7,013 research outputs found
Use-cases on evolution
This report presents a set of use cases for evolution and reactivity for data in the Web and
Semantic Web. This set is organized around three different case study scenarios, each of them
is related to one of the three different areas of application within Rewerse. Namely, the scenarios
are: “The Rewerse Information System and Portal”, closely related to the work of A3
– Personalised Information Systems; “Organizing Travels”, that may be related to the work
of A1 – Events, Time, and Locations; “Updates and evolution in bioinformatics data sources”
related to the work of A2 – Towards a Bioinformatics Web
Devices, Information, and People: Abstracting the Internet of Things for End-User Personalization
Nowadays, end users can take advantage of end-user development platforms to personalize the Internet of Things. These platforms typically adopt a vendor-centric abstraction, by letting users to customize each of their smart device and/or online service through different trigger-action rules. Despite the popularity of such an approach, several research challenges in this domain are still underexplored. Which "things" would users personalize, and in which contexts? Are there any other effective abstractions besides the vendor-centric one? Would users adopt different abstractions in different contexts? To answer these questions, we report on the results of a 1-week-long diary study during which 24 participants noted down trigger-action rules arising during their daily activities. Results show that users would adopt multiple abstractions by personalizing devices, information, and people-related behaviors where the individual is at the center of the interaction. We found, in particular, that the adopted abstraction may depend on different factors, ranging from the user profile to the context in which the personalization is introduced. While users are inclined to personalize physical objects in the home, for example, they often go "beyond devices" in the city, where they are more interested in the underlying information. Our findings identify new design opportunities in HCI to improve the relationship between the Internet of Things, personalization paradigms, and users
Challenges in context-aware mobile language learning: the MASELTOV approach
Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment
Authoring Support for Mobile Interaction with the Real World.
Mobile phones have been established as devices for the interaction with objects from the everyday world, such as posters, advertisements or points of interest. However, the usage of physical mobile applications is often still restricted by fixed content and behavior, whose authoring usually requires a considerable coding effort. This paper presents an approach to an authoring tool that separates the creative process of authoring content and behavior for mobile applications from its technical deployment. The tool supports non-technical users in the creation of content and behavior for the mobile guiding application MOPS that associates its content with points of interest in the real world through Physical Mobile Interaction
Modeling Interdependent and Periodic Real-World Action Sequences
Mobile health applications, including those that track activities such as
exercise, sleep, and diet, are becoming widely used. Accurately predicting
human actions is essential for targeted recommendations that could improve our
health and for personalization of these applications. However, making such
predictions is extremely difficult due to the complexities of human behavior,
which consists of a large number of potential actions that vary over time,
depend on each other, and are periodic. Previous work has not jointly modeled
these dynamics and has largely focused on item consumption patterns instead of
broader types of behaviors such as eating, commuting or exercising. In this
work, we develop a novel statistical model for Time-varying, Interdependent,
and Periodic Action Sequences. Our approach is based on personalized,
multivariate temporal point processes that model time-varying action
propensities through a mixture of Gaussian intensities. Our model captures
short-term and long-term periodic interdependencies between actions through
Hawkes process-based self-excitations. We evaluate our approach on two activity
logging datasets comprising 12 million actions taken by 20 thousand users over
17 months. We demonstrate that our approach allows us to make successful
predictions of future user actions and their timing. Specifically, our model
improves predictions of actions, and their timing, over existing methods across
multiple datasets by up to 156%, and up to 37%, respectively. Performance
improvements are particularly large for relatively rare and periodic actions
such as walking and biking, improving over baselines by up to 256%. This
demonstrates that explicit modeling of dependencies and periodicities in
real-world behavior enables successful predictions of future actions, with
implications for modeling human behavior, app personalization, and targeting of
health interventions.Comment: Accepted at WWW 201
HeyTAP: Bridging the Gaps Between Users' Needs and Technology in IF-THEN Rules via Conversation
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 IF-THEN rules. Unfortunately, how to make such a personalization effective and appreciated is still largely unknown. On the one hand, contemporary platforms to compose IF-THEN rules adopt representation models that strongly depend on the exploited technologies, thus making end-user personalization a complex task. On the other hand, the usage of technology-independent rules envisioned by recent studies opens up new questions, and the identification of available connected entities able to execute abstract users' needs become crucial. To this end, we present HeyTAP, a conversational and semantic-powered trigger-action programming platform able to map abstract users' needs to executable IF-THEN rules. By interacting with a conversational agent, the user communicates her personalization intentions and preferences. User's inputs, along with contextual and semantic information related to the available connected entities, are then used to recommend a set of IF-THEN rules that satisfies the user's needs. An exploratory study on 8 end users preliminary confirms the effectiveness and the appreciation of the approach, and shows that HeyTAP can successfully guide users from their needs to specific rules
End User Development in the IoT: a Semantic Approach
The Internet of Things (IoT) is, nowadays, a well recognized paradigm. In this field, End User Development (EUD) is a promising approach that allows users to program their devices and services. The representation models adopted by contemporary EUD interfaces, however, are often highly technology-dependent, and the interaction between users and the IoT ecosystem is put to a hard test. The goal of my research is to explore new approaches and tools for helping end-users to program their technological devices and services. For this purpose, I proposed EUPont, an ontological model able to represent abstract and technology independent trigger-action rules, that can be adapted to different contextual situations. EUPont has been evaluated in terms of understandability, completeness, and usefulness. Currently, I am using the semantic features of the model in different research projects, e.g., to optimize the layout of EUD interfaces, and to design a recommender system of trigger-action rules. Preliminary results are promising, and confirm the benefit of using the semantic information of EUPont for helping end-users to better deal with the forthcoming IoT world
The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge
Nudging provides a way to gently influence people to change behavior towards a desired
goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and
context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by
tailoring nudges to the current situation of each individual user. However, designing smart nudges is
challenging, as different users may need different supports to improve their behavior. Determining
the next nudge for a specific user must be done based on the user’s current situation, abilities, and
potential for improvement. In this paper, we focus on the challenge of designing the next nudge by
presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible
for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user
can follow. The classification is tailored to individual users based on user profiles, current situations,
and knowledge of previous behaviors. This paper describes steps in the nudge design process and a
novel set of principles for designing smart nudges
PersoBOX: A Personalization Engine between ERP System and Web Frontend
The demand for personalization functions in e-shops is increasing steadily. In order to fulfil customer requirements best and to stimulate the customer’s buying experience positively, companies are aiming at an easy technical solution to the integration of ERP master data, CRM data, and transactional data from web shops. The current paper presents the state of the art in personalization in e-commerce and summarizes remaining problems. An integrated toolset, the so called PersoBOX, is introduced as a solution which connects the realm of ERP systems with web shops. We present a schematic architecture of the PersoBOX describing the data flows, as well as processes and functions to be implemented. The presentation of the architecture is a preliminary result of an ongoing research project in the area of personalization
My IoT Puzzle: Debugging IF-THEN Rules Through the Jigsaw Metaphor
End users can nowadays define applications in the format of IF-THEN rules to personalize their IoT devices and online services. Along with the possibility to compose such applications, however, comes the need to debug them, e.g., to avoid unpredictable and dangerous behaviors. In this context, different questions are still unexplored: which visual languages are more appropriate for debugging IF-THEN rules? Which information do end users need to understand, identify, and correct errors? To answer these questions, we first conducted a literature analysis by reviewing previous works on end-user debugging, with the aim of extracting design guidelines. Then, we developed My IoT Puzzle, a tool to compose and debug IF-THEN rules based on the Jigsaw metaphor. My IoT Puzzle interactively assists users in the debugging process with different real-time feedback, and it allows the resolution of conflicts by providing textual and graphical explanations. An exploratory study with 6 participants preliminary confirms the effectiveness of our approach, showing that the usage of the Jigsaw metaphor, along with real-time feedback and explanations, helps users understand and fix conflicts among IF-THEN rules
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