Abstract Context Security issues in Open-Source (OS) software systems emerge from everyday coding activities carried out by developers. As security best practices evolve toward “shift-left” paradigms—emphasizing early and continuous integration of security into the development process—understanding how these issues are introduced and fixed has become increasingly important. Objective Our primary goal is to study the spread and evolution of security issues that lie in the source code of OS Python software systems at commit level. Method We conducted a mining study in which we quantitatively analyzed the commit histories of 361 OS Python software systems, whose repositories were publicly available on GitHub , for a total of 380,931 commits analyzed. To identify security issues at the commit level, we used SonarQube , a popular and widely used both in academic and industrial contexts Static Application Security Testing ( SAST ) tool. Results We observed that security issues are spread in OS Python software systems (on average, there are about 14 security issues per commit) and tend to survive for 11 days and 14 commits. Critical security issues, despite their high severity level, are the most spread and tend to survive the most. Furthermore, we noticed that 55 kinds of security issues—belonging to 62 OWASP Top 10 and CWE security classes—were introduced, and the top six (per number of introductions) are mostly critical and account for 77% of all introduced security issues. Conclusions Python developers need to give utmost importance to security issues, particularly critical ones. To that end, we can suggest developers that use secure coding practices, automated tools, or even DevSecOps to limit/avoid the introduction of security issues into their source code or fix them as soon as possible.

Abstract The assessment of disorganized thought and language is essential for diagnosing schizophrenia (SZ), still remaining a subjective and labor-intensive clinical process. We present ClearThought , an automated framework that uses large language models (LLMs) to evaluate the Thought and Language Disorder (TALD) scale through few-shot prompting on transcribed psychiatric interviews. The model generates 0–4 item-level severity scores and structured justifications aligned with the TALD rubric. We evaluated ClearThought on a dataset of 33 SZ patient interviews, comparing model outputs to clinician ratings using both ordinal and binary performance metrics. For ordinal scoring, the system achieved macro F1 $$\ge$$ 0.80 on 11 items and very strong Spearman correlations ( $$\varvec{\rho \ge 0.80}$$ ) on 15 items, particularly for disorders that show clear and noticeable patterns in spoken language, such as Blocking and Restricted Thinking . Entropy-aware analysis revealed that high performance was most meaningful when accompanied by sufficient label variability. For binary detection, the model accurately identified disorder presence in 26 of 30 items (F1 > 0.80), with over 11 items exceeding F1 = 0.90. However, high scores on low-entropy items (e.g., Clanging , Neologisms ) were often driven by consistent disorder absence, highlighting the need for caution when interpreting results from skewed label distributions. Clinicians rated the model’s justifications as clinically sound and interpretable, with average scores above 4.0/5.0 for scoring accuracy, clarity, and overall trust. These results suggest that LLMs, guided by prompt-based scoring and structured justification, can support objective, interpretable, and scalable TALD assessments. The framework performs best for linguistically relevant disorders and provides a transparent interface to assist clinical reasoning. Future work will target rare disorders, enhance justification, and extend validation across broader clinical cohorts.

Emotional artificial intelligence (AI) can enhance personalization in computational social systems by enabling machines to respond to users’ affective states. In education, adapting instruction based on emotions such as engagement and stress may lead to more supportive learning experiences. This study examines whether an emotionally adaptive social robot, driven by real-time electroencephalography (EEG) signals, can improve learners’ engagement and perception of the robot. A between-subjects experiment is conducted with 30 undergraduate students during an XML lecture delivered by the Furhat social robot in one-to-one modality. In the empathetic condition, the robot adapts its behavior in response to EEG-detected engagement and stress levels; in the nonempathetic condition, the robot follows a fixed script. The empathetic robot significantly improved users’ perceptions across all Godspeed dimensions and enhanced perceived usability and reward. EEG data showed a significant increase in engagement. Learning outcomes were statistically similar across conditions. Real-time EEG-guided emotional adaptation improves user engagement and perception of social robots in educational contexts. These findings support the role of affect-driven adaptivity in enhancing user experience in intelligent tutoring systems.

GitHub’s dependency graph is a tool that eases Software Composition Analysis (SCA), and it is leveraged not only by other tools or by practitioners in their analyses but also by researchers when conducting studies on open-source projects. However, its potential inaccuracy may seriously harm its applicability and usefulness. This paper quantitatively and qualitatively analyzes the accuracy of GitHub’s dependency graphs for Java and Python projects, how such accuracy has changed over time, and what the likely pitfalls and limitations of the dependency graph are. After creating statistically significant samples of Java and Python projects, we analyzed their dependency graph in two directions, forward (by looking at dependencies), backward (by looking at dependents), and inspected their manifest/lock files. Results indicate that in our sample, dependencies have over 27% of inaccuracy, and dependents up to 10%. Errors depend on several reasons, among others, an oversimplified processing of manifest/lock files by the dependency graph generator. Our results provide (i) guidelines for researchers to understand the threats arising in studies based on the dependency graph and (ii) insights to practitioners and tool builders to enhance their SCA, given the current limitations of the dependency graph. • We show that GitHub dependency graph is inaccurate ( ≃ 20% errors in dependencies and ≃ 10 errors in dependents). • We report a qualitative categorization of dependency graph inaccuracies root causes. • The findings of our research warn about the accuracy of tools and studies leveraging the dependency graph.

A Software Bill of Materials ( SBOM ) formally lists the open-source and proprietary components that constitute a software product, including their licenses, versions, vendors, vulnerabilities, and supply chain relationships. SBOMs enable software producers and consumers to gain visibility into the software supply chain and monitor the risks associated with software security, licensing, and more. This paper presents the results of an exploratory mining study investigating the adoption of SBOMs by open-source software projects. To that end, we mined GitHub and identified 186 public software repositories using SBOM generation tools owned by SPDX and CycloneDX . Although the adoption of SBOMs is low, it is increasing. Moreover, SBOMs are under version control or available in public release versions of less than half the software projects analyzed. Finally, only a limited fraction of SBOMs contain minimum/recommended information, and some SBOMs are also uncompliant with existing SBOM standards. Our study reveals that software producers are paying more attention to SBOMs, but even so, these may be incomplete. We urge software producers to adopt SBOMs and meet the new software supply chain standards. As for researchers, we foster further investigations on adopting SBOMs and their correct use.

In this poster, we present a multimodal conversational interface, named Muxi, aiming to simplify meeting management in a collaborative work environment. The interaction is performed in two modalities: (i) a vocal conversation with a chatbot avatar and (ii) an interactive board where supporting information is provided.

Hair-related illnesses may significantly impact the Quality of Life of people suffering from them. Deep Learning models and techniques may be adopted for early detection of these diseases. In this paper, we propose a mobile app, named Skinner, aims to support the end-users in the detection of their skin disease. The app’s aim is only to alert the user, it does not substitute the clinicians, indeed it suggests the most appropriate medical center in the neighborhood. Skinner is based on a Resnet50 neural network that reaches 78.84on ten classes of disease. It also adopts explainable AI techniques for improving transparency and the user’s trust.

In this folklore-confirmation short paper, we present the results of a study on the removal of dead code. We aim to gather evidence on the impact of dead-method removal on the internal structure of source code and the usage of resources such as compilation time and space to store executable code. To that end, we studied 23 open-source Java desktop applications hosted on GitHub. We cleaned up each of these applications from its dead methods to obtain two versions: one with dead methods (i.e., original version) and another one without them (i.e., cleaned version). For each of these applications, we compared its versions (original and cleaned) to determine whether, and to what extent, the removal of dead methods affects the internal structure of source code and the usage of resources. We observed that, after removing dead methods, the internal structure of source code significantly improves, while the time to compile source code significantly diminishes as well as the space to store executable code.

GitHub’s dependency graph shows dependency relationships between repositories. This feature is leveraged by tools such as Dependabot, or GitHub’s feature to export SBOM (Software Bill of Materials) files. Also, it has been used in empirical studies. Inaccuracies in the dependency graph might negatively affect both the effectiveness of tools and the results of the conducted studies. In this paper, we present the results of a mining study to assess the accuracy of GitHub’s dependency graph in Java and Python open-source software projects. In particular, on April 16th, 2023, we randomly sampled 297 software projects developed in Java and 338 developed in Python (all hosted on GitHub), each using GitHub’s dependency graph. Then, we performed three analyses to assess how accurate GitHub’s dependency graph is: (i) backward analysis, focusing on the accuracy of the dependencies of a given repository, as reported in GitHub’s dependency graph; (ii) forward analysis, focusing on the accuracy of the dependents of a given repository, as reported in GitHub’s dependency graph; and (iii) manifest/lock file analysis, focusing on the correspondence between the dependencies reported in the dependency graph of a given repository and what was reported in the corresponding manifest/lock files. The obtained results highlight several inaccuracies in GitHub’s dependency graph, which might affect the output of tools based on GitHub’s dependency graph (e.g., Dependabot and SBOM generators) as well as the outcomes of past empirical studies. We also provide qualitative insights into these inaccuracies and implications for practitioners and researchers.

MSR4SBOM (Mining Software Repositories for enhanced Software Bills of Materials) is a project whose main goal is to deliver a framework that analyzes the content of software repositories and SBOMs to provide context-sensitive recommendations. The expected outputs are (i) a set of approaches and tools released as open-source projects, making them exploitable in industrial, academic, and open-source contexts; and (ii) replication packages of our empirical studies and repositories of datasets collected while developing, calibrating, and validating the MSR4SBOM approaches and tools.

In this paper, we present the results of a confirmation study on the impact of dead-method removal on the internal structure of source code, time to compile source code, and space to store compilation results (i.e., executable code). To that end, we cleaned up 23 open-source Java desktop applications hosted on GitHub from their dead methods, obtaining two versions: one with dead methods (i.e., original version) and another one without them (i.e., cleaned version). For each of these applications, we compared its versions (original and cleaned) and found that, after removing dead methods, the internal structure of source code significantly improves, while the time to compile source code significantly diminishes as well as the space to store compilation results. We also performed correlation analyses that allowed us to conclude that the more dead methods are removed, the greater the improvement to the internal structure of source code and the less space to store executable code.

A Software Bill of Materials (SBOM) describes, in a structured, machine-readable format, the open-source and proprietary components that constitute a software product, including their licenses, versions, vendors, vulnerabilities, and dependency relationships. SBOMs enable practitioners to gain visibility into the software supply chain and monitor any risks associated with software security, licensing, and more. In this industry paper, we present the findings of 10 semi-structured interviews with practitioners with different roles in six different software companies operating in Italy, some of which being very large multinationals. The gathered information indicates that the adoption of SBOMs is low, yet the attention of the software industry to software supply chain-related challenges is high. A possible reason behind this outcome is that the software industry has limited knowledge of SBOMs and software supply chain regulations. Although some participants showed a growing interest in SBOMs, the Italian software industry seems to respond less promptly to this technology. We plan to use these results and those from past research to design a survey with practitioners to have a complete picture of SBOM usage in the software industry.

Technological evolution has enabled the development of new artificial intelligence (AI) models with generative capabilities. Among them, one of the most discussed is the virtual agent ChatGPT. This chatbot may occasionally produce fake information, as also declared by the producer OpenAI. Such a model may provide very useful support in several tasks, ranging from text summarization to programming. The research community has marginally investigated the impact that fake information created by AI models has on the users’ perceptions and on their belief in AI. We analyzed the impact of the fake information produced by AI on user perceptions, specifically trust and satisfaction, by performing a user study on ChatGPT. An additional issue is assessing whether the early or late knowledge of the possibility of the tool generating fake information has a different impact on the users’ perceptions. We conducted an experiment, involving 62 university students, a category of users who may employ tools such as ChatGPT extensively. The experiment consisted of a guided interaction with ChatGPT. Some of the participants experienced the failure of the chatbot, while a control group only received correct and reliable answers. We collected participants’ perceptions of trust, satisfaction, and usability, together with the net promoter score (NPS). The results demonstrated a statistically significant difference in trust and satisfaction between the users who early experienced fake information production compared to those who discovered ChatGPT’s faulty behaviors later during the interaction. Also, there is no statistically significant difference among the users who received the late fake information and the control group (no fake information). Usability and the NPS also resulted higher when the fake news was detected in the late interaction. When users are aware of the fake information generated by ChatGPT their trust and satisfaction decrease, especially when they impact on this at the early stage of use of the chatbot. Nevertheless, the perception of trust and satisfaction still remains high, as some of the users are still enthusiastic; others consider a more conscious use of the tool in terms of support to be verified. A useful strategy could be to favor a critical use of ChatGPT, letting young people to verify the provided information. This should be a new way to perform learning activities.

Background: There are a number of academic courses in the Bachelor Program in Computer Science (CS) on the design of Web apps. Often the internal and external quality of the developed Web apps is not adequately taken into account. Aim: We aimed to (i) estimate the quality of Web apps developed by bachelor CS students in a Software Technologies for the Web (STW) course (a.y. 2021-22) and (ii) define a training plan (on the base of the results of the first step) for the students enrolled to this course for the a.y. 2022-23 to let them design and implement better Web apps, and (iii) experimenting the training plan by comparing the quality of Web apps developed in a.y. 2021-22 and a.y. 2022-23. Method: We designed a prospective empirical investigation to study STW with respect to the training of bachelor students with respect to the quality (internal and external) of the developed Web apps. Results: We observed that quality concerns are widespread in the code of the Web apps the STW students developed in the a.y. 2021-22. Therefore, we plan to ask the students of the a.y. 2022-23 to use in their development pipeline a Static Analysis Tool (SAT) to detect quality concerns in the developed Web apps and deal with them. This second step represents an ongoing stage of our research. Conclusions: Our preliminary outcomes suggest that students must be aware that quality is of primary relevance for the development of Web apps and prepared to use SAT in the development pipeline.

Background: Software vulnerabilities are flaws in application source code that can be exploited to cause harm, hence companies must devise strategies to manage them.Aim: We want to understand how software vulnerabilities are managed in a big IT (Information Technology) service and consulting company like NTT Data.Method: We conducted a focus group involving six software professionals working at NTT Data and analyzed the gathered data through a thematic analysis approach.Results: We found that application security standards are defined based on the needs of the clients (i.e., companies that commissioned NTT Data the software to be developed) and the projects’ nature (i.e., the development of greenfield projects vs. maintenance of existing ones). Also, to detect software vulnerabilities, SAST (Static Application Security Testing) tools are mainly used; among these, SonarLint and SonarQube appear to be the de-facto standards for NTT Data. Finally, not all software vulnerabilities are fixed; for example, the presence of some software vulnerabilities is tolerated by the clients, who take on the responsibility of not removing these vulnerabilities.Conclusions: It seems that developers and NTT Data clients are not averse to securing their code. NTT Data follows the application security standards established with their clients. To detect software vulnerabilities, SonarLint and SonarQube appear to be the de-facto standards, so explaining to some extent the increasing attention on these tools by the software engineering research community.

We conducted a large-scale fine-grained empirical study in which we quantitatively analyzed the commit histories of 200 Open-Source (OS) Python software systems, whose software repositories were publicly available on GitHub, for a total of 164,980 commits analyzed. We focused on commits—this is why our study is considered fine-grained—to investigate the spread and evolution of security concerns. To detect security concerns at a commit level, we used SonarQube, a popular Static Application Security Testing (SAST) tool. We found, among other things, that: security concerns are spread in OS Python software systems (on average, about 11 security concerns per commit) and tend to survive more than a couple of weeks and a dozen commits; and critical security concerns, despite their high severity level, are the most spread and tend to survive the most. Also, we found that 47 different kinds of security concerns were introduced into the source code of the studied software systems, and the top eight (per number of introductions) are severe and account for 87% of all introduced security concerns. Python developers should pay more attention to security concerns, especially those critical, and use secure coding practices, automated tools, or even DevSecOps to avoid the introduction of security concerns into their source code or fix them as soon as possible.

A Software Bill of Materials (SBOM) is a complete, formally structured list of all the open-source and proprietary software components present in a software product, including their licenses, versions, and vendors. SBOMs enable software creators and consumers to gain visibility into the software supply chain and monitor any risks associated with security or licensing. Thereby, the United States Government and the European Union have brought SBOMs to the forefront of digital policy. In this paper, we present the results of an exploratory mining study that aims to investigate the adoption of SBOMs by open-source software projects. To that end, we mined GitHub to identify repositories that use SBOM generation tools developed by SPDX and CycloneDX, identifying a total of 186 public repositories adopting SBOMs. We found that the adoption of SBOMs is low, yet it has an increasing trend. Moreover, SBOM files are available in the repository or published release versions in 46% of the software projects analyzed. SBOMs are getting an increasing attention from software creators and consumers. There is a pressing need for organizations to update their software to meet the new standards required for the software supply chain.

People with Autism Spectrum Disorder (ASD) have difficulties in social communication and interaction. Their caregivers help them in overcoming these challenges. Social stories are tools largely adopted to improve the interaction and communication capabilities of people with ASD. The creation of a social story is not an easy task. Several guidelines have been defined to build them. In this paper, we propose an interactive mobile application aimed at empowering caregivers of people with ASD in generating social stories. The application integrates AI processing and AI-based audio transcription for story generation, effective audio data extraction, and data processing. The stories are presented in a slide-show format to the learner in different modalities according to the learner's capabilities. A preliminary usability study of the application has been performed and the first results are encouraging.

This paper proposes a multimodal conversational interface that supports caregivers of people with ASD in the semiautomatic creation of social stories. With the support of the multimodal interface the editor lets caregivers select the appropriate elements and the content representations to be included in the social stories by following specific guidelines. We describe the interface design and a preliminary evaluation of the mobile application. Seven caregivers of people with ASD have participated in the evaluation. Usability results are encouraging.

In recent years it has been assessed that in people with type 2 diabetes the likelihood of developing Alzheimer's disease increases by more than 50%. The purpose of the analysis proposed in this paper is to identify visually the correlation between Alzheimer's disease and type 2 diabetes and determine whether Alzheimer's disease is a form of brain diabetes mellitus. A dataset containing genomic microarray data relating to the two diseases is used for the analysis. First, we conduct an exploratory analysis using clustering techniques to perform a first screening of the samples and divide them into two different clusters. Then, we propose a predictive model for the classification and identify the genes equally expressed in the two types of samples. This makes it possible to select genes with significant values for the research in progress, on which pathway analysis must be performed to identify the classes they belong to. We also study the gene expression alterations of genes belonging to a specific pathway to determine if the differential expression is statistically significant. We provide a visual representation of connections in the pathways of both the diseases. Results indicate that there is a set of genes of significant importance for both type 2 diabetes and Alzheimer's disease, but that there is also a significant correlation with other neurodegenerative diseases. Consequently, it is possible to define the Alzheimer's disease as a form of cerebral diabetes mellitus.

Acute Leukemia is classified in terms of two distinct classes: Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). This paper aims at defining a feature selection analysis process mainly based on Deep Learning for classifying the acute leukemia type. The considered dataset consists in data of patients affected by both the leukemia types. Both the leukemia types are characterized by a list of identical genes for all the patients. The analysis exploits feature selection techniques for reducing the consistent number of variables (genes). To this aim, we use linear models for differential expression for microarray data, and an autoencoder based unsupervised deep learning model to simplify and speed up the classification. Then, classification models have been implemented with the use of a deep neural network (DNN), obtaining an accuracy of approximately 92%. Moreover, the results have been compared with the ones provided by an approach based on support vector machines (SVM), giving an accuracy of 87,39%. Another feature selection approach based on genetic algorithms has been experimented, with worse performances. We also conducted a gene enrichment analysis based on the functional annotation of the differentially expressed genes. As a result, a differentially expressed pathway between the two pathologies has been detected.

In this paper, we conduct an empirical study aiming at investigating how personality traits can affect the productivity of software developers in the context of the distributed development of multi-platform apps within a software project stored in GitHub. Participants were 31 master’s students in Computer Science grouped in 13 teams. Data were gathered from the compilation of the IPIP-NEO-120 questionnaire, a largely adopted tool to estimate personality traits, and from the software projects. We analyzed the correlation between personality traits (and their facets) and the productivity metrics. The results of this preliminary study seem to reveal that the most productive participants are those with the highest scores for the personality traits of Agreeableness and Conscientiousness.

Autism Spectrum Disorder (ASD) is a term used to describe a constellation of early-onset social communication deficits and repetitive sensorimotor behaviours associated with a strong genetic component as well as other causes. This paper aims at creating a tool for automatically isolating segments of the speech useful for extract prosody features for identifying children with ASD. In particular, in this first phase of the research, we are interested in the creation of a large dataset of ’a’ vowels of ASD and not ASD people. The ’a’ vowel contains relevant information on the voice quality and emotional states. The proposed methodology is divided into 2 phases. In the former the input audio is analyzed to determine the vowel onset and offset points, useful to extract the vowel regions. Then a spectrogram graphically visualizing the identified vowels is provided as input to the second phase, where a convolutional neural network classifies whether the histogram represents the vowel ’a’. The convolutional network reaches an average accuracy of 95.00% (standard deviation ± 2.60%) on a dataset of 640 samples with Stratified 5-Fold Cross-Validation.

The Therapeutic Alliance (TA) between patient and health provider (therapist or clinician) is one of the most relevant factors for the success of a therapy. In the case of people suffering from Autism Spectrum Disorder (ASD), the alliance is extended to all the people involved in their care (i.e., teachers, therapists, clinicians, relatives). In this paper, we propose a multimedia application named Thea for empowering the TA of children with ASD by improving the communication among the TA members, sharing guidelines, multimedia contents, and strategies to comply with challenging behaviors and progress with particular attention towards end-users who are occasional smart-users. A detailed process for empowering the TA members by enhancing the informed interaction among all of them is proposed and implemented. A vocal assistant also supports patients/caregivers and therapists in documenting their activity with the person with ASD by recording videos in a free-hand modality. After a contextual analysis based on Thematic Analysis Template, Thea has been implemented using a user-centered development approach. We performed three iterations involving the end-users. A user study is performed at the third iteration. Results of the user study revealed a positive attitude towards the application. In particular, the perception of empowerment of participants increased after the tool had been used. We also highlighted the guidelines and tools that may be adopted for empowering different kinds of patients. The first results seem to suggest that the use of Thea may increase the belief of the caregivers of a person with ASD to be able to better take care of her, in a more controlled and informed way.

The use of smartphones is dramatically increasing. As a consequence, many organizations have the need of migrating their Java desktop applications towards the mobile technology. In this paper we present a miniaturization approach (process and supporting tool) named miniJava for the automatic miniaturization of Java desktop applications towards Android. The Java business logic is unvaried, while the calls to the Java objects of the interface are mapped into call to objects of the target technology. Semi-automatic layout fragmentation enables us to partition a desktop Java interface in various mobile screens. The approach also migrates the application files and enables the network connection. We conduct a user study where we assess the user perception in terms of user experience and affective reaction of the miniaturized application generated by a real Java desktop application which also has real Android variant. The end-user sample consisted of 18 participants. Results of this preliminary evaluation are encouraging: they do not reveal particular problems when using the miniaturized version automatically generated of the real desktop app with respect to its original Android variant, except for the novelty, which is better perceived for the native Android one.

Over the last few years, biometrics has emerged as an increasingly reliable solution to recognize people using their physiological or behavioural characteristics. Despite their advantages, biometric systems raise many practical, ethical and legal issues. While, understandably, main concerns involve privacy and the risk of covert surveillance, profiling, and social control, another relevant question is the potential exclusion of individuals that, due to injuries, disability or genetic defects, may not meet the physical requirements used for the identification. In such situations, the risk comes out from the limits of current biometrics systems, which could exclude entire classes of individuals with negative spillovers on the possibility of access services and even exercise rights. In this paper, we focus on the recognition of iris suffering from Coloboma, a congenital abnormality of membranes of the eye. We first show how this pathological state impacts on the performance of the Daugman's algorithm, which represents the most widespread method used for the iris localization step in eye-based biometrics. Second, we designed and tested a classifier based on Convolutional Neural Network able to detect the presence of Coloboma with 95.45% accuracy. This result opens up new perspectives towards the definition of more sophisticated "diversity-aware" biometric systems.

Melanoma is the deadliest form of skin cancer. It mainly requires a visual diagnosis by dermatologists. However, a dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose correctly it. To this aim, in the last twenty years, Computer-Aided Diagnosis systems based on artificial vision are increasingly adopted to support dermatologists in the early diagnosis of melanoma. However, these systems exploits only a reduced set of parameters or they implement a melanoma classifier that tries to substitute the dermatologists, without supporting their experience in the classification of skin lesions. This paper proposes a mobile application for supporting the clinician decision in the diagnosis of melanoma directly in the dermatologist environment by using Augmented Reality technology. In particular, computer-generated perceptual information is added to the image of patient skin reporting the values of various parameters and the lesion classification based on deep learning approach for analyzing skin lesions and identifying melanoma.

Abstract Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.

Dyscalculia should be detected in the third primary class, but in many cases this disturb is diagnosed in later ages. In this paper we present a tool for supporting the clinician in the generation of responsive web-based tests for individuating people with disorder in basic numerical and arithmetic skills. Tests are created by combining specific kinds of questions which are delivered to a sample of people belonging to a specific target. The tool provides also support in the data analysis and in setting the alert thresholds for the selected user target. Results may be graphically visualized in summarized and single user way. Once the setting process is terminated, the test may be adopted. A case study on adult people is also presented.

In recent years, the integration of researches in Computer Science and medical fields has made available to the scientific community an enormous amount of data, stored in databases. In this paper, we analyze the data available in the Parkinson's Progression Markers Initiative (PPMI), a comprehensive observational, multi-center study designed to identify progression biomarkers important for better treatments for Parkinson's disease. The data of PPMI participants are collected through a comprehensive battery of tests and assessments including Magnetic Resonance Imaging and DATscan imaging, collection of blood, cerebral spinal fluid, and urine samples, as well as cognitive and motor evaluations. To this aim, we propose a technique to identify a correlation between the biomedical data in the PPMI dataset for verifying the consistency of medical reports formulated during the visits and allow to correctly categorize the various patients. To correlate the information of each patient's medical report, Information Retrieval techniques have been adopted, including the Latent Semantic Analysis technique suitable for constructing a concept space on patient information. Then, patients are grouped and classified into affected or not by using clustering algorithms according to the similarity of medical reports projected in the concept space. Results revealed that the proposed technique reached 95% of effectiveness in the classification of patients.

In this paper we present the Hunting Game Generator (HGG) system, a tool and a methodology for supporting geolocalized learning activities in Augmented Reality modality. The tool enables the teacher to create treasure hunting games directly on the mobile device. The teacher defines also the quizzes and advancing mechanism of the game. In this study we also conducted a qualitative investigation in terms of a focus group involving secondary school students aiming at evaluating their viewpoint on the relevance of the support the tool provides in learning activities. Students appeared very motivated by the tool, which is seen as a relevant support to traditional lectures.

MetricAttitude [18] is a visualization approach implemented in an environment that provides a mental picture of an object-oriented software by means of polymetric views of classes. In this paper, we describe a qualitative investigation we have conducted with a focus group involving developers aiming at evaluating their viewpoint on the relevance of the support MetricAttitude provides to perform comprehension tasks on source code. This investigation also allowed us to gather information on the developers' opinion on the MetricAttitude features and its software visualization metaphors. The discussion was animated and participants provided a number of useful suggestions for improving the visualization. The tool was considered very useful, while some usability problems have to be addressed. Specifically, the information provided has to be further filtered to easier software comprehension tasks.

MetricAttitude is a visualization tool based on static analysis that provides a mental picture by viewing an object-oriented software by means of polymetric views. In this tool demonstration paper, we integrate an information retrieval engine in MetricAttitude and name this new version as MetricAttitude++. This new tool allows the software engineer to formulate free-form textual queries and shows results on the polymetric views. In particular, MetricAttitude++ shows on the visual representation of a subject software the elements that are more similar to that query. The navigation among elements of interest can be then driven by the polymetric views of the depicted elements and/or reformulating the query and applying customizable filters on the software view. Due to its peculiarities, MetricAttitude++ can be applicable to many kinds of software maintenance and evolution tasks (e.g., concept location and program comprehension).

In this paper, we present LifeBook, a Personal Information Management (PIM) system that handles information on events captured by all the user's devices. Our PIM retrieves events on the basis of both the user's context and event similarity, which is computed by exploiting an information retrieval technique. We aggregated together the similarity of content, location, time, and event type to relate and surf the events. To this aim, we propose a re-find interface enabling the user to search and visualize information already seen before, of which he remembers some context aspects, such as time and/or place. The events captured on different devices are stored on the cloud without user intervention. A preliminary quantitative and qualitative evaluation has been also conducted to assess the effectiveness of LifeBook. Results in terms of time, effort and relevance of the information provided suggest that LifeBook be a viable means to retrieve personal information. Participants in the empirical investigation also considered the tool appropriate for supporting information re-finding tasks.

MetricAttitude is a visualization approach implemented in an environment that provides a mental picture by viewing an object-oriented software by means of polymetric views of classes (i.e., fine-grained). In this paper, we present an extension of MetricAttitude which visualizes a software by levels considering not only its class view but also its package views in terms of nested packages (i.e., coarse-grained). Packages are represented by using visual properties associated to Martin's metrics [15]. The new approach and its supporting visualization environment also allow showing relationships among packages.

We present an approach to enable end-users to graphically compose their own applications directly on their mobile phone, mainly integrating the functionalities available on the device and those provided by pervasive and Internet services. To this aim, we propose a methodology and a graphical notation enabling the user to compose mobile applications, named MicroApps: the user creates an application following an incremental and iterative development process; he composes icons representing (pervasive) services mainly by touch-based selection and following a data-flow approach. He is not in charge of the creation of the user interface, which is automatically generated. The methodology enables the end-user to develop applications and/or compose services on the smartphone, so paving the way towards new scenarios where smartphones replace and overtake the Personal Computer, given their native possibility of wide connectivity, when augmented by features for interaction with remote systems and sensors. The methodology has been evaluated through an empirical analysis that revealed that in spite of the reduced size of the screen the use of the MicroApp Generator tool improves the effectiveness in terms of time and editing errors with respect to the use of MIT App Inventor [1] .

I have enhanced Metric Attitude. It is a visualization tool based on static analysis that provides a mental picture by viewing an object-oriented software system by means of polymetric views. In particular, we have integrated an Information Retrieval engine and named this new version of visualization tool as Metric Attitude++. It allows the user to formulate a textual query and to show on the visual representation of the subject software the elements that are more similar to that query. This could be useful in all those cases in which a user needs to identify (or to localize) features implemented in the source code. Several filters are also available to hide possibly irrelevant details and to ease the browsing and then the comprehension of a software system. Finally, we have applied Metric Attitude++ on a number of object-oriented software systems. In this paper, we report preliminary results of a quantitative study on a widely studied open-source software, namely JEdit. On the basis of our results it seems that Metric Attitude++ can be effectively applied to different kinds of source code comprehension tasks and to concept location in source code, in particular.

MetricAttitude is a visualization tool based on static analysis that provides a mental picture by viewing an object-oriented software system by means of polymetric views. In this paper, we present a preliminary empirical investigation based on a questionnaire-based survey to assess Metric Attitude with respect to source code comprehension tasks. Participants involved in this study were Computer Science students and software professionals. The results suggest that Metric Attitude is a viable means to comprehend source code and that both kinds of participants in the empirical investigation considered it to be appropriate in source code comprehension.

Speaking in public may be a challenging task in terms of self-control and attention to the concepts to expose and to non-verbal communication. Presentation software, like Microsoft PowerPoint™ or OpenOffice, may support the speaker in organizing and controlling the flow of his/her discussion by commanding the slide change. In this paper we describe an approach exploiting the availability of the Microsoft Kinect™ advanced game controller to manage a presentation software through a Natural User Interface (NUI). The approach, named Kinect Presenter (KiP), adopts motion capture to recognize body gestures representing interaction metaphors. We perform a preliminary evaluation aiming at assessing the degree of support provided by the proposed interaction approach to the speaker activities. The assessment is based on the combined usage of two techniques: a questionnaire-based survey and an empirical analysis. The context of this study was constituted of Bachelor and PhD students in Computer Science at the University of Salerno, and teachers and employees from the same university. First results were adequate both in terms of satisfaction and performances, also when compared with a wireless mouse-based interaction approach.

The recent diffusion of advanced controllers, initially designed for the home game console, has been rapidly followed by the release of proprietary or third part PC drivers and SDKs suitable for implementing new forms of 3D user interfaces based on gestures. Exploiting the devices currently available on the game market, it is now possible to enrich, with low cost motion capture, the user interaction with desktop computers by building new forms of natural interfaces and new action metaphors that add the third dimension as well as a physical extension to interaction with users. This paper presents two systems specifically designed for 3D gestural user interaction on 3D geographical maps. The proposed applications rely on two consumer technologies both capable of motion tracking: the Nintendo Wii and the Microsoft Kinect devices. The work also evaluates, in terms of subjective usability and perceived sense of Presence and Immersion, the effects on users of the two different controllers and of the 3D navigation metaphors adopted. Results are really encouraging and reveal that, users feel deeply immerse in the 3D dynamic experience, the gestural interfaces quickly bring the interaction from novice to expert style and enrich the synthetic nature of the explored environment exploiting user physicality.

This paper presents an investigation, based on the combined use of two techniques: a questionnaire-based survey and an empirical analysis, to assess the effectiveness and efficacy of the MicroApp environment to support End-Users in the visual composition of their own applications directly on their mobile phone. The satisfaction of the End-Users has been investigated as well. The context of this study was constituted of students, administrative personnel and consultants of the University of Salerno. The survey shows a positive satisfaction degree of all the involved subjects, while the empirical analysis reveals that the use of the Micro App tool increases the efficiency and, in case of complex tasks, also the simplicity with respect to the use of a PC-based similar tool proposed by Google.

Abstract Research and commercial interest toward 3D virtual worlds are recently growing because they probably represent the new direction for the next generation of web applications. Although these environments present several features that are useful for informal collaboration, structured collaboration is required to effectively use them in a working or in a didactical setting. This paper presents a system supporting synchronous collaborative learning by naturally enriching Learning Management System services with meeting management and multimedia features. Monitoring and moderation of discussions are also managed at a single group and at the teaching level. The Second Life (SL) environment has been integrated with twoad hocdeveloped Moodle plug‐ins and SL objects have been designed, modeled, and programmed to support synchronous role‐based collaborative activities. We also enriched SL with tools to support the capturing and displaying of textual information during collaborative sessions for successive retrieval. In addition, the multimedia support has been enhanced with functionalities for navigating multimedia contents. We also report on an empirical study aiming at evaluating the use of the proposed SL collaborative learning as compared with face‐to‐face group collaboration. Results show that the two approaches are statistically undistinguishable in terms of performance, comfort with communication, and overall satisfaction. Copyright © 2009 John Wiley & Sons, Ltd.

Second Life is a virtual world which is often used for the synchronous meeting of teams. However, supporting distributed meeting goes beyond supporting user activities during the meeting itself, because it is also necessary to facilitate their coordination, arrangement and set up.

In this paper we describe how to exploit the 3D programmable virtual world provided by second life to create an environment and a location for collaborative learning. To this aim second life objects have been modeled and programmed to support the synchronous role-based collaborative activities required by the jigsaw learning technique in a 3D virtual meeting setting. We have also integrated this approach with Moodle, in such a way to naturally enrich LMS services with meeting management, set-up features, and synchronous collaborative learning.

Abstract Video lectures are an old distance learning approach that offers only basic interaction and retrieval features to the user. Thus, to follow the new learning paradigms, we need to re‐engineer the e‐learning processes while preserving the investments made in the past. In this paper we present an approach for migrating video lectures to multimedia learning objects. Two essential problems are tackled: the detection of slide transitions and the generation of the learning objects. To this aim, the video of the lecture is scanned to detect the slide changes, while the learning object metadata and the slide pictures are extracted from the presentation document. A tool named VLMigrator (video lecture migrator) has been developed to support the migration of video lectures and the restructuring of their contents in terms of learning objects. Both the migration strategy and the tool have been experimented in a case study. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract This paper presents the research results of an ongoing technology transfer project carried out in cooperation between the University of Salerno and a small software company. The project is aimed at developing and transferring migration technology to the industrial partner. The partner should be enabled to migrate monolithic multi‐user COBOL legacy systems to a multi‐tier Web‐based architecture. The assessment of the legacy systems of the partner company revealed that these systems had a very low level of decomposability with spaghetti‐like code and embedded control flow and database accesses within the user interface descriptions. For this reason, it was decided to adopt an incremental migration strategy based on the reengineering of the user interface using Web technology, on the transformation of interactive legacy programs into batch programs, and the wrapping of the legacy programs. A middleware framework links the new Web‐based user interface with the Wrapped Legacy System. An Eclipse plug‐in, named MELIS (migration environment for legacy information systems), was also developed to support the migration process. Both the migration strategy and the tool have been applied to two essential subsystems of the most business critical legacy system of the partner company. Copyright © 2008 John Wiley & Sons, Ltd.

We present the results of two controlled experiments aimed at assessing MELIS (migration environment for legacy information systems), a tool developed within a technology transfer project to support the migration of COBOL legacy information systems to a J2EE web-enabled multi-tier target architecture. The first controlled experiment was conducted within an academic research laboratory with master students in Computer Science at the University of Salerno, while a replicated experiment was conducted in the laboratory of our partner company with both professional programmers and academic researchers. The results revealed that the use of MELIS decrease the time to migrate legacy systems to the web with respect to the use of traditional development tools. Also, less expert software engineers benefit more of the use of MELIS.

In this paper we propose a tool, named VLMigrator, for interactively restructuring a lecture and the associated Powerpoint presentation into one or more multimedia Learning Objects. It also enables to fill the Learning Object metadata by automatically extracting information from the Powerpoint presentation. To easily perform these tasks, the VLMigrator interface exploits continuous semantic zooming and visual contextualization of information.

We present an incremental approach to the migration of non decomposable COBOL applications to a Web-enabled multi-tier architecture. The relevant software components of the target architecture, namely the communication middleware and the generator of graphical user interfaces, are developed once for all in order to reduce the migration effort. An Eclipse plug-in has also been developed to support the software engineer in the migration of the graphical user interface and in the restructuring and wrapping of the original legacy code. A pilot project on a COBOL legacy system evolved during the last thirty years has been used to experiment the migration strategy and the plug-in

Banking legacy systems intensively exchange messages in electronic format. Such systems are, for their nature, difficult to update and maintain. As a consequence, the introduction of new types of messages is a hard task. Adding new functionalities requires custom coding and software reuse is seen as a key to obtain a better time-to-market factor, risk and cost reduction. In this paper we describe the architecture and part of the implementation of the SI.RE. Visual System, an Interbanking Network Information System that lets us generate gateways for the exchange of electronic messages among banking legacy systems and supports software reuse. In particular, the SI.RE visual system contains a Visual Programming Environment that allows us to obtain a rapid development of the message handling functions. This environment implements a Visual Programming Language UVG that allows a programmer to reuse COBOL routines.

We present the Component-Based Visual Environment Development (CB-VED) process for building visual language environments and introduce the Visual Language Desk (VLDesk) system supporting its implementation. The proposed approach is based on software reuse at different granularity levels and enables incremental development. The VLDesk exploits all the knowledge gained from the development of the Visual Language Compiler-Compiler tool extending its functionalities with many adjunctive features useful in the presented development process. One of the aims of this research consists of the application of software engineering techniques to the incremental development of visual language environments.

The modeling of multimedia databases in the context of multimedia information systems is a complex task. The designer has to model the structure and the dynamic behavior of multimedia objects, together with possible user interactions with them. These can include content-based queries, and dynamic presentations, which might both require the construction of special index structures on the stored data. In this paper we present a methodology for the design of multimedia databases. The methodology extends some of the Unified Modeling Language (UML) diagrams with constructs to model the structure of multimedia objects, their dynamic behavior, presentation layout, and user interactions. These constructs allow a designer to model important aspects of database oriented multimedia applications. As a consequence, it is possible to construct refinement techniques to map our high level models onto logical data models of several target multimedia DBMSs. In particular, we describe how to map our conceptual models onto extended relational data models.