The aim of the study is to show whether it is possible to predict infectious disease outbreaks early, by using machine learning. This study was carried out following the guidelines of the Cochrane Collaboration and the meta-analysis of observational studies in epidemiology and the preferred reporting items for systematic reviews and meta-analyses. The suitable bibliography on PubMed/Medline and Scopus was searched by combining text, words, and titles on medical topics. At the end of the search, this systematic review contained 75 records. The studies analyzed in this systematic review demonstrate that it is possible to predict the incidence and trends of some infectious diseases; by combining several techniques and types of machine learning, it is possible to obtain accurate and plausible results.

It is well known from prior work, that people interacting as well as attending to a public display attract further people to interact. This behavior is commonly referred to as the honeypot effect. At the same time, there are often situations where an audience is present in the vicinity of a public display that does not actively engage or pay attention to the display or an approaching user. However, it is largely unknown how such a passive audience impacts on users or people who intend to interact. In this paper, we investigate the influence of a passive audience on the engagement of people with a public display. In more detail, we report on the deployment of a display in a public space. We collected and analyzed video logs to understand how people react to passive audience in the vicinity of public displays. We found an influence on where interacting users position themselves relative to both display and passive audience as well as on their behavior. Our findings are valuable for display providers and space owners who want to maximize the display's benefits.

Public displays have lately become ubiquitous thanks to the decreasing cost of such technology and public policies supporting the development of smart cities. Depending on form factor, those displays might use touchless gestural interfaces that therefore are becoming more often the subject of public and private research. In this paper, we focus on touchless interactions with situated public displays, and introduce a pilot study on comparing two interfaces: An interface based on the Microsoft Human Interface Guidelines (HIG), a de facto standard in the field, and a novel interface, designed by us. Differently from the HIG-based one, our interface displays an avatar, which does not require any activation gestures to trigger actions. Our aim is to study how the two interfaces address the so-called interaction blindness - the inability of the users to recognize the interactive capabilities of those displays. According to our pilot study, although providing a different approach, both interfaces has proven effective in the proposed scenario: A public display in a hall inside a University campus building

The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint principal lines extraction method. Our approach consists of six simple steps: normalization, median filtering, average filters along four prefixed directions, grayscale bottom-hat filtering, combination of bottom-hat filtering, binarization and post processing. The contribution of our work is a new method for palmprint principal lines detection and a new dataset of hand labeled principal lines images (that we use as ground truth in the experiments). Preliminary experimental results showed good performance in terms of accuracy with respect to three methods of the state of the art.

In recent years, touchless-enabling technologies have been more and more adopted for providing public displays with gestural interactivity. This has led to the need for novel visual interfaces aimed at solving issues such as communicating interactivity to users, as well as supporting immediate usability and "natural" interactions. In this paper, we focus our investigation on a visual interface based only on the use of in-air direct manipulations. Our study aims at evaluating whether and how the presence of an Avatar that replays user's movements may decrease the perceived cognitive workload during interactions. Moreover, we conducted a brief evaluation of the relationship between the presence of the Avatar and the use of one or two hands during the interactions. To this end, we compared two versions of the same interface, differing only for the presence/absence of the user's Avatar. Our results showed that the Avatar contributes to lower the perceived cognitive workload during the interactions.

This paper presents a novel method to reproduce in real time the opening and closing gestures of a human hand, animating a three-dimensional model of it. In other works, this result can be achieved by mapping a set of significant points of a real hand on the corresponding points of the model to animate. We propose an alternative way to produce the same effect without mapping points, but using a level-based estimation of the degree of opening of the hand. The experiments have been executed using Microsoft KinectTM, but the method would work on any other Kinect-like devices (as defined herein). The results obtained are particularly encouraging and demonstrate real-time performance of the system.