1,721,040 research outputs found
1st International workshop on social media sensing (SMS'19): message from the workshop chairs
No full textProviding accessible and portable video lecturing using content transcoding
No full textE-learning has the potential to reach students worldwide, making education a resource available to every user, anywhere, anytime, and from any network and device. However, many people will be excluded from the benefits of Web-based education if constrains to guarantee so wide an access are not considered in the design stage. Offering universal access to E-learning and T-learning does not mean sacrificing interactivity or appeal by producing textbased lectures. Furthermore, as technologies develop, rich media are becoming common resources for e-learning activities and a set of transcoding mechanisms could effectively improve portability and accessibility of on-line lectures. In this context, we present an application designed to support accessibility of on-line education that automatically produces accessible and portable versions of lectures, to be experienced on different networks, applications and devices
Stratigraphic interpretation of amplitude processed seismic data: a case history in the Lombardian Plain
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Intelligent and Good Machines? The Role of Domain and Context Codification
No full textThere is a core problem with the modern Artificial Intelligence (AI) technologies, based on the current new wave of Artificial Neural Networks (ANNs). Whether they have been used in healthcare or for exploring Mars, we, the programmers who build them, do not know well why they make some decisions over others. Many are putting into question, hence, this aura of AI objectivity and infallibility; on our side, we, instead, identify a key issue around the problem of AI errors and bias into an insufficient human ability to determine the limits of the context, where the ANNs will have to operate. In fact, while it is of great amplitude the range of what the rational side of the human mind can master, machine intelligence has limited capacity to learn in completely unknown scenarios. Simply, an inaccurate or incomplete codification of the context may result into AI failures. We present here a simple cognification ANN-based case study, in an underwater scenario, where the difficulty of identifying and then codifying all the relevant contextual features has led to a situation of partial failure. This paper reports on our reflections, and subsequent technical actions taken to recover from this situatio
Hierarchical clustering as an unsurpervised machine learning algorithm for hyperspectral image segmentation of films
No full textIn recent years, hyperspectral imaging has been increasingly applied to cultural heritage analysis, conservation, as well as digital restoration practice. However, the processing of such a large amount of data acquired by the instrument remains a challenging problem. In the absence of a machine learning pipeline, the segmentation task conducted in conventional methods is usually time-consuming and labor-intensive or requires pre-knowledge of the chemical nature of the art objects. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative and necessary machine learning approach to segment the spectral images into clusters with different hierarchies, to maximize the information we can obtain from the high-dimensional hyperspectral data. Data were acquired using a custom-made push-broom VNIR hyperspectral camera (380-780 nm) and the materials used were a set of cinematic film samples with different degradation degrees. As preliminary results, different fading areas in the entire dataset are successfully classified and segmented. With the help of the automating and unsupervised algorithm, the effective segmentation of those degradation areas could be beneficial as it provides the basis for the future digital unfading treatment
On exploiting Gamification for the Crowdsensing of Air Pollution: A Case Study on a Bicycle-based System
No full textCities all over the world struggle with air pollution. With the ever-increasing concentration of people in urban areas, more and more people suffer from the negative effects of air pollutants. Crowdsensing systems are a unique chance to increase the users' awareness of this problem and to provide more fine-grained data to policymakers so that they can adopt appropriate strategies. In this paper, we present a crowdsensing system to collect air pollution in urban and suburban environments through the use of bicycles. It consists of a Web application, enriched with gamification elements, that communicates with a portable low-cost sensor. The user interface of such a system, as well as the adopted gamification mechanisms, has been designed by involving a group of target users, with the aim of better meeting users' preferences and needs, and, then, better engaging them
What influences sentiment analysis on social networks: A case study
No full textSentiment 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
Gamification and Accessibility
No full textMany 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
Designing Interfaces to Display Sensor Data: A Case Study in the Human-Building Interaction Field Targeting a University Community
Get PDFThe increase of smart buildings with Building Information Modeling (BIM) and Building Management Systems (BMS) has created a large amount of data, including those coming from sensors. These data are intended for monitoring the building conditions by authorized personnel, not being available to all building occupants. In this paper, we evaluate, from a qualitative point of view, if a user interface designed for a specific community can increase occupants’ context-awareness about environmental issues within a building, supporting them to make more informed decisions that best suit their needs. We designed a user interface addressed to the student community of a smart campus, adopting an Iterative Design Cycle methodology, and engaged 48 students by means of structured interviews with the aim of collecting their feedback and conducting a qualitative analysis. The results obtained show the interest of this community in having access to information about the environmental data within smart campus buildings. For example, students were more interested in data about temperature and brightness, rather than humidity. As a further result of this study, we have extrapolated a series of design recommendations to support the creation of map-based user interfaces that we found to be effective in such contexts
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