Context-based multi-agent recommender system, supported on IoT, for guiding the occupants of a building in case of a fire

The evacuation of buildings in case of fire is a sensitive issue for civil society that also motivates the academic community to develop and study solutions to improve the efficiency of evacuating these spaces. The study of human behavior in fire emergencies has been one of the areas that have deserved the attention of researchers. However, this modeling of human behavior is difficult and complex because it depends on factors that are difficult to know and that vary from country to country. In this paper, a paradigm shift is proposed which, instead of focusing on modeling the behavior of occupants, focuses on conditioning this behavior by providing real-time information on the most efficient evacuation routes. Making this information available to occupants is possible with a solution that takes advantage of the growing use of the IoT (Internet of Things) in buildings to help occupants adapt to the environment. Supported by the IoT, multi-agent recommender systems can help users to adapt to the environment and provide the occupants with the most efficient evacuation routes. This paradigm shift is achieved through a context-based multi-agent recommender system based on contextual data obtained from IoT devices, which recommends the most efficient evacuation routes at any given time. The obtained results suggest that the proposed solution can improve the efficiency of evacuating buildings in the event of a fire; for a scenario with two hundred people following the system recommendations, the time they take to reach a safe place decreases by 17.7%.info:eu-repo/semantics/publishedVersio

A Framework for Exploiting Internet of Things for Context-Aware Trust-based Personalized Services

In the last years, we have witnessed the introduction of Internet of Things as an integral part of the Internet with billions of interconnected and addressable everyday objects. On the one hand, these objects generate massive volume of data that can be exploited to gain useful insights into our day-to-day needs. On the other hand, context-aware recommender systems (CARSs) are intelligent systems that assist users to make service consumption choices that satisfy their preferences based on their contextual situations. However, one of the major challenges in developing CARSs is the lack of functionality providing dynamic and reliable context information required by the recommendation decision process based on the objects that users interact with in their environments. Thus, contextual information obtained from IoT objects and other sources can be exploited to build CARSs that satisfy users’ preferences, improve quality of experience and recommendation accuracy. This article describes various components of a conceptual IoT based framework for context-aware personalized recommendations. The framework addresses the weakness whereby CARSs rely on static and limited contextual information from user’s mobile phone, by providing additional components for reliable and dynamic contextual information, using IoT context sources. The core of the framework consists of context recognition and reasoning management, dynamic user profile model incorporating trust to improve accuracy of context-aware personalized recommendations. Experimental evaluations show that incorporating context and trust in personalized recommendations can improve its accuracy

Co-creating a smart tourism local service system in rural areas: a case study from south

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe most recent trends show an increase in the urbanization of cities, and, consequently, inner territories become more depopulated, business activities get closed, services get reduced and the overall services become poor and not able to offer quality offers to visitors (Bolay, 2020). According to (United Nations, 2019), by 2050 more than three out of four people will be living in urban areas. Nowadays, many studies have addressed the evolution and features of Smart Cities (Van Dijk & Teuben, 2015) and tourism is also one of those spheres that got digitally transformed by Smart Cities (Khan, Woo, Nam, & Chathoth, 2017). One of the features of smart applications is the possibility to let the user be a driver of value in creating and sharing contents (Kontogianni & Alepis, 2020). However, the explosion of smart solutions enabled by the latest technological innovations has been mostly contextualized in urban environments while fewer solutions have been developed in less urbanized rural areas (Steyn & Johanson, 2010). The methodology used employs the merging of two of the core contemporary service research approaches: Service Science and Service-Dominant logic; the first offers an organizational framework to generate and integrate value co-creation in terms of a smart service systems (Polese, Botti, Grimaldi, Monta & Vesci, 2018). For the same purpose, but differently, the second proposes a different layout called service ecosystems (Vargo & Lusch, 2016). This combination of approaches overcomes individual model limitations by setting an integrated model that can be employed to hypercompetitive and experience-based sectors (Polese, Botti, Grimaldi, Monta & Vesci, 2018), and that was adopted by using a case study methodology, relying on semi-structured interviews

A Unified Recommendation Framework for Data-driven, People-centric Smart Home Applications

With the rapid growth in the number of things that can be connected to the internet, Recommendation Systems for the IoT (RSIoT) have become more significant in helping a variety of applications to meet user preferences, and such applications can be smart home, smart tourism, smart parking, m-health and so on. In this thesis, we propose a unified recommendation framework for data-driven, people-centric smart home applications. The framework involves three main stages: complex activity detection, constructing recommendations in timely manner, and insuring the data integrity. First, we review the latest state-of-the-art recommendations methods and development of applications for recommender system in the IoT so, as to form an overview of the current research progress. Challenges of using IoT for recommendation systems are introduced and explained. A reference framework to compare the existing studies and guide future research and practices is provided. In order to meet the requirements of complex activity detection that helps our system to understand what activity or activities our user is undertaking in relatively high level. We provide adequate resources to be fit for the recommender system. Furthermore, we consider two inherent challenges of RSIoT, that is, capturing dynamicity patterns of human activities and system update without a focus on user feedback. Based on these, we design a Reminder Care System (RCS) which harnesses the advantages of deep reinforcement learning (DQN) to further address these challenges. Then we utilize a contextual bandit approach for improving the quality of recommendations by considering the context as an input. We aim to address not only the two previous challenges of RSIoT but also to learn the best action in different scenarios and treat each state independently. Last but not least, we utilize a blockchain technology to ensure the safety of data storage in addition to decentralized feature. In the last part, we discuss a few open issues and provide some insights for future directions

Model-driven Personalisation of Human-Computer Interaction across Ubiquitous Computing Applications

Personalisation is essential to Ubiquitous Computing (Ubicomp), which focuses on a human-centred paradigm aiming to provide interaction with adaptive content, services, and interfaces towards each one of its users, according to the context of the applications’ scenarios. However, the provision of that appropriated personalised interaction is a true challenge due to different reasons, such as the user interests, heterogeneous environments and devices, dynamic user behaviour and data capture. This dissertation focuses on a model-driven personalisation solution that has the main goal of facili-tating the implementation of a personalised human-computer interaction across different Ubicomp scenarios and applications. The research reported here investigates how a generic and interoperable model for personalisation can be used, shared and processed by different applications, among diverse devices, and across different scenarios, studying how it can enrich human-computer interaction. The research started by the definition of a consistent user model with the integration of context to end in a pervasive model for the definition of personalisations across different applications. Besides the model proposal, the other key contributions within the solution are the modelling frame-work, which encapsulates the model and integrates the user profiling module, and a cloud-based platform to pervasively support developers in the implementation of personalisation across different applications and scenarios. This platform provides tools to put end users in control of their data and to support developers through web services based operations implemented on top of a personalisa-tion API, which can also be used independently of the platform for testing purposes, for instance. Several Ubicomp applications prototypes were designed and used to evaluate, at different phases, both the solution as a whole and each one of its components. Some were specially created with the goal of evaluating specific research questions of this work. Others were being developed with a pur-pose other than for personalisation evaluation, but they ended up as personalised prototypes to better address their initial goals. The process of applying the personalisation model to the design of the latter should also work as a proof of concept on the developer side. On the one hand, developers have been probed with the implementation of personalised applications using the proposed solution, or a part of it, to assess how it works and can help them. The usage of our solution by developers was also important to assess how the model and the platform respond to the developers’ needs. On the other hand, some prototypes that implement our model-driven per-sonalisation solution have been selected for end user evaluation. Usually, user testing was conducted at two different stages of the development, using: (1) a non-personalised version; (2) the final per-sonalised version. This procedure allowed us to assess if personalisation improved the human-com-puter interaction. The first stage was also important to know who were the end users and gather interaction data to come up with personalisation proposals for each prototype. Globally, the results of both developers and end users tests were very positive. Finally, this dissertation proposes further work, which is already ongoing, related to the study of a methodology to the implementation and evaluation of personalised applications, supported by the development of three mobile health applications for rehabilitation

Human-data interaction

We have moved from a world where computing is siloed and specialised, to a world where computing is ubiquitous and everyday. In many, if not most, parts of the world, networked computing is now mundane as both foreground (e.g., smartphones, tablets) and background (e.g., road tra c management, financial systems) technologies. This has permitted, and continues to permit, new gloss on existing interactions (e.g., online banking) as well as distinctively new interactions (e.g., massively scalable distributed real-time mobile gaming). An e ect of this increasing pervasiveness of networked computation in our environments and our lives is that data are also now ubiquitous: in many places, much of society is rapidly becoming “data driven”

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Untangle sustainable development goal 8 through data visualization and HCI methods

Following the approval of the 2030 Agenda for Sustainable Development in 2015, sustainability became a hotly debated topic. In order to build a better and more sustainable future by 2030, this agenda addressed several global issues, including inequality, climate change, peace, and justice, in the form of 17 Sustainable Development Goals (SDGs), that should be understood and pursued by nations, corporations, institutions, and individuals. In this thesis, we researched how to exploit and integrate Human-Computer Interaction (HCI) and Data Visualization to promote knowledge and awareness about SDG 8, which wants to encourage lasting, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. In particular, we focused on three targets: green economy, sustainable tourism, employment, decent work for all, and social protection. The primary goal of this research is to determine whether HCI approaches may be used to create and validate interactive data visualization that can serve as helpful decision-making aids for specific groups and raise their knowledge of public-interest issues. To accomplish this goal, we analyzed four case studies. In the first two, we wanted to promote knowledge and awareness about green economy issues: we investigated the Human-Building Interaction inside a Smart Campus and the dematerialization process inside a University. In the third, we focused on smart tourism, investigating the relationship between locals and tourists to create meaningful connections and promote more sustainable tourism. In the fourth, we explored the industry context to highlight sustainability policies inside well-known companies. This research focuses on the hypothesis that interactive data visualization tools can make communities aware of sustainability aspects related to SDG8 and its targets. The research questions addressed are two: "how to promote awareness about SDG8 and its targets through interactive data visualizations?" and "to what extent are these interactive data visualizations effective?"

Green Cities Artificial Intelligence

119 pagesIn an era defined by rapid urbanization, the effective planning and management of cities have become paramount to ensure sustainable development, efficient resource allocation, and enhanced quality of life for residents. Traditional methods of urban planning and management are grappling with the complexities and challenges presented by modern cities. Enter Artificial Intelligence (AI), a disruptive technology that holds immense potential to revolutionize the way cities are planned, designed, and operated. The primary aim of this report is to provide an in-depth exploration of the multifaceted role that Artificial Intelligence plays in modern city planning and management. Through a comprehensive analysis of key AI applications, case studies, challenges, and ethical considerations, the report aims to provide resources for urban planners, City staff, and elected officials responsible for community planning and development. These include a model City policy, draft informational public meeting format, AI software and applications, implementation actions, AI timeline, glossary, and research references. This report represents the cumulative efforts of many participants and is sponsored by the City of Salem and Sustainable City Year Program. The Green Cities AI project website is at: https://blogs.uoregon.edu/artificialintelligence/. As cities continue to evolve into complex ecosystems, the integration of Artificial Intelligence stands as a pivotal force in shaping their trajectories. Through this report, we aim to provide a comprehensive understanding of how AI is transforming the way cities are planned, operated, and experienced. By analyzing the tools, applications, and ethical considerations, we hope to equip policymakers, urban planners, and stakeholders with the insights needed to navigate the AI-driven urban landscape effectively and create cities that are not only smart but also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues