Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Challenging Social Media Threats using Collective Well-being Aware Recommendation Algorithms and an Educational Virtual Companion

Social media (SM) have become an integral part of our lives, expanding our inter-linking capabilities to new levels. There is plenty to be said about their positive effects. On the other hand however, some serious negative implications of SM have repeatedly been highlighted in recent years, pointing at various SM threats for society, and its teenagers in particular: from common issues (e.g. digital addiction and polarization) and manipulative influences of algorithms to teenager-specific issues (e.g. body stereotyping). The full impact of current SM platform design -- both at an individual and societal level -- asks for a comprehensive evaluation and conceptual improvement. We extend measures of Collective Well-Being (CWB) to SM communities. As users' relationships and interactions are a central component of CWB, education is crucial to improve CWB. We thus propose a framework based on an adaptive "social media virtual companion" for educating and supporting the entire students' community to interact with SM. The virtual companion will be powered by a Recommender System (CWB-RS) that will optimize a CWB metric instead of engagement or platform profit, which currently largely drives recommender systems thereby disregarding any societal collateral effect. CWB-RS will optimize CWB both in the short term, by balancing the level of SM threat the students are exposed to, as well as in the long term, by adopting an Intelligent Tutor System role and enabling adaptive and personalized sequencing of playful learning activities. This framework offers an initial step on understanding how to design SM systems and embedded educational interventions that favor a more healthy and positive society

Social contextuality and conversational recommender systems

As people continue to become more involved in both creating and consuming information, new interactive methods of retrieval are being developed. In this thesis we examine conversational approaches to recommendation, that is, the act of suggesting items to users based on the system’s understanding of them. Conversational recommendation is a recent contribution to the task of information discovery. We propose a novel approach to conversation around recommendation, examining how it is improved to work with collaborative filtering, a common recommendation algorithm. In developing new ways to recommend information to people we also examine their methods of information seeking, exploring the role of conversational recommendation, using both interview and sensed brain signals. We also look at the implications of the wealth of social and sensed information now available and how it improves the task of accurate recommendation. By allowing systems to better understand the connections between users and how their social impact can be tracked we show improved recommendation accuracy. We look at the social information around recommendations, proposing a directed influence approach between socially connected individuals, for the purpose of weighting recommendations with the wisdom of influencers. We then look at the semantic relationships that might seem to indicate wisdom (i.e. authors on a book-ranking site) to see if the ``wisdom of the few'' can be traced back to those conventionally considered wise in the area. Finally we look at ``contextuality'' (the ability of sets of contextual sensors to accurately recommend items across groups of people) in recommendation, showing that different users have very different uses for context within recommendation. This thesis shows that conversational recommendation can be generalised to work well with collaborative filtering, that social influence contributes to recommendation accuracy, and that contextual factors should not be treated the same for each user

Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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Supporting Serendipity through Interactive Recommender Systems in Higher Education

Serendipiteetin käsite viittaa onnekkaisiin sattumuksiin, jossa hyödyllistä tietoa tai muita arvokkaita asioita löydetään yllättäen. Suosittelujärjestelmien tutkimuksessa serendipiteetistä on tullut keskeinen kokemuksellinen tavoite. Ihmisen ja tietokoneen vuorovaikutuksen kannalta olennainen kysymys siitä, kuinka käyttöliittymäsuunnittelu suosittelujärjestelmissä voisi tukea serendipiteetin kokemusta, on kuitenkin saanut vain vähän huomiota. Tässä työssä tutkitaan, kuinka suosittelijajärjestelmän mahdollistamaa serendipiteetin kokemusta voidaan soveltaa tutkimusartikkelien suositteluihin korkeakouluopetuksen kontekstissa. Erityisesti työ tarkastelee suositusjärjestelmäsovellusten käyttöä kehittyvissä maissa, sillä suurin osa kehittyvissä maissa tehdyistä tutkimuksista on keskittynyt pelkästään järjestelmien toteutukseen. Tässä väitöskirjassa kuvataan suosittelujärjestelmien käyttöliittymien suunnittelua ja kehittämistä, tavoitteena ymmärtää paremmin serendipiteetin kokemuksen tukemista käyttöliittymäratkaisuilla. Tutkimalla näitä järjestelmiä kehittyvässä maassa (Pakistan), tämä väitöskirja asettaa suosittelujärjestelmien käytön vastakkain aikaisempien teollisuusmaissa tehtyjen tutkimusten kanssa, ja siten mahdollistaa suositusjärjestelmien soveltamiseen liittyvien kontekstuaalisten ja kulttuuristen haasteiden tarkastelua. Väitöskirja koostuu viidestä empiirisestä käyttäjätutkimuksesta ja kirjallisuuskatsausartikkelista, ja työ tarjoaa uusia käyttöliittymäideoita, avoimen lähdekoodin ohjelmistoratkaisuja sekä empiirisiä analyyseja suositusjärjestelmiin liittyvistä käyttäjäkokemuksista pakistanilaisessa korkeakoulussa. Onnekkaita löytöjä tarkastellaan liittyen tutkimusartikkelien löytämiseen suositusjärjestelmän avulla. Väitöstyö kattaa sekä konstruktiivista että kokeellista tutkimusta. Väitöskirjan artikkelit esittelevät alkuperäistä tutkimusta, jossa kokeillaan erilaisia käyttöliittymämalleja, pohditaan sidosryhmien vaatimuksia, arvioidaan käyttäjien kokemuksia suositelluista artikkeleista ja esitellään tutkimusta suositusjärjestelmien tehtäväkuormitusanalyysistä.Serendipity is defined as the surprising discovery of useful information or other valuable things. In recommender systems research, serendipity has become an essential experiential goal. However, relevant to Human-Computer Interaction, the question of how the user interfaces of recommender systems could facilitate serendipity has received little attention. This work investigates how recommender system-facilitated serendipity can be applied to research article recommendation processes in the context of higher education. In particular, this work investigates the use of recommender system applications in developing countries as most studies in developing countries have focused solely on implementation, rather than user experiences. This dissertation describes the design and development of several user interfaces for recommender systems in an attempt to improve our understanding of serendipity facilitation with the help of user interfaces. By studying these systems in a developing country, this dissertation contrasts the study of recommender systems in developed countries, examining the contextual and cultural challenges associated with the application of recommender systems. This dissertation consists of five empirical user studies and a literature review article, contributing novel user interface designs, open-source software, and empirical analyses of user experiences related to recommender systems in a Pakistani higher education institution. The fortunate discoveries of recommendations are studied in the context of exploring research articles with the help of a recommender system. This dissertation covers both constructive and experimental research. The articles included in this dissertation present original research experimenting with different user interface designs in recommender systems facilitating serendipity, discuss stakeholder requirements, assess user experiences with recommended articles, and present a study on task load analysis of recommender systems. The key findings of this research are that serendipity of recommendations can be facilitated to users with the user interface. Recommender systems can become an instrumental technology in the higher education research and developing countries can benefit from recommender systems applications in higher education institutions

Applicability of artificial intelligence in e-commerce fashion platforms

A inovação tecnológica e a democratização da inteligência artificial (IA) têm vindo a alavancar o potencial de sucesso em todas as áreas que conhecemos hoje, com expectativas do que ainda está para vir. A presente dissertação propõe uma análise das aplicações da IA na indústria da moda, particularmente nas plataformas de marcas de moda do comércio eletrónico, e de que forma está a ter impacto na esfera pessoal do consumidor, particularmente no processo de tomada de decisão dos consumidores da Geração Z. O âmbito da IA tem vindo a evoluir de tal forma que permitiu às empresas não só melhorar a sua oferta e a procura dos clientes, como também proporcionar uma experiência de compra que vai para além da “seleção e compra” mecânica: os pontos de contacto impulsionados pela IA influenciam e enriquecem cada fase do processo de tomada de decisão, seja de forma mais positiva ou negativa. Em última análise, esta dissertação pretende proporcionar ao leitor um melhor conhecimento sobre a IA e o comércio eletrónico de moda, bem como delinear o seu impacto no comportamento online do consumidor.Technological innovation and democratization of artificial intelligence (AI) have been leveraging the potential success in every field we know today, while more is yet to come. The following dissertation proposes an analysis of AI achievements within the fashion industry, particularly in e-commerce fashion brand platforms, and how it is impacting the consumer personal sphere, particularly the decision-making process of Gen-Z consumers. The field of AI has been evolving in such a way that allows companies to not only improve their supply and customer demand, but also provide a shopping experience that goes beyond the mechanical “select and buy“: AI-driven touchpoints influence and enrich each stage of the decision-making process, whether more positively or negatively. Ultimately, this dissertation intends to provide the reader a better knowledge of AI and fashion e-commerce joining applications, and to delineate its impact on the online customer journey