The deployment of Digital Twins (DTs) in heterogeneous Internet of Things (IoT) environments remains challenging due to protocol diversity, infrastructure complexity, and the absence of standardized methodologies. Existing solutions rely on ad-hoc architectures with manual configuration, limiting scalability and adaptability. This paper introduces DTDeployer, a platform for automated DT deployment and lifecycle management inspired by container orchestration. DTDeployer adopts a declarative configuration model, enabling users to define complex ecosystems through simple specification files, with automated provisioning of ingestion, processing, and output components. Key features include template-driven composability, multi-protocol data ingestion (HTTP, WebSockets, MQTT, AMQP, Kafka), integrated time-series storage, and developer-centric code generation for type-safe processing in Java and Python. By combining requirements analysis with a comprehensive architecture, this work establishes DTDeployer as a foundation for enterprise-grade DT deployments, lowering adoption barriers while supporting complex industrial applications.

Large Language Models (LLMs) are rapidly being embedded in consumer and service robots, enabling richer human–robot interaction, multimodal reasoning, and language-driven autonomy. However, the computational and lifecycle costs of training, inference, and continuous upgrade cycles raise urgent digital sustainability concerns: energy consumption, network dependency, privacy exposure, and hardware obsolescence. In this conceptual paper, we introduce the Sustainable Language-Driven Autonomy Framework (SLAF), a modular architecture and set of operational policies that align multimodal LLM reasoning with sustainability goals. SLAF decomposes the intelligence stack into Perception & Preprocessing, Local Cognition (Edge), High-level Reasoning (LLM), and Control & Execution layers, mediated by an Adapter responsible for compact semantic encoding, adaptive triggers, caching, and energy budgets. We propose quantitative primitives and trade-off models (e.g., energy-per-inference Einf, calls-per-mission Ncalls, mission energy Emission) and an evaluation protocol to make sustainability claims comparable and auditable. Finally, we map how SLAF addresses four research questions on zero-shot generalization, energy-efficient architectures, software-first lifespan extension, and cloud/on-device trade-offs. We conclude with a roadmap for empirical validation, lifecycle analysis, and user-centered studies to operationalize sustainable, language-enabled robotics.

Climate change poses a growing threat to biodiversity, particularly in fragile ecosystems such as wetlands and coastal regions. The Po Delta, one of Europe’s most significant wetland systems, is particularly vulnerable due to rising sea levels, changing salinity levels, and habitat loss. Within this context, the DISCOV.ER project aims to enhance public awareness and promote sustainable tourism by combining digital innovation and citizen science. This paper presents the DISCOV.ER mobile application, a prototype designed to engage both tourists and locals in exploring the Po Delta natural and cultural heritage in a gamified way, while participating in citizen science activities that help in monitoring the park’s ecosystem. The application merges location-based interactive quests, AI-assisted storytelling elements and collectible strategies to motivate users. We briefly describe the design process, which was carried out through participatory methods, and illustrate the application’s architecture and key features. Finally, we report on preliminary user testing, highlighting the strengths, challenges, and lessons learned in developing an AI-powered application for engaging and educational citizen science experiences that foster biodiversity awareness and monitoring.

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. Information and communication technology can be of great help in supporting the monitoring and management of crowds. Following the “digital twin concept”, a group or crowd of people – as a dynamic physical entity – is amenable to be represented and managed through digital models. Accordingly, in this work we present and develop a general notion of a Crowd Digital Twin (CDT) , intended as the digital twin associated to physical gatherings of humans. We motivate it as a fundamental component of crowd management systems, and investigate the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what the fundamental components of CDT systems are, and accordingly delineate a reference architecture aimed at flexibly supporting the corresponding physical-digital thread. We also discuss methods and tools that might be useful for supporting implementations of such a CDT concept, and delineate a set of challenges that may serve as a roadmap for future research on the topic.

Crowd management is the problem of dealing with physical crowds of humans, e.g., for safety reasons or to improve services through crowd-awareness. In previous work, the notion of a Crowd Digital Twin (CDT), namely the digital twin associated to physical gatherings of humans, was motivated and proposed as a fundamental component of crowd management systems. In this work, we better characterise the CDT notion, by investigating the functional and non-functional requirements that such a technical solution should satisfy. Then, we consider what are the fundamental components of CDT systems, and accordingly delineate a general architecture aimed at flexibly supporting the corresponding physical-digital thread.

The development of a crowd digital twin offers significant potential for enhancing public safety, urban planning, and event management. A key challenge in creating such a digital twin lies in the efficient and accurate acquisition of crowd-related data, particularly through object detection models deployed on resource-constrained devices. Through a series of experiments, we compare TinyML and Edge approaches in terms of detection accuracy, inferencing time, and resource utilization. Our findings highlight the trade-offs inherent in selecting detection models for crowd digital twin applications, underscoring the importance of aligning model choice with specific deployment needs.

Urban environments face significant challenges due to deteriorating road pavements, affecting all transportation modes' safety and comfort. Traditional methods of road assessment are costly and infrequent, but advancements in mobile technology and crowdsensing offer real-time, large-scale data collection solutions. This paper introduces “ShakeSensing”, a mobile application that uses Android device sensors to measure vibrations experienced by cyclists and scooter users, providing insights into road quality. The collected data, analyzed to assess road conditions, can benefit both users and municipal authorities, promoting safer and more comfortable travel.

Accurate demand forecasting is essential for optimizing public transportation systems, reducing congestion, and improving service efficiency. Traditional methods often struggle to capture the dynamic nature of urban mobility, leading to increasing reliance on machine learning and deep learning techniques. This paper explores various machine learning models for bus transportation demand forecasting, leveraging real-world data to evaluate their effectiveness. Our findings contribute to the development of intelligent public transport systems, supporting data-driven decision-making for adaptive and sustainable urban mobility solutions.

Vision equipment plays a crucial role in wildlife conservation by enabling the detection and collection of wild animal images, thereby providing an efficient way for preserving biodiversity observation. However, traditional manual detection methods are inefficient and costly. While cloud server-based methods offer an alternative, they introduce challenges such as transmission delays and data security concerns. To address these limitations, we propose an edge computing-based AI vision terminal for autonomous wildlife monitoring. Evaluations using the NCNN framework and varying input resolutions (640,320, 160 pixels) revealed that YOLOv8n models resulted in significantly faster inference times (up to 7.8-14.5x speedup at 160 pixels compared to 640 pixels). We implemented and evaluated quantized YOLOv8n and YOLOv8s models using NCNN on a Raspberry Pi, achieving significant inference speedups (18.840.1% reduction in inference time) compared to non-quantized models across various image sizes. YOLOv8s-int8 offered a better speed-precision trade-off ($24 \%$ faster for $6.8 \%$ lower precision) than YOLOv8n-int8 ($\mathbf{1 2 . 8 \%}$ faster for $\mathbf{1 1 . 6 \%}$ lower precision). This approach enables real-time animal detection with approximately 3 W power consumption, demonstrating the feasibility of deploying intelligent wildlife monitoring systems in remote, resource-constrained environments. Furthermore, the edge device exhibits robust detection performance for complex backgrounds and small targets.

Real-world handwritten documents often contain noise and complex elements, such as notes with colored markings, naturally degraded handwriting, and diverse paper backgrounds. Based on the strong demand for techniques that convert text images into editable digital formats, this study focuses on recognizing line-level handwritten Chinese text in complex backgrounds to improve recognition accuracy. Through a comparative analysis of five experimental settings, including no preprocessing, different preprocessing techniques, and advanced enhancement methods leveraging the self-attention mechanism from the transformer network, our reimagined CRNN model achieves the highest accuracy. These results confirm the effectiveness of the selfattention mechanism in boosting recognition performance under challenging conditions, offering valuable insights for future advancements in handwritten text recognition technologies.

The widespread diffusion of Large Language Models (LLMs) has ushered in a transformative era across numerous research domains, including web accessibility. In fact, they can potentially offer automated solutions for generating accessible content, performing accessibility testing, and enhancing the overall user experience for individuals with disabilities. In this paper, we investigate how LLMs can be successfully employed to evaluate and correct web accessibility. Then, we delve into the positive implications and the current challenges derived from the interaction between developers and LLMs in this specific context. Finally, we present some future directions that could be explored to ensure that web content remains accessible to all.

Driving involves numerous factors demanding a driver's attention. To enhance road safety, there has been a significant increase in the development and implementation of vehicle sensors. These sensors, in conjunction with mobile applications, can assess a driver's emotional state and focus, providing feedback to enhance their attention. This paper explores the monitoring of driver behavior, emphasizing the effects of distractions and emotions on driving performance. Stemming from the European initiative, NextPerception, this research focuses on advancing perception sensors and refining distributed intelligence models in various domains, including automotive. The initiative aims to develop a range of sensors, from obstacle detection tools to those monitoring a driver's eye movements and vital signs. A key goal is to define a “fitness-to-drive” metric, representing the driver's attentiveness level. The project also seeks to use gamification to emphasize the importance of this metric, increasing drivers' awareness of their driving skills. The ultimate aim is to create a system that determines fitness-to-drive based on distractions and emotions, integrating this into a prototype simulating sensor data, and introducing a web-based application to display this data for a community of drivers.

The term Digital Twin (DT) refers to an emerging concept and technology extending the value of physical assets through the services supported by a bidirectional connection with their digital counterparts. Since its very introduction, the DT concept has been applied to empower several kinds of physical assets across multiple domains, including Industry 4.0, infrastructures, autonomous vehicles, e-health, to name a few. The rise of socio-technical systems and cyber-physical-human systems, which feature humans as key system components, has led to the extension of the DT concept to humans themselves, bringing in the notion of "human digital twin", supporting human-system integration. However, in several contexts, humans are not just isolated entities, but rather form groups or crowds. The peculiar dynamics of such groups have to be carefully considered in various scenarios like emergency management or logistics scenarios. Motivated also by recent trends in social Internet of Things and collective computing, in this paper, we propose the notion of a "crowd digital twin", meant to support services considering the crowd and for the crowd itself. Supporting a bidirectional connection with a physical crowd, this notion is shown to present peculiar requirements, opportunities, and challenges, including low-latency synchronisation, environment-driven prediction, actuation, and validation of emergent behaviour.

Several elements can affect the drivers' behaviour while they are performing driving activities. Ranging from visual to cognitive distractions, emotions and other drivers' conditions (that could emerge from biometric data, such as temperature, heartbeat, pressure, etc.) can play a significant role, performing as a factor that can increase drivers' response time. This could be crucial in avoiding dangerous situations and in deciding and performing actions that could influence the happening of car accidents. This paper introduces the concept of the “Fitness-to-Drive” index and aims to evaluate how the arousal effects can influence the drivers' status. The paper presents some experimental evaluations we have conducted on a driver simulator, discussing the obtained results.

Virtual tours are tools that could be effectively used in many different contexts. The COVID-19 pandemic has let emerge how they can be useful and efficacy in those situations where it is not possible to access physical places and attend events. Besides that, they can be used for several goals in different contexts. In fact, in simple words, virtual tours are essentially based on using 3D images and videos, letting the users interact with the virtual environment and its points of interest through them, emphasizing the need for navigation and orienteering mechanisms within the virtual environment. Due to these characteristics, virtual tours represent digital barriers affecting users with disabilities, in particular those people with visual impairments. This paper presents an innovative solution we have adopted by designing and developing a prototype devoted to exploring a University Campus, with accessibility in mind. A preliminary evaluation has been conducted, and the obtained results are presented in the paper, which discusses some potentialities and limitations that emerged,

Recently in the world of boats we hear about “smart boats”, or 3.0 connected boats. For many years, technology has entered the world of boats, especially as regards safety, some systems have become mandatory. With the spread of IoT systems, boats could be considered as sources of information for other boats in the same sea area or for the construction of coastal IoT services. In the marine context, measurements of environmental values such as sea water temperature, wave period and height or direction of currents, are carried out using buoys of considerable size and at a great distance from the coast. In this paper we want to present a Social Internet of Things system that allows the distribution of information between boaters and the integration with fixed IoT networks consisting of buoys and coastal stations. A preliminary LoRa mesh communication test is presented showing a stable mesh network in which each node is able to communicate in the sea with a radius of 5 Km.

Abstract Incidental learning is a human learning paradigm that takes its form as a response to unplanned initiatives, potentially occurring at any time and at any place. It is emerging as very promising in the case of learning a second language, as it multiplies the occurrence of unexpected and unintentional learning situations. Still open, and subject of debate, is the issue of which is the right methodology to be applied when mobile computer-based technologies are exploited to provide support to such kind of a learning paradigm. Drawing on the results of an international Project (ILOCALAPP), in this paper we provide our reflections on a specific incidental learning methodology we have adopted, based on the use of a mobile application (the UniOn! App) that was developed at the University of Bologna to support mobility students in their daily activities. Our main goal was to understand if such an approach was effective to let individuals incidentally learn the Italian language and culture driven by our mobile application, while conducting informal and daily activities. Specifically, we aimed to measure how much students have learned Italian, by means of self-evaluation and self-assessment tools and statistical analysis, involving 95 students who incidentally improve their Italian language skills, supported by the app. In particular, we adopted a quasi-experimental design, and we focused our investigation on four different dimensions: target users, users’ language background, duration and frequency of use, context of use. The 95 participants were grouped into 5 case studies, based on the four different dimensions. After having used the app, the participants were asked to answer a survey. The obtained results confirm the viability of our approach, showing that incidental learning is enhanced if certain conditions (i.e., integrated guidance) are met, where more than 55% of the participants reported to be more confident about their acquired skills.

Multistep tasks refer to activities of daily living that require a sequence of actions to be performed (e.g., preparing a sandwich). Engaging persons with intellectual disabilities in such tasks is an important educational goal, but these persons may find it difficult to independently execute the correct sequence of steps necessary to perform the intended activity. In this proof-of-concept study, we evaluated the usability of a web application running on a mobile device to enable three young adults with intellectual and motor disabilities to independently complete multistep tasks. The results of this study provide preliminary evidence about the overall usability of the web application for persons with disability and their caregivers. Future studies may build on current evidence to prove the overall effectiveness of the proposed system.

The idea of equipping smart devices with the capacity to form social connections with their peers is gaining popularity. Indeed, it is a feature with a lot of promise for fostering device collaboration, speeding up and improving service discovery, and evaluating the reliability of devices by utilizing appropriate features based on peer evaluations in social networks. While numerous architectural solutions have been offered along with various concepts for creating the devices’ social network, the question of how to build and sustain the devices’ social connections has been neglected. This paper proposes a possible implementation of a Social Virtual Object as a constituent entity of the Social Internet of Things (SIoT) based on the concept of virtualization. In the experimental evaluation, the performances in the management of the relationships that originate from the proximity among objects and that represent the greatest difficulties of realization are analyzed. The field trials show the effectiveness of the proposed system in creating relationships and highlight some problems that should be addressed in the formalization of the social relationship in the SIoT.

The climatic conditions of places with a high tourist vocation greatly affect the flow of visitors. In the coastal context, measurements of environmental values such as sea water temperature, wave period and height or direction of currents, are carried out using buoys of considerable size and at a great distance from the coast. This results in a lack of information to satisfy the preferences of tourists and guarantee adequate safety in certain sea conditions. In this paper, we show the implementation of a small buoy that can be positioned near the beaches and that, through a Social Internet of Things platform, can share information with tourists. The first results of a real installation are also shown and what challenges need to be addressed for an efficient use of such a system.

Social media are revolutionizing various areas of our society, including the cultural heritage one. The same goes for artificial intelligent strategies and machine learning algorithms, which are exploited in many contexts, and could play an interesting role in promoting and sharing cultural heritage and touristic activities and points of interests in urban environments. An interesting application of these techniques in the cultural and touristic domain could be represented by the automatic recognition and classification of monuments and points of interest. In this work, we present a study on monuments recognition, starting from pictures taken by users by means of their mobile devices, while visiting an urban environment. In particular, we employed images collected by the citizens and tourists with the aim of contributing to valorize monuments or places in a specific case study, that is the city of Cesena (Italy). In this paper, we compared several architectures of convolutional neural networks, that are able to discriminate among a set of outdoor monuments with an accuracy of over 90%.

Moving around a university campus may seem an activity of little importance: students are young, they walk and run without problems, with a simple glance they know where they are and where to go, they understand where a lesson is held, when a teacher is available or where to go when they are hungry. Activities that millions of students do every day. But what about students with some form of physical impairments? For them, moving around a university campus is a daily challenge. Can conversational interfaces improve the accessibility of a University campus? is the research question we address in this study. We involved students (with and without disabilities) in the development and evaluation of a skill for the Amazon Alexa platform. Results show that conversational interfaces are highly appreciated by most of the participants and confirm that such interfaces might improve the daily experience of users within a university campus.

In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations).

The spoken language is the most natural interface for a human being and, thanks to the scientific-technological advances made in recent decades, nowadays we have voice assistance devices to interact with a machine through the use of natural language. Vocal user interfaces (VUI) are now included in many technological devices, such as desktop and laptop computers, smartphones and tablets, navigators, and home speakers, being welcomed by the market. The use of voice assistants can also be interesting and strategic in educational contexts and in public environments. This paper presents a case study based on the design, development, and assessment of a prototype devoted to assist students' during their daily activities in a smart campus context.

Indoor wayfinding systems are still presenting challenges and open issues in providing accurate directions while navigating complex and large buildings. When considering users who are experiencing any kind of vision loss, these issues can become really serious to tackle. To facilitate users (both visually impaired people and people without disabilities) in navigating an indoor environment, we designed and implemented a pervasive system taking advantage of the beacon technology and an accessible mobile application. In this paper, we present the system design issues and implementation challenges we addressed to provide user-friendly services in indoor contexts, exploiting the Proximity technique.

With the amazing success of PokemonGo, pervasive games have definitely caught the attention of the entire world. Their game experience is characterized by the fusion of real and virtual elements. In order to be able to extend the virtual world into the real one, such games have to access the mobile devices sensors like GPS and camera. For this reason, they are implemented as native or hybrid mobile applications. Contrary to this trend, in this paper we present Shoot Them All, a pervasive game implemented as a web application. In Shoot Them All, players have to hit the opponents, trying, at the same time, not to get hit. The battlefield is the real world, while the guns are virtual, as an extension of players' mobile devices. We were able to successfully developed such a game thanks to the availability of Javascript APIs that allow to access and manage device sensors. The success of this case study highlight how these technologies are mature enough, opening the door to web pervasive games.

Conversational interfaces are making web accessibility studies obsolete. Long-time has passed since the introduction of graphical interfaces. They revolutionized the way people used the computer. The desktop metaphor popularized and made easy access to the software but did not consider the specific needs of people with some form of sight and/or motion impairments. Accessibility, at that time, was a minor issue. The web changed everything and empathized the need to include people. Researchers from around the world began working on accessibility, which is still an issue on the agenda of many scientific labs. Despite this, we observe that today, with the diffusion of conversational interfaces, there is less need to bother about colors, fonts, size and many other visual features. Indeed, people might access to web contents through voice interaction. No need to see the graphic interface or to use input devices requiring the use of hands. The voice is everything you need, and accessibility has never been easier. Conversational interfaces are about to revolutionize accessibility and in this provocative paper, we show benefits, problems, and open issues that happen when these interfaces meet accessibility requirements.

"Covid-19 is a virus developed to rule the world" is just one of the many fake-news published on the Web. In this pandemic period, the Web is flooded with real news, allegedly true or blatantly false. To understand how fake news is affecting the Covid-19 perception, we selected 40 news (either true or fake) related to the origin, diffusion, treatment and effects of Covid-19 and we asked 293 volunteers to express their opinion on the truthfulness of the news. Then, we propose an Awareness index to compute knowledge degree of the volunteers. The results highlight a large ignorance on medical news, ignorance that goes beyond educational background. The study highlights the need for Health Institution to enter social media platforms in order to clearly explain what is true and what is false on Covid-19.

Sentiment analysis, social networks analysis, and social media sensing are becoming important tools to extract meaningful information from text, adopted in several contexts, ranging from social interactions, touristic activities, shopping and e-commerce, to name a few. In particular, the current CoVid-19 quarantine the world is witnessing has shown the potential of such tools as a way to monitor and understand people’s mood and feelings, in a time where people are resorting, more than ever, to social networks to engage and communicate with others. Indeed, when performing social network content analysis, privacy is a major concern. On the one hand, privacy issues and international laws and acts drive such analysis (e.g., GDPR), with the aim of protecting persons’ privacy and security. On the other hand, these can limit somehow such activities. Hence, a precise and accurate identification of the strategies to adopt should be done to balance privacy issues and sentiment analysis activities. Taking into account the requirements of a Urban Innovation Action project, which is based on the active involvement of citizens, this work aims to describe limitations and potentialities of social networks monitoring and analysis to understand users’ mood about the project actions adopted in the city of Ravenna (in Italy) to improve specific districts.

Many different environments are looking at gamification to improve education, business, tourism, smart-cities management, etc. Despite its popularity, and despite the availability of many studies that propose approaches to transform a non-game activity into a game, a gamification strategy guideline is missing. Usually, the proposed methods are too general to be effective (e.g., simple rules, incentive mechanisms such as scores or vague prizes). In a society where algorithms personalize everything, and where people with different impairments (either technological or physical) are present, it is important to also understand peoples preferences in terms of games. In this paper, through a questionnaire filled by 22 people, we show that the game preferences (rules, mechanics, focus, motivations, and gaming environment) are assistive-technology dependent. These preferences can be used to customize the gamification process and therefore the study might be helpful to develop effective gamification strategies.

Industry 4.0 is world wide recognized as the current trend of automation and data exchange in manufacturing technologies, driving companies and manufacturing activities in the so-called “smart factory” scenario. In this context, human workers are playing an important role, exploiting their skills and capabilities, acting as creative problem solvers. This means that adequate users interfaces and innovative workers' enabling services and functionalities have to be defined, letting humans effectively understand, monitor, and control the automated processes of Industry 4.0. In this paper, we propose a video monitoring multi-dimensional architecture, in the context of predictive maintenance of machinery monitored by means of smart cameras. Some preliminary tests conducted with the aim of evaluating proper configuration are presented and discussed.

Monitoring environmental conditions can provide great benefits, allowing users to be aware of ambient parameters, such as temperature, humidity, pressure, lights, pollution, etc. Collecting and measuring such kind of information means that, after adequate analyses, specific actions can be decided and applied by policymakers in urban scenarios and in public smart buildings, with the aim of improving humans' comfort. Observing and keeping environmental conditions in a specific range is fundamental also for the conservation and the preservation of goods, such as cultural heritage, pieces of art, and also papery documentations. Hence, IoT and smart objects can play a strategic role in equipping smart buildings with systems devoted to such monitoring activities. In this paper, we present an experiment we have conducted with the aim of comparing sensed data obtained with different assessment scenarios, applied in a library storage within a university campus. We have monitored the indoor environmental conditions for three days for each assessment scenario, collecting data about temperature, relative humidity, pollutants. To do that, we have exploited a low-cost multi-sensor platform, named Canarin II, applying the IoT paradigms. We here discuss the results we have obtained, reflecting on the pros and cons of each sensor configuration in the library storage, for a better preservation of its books and papery documents.

In this paper, using Reddit, we investigated on the efficacy of using an AI-based mood analysis methodology to understand how the prescription of different prenatal diagnostic tests (invasive vs non-invasive), and their corresponding outcomes, may have an impact onto the mood of parents-to-be. We found an interesting causal relationship between tests and mood that deserves a deeper comprehension by means of healthcare professionals. We have also demonstrated that combining AI-based dialogue analysis methodologies with advanced statistics may bring to a smart model, suitable for transforming a large amount of empirical data into correct medical interpretations.

One of the main global challenges to cope with for a sustainable development is transport and mobility. They are undisputed key issues of modern life affecting people's well-being and quality of life, which are significantly impacting the environment. In this scenario, innovative self-driving car technologies and electric solutions, emerging sensing technologies, and interconnected infrastructure can lead to more efficient transport systems and innovative mobility services, keeping into account the urgent need to foster a sustainable development. This workshop aims to gather practitioners with different background and different perspective from both Academia and Industry to further the knowledge in the area of emerging smart technologies and infrastructures for Smart Mobility and Sustainability.

Machine (learning)-based techniques have made substantial advances recently, and there is a general suggestion that they will drive major changes in health care within a few years. Yet, we all suffer from the lack of precise comparative studies on the accuracy of machine-based interpretations of medical data. To fill this gap, in this paper we investigate on the efficacy of using an automated mood analysis methodology to understand how patients react to the prescription to take different kinds of prenatal diagnostic tests (invasive vs non-invasive) and to the corresponding outcomes, based on conversations developed on Reddit. Our study essentially provides answers to research questions concerning: i) the popularity of prenatal diagnosis, ii) the patients' sentiment about different prenatal tests, iii) the existence of a cause-effect relationship between prenatal testing and patients' mood, and iv) the type of dialogues held by patients and physicians on this topic. Nonetheless, a general result emerging from our research is that a machine-based decision loop for now still needs human involvement, at least to alleviate the tension between empirical data and their correct medical interpretation.

Mobility and ambient conditions are key factors in urban environments, affecting well-being and quality of life. In this context, sensors, smart mobility, networks, connectivity can play a significant and strategic role, being exploited with the aim of improving data and information available to public administration and to each citizen. In this way, they can be supported in having more sustainable and aware behaviours and in getting useful information and services, improving their daily activities. In this paper, we present a prototype of smart bike eco-system, designed with the aim of collecting, aggregating and sharing data about air pollution and about the urban environment, which can be exploited in a smart mobility context thanks to sensor and vehicular networks.

Moving across a University campus (outdoor, among the buildings, and indoor, among classrooms and offices) could represent a barrier for students with disabilities, affecting their independence while they conduct their daily activities. Providing support by means of smart phones thanks to location technologies can be a useful means of integration and inclusion, with the effect of facilitating also tourists, newbies, or freshmen. This is particularly true in those contexts where the universities are hosted in historical buildings in old towns, which is a typical situation in the European countries. This paper presents AlmaWhere, a system based on beacon technology, designed and developed with the aim of equipping students of the University of Bologna with an indoor navigation system, providing them support in finding classes, labs, and libraries, with a specific attention those users with disabilities. The paper describes the main design issues, the system architecture, and technologies we have analyzed in order to design a prototype and two scenarios that involve personas.

Learning is considered one of the most important activities for the human being, and many educational institutes are trying to improve the engagement with students through the availability of video lectures. However, the access to video material is not easy as one may think. For millions of people with some form of disabilities the simple act of browsing a video lecture archive represents an insuperable burden. Motivated by the need to improve accessibility to video lecture materials, in this paper we present VLP, which stands for Video Lecture Playlist. The idea is to use low-level audio/video features, video segmentation and OCR analysis to “understand” the content of the video lectures. In this way, students can search for specific topic through keywords and the system browses the entire video lecture archive to find all the pieces of video lectures that cover the searched topic. These pieces are then provided through a playlist, so that a single search and a single playout return a complete view of how the searched topic is covered within the entire archive. A developed prototype shows the feasibility of the approach and results obtained from a survey highlight that students would like to browse video lecture archives with keywords and playlists.

The gamification process is gaining increasing interest in many different environments such as education, business, tourism, smart-cities management. In literature, many studies proposed approaches to transform a non-game activity into a game, but these methods are often too general to be effective (e.g., simple rules, incentive mechanisms such as scores or vague prizes). In a society where algorithms personalize everything, from search engine results, to news on social networks, it is anachronistic to think of a single gamification strategy suited to different categories of people (e.g., what might be motivating for digital natives, might not be for digital immigrants, and vice versa). In this paper, through a questionnaire filled by 226 people, we show that the game preferences (rules, mechanics, focus, motivations, and gaming environment) are age-dependent. These preferences can be used to customize the gamification process and therefore the study might be helpful to develop effective gamification strategies.

Supporting high school students in choosing the degree programme that better suits their aspirations and preferences could play a key role in Universities and Colleges strategies, since it could be a crucial element in limiting the students' drop-out phenomenon. With the aim of welcoming freshmen belonging to the digital native generation, we have defined a two-level design framework, by exploiting user-driven innovation and open innovation approaches in a co-design experience. This paper describes such an experience we have conducted by exploring HCI and ICT to design a mobile application that can drive students in choosing the programme that suits their skills and interests, deploying it in the rich scenario of the University of Bologna (Alma Mater Studiorum). The experiment involved 200 high school students and 59 university students in a co-design and open innovation action, resulting in a mobile app.

The large number of video lectures within digital archives is making critical the indexing and retrieval process. Indeed, most of the systems base the indexing process on few generic text information (e.g., course title and teacher's name) and this creates problems to students who are looking for very specific topics and hence want to browse video in details. Moreover, additional metadata could provide useful information to those users who access the educational materials by means of assistive technologies. In this paper, we propose an approach that allows students to take on-the-fly notes while watching a video lecture and uses these notes to enrich video lectures with metadata that will be helpful to the indexing and retrieval process. In particular, to allow detailed video browsing, our proposed SOcial Learning (SOLE) system defines a set of eight predefined tagnotes and segments the lecture into a sequence of video chapters. Students can use the textual notes to describe and to retrieve the video material, providing hints about its content to users with special needs. To evaluate our approach, we developed a prototype version of SOLE and we asked for volunteer evaluators. Results showed that users feel comfortable in taking notes while watching a video and liked to browse video lectures using notes. According to the results obtained in the evaluation, both students and video lecture providers might appreciate the proposed approach.

The paper presents the design and implementation of a low cost and open source assistive system exploiting a wearable device in order to support deaf-blind people in communication using the Malossi alphabet. More specifically, the system, that we called GlovePi, is composed by three main low cost components, such as: (i) a gardener glove; (ii) a Raspberry Pi; (iii) a MPR121 capacitive touch sensor module with expansion board. The MPR121 module works as bridge between the Raspberry Pi and the sensors in the glove, allowing the data transfer. Instead, the Raspberry Pi works as a hotspot wifi and as a server to transfer data to the client, an android application. In this way, the deaf-blind user can use the glove to deliver messages to other users, using the Malossi alphabet. The characters (and phrases) in this way created, will be sent to the android application and displayed or listen. Evaluation tests have been performed to measure the performance on the basis of false positive and false negative, obtaining interesting results in supporting to the feasibility of the here described approach.

Recently, several proposals and discussions have tried to push the use of HTML for preparing and sharing research works within the scholarly domain. Therefore, several native HTML-based templates and formats have been introduced for allowing researchers to write scientific documents, which could be access even with assistive technologies, e.g., screen readers. While HTML can be a good basis for guaranteeing accessibility of such papers, several issues are still in place when we consider the inclusion of non-textual entities in the main text, such as complex formulas and figures. In this paper we experiment on the use of one of such HTML formats, i.e., the Research Articles in Simplified HTML (RASH) format, extended for creating accessible graphs automatically out of CSV data. In particular, we introduce an extension to RASH to let the creation of accessible graphs. Moreover, we analyze the outcomes of some preliminary experiments we have done by using different screen readers on several operating systems and browsers.

This work presents a use case on multimodal urban paths in a smart mobility context. The proposed solution builds on the experience already matured and developed by the authors in different fields: crowdsourcing and sensing done by users to gather data related to urban barriers and facilities, computation of personalized paths for users with special needs, and integration of open data provided by bus companies to identify the actual accessibility features and estimate the real arrival time of vehicles at stops. In terms of functionality, the first “monolithic” prototype fulfilled the goal of composing the aforementioned pieces of information to support citizens with reduced mobility (users with disabilities and/or elderly people) in their urban movements. In this paper, we describe a service-oriented architecture that exploits the microservices orchestration paradigm to enable the creation of new services and to make the management of the various data sources easier and more effective. The manuscript demonstrates the effectiveness of the approach showing a successful use case of a service that take into account multimodal paths, by involving cyclists, bicycle lanes, and bike sharing services in a urban environments. Such a use case take into account the user's interface and interaction mechanisms, which are strongly affected by the context of use.

The wide availability of video lectures within digital archives is causing significant problems to video providers. Indeed, students search for very specific contents and the few minutes of video lecture that cover the required topic have to be found in an archive composed of thousands of hours of videos. Therefore, the indexing of video material is becoming a critical process. Unfortunately, most of the systems return a list of full-length video lectures, and force students to use VCR-like functions in order to find what they are looking for. This approach is not adequate. Furthermore, the indexing process never considers the quality of the lecture (e.g., “interesting”, “meaningless”, etc.). A recent approach proposed to improve the indexing process by using social tagging. To be effective, this approach requires having a considerable number of tags. In this paper, we propose TagLecture, a Game With A Purpose that introduces game elements into the indexing process in order to involve students in the indexing process of video lectures. Through an experimental investigation we highlight that the gamification of the video indexing process is well accepted by students: more than 80% of participants liked the idea of using quality-based tags to classify video lectures, and more than half of them liked the idea of a game where students challenge each other to tag pieces of video lectures.

This paper presents the user-centered design methodologies applied in the ILOCALAPP project, with the aim of designing and developing a mobile application which applies the incidental learning paradigm in the field of foreign cultures and languages. Such methodologies are strongly based on stakeholders' consultations, by involving potential end-users in a dialogue that plays a crucial role in identifying users' needs and expectations. Results of such methodologies application are presented in this paper and are at the basis of the current stage of the project: the development of the mobile application.

This paper presents a preliminary study we have conducted to understand to which extent colors can influence the way we choose urban walking paths beyond the need to move reasonably quick towards our destinations. Building on a mobile application we discuss ColorPath, an application allowing walkers to choose their daily walking routes as a combination of different urban elements to pass through (pleasurable landscapes, historical buildings and monuments, parks and gardens), each corresponding to a specific primary color. After an evaluation campaign with some few avid walkers, we shortly present here our initial findings, showing that emotions and the pleasure enjoyed while walking along particular routes may play a role which is not secondary with respect to the need for speed and accuracy. As our study is still at an initial stage, we took the decision to communicate our first intriguing results through a short paper.

This work presents a study inviting new reflections on our daily walking experiences as mediated by modern GIS-based route-mapping software tools, which has culminated into ColorPath: an app that allows walkers to follow creative urban paths computed on the basis of their walking preferences and expressed under the form of a color. In particular, users may indicate their preference to follow a route as obtained as an urban mix of: pleasurable landscapes (blue), historical buildings and places (red), and parks and gardens (green). ColorPath computes a corresponding path to be followed. Drawing on a new trend that explores the relations between GIS-enabled computerized navigations and sociocultural practices, ColorPath has been built on top of OpenTripPlanner (OTP) and exploits OpenStreetMap (OSM) data. Studies of 10 people’s use of ColorPath propose insights on how walkers reflect on the differences (pros and drawbacks) between “colored” routes and traditional shortest paths computed by routinary route recommendation algorithms.

Mobility in urban environments is an undisputed key factor that can affect citizens’ well-being and quality of life. This is particularly relevant for those people with disabilities or with reduced mobility who have to face the presence of barriers in urban areas. In this scenario, the availability of information about such architectural elements (together with facilities) can greatly support citizens’ mobility by enhancing their independence and their abilities in conducting daily outdoor activities. With this in mind, we have designed and developed mobile Pervasive Accessibility Social Sensing (mPASS), a system that provides users with personalized paths, computed on the basis of their own preferences and needs, with a customizable and accessible interface. The system collects data from crowdsourcing and crowdsensing to map urban and architectural accessibility by providing reliable information coming from different data sources with different levels of trustworthiness. In this context, reliability can be ensured by properly managing crowdsourced and crowdsensed data, combined when possible with authoritative datasets, provided by disability rights organizations and local authorities. To demonstrate this claim, in this article we present our trustworthiness model and discuss results we have obtained by simulations.

This work presents users evaluation of mPASS (mobile Pervasive Accessibility Social Sensing), a system to provide citizens with personalized accessible way finding. mPASS collects data both from crowdsourcing and from crowdsensing, in order to obtain a detailed georeferenced description of the urban environment accessibility. It combines these data with a user profile, with the aim of tailoring paths and maps to users' preferences and needs. To drive the design of our application, we assessed our first proposal through a preliminary questionnaire, showing mPASS mockups to 60 uses with disability and elderly users. On the basis of the results of such questionnaire, we developed a prototype and we tested it with a small group of pilot users. This paper presents results of both the assessments.

A multimedia application (termed Di Piazza in Piazza) has been designed and developed that allows people to enjoy a narrative experience based on food and culinary traditions. Several kinds of digital content narrating the gastronomic culture of the Emilia Romagna region has been made available through a gestural interface within an installation to be exhibited in public spaces. The idea of using hands to access content came after the consideration that hands play a primary role in the relationship between humans and food. Our multimedia installation has gone on tour through several cities in Italy allowing to gather a huge amount of data whose analysis provides some important results. Among those: (a) the use of hands was mostly appreciated by visitors and (b) regional products and gastronomic traditions raised interest in visitors, thus confirming that a strong cultural relationship exists among people and food, in cultural terms.

This paper presents a mobile serious game, which exploits the Socio-Cognitive Conflict (SCC) by means of a game with the aim of supporting preschool and primary school children in understanding two important concepts: (i) topographical orientation, and (ii) recycling. In fact, an important competency that children deal with is the topographical orientation, particularly as regards the relativity of the objects positions with respect to a specific point. To reach this goal we have designed and developed a serious game for mobile devices (e.g. tablets), where kids need to move a character in a grid, with the aim of helping it in associating specific garbage to the right recycling container (taking into account the relative position of the character and of all the objects in the grid). Such a character represents a humanoid robot named MecWilly. We have conducted a first assessment of our serious game by involving six preschool and primary school kids. The first result we have obtained show that kids were very interested in playing such a game and in experiencing more game levels. Further experimentations have been already planned and will be conducted in three primary schools, stressing the SCC by contrasting the mobile serious game with a similar version of the game played by interacting with humanoid robots.

Once we assumed that Web accessibility is a right, we implicitly state the necessity of a governance of it. Beyond any regulation, institutions must provide themselves with suitable tools to control and support accessibility on typically large scale scenarios of content and resources. No doubt, the economic impact and effectiveness of these tools affect accessibility level. In this paper, we propose an application to effectively monitor Web accessibility from a geo-political point of view, by referring resources to the specific (category of) institutions which are in charge of it and to the geographical places they are addressed to. Snapshots of such a macro level spatial-geo-political analysis can be used to effectively focus investments and skills where they are actually necessary.

Multimedia and Web 2.0 can meet up. Despite the success they have obtained in last years, these two domains present some important limitations. In particular, current Web 2.0 based technologies, such as wikis, blogs, podcasts, commonly resort to sequential combinations of contents, rather than exploiting dynamic and synchronized compositions of discrete and continuous media resources. On the other hand, tools for multimedia content authoring and distribution usually lack cooperativeness, interactivity, use of adequate metatagging structures and application mash-up. This paper discusses how by merging these two worlds it is possible to inherit the advantages while removing their limitations. We describe a system exemplar we developed for the cooperative creation, manipulation and sharing of rich media contents.

Technical challenges for non-experts in creating content for e-learning are not trivial. Many more issues raise when that content is required to be universal, usable and accessible. The high-quality of the output, however, seems to be still indissoluble from the high-complexity of the authoring processes. This paper proposes a novel approach to lighten the burden to authors, and to make them produce sophisticated learning objects with little effort. We also describe ISA-WebLob, an authoring platform which takes that approach to implementation.

The large diffusion of e-learning technologies represents a great opportunity for underserved segments of population. This is particularly true for people with disabilities for whom digital barriers should be overstepped with the aim of reengaging them back into society to education. In essence, before a mass of learners can be engaged in a collective educational process, each single member should be put in the position to enjoy accessible and customized educational experiences, regardless of the wide diversity of their personal characteristics and technological equipment. To respond to this demand, we developed LOT (Learning Object Transcoder), a distributed PHP-based service-oriented system designed to deliver flexible and customized educational services for a multitude of learners, each with his/her own diverse preferences and needs. The main novelty of LOT amounts to a broking service able to manage the transcoding activities needed to convert multimedia digital material into the form which better fits a given student profile. Transcoding activities are performed based on the use of Web service technologies. Experimental results gathered from several field trials with LOT (available online at http://137.204.74.83/∼lot/) have confirmed the viability of our approach.

The use of adaptive systems provides means to ensure \nanytime, anywhere and any device entertainment experiences for \nheterogeneous players. In this paper, we present an approach to \ndynamically transcode edutainment contents and to facilitate \nuser interactions in context-aware scenarios. We focus on those \ntypologies of interactive online applications, for which \nedutainment activities are structured as a combination of \ndelivery of rich media contents interleaved with interactive \nchoices made by the users (e.g., multimedia quizzes). As a matter \nof facts, today’s edutainment contents are mostly produced to be \ndelivered through TV or broadband networks. Thus, typically \nthey are not encoded to be used in nomadic contexts. It turns out \nthat the use of an adaptation process (based on user profiles) \nresults as an important tool to face with the poor computational \nand networking capabilities of (mobile) devices. Moreover, a \nhigher level of accessibility can be guaranteed to users with \ndisabilities, since contents are transcoded based on user \npreferences. Results gathered from a real evaluation assessment \nshow the viability of our solution.

The increasing availability of network connectivity and the simultaneous improvement in hardware miniaturization are promoting the growth of mobile entertainment. Consumers wandering about the city desire to participate in multi-player games through applications that should guarantee high playability standards. In this way, the concept of mobility has entered the field of gaming, bringing also new related problems. In this sense, mobility enhances the unreliability of the wireless/wired networked scenario through handovers, transmission errors and temporary link outages. Furthermore, the entertainment industry and the scientific community are researching methods to successfully run the two sides of a networked game, re-synchronizing them when players come back within the coverage range. In this context, we have developed a participatory framework that is able to handle the communication on the two sides when network faults occur, while maintaining at a higher level of

Content transcoding is becoming a privileged strategy to tailor rich multimedia to be delivered to the wide range of mobile device and through different networks. However, computing the appropriate multimedia format for each of the possible condition could be very expensive and somehow unmanageable. Web services represent a distributive technology that could enhance content adaptation by adding modularity and portability. This paper presents a Web service-based distributed architecture that combines on-the-fly media transcoding with hierarchical caching strategies to provide the user with effective reliable and efficient rich media services.