerous personal discussions of the participants on the margins of the conference, Behave 2018 was a huge success. The members of the organising committee thank all participants, visitors, partners, the media and the ZHAW for their support that made this success possible