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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]

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]

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]

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]