Voice assistants (VAs) are typically evaluated through task performance metrics and self-report questionnaires, but people’s voices themselves carry rich paralinguistic cues that reveal affect, effort, and interaction breakdowns. We present a within-subjects study (N=49) that systematically compared three VA personas across three usage scenarios to investigate whether speech-derived audio features can serve as a proxy for user experience (UX). Participants’ speech was analyzed for temporal, spectral, and linguistic markers, alongside standardized UX measures, brief mood and stress ratings, and a post-study questionnaire. We found correlations between specific speech features and self-reported satisfaction and experience. Furthermore, a machine learning model trained on speech features achieved promising accuracy in classifying UX levels, indicating that this might be a reasonable alternative to self-report instruments. Our findings establish speech as a viable, real-time signal for implicitly measuring UX and point toward adaptive VUIs that respond dynamically to emotional and usability-related vocal cues.

Lights out! With the escalating climate crisis, eco-feedback has gained prominence over the last decade. However, traditional approaches could be underperforming as they often use data-driven strategies and assume that people only need additional information about their consumption to change behavior. A proposed path to overcome this issue is to design eco-feedback to foster emotional connections with users. However, not much is known about the effectiveness of such designs. In this paper, we propose a novel care-based eco-feedback system. Central to the system is a Tamagotchi-inspired digital character named Infi who gets its life force from the user’s energy savings. Additionally, we harness the latest advancements in generative artificial intelligence to enhance emotional attachment through conversational interactions that users can have with Infi. The results of a randomized controlled experiment (N=420) convey the fact that this design increases emotional attachment, which in turn increases energy-saving behavior.

Whether it is sleep, diet, or procrastination, changing behaviours can be challenging. Individuals could design and build their own personalised digital interventions to help them reach their goals, but little is known about this process. Building upon previous research we propose the Behaviour Change with Trigger-Action Programming (BC-TAP) model which describes how individuals could bridge the gap between their current and desired behaviour through the creation of 'Do-It-Yourself' (DIY) digital interventions. We conducted a two-day participatory workshop based on the BC-TAP model with 28 participants. Participants articulated plans to change a behaviour of their choice and represented these plans in mobile device automations. After using their interventions for up to three weeks, participants reflected on their experience. Our findings report opportunities and challenges at each stage of the process. While formulating a digital proxy for certain behaviours was challenging, both failures and successes facilitated participants' awareness of their behaviour, and their ability to change it.

In future radio access networks, machine learning (ML) based strategies for short-term forecasting of vehicular trajectories will be key for anticipatory resource allocation and management at the mobile edge. However, training ML models in a centralized fashion, over data collected from a massive heterogeneous and dynamic set of devices, poses significant scalability, reliability, and efficiency challenges, which are still open to date. In this article, we look at the specific issue of scalable and resource-efficient training of ML models in a vehicular environment. To address such a challenge, we propose a new Gossip Learning scheme, i.e., a fully distributed, collaborative training approach based on direct, opportunistic model exchanges via wireless device-to-device (D2D) communications with no centralized support. Our approach is based on constantly improving each node's own model instance through knowledge transfer among nodes, and on different strategies for estimating the potential contribution of neighboring nodes to the training process at a node. Extensive numerical assessments on a variety of measurement-based dynamic urban scenarios suggest that our schemes are able to converge rapidly and provide sufficiently accurate forecasts of vehicle position for time horizons which are typical of future 5 G/6 G dynamic resource allocation algorithms.

This article focuses on identifying the defining features of a metered Arabic poetic structure. In addition to its research and educational advantages, emphasizing the comprehensibility of the structure, even without language proficiency, enables individuals to grasp its core essence. This understanding opens up opportunities for exploring comparative possibilities and establishing connections with poetic structures in different languages.To achieve this goal, this article presents a novel clustering approach based on the six following poem metrics (Diversity, Evenness, Variability, Repetition, Pattern Potential and Pattern Usage) and utilizing two distinct algorithms. We discovered that the most significant poem metrics are Repetition, Variability and Pattern Usage and two attributes are sufficient to characterize the poetry instead of six.

Doomsurfing, doomscrolling or zombie scrolling. These new additions to the tech vocabulary have become part of our everyday routine, scrolling endlessly through social media feeds. Furthermore, some users report a sense of compulsion, a decrease in mental wellbeing and an increased sense of distraction. A common complaint among users harks back to the Facebook newsfeed. In a field experiment with real Facebook users (), we investigate the difference between a strict newsfeed diet (where the newsfeed is automatically reduced to a minimum) and self-regulated newsfeed diet (where the newsfeed is reduced, but users can then manage its content). Our results indicate that both of these newsfeed diets are effective at reducing the time spent on Facebook’s platform ( for the strict diet, % for the self-regulated diet). Our findings also suggest that these design interventions come with positive and negative user experiences such as increased self-awareness and fear of missing out (FOMO).

With daily social media consumption among teens exceeding eight hours, it becomes increasingly important to raise awareness about the digital traces they leave behind. However, concepts of privacy literacy such as data and metadata can seem abstract and difficult to grasp. In this short research paper, we tackle this issue by designing, implementing and presenting an evaluation of a novel technology-enhanced pedagogical scenario for high school students. The scenario covers two main sessions. In a first session the SpeakUp social-media-like classroom interaction app is used to support a digitally mediated debate. In the second session, the actual learning traces from the digitally mediated debate are used as an object of study to enable students to reflect on the traces they leave behind on social media platforms. In order to enable this scenario we extended the existing SpeakUp app to the specifics of the context. The scenario was implemented and evaluated in real classrooms during a semester-long course on digital skills with 45 high school students. Our results show that the learning scenario is appreciated by students and even though non-STEM students might require more onboarding to be fully engaged in the digitally mediated debate, students from both STEM and non-STEM classes learn effectively. We discuss shortcomings and future research avenues.

Research output related to digital nudging has increased ten-fold over the last five years. Nudging in the digital realm differs from its analog counterpart in important ways. For instance, online, choice architectures can be interconnected and personalized using real time data. In the face of this development, it is crucial to understand the current state of the literature and to map out possible research gaps. This paper addresses this issue and provides a systematic review of empirical studies where digital nudges have been evaluated. The systematic review covers 73 peer-reviewed papers containing 109 separate studies where 231 digital nudges have been evaluated. Our results lead to nine open research questions to be addressed in the future by the research community.

Nowcasting, i.e., short-term forecasting, of end user location is becoming increasingly important for anticipatory resource management in radio access networks (RAN). In this paper, we look at the case of vehicles moving in dense urban environments, and we tackle the location nowcasting problem with a particular class of machine learning (ML) algorithms that goes under the name Gossip Learning (GL). GL is a peer-to-peer machine learning approach based on direct, opportunistic exchange of models among nodes via wireless device-to-device (D2D) communications, and on collaborative model training. It has recently proven to scale efficiently to large numbers of static nodes, and to offer better privacy guarantees than traditional centralized learning architectures. We present new decentralized algorithms for GL, suitable for setups with dynamic nodes. In our approach, nodes improve their personalized model instance by sharing it with neighbors, and by weighting neighbors' contributions according to an estimate of their marginal utility. Our results show that the proposed GL algorithms are capable of providing accurate vehicle position predictions for time horizons of a few seconds, which are sufficient to implement effective anticipatory radio resource management.

Arabic classical poetry meter is a sequence of patterns. A poetry verse is characterized by a meter and consists of two parts. Detecting classical poetry meter is important for teaching purposes, for poetry and prose categorization, for authorship recognition and for computational aesthetics. Automatically detecting the meter of any single verse written as a normal sentence is challenging. We need a global approach that processes phonological verse preparation, deals with distinguishing the first part of the verse from the second, handles meters disambiguation and covers the verse parts concordance. To tackle this challenge, we introduce a novel solution called the Arabic Meters Identification System (AMIS) that combines an exhaustive pattern data set, pattern matching and similarity. We evaluate our system on a vocalized poetry corpus and reach a precision of 99.3%.

Humanitarian organisations provide invaluable work to improve the lives of individuals impacted by natural and anthropogenic hazards. While humanitarian organisations are highly knowledge intensive, they often fail to manage knowledge effectively. Providing adequate incentives to foster knowledge sharing on a knowledge management system is a challenge for many organisations. It is especially important in the humanitarian context where organisational efficiency leads to saving more lives. We argue that gamification (i.e., the integration of game-like features, such as points or badges in non-game systems) is a viable solution to address the lack of knowledge sharing often encountered. To do so, we embark on a design science journey with Doctors Without Borders to investigate how gamification within (e.g., using a personal profile) and outside (e.g., using a connected ambient object) a knowledge management system can improve knowledge sharing. Our findings demonstrate that well-designed gamification can increase engagement and knowledge sharing, in particular for altruistic individuals.

Digital capacity building is a key policy for states to meet the Sustainable Development Goals. This paper1 presents one of the actions carried out by the United Nations Conference on Trade and Development (UNCTAD) in collaboration with the Swiss Federal Institute of Technology in Lausanne (EPFL) to tackle the multifaceted digitization challenges in developing and transition economies by introducing blended learning in one of its flagship executive courses. The main outcome is the obvious but generally neglected importance of focusing on the added values, the competence development, and confidence building for all stakeholders to trigger adoption. These objectives are partially tackled by blending digital and physical communication and awareness channels in face-to-face executive training sessions.

The humanitarian sector is entering in the network age. The explosion in access to new information and communication technology (ICT) like mobile phones, the internet, and social media, is changing the way people communicate in emergency situations. However, the use of new ICT has not yet been adequately accompanied by an analysis of the challenges it represents for the application of the four humanitarian principles, namely humanity, neutrality, impartiality and independence. These principles provide the foundations for humanitarian action and are central to establishing and maintaining access to affected people. This article addresses that issue and provides a design framework to guide humanitarian agencies towards a principled approach to ICT.

Interacting with knowledge in a timely fashion is critical for the success of humanitarian missions. At the same time, the places where humanitarian action is needed are often those where Internet connection is poor or not available at all, making digital knowledge access difficult. In this paper, we propose a novel knowledge delivery model that relies on a peer-to-peer middleware and uses low-cost computers for local knowledge replication. We have developed a system implementing the model and evaluated it during eight deployments in Médecins Sans Frontières missions. The evaluation demonstrated knowledge delivery abilities of the system and its usefulness for the field staff.

While co-located social media previously has been employed to enhance interaction in community building activities in previous work, its range of effects have not been quantitatively described. In this study we introduce a co-located social media app called SpeakUp to a community building project in the Za'atari Syrian refugee camp in Jordan. To overcome issues such as the lack of Internet access, we purposefully adapted the design of the application and ported its server to a low cost single board computer on a Raspberry Pi. We explore the effects of SpeakUp through field experiments with one control and two treatment groups, as well as with interviews and observations. Our results show that SpeakUp significantly increases refugees' level of participation and sense of community. Importantly, with the use of the application, female and male participants demonstrate no significant differences in participation, showing that the use of such applications can be equalizing. We also found that co-located social media supports asynchronous interaction when outside-the-classroom activities are involved.

Non-Governmental Organizations in the humanitarian field are particularly knowledge intensive structures. However, they often fail to manage this knowledge efficiently and thus waste resources repeating avoidable mistakes. Providing adequate incentives for knowledge sharing is a central issue in any knowledge management system and is still largely unresolved. In this position paper, we advocate for advances on the topic and lay out a research agenda to address the issue.

Research on learning dashboards aims to identify what data is meaningful to different stakeholders in education, and how data can be presented to support sense-making processes. This paper summarizes the main outcomes of a systematic literature review on learning dashboards, in the fields of Learning Analytics and Educational Data Mining. The query was run in five main academic databases and enriched with papers coming from GScholar, resulting in 346 papers out of which 55 were included in the final analysis. Our review distinguishes different kinds of research studies as well as different aspects of learning dashboards and their maturity in terms of evaluation. As the research field is still relatively young, many of the studies are exploratory and proof-of-concept. Among the main open issues and future lines of work in the area of learning dashboards, we identify the need for longitudinal research in authentic settings, as well as studies that systematically compare different dashboard design options.

Developing educational apps that cover a wide range of learning contexts and languages is a challenging task. In this paper, we introduce the AppComposer Web app to address this issue. The AppComposer aims at empowering teachers to easily translate and adapt existing apps that fit their educational contexts. Developers do not need to provide extensive translations and configurations of their apps and can simply follow certain guidelines to make their apps translatable and adaptable by the AppComposer. Since the AppComposer makes use of the standard internationalization specification used by OpenSocial, even external apps can be translated without contacting the original developer.

Timely access to critical information is crucial for any organization operating in situations of emergency. Deploying an adequate information system tailored to specific organizational needs and matched to the organizational structure is essential. To understand the knowledge management needs of Médecins Sans Frontières, one of the leading NGOs in the humanitarian and medical fields, we conducted in total 145 hours of in-depth interviews. This paper presents three identified key requirements for an effective knowledge management system specifically designed for large distributed NGOs, like MSF. Additionally, we introduce a novel social media called Graspeo designed and built from ground up to fulfill these requirements.

Peer assessment is seen as a powerful supporting tool to achieve scalability in the evaluation of complex assignments in large courses, possibly virtual ones, as in the context of massive open online courses (MOOCs). However, the adoption of peer assessment is slow due in part to the lack of ready-to-use systems. Furthermore, the validity of peer assessment is still under discussion. In this paper, in order to tackle some of these issues, we present as a proof-of-concept of a novel extension of Graasp, a social media platform, to setup a peer assessment activity. We then report a case study of peer assessment using Graasp in a Social Media course with 60 master's level university students and analyze the level of agreement between students and instructors in the evaluation of short individual reports. Finally, to see if both instructor and student evaluations were based on appearance of project reports rather than on content, we conducted a study with 40 kids who rated reports solely on their look. Our results convey the fact that unlike the kid evaluation, which shows a low level of agreement with instructors, student assessment is reliable since the level of agreement between instructors and students was high.

Location-based multicast addressing (LMA) yields an important building block for context-aware applications in mobile ad hoc networks (MANETs). In LMA, messages are routed based on their content as well as on the location of the sending and the receiving nodes. The same dynamism that motivates locations as part of the addressing mechanism for multicast applications in MANETs, makes such a multicast challenging to implement both efficiently and reliably across application scenarios. Different implementation strategies have been proposed in literature for abstractions similar to LMA, motivated and validated by specific applications. The goal of this paper is to devise specific implementation strategies for LMA and compare these strategies in the context of several application scenarios, in order to aid in the selection of a scheme for a given application. To that end, we first detail three algorithms for implementing LMA. The first, message-centric, strategy uses geographically scoped gossiping to propagate messages. The second, query-centric, strategy propagates queries of receivers to subsequently route messages. The third, hybrid, strategy strives for the best of both worlds through a restricted multicasting of both messages and queries. We compare these algorithms both analytically and empirically. We pinpoint differences and break-even points among the approaches based on communication patterns, contrasting our findings with common expectations and our analysis. Our evaluations show that the hybrid approach invariably outperforms at least one of the other approaches, making it a safe choice for settings with varying or unknown communication patterns.

We introduce spot cast, a new communication abstraction specifically aimed at the development of mobile proximity-based applications running in mobile ad hoc networks (MANETs). Our motivation lies in the fact that traditional communication abstractions, typically broadcast primitives with strong consistency guarantees, do not adequately capture the intrinsic here-and-now nature of such applications. Rather, developers need a communication abstraction offering the notion of proximity-based diffusion and some level of message durability, which is precisely what spot cast provides. We illustrate how spot cast can be used to implement mobile applications and we shortly discuss the correctness and the implement ability of the spot cast abstraction in MANETs.

Broadcast is an important building block in ad hoc networks. Its challenge is to deliver a message to all nodes in the network for a reasonable cost in terms of message load and delay. Several context-aware broadcasting protocols have been proposed in order to meet this challenge, using location or proximity information in order to fine-tune retransmission decisions. However, existing protocols often target one specific setting and can reveal to be sub-optimal when settings change. Typically, optimal parameters for dense networks will differ from optimal parameters for sparse networks. To address this issue, we propose PLAN-B an adaptive proximity-based broadcast protocol that offers the possibility to define policies in order to adapt its parameters for different network settings at runtime. Our performance evaluations show that PLAN-B outperforms existing static and adaptive protocols in terms of message load in changing and unknown densities up to a factor of 2.

As pervasive computing is becoming a reality, vehicular networking is becoming increasingly popular in the research community. The prospects of increasing safety and reducing energy expenditures lay at the heart of this surge of interest. A promising technology that allows to increase both safety and energy efficiency is automated driving, where vehicles follow each other and coordinate by message passing. It is essential to build such systems on top of solid communication building blocks, such as reliable and efficient broadcast protocols. In this paper we present BROADTRIP, a specialized broadcasting protocol for vehicles transiting in platoons. It leverages on location information and network coding in order to reduce the number of retransmissions needed to broadcast messages in platoons. We compare it with existing protocols through simulations, and show that BROADTRIP outperforms the next best comparable approach by 12% to 38% depending on settings.

In this paper, we introduce six-shot multicast (6Shot), a location-aware multicast algorithm devised for mobile ad hoc networks. Multicast is a one-to-many communication scheme and has largely been studied in such networks. Indeed, this communication primitive can be used as a building block for popular services, such as data streaming or group communication. The particularity of 6Shot is the location- aware routing scheme of its implementation, which offers improved efficiency in terms of message overhead compared to existing algorithms, for a reasonable cost in terms of reliability.

Applications aimed at enhancing the experience of vehicular transportation have been increasing in recent years with the widespread diffusion of smart mobile devices with network capabilities and access to user location. Such applications include navigation systems and location-based timetables. However, most of these applications only use individual contextual information in order to provide useful services to the end user. Sharing contextual information with other users can open a host of new possibilities, such as providing live traffic monitoring, where the location and the speed of individual cars are shared and indicates the flow of traffic; or friend locating, where the location of friends can be displayed on a map. In this paper, we argue that there is a lack of specialized programming support for such applications and we present DYMES, a dynamic messaging service devised to help fill this gap. Central to DYMES is a dynamic publish/subscribe system, which allows the publication of dynamic contextual information and the creation of dynamic context-based message filters. We present the core APIs provided by DYMES and illustrate their usage via two typical VANET applications. Furthermore, we identify and discuss implementation issues which guide the architectural choices in our ongoing work.

This paper introduces Pervaho, a platform for developing and testing mobile ad hoc applications. The Pervaho platform is founded on a location-based publish/subscribe service, which allows mobile peers to interact in a flexible and anonymous manner, based on their collocation at the time of the interaction. To validate the semantics of location-based criteria-a general issue in mobile ad hoc applications-we also propose a phone motion simulator as second cornerstone of our platform. Finally, we evaluate the usability of Pervaho by developing a concrete application with and without Pervaho

This paper introduces the concept of location-based publish/subscribe (LPS), which allows mobile ad hoc applications to anonymously communicate with each other, depending on their locations. With this concept, publish/subscribe topics are typically expressed in a dynamic manner including proximity criteria, e.g., "I subscribe to all events on topic T published within some range R". We advocate that location-based publish/subscribe is a key programming paradigm for building mobile ad hoc application, and sketch our current implementation, which is based on standard APIs of the Java 2 platform, Micro Edition