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Nowadays, end users can exploit end-user development (EUD) platforms to personalise their Internet of Things (IoT) ecosystems, typically through trigger–action rules. Unfortunately, within such platforms, users are forced to adopt a unique, vendor-centric abstraction: to define triggers and actions, they must specifically refer to every single device or online service needed to execute the intended behaviours. As a consequence, little social and practical benefits of EUD in this domain have emerged so far. In this paper, we build on the idea that other abstractions besides the vendor-centric one are possible, and that the growth of end-user personalisation in the IoT may depend on their identification. Specifically, we report on the results of a 1-week-long diary study during which 24 participants were free to collect trigger–action rules arising during their daily activities. First, we demonstrate that users would adopt different abstractions by personalising devices, information and people-related behaviours, where the individual is at the centre of the interaction. Then, we show that the adopted abstraction may depend on different factors, ranging from the user profile, e.g. their programming experience, to the context in which the personalisation is introduced. While users are inclined to personalise physical objects in the home, for example, they often go 'beyond devices' in the city, where they are more interested in the underlying information. Finally, we discuss the retrieved results by identifying new design opportunities to improve the relationship between users and the IoT. [ABSTRACT FROM AUTHOR]

People can nowadays take advantage of Digital Self-Control Tools (DSCTs) to regulate their technology use, e.g., by defining a usage timer or by removing some distractive functionality like recommendations or a newsfeed. In this paper, we aim to overcome the limits of these restrictive methods, e.g., their low effectiveness in the long term, by speculating on tools that can teach users how to use technology better. To this end, we present StepByStep, a novel mobile DSCT that proactively assists users in learning how to regulate smartphone use through adaptable and continuously variable interventions. The DSCT is the result of a 2-phase iterative design process. StepByStep starts conversations with users to suggest possible problematic smartphone behaviors, and it establishes personalized "learning paths" via gamification. It then progressively reduces its degree of support until the user acquires a sufficient level of independence, i.e., it is able to sustain a new behavior without the help of the tool. Two exploratory in-the-wild studies with 8 and 41 participants, respectively, show initial evidence that the adopted approach is promising and could effectively assist users into understanding when and how to reduce the time spent on their smartphones, thus avoiding unwanted smartphone habits. • Present a digital self-control that teaches users how to regulate smartphone use. • Describe how the app integrates established psychological theories and constructs. • Evaluate StepByStep in two in-the-wild studies with 8 and 41 participants. [ABSTRACT FROM AUTHOR]

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as "IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen." Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present HeyTAP2, a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user's need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, HeyTAP2 implements a semantic recommendation process that takes into account (a) the current user's intention, (b) the connected entities owned by the user, and (c) the user's long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference, thus allowing HeyTAP2 to provide refined recommendations that better align with the original intention. We evaluate HeyTAP2 by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare HeyTAP2 with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of HeyTAP2 in recommending IF-THEN rules that satisfy the current personalization intention of the user. [ABSTRACT FROM AUTHOR]

With the increase of connected devices and online services, and recently IoT devices, the number of notifications received by every person is growing. The importance of notifications, as they become part of people's lives, often depends on various factors that can differently influence the reaction and the disruption of recipients. For this reason, the generation and the distribution of notifications has been gaining importance in the design of new applications, services, and smart devices. Nowadays, developers have not yet fully exploited all the advantages that the availability of multiple devices could bring in the customization and the distribution of notifications, e.g., exploiting a cross-device approach. This paper presents XDN (cross device notification), a framework to assist developers in creating cross-device notifications by scripting. The XDN architecture includes: a library to enable developers to design personalized notifications to be distributed among ad-hoc networks of IoT/mobile devices; a GUI to assist developers in implementing and testing (through a simulator) notification strategies; a server-side runtime environment; and an XDN IoT/mobile library for device support. We discuss the requirements for cross device notification systems, and we present the features of the XDN framework, in particular from the point of view of developer advantages, validated through different scenarios. [ABSTRACT FROM AUTHOR]

Highlights • Investigate whether a high-level representation can help end-user better create trigger-action rules in the IoT. • Present EUPOnt, a high-level semantic model for end-user development in the IoT. • Propose an end-user development platform to execute high-level rules. • Evaluate the suitability of the EUPont approach with a 30-people user study. Abstract Various programming environments for End-User Development (EUD) allow the composition of Internet of Things (IoT) applications, i.e., connections between IoT objects to personalize their joint behavior. These environments, however, only support a one-to-one mapping between pairs of object instances, and adopt a low level of abstraction that forces users to be aware of every single technology they may encounter in their applications. As a consequence, numerous open questions remain: would a "higher level" of abstraction help users creating their IoT applications more effectively and efficiently compared with the contemporary low-level representation? Which representation would users prefer? How high-level IoT applications could be actually executed? To answer these questions, we introduce EUPont, a high-level semantic model for EUD in the IoT. EUPont allows the creation of high-level IoT applications, able to adapt to different contextual situations. By integrating the ontology in the architecture of an EUD platform, we demonstrate how the semantic capabilities of the model allow the execution of high-level IoT applications. Furthermore, we evaluate the approach in a user study with 30 participants, by comparing a Web interface for composing IoT applications powered by EUPont with the one employed by a widely used EUD platform. Results show that the high-level approach is understandable, and it allows users to create IoT applications more correctly and quickly than contemporary solutions. [ABSTRACT FROM AUTHOR]

A growing - yet debated - discussion around the negative aspects of overusing technology led researchers and practitioners to consider a new kind of psychological digital wellbeing, giving rise to the flourishing of digital self-control tools (DSCTs). As many people nowadays own and use several devices, there is a need to understand better their overlapping use (and non-use) and the relationships to digital wellbeing. This special issue aims to provide the academic and industry research communities a venue for work at the state-of-the-art on digital wellbeing and DSCTs, with a particular focus on digital wellbeing within complex technological and social contexts. [ABSTRACT FROM AUTHOR]

The increasing complexity of the new breed of distributed intelligent systems, such as the Internet of Things, which require a diversity of languages and protocols, can only be tamed with design and programming best practices. Interest is also growing for including the human factor, as advocated by the ambient intelligence (AmI) research field, whose focus is on transparently and intelligently supporting people. These new design methodologies are increasingly needed in the toolbox of new electronic and computer engineers, and teaching strategies should be devised that allow students to acquire a systems-level view instead of getting lost in technology-oriented approaches. This paper describes a study carried out over two academic years, in a course in AmI at Politecnico di Torino, Italy. In the course, a project-based learning approach was adopted, in which students design and prototype an AmI system, and their progress is closely monitored throughout the semester. This paper presents the learning goals and teaching strategies, analyzes the learning outcomes from the qualitative and quantitative points of view, and highlights the lessons learned in the process. [ABSTRACT FROM PUBLISHER]

• Propose a literature programming approach for prototyping Internet-of-Things systems. • Present the design and implementation of the IoT notebook, together with its novel features and architecture. • Evaluate the IoT notebook in an exploratory user study with 13 novice IoT developers working for a large IT company. Computational notebooks create narratives that consolidate text, executable code, and visualizations in a single document. They are widely used in data science, enabling data scientists to accurately document and execute the steps of their analyses in an exploratory and iterative manner. Prototyping Internet of Things (IoT) systems is also complex because of IoT heterogeneous and interconnected nature. Indeed, IoT system prototyping spans multiple development and execution environments, and developers, besides focusing on the code, are required to configure various devices. To ascertain if and how computational notebooks' capabilities might be useful in the IoT scenario, this work presents an IoT-tailored notebook architecture aimed at helping developers build and share a computational narrative around the prototyping of IoT systems. To that end, we propose the concept of "IoT notebook", for which we analyze the required features and present its first implementation. We evaluate our proposal by conducting an exploratory user study among non-expert IoT developers working in a large IT company. Finally, we point out the potential of this approach. [ABSTRACT FROM AUTHOR]

In the era of green energy and smart grids, the ability to access energy information and effectively analyze such data to extract key performance indicators is a crucial factor for successful building management. Energy data can in fact be exploited both in long-term policy adaptation and in shorter term habits modification, providing the basis for stable improvements of the overall efficiency of buildings and dwellings. To reach the ambitious goal of actually improving how buildings consume energy, four main challenges emerge from literature: (a) lack of skills and experience of energy managers, (b) complex and disparate data sets, which are currently blocking decision making processes, (c) mostly manual work-flows that struggle to find relevant information into overwhelming streams of data sourced by monitoring systems, and (d) lack of collaborations between organizational departments. This paper provides deeper insights on these challenges, by investigating the kind of analysis currently performed by energy managers (in Italy) and the expectations they have if required to reason about systems that will be available within the next five years, and proposes design recommendations for next generation energy intelligence systems. [ABSTRACT FROM AUTHOR]

• Investigate the habitualness of smarphone app switching behavior, i.e., transitioning from one app to another in the same usage session to consume content. • Present RecApps, an interactive floating widget designed to proactively support the transition from one app to another. • Evaluate RecApps in a three-week long in-the-wild study with 18 smartphone users. • Discuss the design space for proactively supporting app switching behaviors with a reference to different design dimensions, ranging from visibility to cognitive and physical demand. Despite a large body of literature analyzing mobile device usage, app switching is still an overlooked interaction. To better understand and streamline the app switching experience in modern smartphones, we first explore how to automatically extract and characterize habitual app switching behaviors from smartphone usage data. By applying a data analytic methodology based on association rules to a large dataset of smartphone usage, in particular, we demonstrate that users repeatedly switch between the same applications under different contexts (e.g., location and time). We then implemented the methodology in RecApps, an interactive floating widget that proactively suggests the next apps to be used while the user is interacting with their smartphone. We evaluate RecApps through an in-the-wild study with 18 participants. Findings show that RecApps simplifies and supports the transitions between the users' favorite apps, while highlighting the need for novel interactions supporting app switching behavior. We use such results to explore trade-offs in the design space for proactively supporting app switching behavior in mobile interaction. [ABSTRACT FROM AUTHOR]

This review aims to analyze the recent literature to find how the preattentive visual process is currently used in information visualization, particularly to improve the cognitive process in chart comprehension (i.e., perceptual effectiveness). The purpose of our literature review is to provide an overview of how concepts related to the preattentive process are used pragmatically in recent research. We searched different bibliography sources between 2010 and 2021, getting 29 articles that fit the review focus. In general, we discovered that the research work on exploiting the preattentive process in information visualization is currently not thoroughly explored. The main contribution of the paper is the analysis of the papers, from which we identified two categories of research directions according to the primary uses of preattentive concepts: "preattentive attributes as design components," and "the preattentive process as a measuring tool," with a gap between these two approaches. The review also highlighted two limitations in the current research literature: most works tend to focus on a particular chart type, only, with difficult to generalize results, and the manipulation of preattentive attributes is done implicitly, without providing to the graph designer the suitable awareness over the design decisions impact. We finally present a proposal about how to use the knowledge about the preattentive process in the first stages of design in information visualization to start closing the gap mentioned above with the graph designer. [ABSTRACT FROM AUTHOR]

Abstract: Buildings account for a relevant fraction of the energy consumed by a country, up to 20-40% of the yearly energy consumption. If only electricity is considered, the fraction is even bigger, reaching around 73% of the total electricity consumption, equally divided into residential and commercial dwellings. Building and Home Automation have a potential to profoundly impact current and future buildings’ energy efficiency by informing users about their current consumption patterns, by suggesting more efficient behaviors, and by pro-actively changing/modifying user actions for reducing the associated energy wastes. In this paper we investigate the capability of an automated home to automatically, and timely, inform users about energy consumption, by harvesting opinions of residential inhabitants on energy feedback interfaces. We report here the results of an on-line survey, involving nearly a thousand participants, about feedback mechanisms suggested by the research community, with the goal of understanding what feedback is felt by home inhabitants easier to understand, more likely to be used, and more effective in promoting behavior changes. Contextually, we also collect and distill users’ attitude towards in-home energy displays and their preferred locations, gathering useful insights on user-driven design of more effective in-home energy displays. [Copyright &y& Elsevier]

Abstract: Nowadays home automation, with its increased availability, reliability and with its ever reducing costs is gaining momentum and is starting to become a viable solution for enabling people with disabilities to autonomously interact with their homes and to better communicate with other people. However, especially for people with severe mobility impairments, there is still a lack of tools and interfaces for effective control and interaction with home automation systems, and general–purpose solutions are seldom applicable due to the complexity, asynchronicity, time dependent behavior, and safety concerns typical of the home environment. This paper focuses on user–environment interfaces based on the eye tracking technology, which often is the only viable interaction modality for users as such. We propose an eye-based interface tackling the specific requirements of smart environments, already outlined in a public Recommendation issued by the COGAIN European Network of Excellence. The proposed interface has been implemented as a software prototype based on the ETU universal driver, thus being potentially able to run on a variety of eye trackers, and it is compatible with a wide set of smart home technologies, handled by the Domotic OSGi Gateway. A first interface evaluation, with user testing sessions, has been carried and results show that the interface is quite effective and usable without discomfort by people with almost regular eye movement control. [Copyright &y& Elsevier]