Collaborative design of software applications: the role of users

Abstract Drawing on a 1-year application design, implementation and evaluation experience, this paper examines how engaging users in the early design phases of a software application is tightly bound to the success of that application in use. Through the comparison between two different approaches to collaborative application design (namely, user-centered vs participatory), we reveal how sensitivity to the role that users may play during that collaborative practice rebounds to a good level of user satisfaction during the evaluation process. Our paper also contributes to conversations and reflections on the differences between those two design approaches, while providing evidences that the participatory approach may better sensitize designers to issues of users' satisfaction. We finally offer our study as a resource and a methodology for recognizing and understanding the role of active users during a process of development of a software application

On the Feasibility of Social Network-based Pollution Sensing in ITSs

Intense vehicular traffic is recognized as a global societal problem, with a multifaceted influence on the quality of life of a person. Intelligent Transportation Systems (ITS) can play an important role in combating such problem, decreasing pollution levels and, consequently, their negative effects. One of the goals of ITSs, in fact, is that of controlling traffic flows, measuring traffic states, providing vehicles with routes that globally pursue low pollution conditions. How such systems measure and enforce given traffic states has been at the center of multiple research efforts in the past few years. Although many different solutions have been proposed, very limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks, in general, provide direct feedback from people and, as such, potentially very valuable information. A post that tells, for example, how a person feels about pollution at a given time in a given location, could be put to good use by an environment aware ITS aiming at minimizing contaminant emissions in residential areas. This work verifies the feasibility of using pollution related social network feeds into ITS operations. In particular, it concentrates on understanding how reliable such information is, producing an analysis that confronts over 1,500,000 posts and pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma

A theory of processes with durational actions

AbstractA new bisimulation based semantics, called performance equivalence, is proposed for a process algebra equipped with the TCSP parallel operator. This semantics relies on the basic assumption that actions are time-consuming, where their duration is statically fixed. Performance equivalence equates systems whenever they perform the same actions in the same amount of time, thus introducing a simple form of performance evaluation in process algebras. A comparison with other equivalences is provided; in particular, we show that performance equivalence is strictly finer than step bisimulation equivalence and strictly coarser than partial ordering bisimulation equivalence

Is bigger always better? A controversial journey to the center of machine learning design, with uses and misuses of big data for predicting water meter failures

Abstract In this paper, we describe the design of a machine learning-based classifier, tailored to predict whether a water meter will fail or need a replacement. Our initial attempt to train a recurrent deep neural network (RNN), based on the use of 15 million of readings gathered from 1 million of mechanical water meters, spread throughout Northern Italy, led to non-positive results. We learned this was due to a lack of specific attention devoted to the quality of the analyzed data. We, hence, developed a novel methodology, based on a new semantics which we enforced on the training data. This allowed us to extract only those samples which are representative of the complex phenomenon of defective water meters. Adopting such a methodology, the accuracy of our RNN exceeded the 80% threshold. We simultaneously realized that the new training dataset differed significantly, in statistical terms, from the initial dataset, leading to an apparent paradox. Thus, with our contribution, we have demonstrated how to reconcile such a paradox, showing that our classifier can help detecting defective meters, while simplifying replacement procedures

An alternative approach to dimension reduction for pareto distributed data: a case study

Deep learning models are tools for data analysis suitable for approximating (non-linear) relationships among variables for the best prediction of an outcome. While these models can be used to answer many important questions, their utility is still harshly criticized, being extremely challenging to identify which data descriptors are the most adequate to represent a given specific phenomenon of interest. With a recent experience in the development of a deep learning model designed to detect failures in mechanical water meter devices, we have learnt that a sensible deterioration of the prediction accuracy can occur if one tries to train a deep learning model by adding specific device descriptors, based on categorical data. This can happen because of an excessive increase in the dimensions of the data, with a correspondent loss of statistical significance. After several unsuccessful experiments conducted with alternative methodologies that either permit to reduce the data space dimensionality or employ more traditional machine learning algorithms, we changed the training strategy, reconsidering that categorical data, in the light of a Pareto analysis. In essence, we used those categorical descriptors, not as an input on which to train our deep learning model, but as a tool to give a new shape to the dataset, based on the Pareto rule. With this data adjustment, we trained a more performative deep learning model able to detect defective water meter devices with a prediction accuracy in the range 87-90%, even in the presence of categorical descriptors

On the Design and Run of VANET Road Experiments

Abstract-Vehicular ad hoc networks (VANETs) are an emerging area of communication that offer a wide variety of possible applications, ranging from safety to multimedia and games. In a near future, in fact, we may easily envision safety and gaming applications where the real-time video captured from a vehicle is streamed to all connected ones, within some given range. We can therefore expect that the standardization of inter-vehicular communication protocols will support the emergence of such type of new applications and that multimedia and gaming, putting to good use such technologies, will rapidly grow. However, one of the obstacles to the exploitation of such applications in the context of VANETs is given by the practical impossibility to test those solutions in real life conditions, as a great number of vehicles are required to gather any significant amount of relevant experimental data. Hence, we here present an approach that makes the practicality of field tests come true, applying a novel methodology apt to experiment with multimedia applications and games in vehicular environments, as it can cope with a very limited amount of resources. The results gained by applying this approach represent a solid leapfrog in the study of such systems. We here discuss in detail the experiments that were run on the road with such methodology and the positive implications that such results reveal for the context of VANET-based multimedia and gaming

Exploiting fashion x-commerce through the empowerment of voice in the fashion virtual reality arena. Integrating voice assistant and virtual reality technologies for fashion communication

The ongoing development of eXtended Reality (XR) technologies is supporting a rapid increase of their performances along with a progressive decrease of their costs, making them more and more attractive for a large class of consumers. As a result, their widespread use is expected within the next few years. This may foster new opportunities for e-commerce strategies, giving birth to an XR-based commerce (x-commerce) ecosystem. With respect to web and mobile-based shopping experiences, x-commerce could more easily support brick-and-mortar store-like experiences. One interesting and consolidated one amounts to the interactions among customers and shop assistants inside fashion stores. In this work, we concentrate on such aspects with the design and implementation of an XR-based shopping experience, where vocal dialogues with an Amazon Alexa virtual assistant are supported, to experiment with a more natural and familiar contact with the store environment. To verify the validity of such an approach, we asked a group of fashion experts to try two different XR store experiences: with and without the voice assistant integration. The users are then asked to answer a questionnaire to rate their experiences. The results support the hypothesis that vocal interactions may contribute to increasing the acceptance and comfortable perception of XR-based fashion shopping

Web Content Search and Adaptation for IDTV: One Step Forward in the Mediamorphosis Process toward Personal-TV

We are on the threshold of a mediamorphosis that will revolutionize the way we interact with our TV sets. The combination between interactive digital TV (IDTV) and the Web fosters the development of new interactive multimedia services enjoyable even through a TV screen and a remote control. Yet, several design constraints complicate the deployment of this new pattern of services. Prominent unresolved issues involve macro-problems such as collecting information on the Web based on users' preferences and appropriately presenting retrieved Web contents on the TV screen. To this aim, we propose a system able to dynamically convey contents from the Web to IDTV systems. Our system presents solutions both for personalized Web content search and automatic TV-format adaptation of retrieved documents. As we demonstrate through two case study applications, our system merges the best of IDTV and Web domains spinning the TV mediamorphosis toward the creation of the personal-TV concept

Attitudes of Crohn's Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts

Background: Data concerning patients originates from a variety of sources on social media. Objective: The aim of this study was to show how methodologies borrowed from different areas including computer science, econometrics, statistics, data mining, and sociology may be used to analyze Facebook data to investigate the patients’ perspectives on a given medical prescription. Methods: To shed light on patients’ behavior and concerns, we focused on Crohn’s disease, a chronic inflammatory bowel disease, and the specific therapy with the biological drug Infliximab. To gain information from the basin of big data, we analyzed Facebook posts in the time frame from October 2011 to August 2015. We selected posts from patients affected by Crohn’s disease who were experiencing or had previously been treated with the monoclonal antibody drug Infliximab. The selected posts underwent further characterization and sentiment analysis. Finally, an ethnographic review was carried out by experts from different scientific research fields (eg, computer science vs gastroenterology) and by a software system running a sentiment analysis tool. The patient feeling toward the Infliximab treatment was classified as positive, neutral, or negative, and the results from computer science, gastroenterologist, and software tool were compared using the square weighted Cohen’s kappa coefficient method. Results: The first automatic selection process returned 56,000 Facebook posts, 261 of which exhibited a patient opinion concerning Infliximab. The ethnographic analysis of these 261 selected posts gave similar results, with an interrater agreement between the computer science and gastroenterology experts amounting to 87.3% (228/261), a substantial agreement according to the square weighted Cohen’s kappa coefficient method (w2K=0.6470). A positive, neutral, and negative feeling was attributed to 36%, 27%, and 37% of posts by the computer science expert and 38%, 30%, and 32% by the gastroenterologist, respectively. Only a slight agreement was found between the experts’ opinion and the software tool. Conclusions: We show how data posted on Facebook by Crohn’s disease patients are a useful dataset to understand the patient’s perspective on the specific treatment with Infliximab. The genuine, nonmedically influenced patients’ opinion obtained from Facebook pages can be easily reviewed by experts from different research backgrounds, with a substantial agreement on the classification of patients’ sentiment. The described method allows a fast collection of big amounts of data, which can be easily analyzed to gain insight into the patients’ perspective on a specific medical therapy