36 research outputs found
Digital Wellbeing for Teens: Designing Educational Systems (DIGI-Teens 2024)
No full textRecent research has identified the detrimental consequences stemming from the pervasive and excessive use of digital devices, prompting the emergence of the concept of digital wellbeing. This workshop serves as a platform for both researchers and practitioners to convene and delve into discussions surrounding the imperative task of educating young generations on digital wellbeing. Participants will engage in a collaborative exploration of innovative strategies and tools aimed at fostering a more mindful and conscious engagement with digital platforms. Through shared insights and collective expertise, the workshop aims at paving the way for a healthier and more balanced relationship with technology among today's teenagers
Design Recommendations for Smart Energy Monitoring: a Case Study in Italy
No full textIn 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 system
Estimate User Meaningful Places through Low-Energy Mobile Sensing
No full textDue to the increasing spread of location-aware applications, developers interest in user location estimation has grown in recent years. As users spend the majority of their time in few meaningful places (i.e., groups of near locations that can be considered as a unique place, such as home, school or the workplace), this paper presents a new energy efficient method to estimate user presence in a meaningful place. Specifically, instead of using commonly used but energy hungry methods such as GPS and network positioning techniques, the proposed method applies a Machine Learning algorithm based on Decision Trees, to predict the user presence in a meaningful place by collecting and analyzing: a) user activity, b) information from received notifications (receipt time, generating service, sender-receiver relationship), and c) device status (battery level and ringtone mode). The results demonstrate that, using 20 days of training data and testing the system with data coming from 14 persons, the accuracy (percentage of correct predictions) is 89.40% (standard deviation: 8.27%) with a precision of 89.04% and a recall of 89.40%. Furthermore, the paper analyzes the importance of each considered feature, by comparing the prediction accuracy obtained with different combinations of features
A Context and User Aware Smart Notification System
No full textNowadays, notifications are increasingly gaining momentum in our society. New smart devices and appliances are developed everyday with the ability to generate, send and show messages about their status, acquired data and/or information received from other devices and users. Consequently, the number of notifications received by a user is growing and the tolerance to them could decrease in a short time. This paper presents a smart notification system that uses machine learning algorithms to adequately manage incoming notifications. According to context awareness and user habits, the system decides: a) who should receive an incoming notification; b) what is the best moment to show the notification to the chosen user(s); c) on which device(s) the chosen user(s) should receive the notification; d) which is the best way to notify the incoming notification. After the design of a general architecture, as a first step in building such a system, three different machine learning algorithms were compared in the task of establishing the best device on which the incoming notification should be delivered. The algorithms were applied to a dataset derived from real data provided by the MIT Media Laboratory Reality Mining project, enriched with additional synthetic information
An Ontology-Based Approach for Setting Security Policies in Smart Homes
No full textTo preserve the security and the integrity of smart home environments, a smart home system should provide end users with mechanisms to define security-based policies on their devices and services without the need to know (and specify) details that strongly depend on the underlying technology. To this end, this paper presents an End-User Development tool that allows users to a) define high-level security policies like "do not record any sound in the living room tonight," b) check and debug high-level security policies against inconsistencies and redundancies, and c) translate high-level security policies into device-specific policies that can be applied at run-time. The tool implements a trigger-action programming paradigm, and it exploits a hybrid formalism based on ontologies and Petri Networks
Home Energy Consumption Feedback: A User Survey
No full textBuildings 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 display
IoT for Ambient Assisted Living: Care4Me - A Healthcare Support System
No full textResearch activities on healthcare support systems mainly focus on people in their own homes or nurses and doctors in hospitals. A limited amount of research aims at supporting caregivers that work with people with disabilities in assisted living facilities (ALFs). This chapter explores and applies the Internet of Things to the ALF context. In particular, it presents the design, the implementation, and the experimental evaluation of Care4Me, a system supporting the daily activities of assistants. The requirements for designing and implementing Care4Me derive from a literature analysis and from a user study. The solution combines wearable and mobile technologies. With this healthcare support system, caregivers can be automatically alerted of potentially hazardous situations. Furthermore, inhabitants can require assistance instantly and from any point of the facility. The system was evaluated in two ways. The authors performed a functional test with a group of professional caregivers, and deployed the system in an ALF in Italy, collecting the opinions of caregivers and inhabitants
EUDoptimizer: Assisting End Users in Composing IF-THEN Rules Through Optimization
No full textNowadays, several interfaces for End-User Development (EUD) empower end users to jointly program the behavior of their smart devices and online services, typically through trigger-action rules. Despite their popularity, such interfaces often expose too much functionality, and force user to search among a large number of supported technologies disposed in confused grid menus. This paper contributes to the EUD with the aim of interactively assisting end users in composing IF-THEN rules with an optimizer in the loop. The goal, in particular, is to automatically redesign the layout of EUD interfaces to facilitate users in defining triggers and actions. For this purpose, a) we define a predictive model to characterize the composition of trigger-action rules on the basis of their final functionality; b) we adapt different optimization algorithms to explore the design space; and c) we present EUDoptimizer, the integration of our approach in IFTTT, one of the most popular EUD interfaces.We demonstrate that good layout solutions can be obtained in a reasonable amount of time. Furthermore, an empirical evaluation with 12 end users show evidence that EUDoptimizer reduces the efforts needed to compose trigger-action rules
JEERP: Energy Aware Enterprise Resource Planning
No full textEver increasing energy costs, and saving requirements, especially in enterprise contexts, are pushing the limits of Enterprise Resource Planning to better account energy, with component-level asset granularity. Using an application-oriented approach we discuss the different aspects involved in designing Energy Aware ERPs and we show a prototypical open source implementation based on the Dog Domotic Gateway and the Oratio ER
From Users' Intentions to IF-THEN Rules in the Internet of Things
No full textIn 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 make 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, 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 her 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
