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Daily experiences in working with various types of computer systems show that, despite the offered functionalities, users have many difficulties, which affect their overall User eXperience (UX). The UX focus is on aesthetics, emotions and social involvement, but usability has a great influence on UX. Usability evaluation is acknowledged as a fundamental activity of the entire development process in software practices. Research in Human-Computer Interaction has proposed methods and tools to support usability evaluation. However, when performing an evaluation study, novice evaluators still have difficulties to identify usability problems and to understand their causes: they would need easier to use and possibly automated tools. This article describes four visualization techniques whose aim is to support the work of evaluators when performing usability tests to evaluate websites. Specifically, they help detect “usability smells”, i.e. hints on web pages that might present usability problems, by visualizing the paths followed by the test participants when navigating in a website to perform a test task. A user study with 15 participants compared the four techniques and revealed that the proposed visualizations have the potential to be valuable tools for novice usability evaluators. These first results should push researchers towards the development of further tools that are capable to support the detection of other types of UX smells in the evaluation of computer systems and that can be translated into common industry practices.

Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of intra-vehicle communication, that is controller area network (CAN), local interconnect network (LIN), FlexRay, media oriented systems transport (MOST), and Ethernet. Therefore, to achieve this goal, security information and event management (SIEM) was used to monitor and detect malicious attacks in intra-vehicle and inter-vehicle communications: messages transmitted between vehicle ECUs; infotainment and telematics systems, which provide passengers with entertainment capabilities and information about the vehicle system; and vehicular ports, which allow vehicles to connect to diagnostic devices, upload content of various types. As a result, this allows the automation and improvement of threat detection and incident response processes. Furthermore, the V-SOC4AS allows the classification of the received message as malicious and non-malicious and acquisition of additional information about the type of attack. Thus, this reduces the detection time and provides more support for response activities. Experimental evaluation was conducted on two state-of-the-art attacks: denial of service (DoS) and fuzzing. An open-source dataset was used to simulate the vehicles. V-SOC4AS exploits security information and event management to analyze the packets sent by a vehicle using a rule-based mechanism. If the payload contains a CAN frame attack, it is notified to the SOC analysts.

The potential for visually detectable clinical signs of anaemia and their correlation with the severity of the pathology have supported research on non-invasive prevention methods. Physical examination for a suspected diagnosis of anaemia is a practice performed by a specialist to evaluate the pallor of the exposed tissues. The aim of the research presented herein is to quantify and minimize the subjective nature of the examination of the palpebral conjunctiva, suggesting a method of diagnostic support and autonomous monitoring. Here we describe the methodology and system for extracting key data from the digital image of the conjunctiva, which is also based on analysis of the dominant colour classes. Effective features have been used herein to establish the inclusion of each image in a diagnosis probability class for anaemia. The images of the conjunctiva were taken using a new low cost and easy to use device, designed to optimize the properties of independence from ambient light. The performance of the system was tested either by extracting manually the palpebral conjunctiva from images or by extracting them in a semi-automatic way based on the SLIC Superpixel algorithm. Tests were conducted on images obtained from 102 people. The dataset was unbalanced, since many more samples of healthy people were available, as often happens in the medical field. The SMOTE and ROSE algorithms were evaluated to balance the dataset, and some classification algorithms for assessing the anaemic condition were tested, yielding very good results. Taking a photo of the palpebral conjunctiva can aid the decision whether a blood sample is needed or even whether a patient should inform a physician, considerably reducing the number of candidate subjects for blood sampling. It also could highlight the suspected anaemia, allowing screening for anaemia in a large number of people, even in resource-poor settings.

Medical practice for determining haemoglobin (Hb) concentration, which is important for anemic patients in need of blood transfusion, requires a blood sample. In this paper, we propose a non-invasive approach to Hb estimation based on the image analysis of a specific conjunctival region. Our goal is to develop a device that is not expensive and simple to use for assessing the anemic condition; this device could be used by the physician to decide whether to take a blood sample or even by a patient at home to decide whether to inform a physician; in this way, we can avoid having the patient go frequently to the laboratory to take a blood sample. This device also allows us to rapidly screen for Anemia in a large number of persons, for example, groups of girls who are at the beginning of the menses. Here, we detail the prototype of our device and the methodology for extracting key information from the color values of the acquired image. Tests conducted on 113 anemic and healthy persons show a strong correlation between the real Hb value obtained through blood sampling and the value estimated by our device. A k-nearest neighbor classification algorithm for assessing the (non)anemic condition yielded good results and helps doctors avoid a significant number of blood tests.

Patients with Parkinson's disease (PD) may have difficulties in speaking because of reduced coordination of the muscles that control breathing, phonation, articulation, and prosody. Symptoms that may occur are weakening of the volume of the voice, voice monotony, changes in the quality of the voice, the speed of speech, uncontrolled repetition of words, and difficult speech intelligibility. To date, evaluation of the speech intelligibility is performed based on the unified PD rating scale. Specifically, section 3.1 (eloquence) of the cited scale provides the specialist with some tips to evaluate the patient's speech ability. With the aim of evaluating the speech intelligibility by measuring the variation in parameters in an objective manner, we show that a speech-to-text (STT) system could help specialists to obtain an accurate and objective measure of speech, phrase, and word intelligibility in PD. STT systems are based on methodologies and technologies that enable the recognition and translation of spoken language into text by computers and computerized devices. We decided to base our study on Google STT conversion. We expand Voxtester, a software system for digital assessment of voice and speech changes in PD, in order to perform this study. No previous studies have been presented to address the mentioned challenges based on STT. The experiments here presented are related with detection/classification between pathological speech from patients with PD and regular speech from healthy control group. The results are very interesting and are an important step toward assessing the intelligibility of the speech of PD patients.

Integrated clinical pathways (ICPs) are task-oriented care plans detailing the essential steps of the therapeutic pathway referring to a specific clinical problem with a patient’s expected clinical course. ICPs represent an effective tool for resource management in the public and private health domains. To be automatically executed, the ICP process has to be described by means of complex general purpose description language (GPDL) formalisms. However, GPDLs make the process model difficult to grasp by a human. On the other hand, the adoption of a reduced set of graphical constructs prevents a fully automated process execution due to the lack of information required by a machine. Unfortunately, it is difficult to find a balance between modelling language expressiveness and the automated execution of the modelled processes. In this paper, we present a meta-model based on a GPDL to organize the ICP process knowledge. This meta-model allows the management of ICP information in a way that is independent from the graphic representation of the adopted modelling standard. We also propose a general framework and a methodology that aim to guarantee a high degree of automation in process execution. In particular, the corresponding execution engine is implemented as a chatbot (integrated with social media), which plays a two-fold role: during the actual execution of the entire ICP, it acts as a virtual assistant and gathers the patient’s health data. Tests performed on a real ICP showed that, thanks to the proposed solution, the chatbot engine is able to engage in a dialogue with the patient. We provide discussion about how the system could be extended and how it could be seen as an alternative to Artificial Intelligence (AI) and Natural Language Processing (NLP)-based approaches.

The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.

Contribution: This paper examines the literature dealing with the non-invasive estimate of anemia (NEA), and analyzes if the research is developing scientifically with adequate empirical validation. This paper reveals a trend in NEA studies towards an increasing interest in estimating anemia using conjunctiva pallor. Background: Supporting clinical developments and processes to reduce personal discomfort and allow extensive screening needs substantial efforts in researching non-invasive techniques to evaluate anemia. Research Questions: The main aims of this study are the analysis of the area of interest explored in the NEA literature, the evaluation of the peculiarities of papers, giving special consideration to empirical ones, examining them from the point of view of the daily improvement of doctors and healthcare personnel activities and the daily life of patients; and the identification of any considerable research gap to encourage further investigations on new topics. Methodology: The systematic mapping study has been elected as the optimal approach to probe the NEA literature since it defines a rigorous process for data retrieving and interpretation. Findings: Research in this sector is very active, especially in the most populated countries in the world and focuses on improving the technologies currently on the market and on proposing new solutions, especially portable and usable by everyone. A new trend in camera and smartphone-based devices is identified.

The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X–Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF–K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages.

Context. A Smart city is intended as a city able to offer advanced integrated services, based on information and communication technology (ICT) technologies and intelligent (smart) use of urban infrastructures for improving the quality of life of its citizens. This goal is pursued by numerous cities worldwide, through smart projects that should contribute to the realization of an integrated vision capable of harmonizing the technologies used and the services developed in various application domains on which a Smart city operates. However, the current scenario is quite different. The projects carried out are independent of each other, often redundant in the services provided, unable to fully exploit the available technologies and reuse the results already obtained in previous projects. Each project is more like a silo than a brick that contributes to the creation of an integrated vision. Therefore, reference models and managerial practices are needed to bring together the efforts in progress towards a shared, integrated, and intelligent vision of a Smart city. Objective. Given these premises, the goal of this research work is to propose a Smart City Integrated Model together with a Smart Program Management approach for managing the interdependencies between project, strategy, and execution, and investigate the potential benefits that derive from using them. Method. Starting from a Smart city worldwide analysis, the Italian scenario was selected, and we carried out a retrospective analysis on a set of 378 projects belonging to nine different Italian Smart cities. Each project was evaluated according to three different perspectives: application domain transversality, technological depth, and interdependences. Results. The results obtained show that the current scenario is far from being considered “smart” and motivates the adoption of a Smart integrated model and Smart program management in the context of a Smart city. Conclusions. The development of a Smart city requires the use of Smart program management, which may significantly improve the level of integration between the application domain transversality and technological depth.