A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Living Innovation Laboratory Model Design and Implementation

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published and trying to discuss and define the model and architecture of LIL. People all acknowledge about the three characteristics of LIL: user centered, co-creation, and context aware, which make it distinguished from test platform and other innovation approaches. Its existing model consists of five phases: initialization, preparation, formation, development, and evaluation. Goal Net is a goal-oriented methodology to formularize a progress. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To prove the feasibility of LIL Goal Net Model, it was applied to two real cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully, based on LIL goal net based methodology. The two projects were evaluated by phenomenography, which was a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the positive evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf

Software-based dialogue systems: Survey, taxonomy and challenges

The use of natural language interfaces in the field of human-computer interaction is undergoing intense study through dedicated scientific and industrial research. The latest contributions in the field, including deep learning approaches like recurrent neural networks, the potential of context-aware strategies and user-centred design approaches, have brought back the attention of the community to software-based dialogue systems, generally known as conversational agents or chatbots. Nonetheless, and given the novelty of the field, a generic, context-independent overview on the current state of research of conversational agents covering all research perspectives involved is missing. Motivated by this context, this paper reports a survey of the current state of research of conversational agents through a systematic literature review of secondary studies. The conducted research is designed to develop an exhaustive perspective through a clear presentation of the aggregated knowledge published by recent literature within a variety of domains, research focuses and contexts. As a result, this research proposes a holistic taxonomy of the different dimensions involved in the conversational agents’ field, which is expected to help researchers and to lay the groundwork for future research in the field of natural language interfaces.With the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. The corresponding author gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the inancial support of his predoctoral grant FPI-UPC. This paper has been funded by the Spanish Ministerio de Ciencia e Innovación under project / funding scheme PID2020-117191RB-I00 / AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft

Detecting Low Rapport During Natural Interactions in Small Groups from Non-Verbal Behaviour

Rapport, the close and harmonious relationship in which interaction partners are "in sync" with each other, was shown to result in smoother social interactions, improved collaboration, and improved interpersonal outcomes. In this work, we are first to investigate automatic prediction of low rapport during natural interactions within small groups. This task is challenging given that rapport only manifests in subtle non-verbal signals that are, in addition, subject to influences of group dynamics as well as inter-personal idiosyncrasies. We record videos of unscripted discussions of three to four people using a multi-view camera system and microphones. We analyse a rich set of non-verbal signals for rapport detection, namely facial expressions, hand motion, gaze, speaker turns, and speech prosody. Using facial features, we can detect low rapport with an average precision of 0.7 (chance level at 0.25), while incorporating prior knowledge of participants' personalities can even achieve early prediction without a drop in performance. We further provide a detailed analysis of different feature sets and the amount of information contained in different temporal segments of the interactions.Comment: 12 pages, 6 figure

To whom to explain and what? : Systematic literature review on empirical studies on Explainable Artificial Intelligence (XAI)

Expectations towards artificial intelligence (AI) have risen continuously because of machine learning models’ evolution. However, the models’ decisions are often not intuitively understandable. For this reason, the field of Explainable AI (XAI) has emerged, which tries to create different techniques to help users understand AI better. As AI’s use spreads more broadly in society, it becomes like a co-worker that people need to understand. For this reason, AI-human interaction in research is of broad and current interest. This thesis outlines the current empirical XAI research literature themes from the human-computer interaction (HCI) perspective. This study's method is an explorative, systematic literature review carried out following the PRISMA (Preferred Research Items for Systematic Reviews) method. In total, 29 articles that concluded an empirical study into XAI from the HCI perspective were included in the review. The material was collected based on database searches and snowball sampling. The articles were analyzed based on their descriptive statistics, stakeholder groups, research questions, and theoretical approaches. This study aims to determine what factors made users consider XAI transparent, explainable, or trustworthy and to whom the XAI research was intended. Based on the analysis, three stakeholder groups to whom the current XAI literature was aimed for emerged: end-users, domain experts, and developers. This study’s findings show that domain experts’ needs towards XAI vary greatly between domains, whereas developers need better tools to create XAI systems. The end-users, on their part, considered case-based explanations unfair and wanted to have explanations that “speak their language”. Also, the results indicate that the effect of current XAI solutions on users’ trust towards AI systems is relatively small or even non-existing. The studies’ direct theoretical contributions and the number of theoretical lenses used were both found out to be relatively low. This thesis’s most immense contribution is to provide a synthesis of the extant empirical XAI literature from the HCI perspective, which previous studies have rarely brought together. Continuing this thesis, researchers can further investigate research avenues such as explanation quality methodologies, algorithm auditing methods, users’ mental models, and prior conceptions about AI.Odotukset tekoälyä kohtaan ovat kohonneet jatkuvasti koneoppimismallien kehittymisen vuoksi. Mallien tekemät päätökset eivät usein ole ihmiskäyttäjälle vaistonvaraisesti ymmärrettävissä. Tätä ongelmaa ratkomaan on syntynyt selittävän tekoälyn tutkimuskenttä, joka luo erilaisia tekniikoita käyttäjien ymmärryksen tueksi. Kun tekoälyn käyttö yhteiskunnassa yleistyy laajemmin, tulee siitä ikään kuin työkaveri, jota ihmisten tulee ymmärtää. Tästä syystä tekoälyn ja ihmisen välisen vuorovaikutuksen tutkiminen on nyt laajan mielenkiinnon kohteena. Tässä pro gradu -tutkielmassa hahmotellaan selittävän tekoälyn tutkimuskentän ajankohtaisia teemoja, ihmisen ja tietokoneen välisen vuorovaikutuksen näkökulmasta. Tutkielman metodi on tutkiva, systemaattinen kirjallisuuskatsaus, ja se suoritettiin seuraten PRISMA-ohjeistusta. Katsaukseen valikoitui yhteensä 29 ihmisen ja tietokoneen vuorovaikutuksen näkökulmasta selittävää tekoälyä empiirisesti tutkinutta artikkelia. Aineisto kerättiin tietokantahakujen ja lumipallo-otannan avulla. Tutkimuksia eriteltiin artikkeleja kuvailevien tietojen, niiden kohdeyleisön, tutkimuskysymysten sekä teoreettisten lähestymistapojen kautta. Tutkielman tarkoituksena on selvittää, millaiset tekijät saivat käyttäjät pitämään tekoälyä läpinäkyvänä, selitettävissä olevana tai luotettavana, sekä kenelle aihepiirin tutkimus oli suunnattu. Analyysin perusteella löytyi kolme ryhmää, joille nykyistä kirjallisuutta on suunnattu: loppukäyttäjät, toimialojen asiantuntijat sekä tekoälyn kehittäjät. Tutkielman tulokset osoittavat, että asiantuntijoiden tarpeet selittävää tekoälyä kohtaan vaihtelevat laajasti toimialojen välillä, kun taas sen kehittäjät kaipaisivat parempia työkaluja tuekseen. Loppukäyttäjien havaittiin pitävän tekoälyn antamia tapauskohtaisia esimerkkejä epäreiluina, ja haluavan juuri heitä puhuttelevia selityksiä. Tulokset ilmaisevat, että nykyisten selittävien tekoälytekniikoiden vaikutukset käyttäjien luottamukseen tekoälyä kohtaan ovat vähäisiä. Tutkimusten tieteellisen panosten ja niiden käyttämien teoreettisten näkökulmien määrän havaittiin olevan suhteellisen pieniä. Tämän tutkielman suurin tieteellinen panos on luoda yhteenveto empiiriseen, selittävän tekoälyn tutkimuskirjallisuuteen, ihmisen ja tietokoneen välisen vuorovaikutuksen näkökulmasta. Tätä näkökulmaa aiempi kirjallisuus on vain harvoin saattanut kokoon. Tutkielma avaa useita näkymiä jatkotutkimukselle, esimerkiksi selitysten laatumetodien, algoritmien auditointimenetelmien, käyttäjien ajatusmallien sekä aiempien käsitysten vaikutusten näkökulmista

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

An overview of the features of chatbots in mental health: A scoping review

Background: Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual languages. Chatbots have the potential to be useful tools for individuals with mental disorders, especially those who are reluctant to seek mental health advice due to stigmatization. While numerous studies have been conducted about using chatbots for mental health, there is a need to systematically bring this evidence together in order to inform mental health providers and potential users about the main features of chatbots and their potential uses, and to inform future research about the main gaps of the previous literature. Objective: We aimed to provide an overview of the features of chatbots used by individuals for their mental health as reported in the empirical literature. Methods: Seven bibliographic databases (Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar) were used in our search. In addition, backward and forward reference list checking of the included studies and relevant reviews was conducted. Study selection and data extraction were carried out by two reviewers independently. Extracted data were synthesised using a narrative approach. Chatbots were classified according to their purposes, platforms, response generation, dialogue initiative, input and output modalities, embodiment, and targeted disorders. Results: Of 1039 citations retrieved, 53 unique studies were included in this review. Those studies assessed 41 different chatbots. Common uses of chatbots were: therapy (n = 17), training (n = 12), and screening (n = 10). Chatbots in most studies were rule-based (n = 49) and implemented in stand-alone software (n = 37). In 46 studies, chatbots controlled and led the conversations. While the most frequently used input modality was writing language only (n = 26), the most frequently used output modality was a combination of written, spoken and visual languages (n = 28). In the majority of studies, chatbots included virtual representations (n = 44). The most common focus of chatbots was depression (n = 16) or autism (n = 10). Conclusion: Research regarding chatbots in mental health is nascent. There are numerous chatbots that are used for various mental disorders and purposes. Healthcare providers should compare chatbots found in this review to help guide potential users to the most appropriate chatbot to support their mental health needs. More reviews are needed to summarise the evidence regarding the effectiveness and acceptability of chatbots in mental health

Improving personalized elderly care: an approach using cognitive agents to better assist elderly people

Tesis por compendio de publicaciones[ES]El envejecimiento de la población a nivel global es una constante cada vez más presente en el día a día y las consecuencias derivadas de este problema son cada vez más impactantes para el correcto funcionamiento y estructuración de la sociedad. En este contexto, hablamos de consecuencias a nivel de crecimiento económico, estilos de vida (y jubilación), relaciones familiares, recursos disponibles por el gobierno a la franja etaria más anciana e inevitablemente la prevalencia de enfermedades crónicas. Es ante esta realidad que surge la necesidad de desarrollo y promoción de estrategias eficaces en el acompañamiento, prevención y estímulo al envejecimiento activo y saludable de la población para garantizar que las personas ancianas continúen teniendo un papel relevante en la sociedad en lugar de someterse al aislamiento y fácil deterioro de las capacidades físicas, cognitivas, emocionales y sociales. De esta forma, tiene todo el sentido aprovechar todos los desarrollos tecnológicos verificados en los últimos años, principalmente en lo que se refiere a avances en las áreas de dispositivos móviles, inteligencia artificial y sistemas de monitoreo y crear soluciones capaces de brindar apoyo diariamente al recopilar datos e indicadores del estado de salud y, en respuesta, proporcionar diversas acciones personalizadas que motiven la adopción de mejores hábitos de salud y medios para lograr este envejecimiento activo y saludable. El desafío consiste en motivar a esta población a conciliar su día a día con el interés y la voluntad de utilizar aplicaciones y sistemas que brinden este apoyo personalizado. Algunas de las abordajes recientemente explorados en la literatura con este objetivo y que han alcanzado resultados prometedores se basan en la utilización de técnicas de gamificación e incentivo al cumplimiento de desafíos a nivel de salud (como si la persona estuviera jugando un juego) y la utilización de interacciones personalizadas con objetos (ya sean físicos como robots o virtuales como avatares) capaces de brindar feedback más personal, creando así una conexión más cercana entre ambas entidades. El trabajo aquí presentado combina estas ideas y resulta en un enfoque inteligente para la promoción del bienestar de la población anciana a través de un sistema de cuidados de salud personalizado. Este sistema incorpora diversas técnicas de gamificación para la promoción de mejores hábitos y comportamientos, y la utilización de un asistente virtual cognitivo capaz de entender las necesidades e intereses del usuario para posibilitar un feedback e interacción personalizados con el fin de ayudar y motivar al cumplimiento de los diferentes desafíos y objetivos que se identifiquen. El enfoque propuesto fue validado a través de un estudio con 12 usuarios ancianos y se lograron resultados significativos en términos de usabilidad, aceptación y efectos de salud. Específicamente, los resultados obtenidos permiten respaldar la importancia y el efecto positivo de combinar técnicas de gamificación e interacción con un asistente virtual cognitivo que traduzca el progreso del estado de salud del usuario, ya que se lograron mejoras significativas en los resultados de salud después de la intervención. Además, los resultados de usabilidad obtenidos mediante la cumplimentación de un cuestionario de usabilidad confirmaron la buena adhesión a el enfoque presentado. Estos resultados validan la hipótesis de la investigación estudiada en el desarrollo de esta disertación