Abstract Most machine learning models for diabetes prediction rely on small, homogeneous datasets and fixed thresholds, producing binary outputs with limited clinical utility. These approaches lack generalizability, probabilistic awareness, and interpretability, which are essential for real-world healthcare adoption. We present Diabetes Risk via Explainable AI Modeling (D.R.E.A.M.), a framework for Type 2 diabetes mellitus (T2DM) risk prediction that delivers continuous, calibrated probabilities with transparent explanations. D.R.E.A.M. integrates two complementary datasets (PIMA and BRFSS 2015) after excluding gestational diabetes cases, applies clinically guided feature engineering and class balancing, and trains ensemble models (Random Forest, XGBoost, LightGBM). Decision thresholds are optimized using precision–recall curve analysis rather than default cutoffs, enabling clinically meaningful stratification. Model interpretability is achieved through SHapley Additive exPlanations (SHAP), providing both global and patient-level insights. All models achieved Area Under the Curves above 0.83 and F1-scores of 0.78, with Random Forest offering the best balance of sensitivity (recall = 0.89 at an optimized threshold of 0.389) and interpretability. SHAP confirmed the contribution of both physiological and behavioral factors, including glucose, BMI, blood pressure, cholesterol, and physical activity. Accessible via a lightweight web interface, D.R.E.A.M. provides real-time, explainable risk scores to support personalized preventive strategies. In summary, D.R.E.A.M. advances beyond conventional post-hoc explainability by integrating calibrated probabilistic predictions, PRC-based thresholding, and direct clinician-facing deployment. This combination transforms it from a research prototype into a transparent and clinically actionable decision support system.

Abstract Biometric identification based on the electrocardiogram (ECG) is gaining attention as a secure and reliable approach to healthcare authentication, employing the unique physiological patterns detected in the ECG signal. Traditional approaches often depend on raw waveform analysis or the extraction of fiducial points, both of which are computationally intensive and challenging to implement in real-time systems. This work presents CardioIdNet, a lightweight convolutional neural network designed to perform biometric identification directly from ECG images, eliminating the need for complex signal preprocessing steps. ECG recordings from 21 subjects in the MIT-BIH arrhythmia database were segmented and converted to grayscale waveform plots, generating a comprehensive well-suited dataset for image-based deep learning classification. The CardioIdNet architecture consists of convolutional and pooling layers for hierarchical feature extraction, followed by fully connected layers for subject classification. Training was carried out using sparse categorical cross-entropy and the Adam optimizer. The dataset was split 80/20 for training and testing, and early stopping was applied to prevent overfitting and improve generalization. The results show that CardioIdNet achieves excellent performance, with accuracy of 99%, precision, recall, and F1-score of 98.18%, an AUC of 99%, and a false negative rate of 1.85%. CardioIdNet suggests to be a promising solution for biometric authentication in healthcare real-time settings, offering a balance of simplicity, interpretability, and efficiency through image-based deep learning.

The accurate evaluation of periodontal risk during orthodontic tooth movement remains a major clinical challenge, as excessive lower incisor displacement beyond anatomical limits can result in bone damage and gingival recession. This study proposes a novel artificial intelligence framework, FusionNet-ARC, designed to predict alveolar bone availability directly from standard two-dimensional cephalometric radiographs, enabling data-driven assessment of periodontal risk without the need for high-radiation imaging modalities. A total of 996 anonymized lateral cephalometric radiographs were processed through a dedicated image analysis pipeline to extract six anatomical landmarks and compute two alveolar parameters: ARC1 (lingual bone availability) and ARC2 (vestibular bone availability). The proposed FusionNet-ARC model combines a convolutional neural network (ResNet50) for visual feature extraction with a Graph Attention Network (GAT) for landmark-based spatial reasoning. The fused embeddings were regressed to predict ARC1 and ARC2 values, and model accuracy was assessed using Mean Absolute Error (MAE), coefficient of determination (R2), and prediction match rate within a ±0.5,mm tolerance. The model achieved high predictive precision for both parameters, with 66.1 % of ARC1 and 61.4 % of ARC2 predictions within clinical tolerance, showing strong agreement with manual cephalometric measurements. When applied to periodontal risk classification, it accurately identified the corresponding Normal, Borderline, and At-Risk categories, confirming its reliability for automated estimation of alveolar bone availability and related periodontal risk from standard cephalometric radiographs. The proposed system provides an objective and quantitative method for assessing alveolar bone support within routine orthodontic workflows.

Gradient-based neural network training traditionally enforces symmetry between forward and backward propagation, requiring activation functions to be differentiable (or sub-differentiable) and strictly monotonic in certain regions to prevent flat gradient areas. This symmetry, linking forward activations closely to backward gradients, significantly restricts the selection of activation functions, particularly excluding those with substantial flat or non-differentiable regions. In this paper, we challenge this assumption through mathematical analysis, demonstrating that precise gradient magnitudes derived from activation functions are largely redundant, provided the gradient direction is preserved. Empirical experiments conducted on foundational architectures—such as Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Binary Neural Networks (BNNs)—confirm that relaxing forward-backward symmetry and substituting traditional gradients with simpler or stochastic alternatives does not impair learning and may even enhance training stability and efficiency. We explicitly demonstrate that neural networks with flat or non-differentiable activation functions, such as the Heaviside step function, can be effectively trained, thereby expanding design flexibility and computational efficiency. Further empirical validation with more complex architectures remains a valuable direction for future research.

Abstract The spread of the COVID-19 pandemic is expected to be uncontrollable by 2020. The main precautions to avoid virus spread have been the introduction of surgical masks or FFP2, sanitization of the hands, and maintaining social distancing. Due to their reliability, molecular tampons are the main detection and prevention methods known as the “Gold Standard”. However, these methods can be particularly uncomfortable. In this case, the analysis of electrocardiogram traces appears to be an alternative method for detecting COVID-19. The dataset used is made up of 1937 images from a study conducted in Pakistan that were preprocessed to train six different neural networks, including MobileNetV2, ResNet-18, ResNet-50, AlexNet, SqueezeNet, and an ad hoc defined neural network. The results show high classification performance, with an accuracy close to 98.94%, as reached by the Resnet-18 network. Moreover, significant attention was devoted to analyzing confusion matrices, revealing the capacity of the networks to identify distinctive features indicative of COVID-19 within ECG data. Finally, it is suggested that in nearly all experiments, including those with low performance, COVID-19 patients are correctly classified, further enhancing the diagnostic potential of ECGs data and DL approach.

Cold spray additive manufacturing (CSAM) is an effective technique for applying metallic layers to various surfaces, particularly beneficial for thermosensitive materials, such as polymers and composites. However, optimizing coating outcomes remains challenging due to several complex factors influencing process efficacy. Machine learning (ML) offers a powerful solution to enhance the quality of CSAM by predicting key coating properties, such as particle penetration depth and flattening. This study addresses the problem of accurately predicting key coating characteristics, specifically particle penetration depth and flattening, by integrating finite element model (FEM) with supervised ML techniques. A dataset of 132 FEM simulations was generated, covering multiple metal–polymer combinations and a wide range of impact velocities. The study evaluates and compares several ML algorithms, including support vector regression, decision trees, Gaussian process regression (GPR), and neural networks (NNs), with the goal of minimizing prediction error measured via root-mean-square error (RMSE). Results show that GPR achieves the best performance for particle flattening (RMSE = 3.9), while a bilayered NN provides the most accurate prediction of penetration depth (RMSE = 2.3). The findings highlight the need for distinct models due to the differing physical mechanisms governing each output: penetration depth exhibits a more linear and predictable relationship with impact velocity and material density, whereas flattening is influenced by complex local deformation and interfacial dynamics. This study demonstrates the feasibility and efficiency of using ML to generalize FEM results, reducing computational cost and enabling fast prediction of coating behavior across varying process conditions.

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.

In the contemporary metaverse landscape, comprehending the intricacies of human interaction is imperative for enhancing communication within Virtual Reality (VR) experiences. At the core of meaningful social relationships lie empathy and trust, pivotal elements nurtured by the capacity to comprehend both self and others’ thoughts and intentions. Conventional face-to-face interactions heavily rely on non-verbal cues, such as body language and facial expressions, to convey messages and display empathy.

This paper introduces "How Are You Today?" (HAYT), a mobile application developed to support the diagnosis and treatment of anxiety and depression. HAYT integrates a digital diary and Natural Language Processing (NLP) to analyze emotional states and predict anxiety or panic attacks. Users document their experiences and emotions while completing a questionnaire based on Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria, helping clinicians monitor symptoms of depression and anxiety. The app combines real-time feedback, Cognitive Behavioral Therapy (CBT) interventions, and direct communication with mental health professionals via a secure messaging system. Preliminary findings from a feasibility study using synthetic data show a significant correlation between sentiment analysis of diary entries and self-reported depressive symptoms. This suggests HAYT’s potential for improving mental health care accessibility and effectiveness by providing continuous monitoring and personalized support.

Diabetes mellitus is one of the most prevalent chronic diseases, affecting millions of people worldwide. Effective management of diabetes, particularly type 1 (T1DM) and type 2 diabetes (T2DM), requires a deep understanding of the complex interactions between clinical, behavioral, and socio-demographic factors. This study leverages Bayesian networks (BNs) to model these interactions, providing a transparent and interpretable visual framework that reveals how variables such as insulin use, BMI, and socio-economic status influence diabetes outcomes. However, constructing the graphical structure of a BN poses significant challenges due to the intricate and multifaceted relationships involved. To ensure a meaningful comparison between T1DM and T2DM, we utilized a cohort of subjects selected to be as demographically homogeneous as possible. This allowed us to reduce confounding effects and focus on the intrinsic differences between the two conditions. We compared network structures derived from three data samples (50%, 80%, and 100%) to explore how variable relationships evolve as the dataset size increases, ensuring that critical interactions were captured at different levels of data availability. The findings highlight key differences in the management of T1DM and T2DM, particularly with regard to behavioral and socio-economic factors.

Trust is a fundamental component of effective therapeutic relationships, significantly influencing patient engagement and treatment outcomes in mental health care. This paper presents a preliminary study aimed at enhancing trust through the co-creation of virtual therapeutic environments using generative artificial intelligence (AI). We propose a multimodal AI model, integrated into a virtual reality (VR) platform developed in Unity, which generates three-dimensional (3D) objects from textual descriptions. This approach allows patients to actively participate in shaping their therapeutic environment, fostering a collaborative atmosphere that enhances trust between patients and therapists. The methodology is structured into four phases, combining non-immersive and immersive experiences to co-create personalized therapeutic spaces and 3D objects symbolizing emotional or psychological states. Preliminary results demonstrate the system’s potential in improving the therapeutic process through the real-time creation of virtual objects that reflect patient needs, with high-quality mesh generation and semantic coherence. This work offers new possibilities for patient-centered care in mental health services, suggesting that virtual co-creation can improve therapeutic efficacy by promoting trust and emotional engagement.

Cardiovascular disease refers to a group of disorders affecting the heart and blood vessels, including conditions like coronary artery disease, stroke, and heart failure. Arrhythmias are irregularities in the rhythm of the heart, where the heart may beat too fast, too slow, or erratically. This study presents a comparison between ChatGPT-4o and a group of cardiologists in the analysis of electrocardiogram images for assisting in the diagnosis of cardiovascular conditions. The purpose of this comparison is to evaluate the potential of using large language models like ChatGPT-4o in clinical environments, specifically for interpreting electrocardiogram traces. To achieve this, we designed an experiment where both the model and a cohort of cardiologists analyzed the same set of ECG images, and their interpretations were compared to assess performance. The evaluation focused on key diagnostic aspects: heart rate determination, rhythm interpretation, and the overall diagnosis of potential cardiovascular abnormalities. Cardiologists were asked to provide their expert insights through a structured survey that captured their diagnostic reasoning. ChatGPT-4o, in turn, was provided with the same set of images and asked to produce diagnostic outputs. Given that large language models are not explicitly trained in medical image analysis, the responses were generated based on the model’s ability to infer from the textual and visual information presented. The model’s outputs were processed and evaluated for accuracy against the responses of the cardiologists and the ground truth labels provided by the dataset. The results revealed notable differences in diagnostic accuracy between the outputs of ChatGPT-4o and the cardiologists’ assessments. ChatGPT-4o achieved an accuracy of 29.20%, sensitivity of 29.20%, and an F1-score of 0.29 when compared to the ground truth labels. In contrast, the cardiologists collectively performed significantly better, achieving an accuracy of 58.70%, sensitivity of 58.70%, and an F1-score of 0.59.

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels due to insufficient insulin production or insulin resistance. It primarily manifests in two forms: Type 1 diabetes, an autoimmune condition typically diagnosed in younger individuals, and Type 2 diabetes, which is more prevalent and often linked to lifestyle factors such as obesity and inactivity. This study evaluates the performance of Long Short-Term Memory networks in diagnosing the two types of diabetes from Italian medical text across four progressively refined pre-processing scenarios. Each scenario incrementally builds on the previous one to enhance text cleaning and data preparation, allowing for a more refined and effective data processing pipeline. In parallel, this study introduces DiabeTalk, a chatbot developed on the WizardLM model, designed to provide specialized advice and support for diabetes diagnosis. While the WE-long short term memory models were fine-tuned with clinical data, DiabeTalk was tested without prior training on clinical diaries, allowing us to evaluate its performance in a real-world context. The results indicate that, despite the lack of domain-specific pre-training, DiabeTalk effectively employs natural language understanding and decision-making algorithms to predict diabetes type and respond to user inquiries. However, the testing revealed limitations in accuracy (77.56% versus 97.80%), with the chatbot achieving a lower performance than the WE-long short term memory model, which was applied to minimally pre-processed raw data. The findings underscore the importance of training large language models on relevant clinical datasets to enhance their response capabilities.

Intelligent diagnosis processes rely on Artificial Intelligence (AI) techniques to provide possible diagnoses by analyzing patient data and medical information. To make accurate and quick diagnoses, it is possible to use AI tools to efficiently analyze huge amounts of data and find patterns that a clinician might miss. In recent years, new large language models (LLMs), such as ChatGPT and Google BARD, have shown remarkable capabilities in several domains, including intelligent diagnostics. This research aims to compare the performances of ChatGPT and traditional machine learning models for making diagnoses of low- and medium- risk diseases only based on their symptoms. On the basis of our study, we defined four research questions: RQ1) What are the benefits and limitations of using ChatGPT in intelligent diagnosis? RQ2) How do traditional machine learning approaches compare to ChatGPT for intelligent diagnosis? RQ3) How does ChatGPT compare with other LLMs and domain-specific natural language processing models in the intelligent diagnosis tasks?, and RQ4) What are the implications of the predictive models and ChatGPT for healthcare, and how can they be used to support people?. To answer these RQs, we first evaluate the performances of different engines of ChatGPT, also introducing a new prompt engineering methodology specifically tailored for achieving accurate diagnostic outcomes. Moreover, we compare these results with those achieved by different predictive models trained for intelligent diagnosis tasks, i.e., Google BARD, and two domain-specific NLP models. Finally, we propose a new interactive bot available for users that relies on the best-performing models evaluated in the previous steps. The experiments have been conducted using two medical datasets for disease prediction consisting of more than 100 symptoms associated with several diagnoses.

The examination of ocular movements has a wide range of applications due to the current developments in sensors that are now able to collect this biometric. This type of investigation is known as “gaze analysis”. The gaze has successfully examined a subject's physical and mental status in the past. As a result, over the last few decades, a large and diverse amount of literature on this subject has been generated and presented. The aim of this study is to collect and debate current gaze analysis methods based on their application field. Due to the context-specific needs for performance and efficiency, the eye movements under research are frequently evaluated from completely distinct perspectives. As a result, a collection of data, methods, and discussions ranging from the medical community to virtual and augmented reality, as well as human computer interface and remote learning, has been produced. In addition to providing a peek of novel observation on the issue of gaze analysis, the gaps between and within areas are also discussed to provide points for researchers to pursue.

Magnetic resonance imaging (MRI) is an effective imaging tool that, due to its non-invasiveness and multiple-parameter nature, is frequently used in medicine. In particular, the MRI's inherent flexibility deriving from the usage of multiple parameters allows to obtain images of variable contrast and quality. However, intrinsic MRI contrast variability often comes with drawbacks in terms of differences in different scanners, thus resulting in the impossibility of standardizing the image contrast. In particular, this variability could negatively affect the automatic analysis of Deep Learning (DL) methods, both in the training phase and in the test phase. In this work, we present several results on how images collected from different MRI scanners are handled by DL methods. To this end, we trained a Siamese network (SNN), based on the EfficientNet-B0 Convolutional Neural Network (EN-CNN), to learn how to recognize the scanner that has generated a given image. The output encoding features of the SNN have been projected into a 2D space with Uniform Manifold Approximation and Projection (UMAP) and have been discussed. Regarding the training phase, the UMAP projects show that the network is capable of separating MR images encoded features from different MRI scanners. Moreover, even if the MR images of different subjects are acquired with the same scanner, the results suggest that there are considerable differences in how the SNN encoded those features. The test phase confirmed that the SNN architecture is capable of recognizing images from different MRI scanners.

BACKGROUND: Melanoma is one of the deadliest tumors in the world. Early detection is critical for first-line therapy in this tumor pathology and it remains challenging due to the need for histological analysis to ensure correctness in diagnosis. Therefore, multiple computer-aided diagnosis (CAD) systems working on melanoma images were proposed to mitigate the need of a biopsy. However, although the high global accuracy is declared in literature results, the CAD systems for the health fields must focus on the lowest false negative rate (FNR) possible to qualify as a diagnosis support system. The final goal must be to avoid classification type 2 errors to prevent life-threatening situations. Another goal could be to create an easy-to-use system for both physicians and patients. RESULTS: To achieve the minimization of type 2 error, we performed a wide exploratory analysis of the principal convolutional neural network (CNN) architectures published for the multiple image classification problem; we adapted these networks to the melanoma clinical image binary classification problem (MCIBCP). We collected and analyzed performance data to identify the best CNN architecture, in terms of FNR, usable for solving the MCIBCP problem. Then, to provide a starting point for an easy-to-use CAD system, we used a clinical image dataset (MED-NODE) because clinical images are easier to access: they can be taken by a smartphone or other hand-size devices. Despite the lower resolution than dermoscopic images, the results in the literature would suggest that it would be possible to achieve high classification performance by using clinical images. In this work, we used MED-NODE, which consists of 170 clinical images (70 images of melanoma and 100 images of naevi). We optimized the following CNNs for the MCIBCP problem: Alexnet, DenseNet, GoogleNet Inception V3, GoogleNet, MobileNet, ShuffleNet, SqueezeNet, and VGG16. CONCLUSIONS: The results suggest that a CNN built on the VGG or AlexNet structure can ensure the lowest FNR (0.07) and (0.13), respectively. In both cases, discrete global performance is ensured: 73% (accuracy), 82% (sensitivity) and 59% (specificity) for VGG; 89% (accuracy), 87% (sensitivity) and 90% (specificity) for AlexNet.

One of the most difficult issues today, is the integration of data from various sources. Thus, it arises the need of automatic Data Integration (DI) methods. However, in the literature there are fully automatic or semi-automatic DI techniques, but they require the involvement of IT-experts with specific domain skills. In this paper we present a novel DI methodology for which it is not required the involvement of IT-experts; in this methodology syntactically/semantically similar entities present in the sources are merged, by exploiting an information retrieval technique, a clustering method and a trained neural network. Although the suggested process is completely automated, we planned some interactions with the Company Manager, a figure who is not required to have IT-skills, but whose only contribution will be to define limits and tolerance thresholds during the DI process, based on the interests of the company. The validity of the proposed approach showed an integration accuracy between 99%−100%.

Today, people of all ages are increasingly using Web platforms for social interaction. Consequently, many tasks are being transferred over social networks, like advertisements, political communications, and so on, yielding vast volumes of data disseminated over the network. However, this raises several concerns regarding the truthfulness of such data and the accounts generating them. Malicious users often manipulate data to gain profit. For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues, it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this article we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach toward the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.

In this work, we develop and analyze the impact of using a gamified application to encourage users to adopt an eco-sustainable driving style. The proposed solution uses elements of gamification such as rankings, scores, prizes, and levels to encourage and engage the driver in an urban context. The score, in terms of eco-sustainable driving, is calculated by considering vehicle speed, acceleration and braking parameters during the driving session. The application also provides real-time feedback in order to help the driver improve his/her driving style. Whenever the application detects a sudden change in speed, known as an unsustainable behavior, it will warn the user so as to let him/her correct the driving style. Our solution is oriented to assist people in eco-sustainable driving while preventing them from loss of interest and from reverting to his/her former bad driving behaviors.

Background: Dead code is a code smell. It can refer to code blocks, variables, parameters, fields, methods, classes, etc. that are unused and/or unreachable. Aim: Results from past empirical studies indicate that dead code is widespread in both desktop and web-based software applications. Also, researchers have shown that both comprehensibility and maintainability of source code are negatively affected when dead code is present. Nevertheless, we still know little about maintenance operations involving dead code. Method: We conducted an exploratory empirical study on 13 open-source Java desktop applications, whose software projects were hosted on GitHub, to provide preliminary evidence on whether, and to what extent, developers modify dead code—more specifically, dead methods—when they deal with the maintenance of open-source Java desktop applications. Results: The most important results of our study can be summarized as follows: (i) developers modify dead methods; (ii) dead methods are modified to a different extent as compared to alive methods; (iii) developers spend time modifying dead methods that are removed in subsequent commits; and (iv) developers modify dead methods that are later revived to a different extent as compared to dead methods that are later removed. Conclusions: One of the conclusions of our study is: developers should remove dead methods, whose presence and purpose are not properly documented, to avoid unnecessary modifications to dead methods during the maintenance of software applications.

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.

Premature ventricular contractions (PVCs) are abnormal heartbeats that begin in the lower ventricles or pumping chambers and disrupt the normal heart rhythm. The electrocardiogram (ECG) is the most often used tool for detecting abnormalities in the heart's electrical activity. PVCs are very frequent and usually harmless, but they can be extremely harmful in patients with significant heart problems. As a result, appropriate prevention combined with adequate treatment can improve patients' lives. This paper presents preliminary results on the main challenge associated with the detection of PVCs: identifying common patterns. The images used were extrapolated from the MIT-BIH Arrhythmia Database and then pre-processed to remove any signal noise before creating a distance matrix based on the wave distances of each pair of analyzed images. Finally, we clustered the distance into four groups using clustering algorithms such as K-means. We used a graph-based structure to graphically represent and explore cluster elements in this work. Preliminary results suggest the presence of four distinct patterns.

BACKGROUND: This research aims to increase our knowledge of amyloidoses. These disorders cause incorrect protein folding, affecting protein functionality (on structure). Fibrillar deposits are the basis of some wellknown diseases, such as Alzheimer, Creutzfeldt-Jakob diseases and type II diabetes. For many of these amyloid proteins, the relative precursors are known. Discovering new protein precursors involved in forming amyloid fibril deposits would improve understanding the pathological processes of amyloidoses. RESULTS: A new classifier, called ENTAIL, was developed using over than 4000 molecular descriptors. ENTAIL was based on the Naive Bayes Classifier with Unbounded Support and Gaussian Kernel Type, with an accuracy on the test set of 81.80%, SN of 100%, SP of 63.63% and an MCC of 0.683 on a balanced dataset. CONCLUSIONS: The analysis carried out has demonstrated how, despite the various configurations of the tests, performances are superior in terms of performance on a balanced dataset.

BACKGROUND: SNARE proteins play an important role in different biological functions. This study aims to investigate the contribution of a new class of molecular descriptors (called SNARER) related to the chemical-physical properties of proteins in order to evaluate the performance of binary classifiers for SNARE proteins. RESULTS: We constructed a SNARE proteins balanced dataset, D128, and an unbalanced one, DUNI, on which we tested and compared the performance of the new descriptors presented here in combination with the feature sets (GAAC, CTDT, CKSAAP and 188D) already present in the literature. The machine learning algorithms used were Random Forest, k-Nearest Neighbors and AdaBoost and oversampling and subsampling techniques were applied to the unbalanced dataset. The addition of the SNARER descriptors increases the precision for all considered ML algorithms. In particular, on the unbalanced DUNI dataset the accuracy increases in parallel with the increase in sensitivity while on the balanced dataset D128 the accuracy increases compared to the counterpart without the addition of SNARER descriptors, with a strong improvement in specificity. Our best result is the combination of our descriptors SNARER with CKSAAP feature on the dataset D128 with 92.3% of accuracy, 90.1% for sensitivity and 95% for specificity with the RF algorithm. CONCLUSIONS: The performed analysis has shown how the introduction of molecular descriptors linked to the chemical-physical and structural characteristics of the proteins can improve the classification performance. Additionally, it was pointed out that performance can change based on using a balanced or unbalanced dataset. The balanced nature of training can significantly improve forecast accuracy.

Abstract In the last few 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 and Machine Learning techniques have been adopted, including the Latent Semantic Analysis, Text2Vec and Doc2Vec techniques. Then, patients are grouped and classified into affected or not by using clustering algorithms according to the similarity of medical reports. Finally, we have adopted a visualization system based on the D3 framework to visualize correlations among medical reports with an interactive chart, and to support the doctor in analyzing the chronological sequence of visits in order to diagnose Parkinson’s disease early.

In the era of digitized images, the goal is to extract information from them and create new knowledge thanks to Computer Vision techniques, Machine Learning and Deep Learning. This enables the use of images for early diagnosis and subsequent treatment of a wide range of diseases. In the dermatological field, deep neural networks are used to distinguish between melanoma and non-melanoma images. In this paper, we have underlined two essential points of melanoma detection research. The first aspect considered is how even a simple modification of the parameters in the dataset determines a change of the accuracy of classifiers. In this case, we investigated the Transfer Learning issues. Following the results of this first analysis, we suggest that continuous training-test iterations are needed to provide robust prediction models. The second point is the need to have a more flexible system architecture that can handle changes in the training datasets. In this context, we proposed the development and implementation of a hybrid architecture based on Cloud, Fog and Edge Computing to provide a Melanoma Detection service based on clinical and dermoscopic images. At the same time, this architecture must deal with the amount of data to be analyzed by reducing the running time of the continuous retrain. This fact has been highlighted with experiments carried out on a single machine and different distribution systems, showing how a distributed approach guarantees output achievement in a much more sufficient time.

This paper presents a patient-centered interaction project of Pepper humanoid robotic therapy applications for children with attention deficit. This new therapeutic methodology was created to support and make therapeutic work more attractive. Pepper comes with a tablet and two identical cameras. The tablet is used to make the patients interact with the application, while the cameras are used to capture their emotions in real-time to understand the degree of attention and any difficulties they may have. The interaction with the tablet takes place through some exercises in the form of games. The exercises performed by the patients are analyzed and combined with the data acquired by the cameras. The combination of these data is elaborated to propose adequate levels of therapeutic activity. This process leads to the digitization of the patients' therapeutic path so that any improvement (or worsening) is monitored and makes Pepper a reliable and predictable technological intermediary for the child. The work was developed in collaboration with a diagnostic and therapeutic center, where it is being tested. By interacting with a humanoid robot, children show greater involvement, which can be explained, according to psychologists, by the fact that a robot is emotionally less rich than humans and the patients feel less fearful.

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.

In recent years, Deep Learning techniques have achieved some success in bioinformatics tasks, including protein conformation prediction. In this work, we propose a Bidirectional Long Short-Term Memory (BLSTM) network system, called Human Proteins Angles Prediction (HPAP), in order to improve the prediction of dihedral angles of proteins. We have introduced a discrete subdivision in classes of 5° for protein torsion angles and four new features related to accessible surface area and volume. In total there are 73 classes (72 classes include the angles between -180° and 180°, a further class is used to code the free angles at the beginning of the sequence) with a maximum expected error of ±2.5°. We have tested three model variants in several parameter combinations. With our model, we have obtained a decrease of the mean absolute error of about 2° for the $\psi$ angle. Although our dataset is reduced in size, the accuracy of $\varphi$ and $\psi$ angles is comparable to the existing methods. Predicting angles accurately is useful for accurately reconstructing the three-dimensional structure of a protein. In this context, the prediction is limited to the $\varphi$ and $\psi$ angles and we will visualize what happens locally when a prediction is correct. In case the prediction is far from true angles, even a small error can deconstruct the backbone.

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.

In this work, we illustrate an innovative treatment for patients affected by Behavioral Disorders, that relies on the use of Pepper humanoid robot. This new therapeutic methodology was created to support and make the therapist's work more attractive. Pepper is equipped with a tablet and two identical cameras. The tablet is used to let the patient interact with the application, while the cameras are used to capture their real-time emotions to understand the degree of attention and any difficulty that they may have. The interaction with the tablet takes place through some exercises in the form of games. The exercises performed by the subject are analyzed and combined with the data captured by the cameras. The combination of these data is processed to propose appropriate levels of therapeutic activities. This process leads to the digitization of the patient's healing path so that any improvement (or worsening) is monitored and causes Pepper to become a reliable and predictable technological intermediary for the child. The work has been developed in collaboration with a diagnostic and therapeutic center. Interacting with a humanoid robot, children exhibit a higher engagement, which can be explained, according to the psychologists, by the fact that a robot is emotionally less rich than human beings, and the patient feels less scared.

The forest fires represent a social emergency that requires significant economic and organizational commitment. Safety and the lack of reliable and timely localization of firefighters is a big problem. In this paper, we present Karya Advanced Sensor, an automatic, accurate, and reliable IT solution able to locate firefighters in harsh environments and support decision making activities at control rooms. The system consists of sensors perfectly integrated into firefighters' uniforms, which are used to monitor in real-time individual operators' activities as well as the entire fire area. In particular, in case a firefighter gets injured, the system will activate the rescue teams quickly, as there will be a constant link between the firefighters and the medical assistance. The firefighter can also specify the reason for the accident, which is critical information for a more timely and appropriate health intervention. Moreover, the system is able to perform an automatic real-time mapping of forest fires and possibly estimate its propagation rate, providing precious support to control rooms, which are the center of the team coordination.

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.

Melanoma is the deadliest form of skin cancer and it is diagnosed mainly visually, starting from initial clinical screening and followed by dermoscopic analysis, biopsy and histopathological examination. A dermatologist's recognition of melanoma may be subject to errors and may take some time to diagnose it. In this regard, deep learning can be useful in the study and classification of skin cancer. In particular, by classifying images with Deep Neural Network methodologies, it is possible to obtain comparable or even superior results compared to those of dermatologists. In this paper, we propose a methodology for the classification of melanoma by adopting different deep learning techniques applied to a common dataset, composed of images from the ISIC dataset and consisting of different types of skin diseases, including melanoma on which we applied a specific pre-processing phase. In particular, a comparison of the results is performed in order to select the best effective neural network to be applied to the problem of recognition and classification of melanoma. Moreover, we also evaluate the impact of the preprocessing phase on the final classification. Different metrics such as accuracy, sensitivity, and specificity have been selected to assess the goodness of the adopted neural networks and compare them also with the manual classification of dermatologists.

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.

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.

Mobile devices are always more diffused in the last years, allowing the users to perform several tasks: communication, web surfing, requiring web services. Given the high amount of sensitive data and operations related to these tasks, securing the mobile devices is becoming a very critical issue. As matter of the fact, malware attacks are on the rise and new mobile malware are continually generated with the aim of stealing private data and performing illegal activities. Since this new malware is mainly obtained by reusing existing malicious code, malware detection is supported by the study and the tracking of the mobile malware phylogeny. This paper proposes a malware phylogeny model obtained by a declarative Process Mining (PM) approach from the analysis of some running malware applications. The main idea is that the set of relations and recurring execution patterns among the syscalls of a running malware application can be modeled to obtain a malware fingerprint. The malware fingerprints are compared and classified by using a fuzzy clustering algorithm to recover the malware phylogeny map of all the considered malware families. The evaluation of the proposed approach is performed on a dataset of more than 4,000 infected applications across 39 malware families obtaining very encouraging results.

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.

In the last years, the growing adoption of cloud-based multi-tiers systems has strongly increased the levels of resource sharing among companies, improving the enterprise efficiency, thanks to a refined business dynamism and a rapid decrease in costs. However, in spite of their advantages, this new business model highlights the emergence of new computational approaches aimed at the distribution and the optimization of resources sharing along so-called multi-tenants system, i.e., cloud-based architecture where a single instance of software runs on a single server and serves multiple companies (tenants). This paper faces this challenging gap by proposing an auto-scaling cloud computing multi-tenancy architecture where process mining and fuzzy-based load-balancing systems synergistically interact to provide an improved and optimized resource management distribution. A case study is carried out to show the proposed architecture in operation.

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.

Crop production problems are common in Sri Lanka which severely effect rural farmers, agriculture sector and the country's economy as a whole. A deeper analysis revealed that the root cause was farmers and other stakeholders in the domain not receiving right information at the right time in the right format. Inspired by the rapid growth of mobile phone usage among farmers a mobile-based solution is sought to overcome this information gap. Farmers needed published information (quasi static) about crops, pests, diseases, land preparation, growing and harvesting methods and real-time situational information (dynamic) such as current crop production and market prices. This situational information is also needed by agriculture department, agro-chemical companies, buyers and various government agencies to ensure food security through effective supply chain planning whilst minimising waste. We developed a notion of context specific actionable information which enables user to act with least amount of further processing. User centered agriculture ontology was developed to convert published quasi static information to actionable information. We adopted empowerment theory to create empowerment-oriented farming processes to motivate farmers to act on this information and aggregated the transaction data to generate situational information. This created a holistic information flow model for agriculture domain similar to energy flow in biological ecosystems. Consequently, the initial Mobile-based Information System evolved into a Digital Knowledge Ecosystem that can predict current production situation in near real enabling government agencies to dynamically adjust the incentives offered to farmers for growing different types of crops to achieve sustainable agriculture production through crop diversification.

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.

The k-nearest neighbor (k-NN) algorithm is one of the most well-known supervised classifiers due to its ease of use and good performance. However, in spite of its popularity, k-NN suffers from some drawbacks such as high computational complexity, high storage requirements, and low noise tolerance. Prototype selection is a successful technique aimed at addressing aforementioned issues by reducing the size of training datasets without deprecating, but improving, the classification accuracy. Recently, evolutionary algorithms have been successfully applied to the optimisation of accuracy and size of reduction of prototype selection because of their innate exploration and exploitation capabilities in visiting the space of solutions of a problem. However, so far, all the evolutionary approaches for prototype selection are based on a so-called multi-objective “a priori” technique, where multiple objectives are aggregated together into a single objective through a weighted combination. This paper proposes to apply, for the first time, an “a posteriori” algorithm, namely SPEA2, to prototype selection problem in order to explicitly deal with both objectives and offer a better trade-off between classification and reduction performance. As shown in the experimental section, the application of SPEA2 allows to hold high accuracy in nearest neighbour classification with a significant reduction of training data thanks to the discovery of higher quality solutions than those detected by a conventional “a priori” approach.

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] .

One of the problems arising upon the evolution of a database schema is that some queries and views defined on the previous schema version might no longer work properly. Thus, evolving a database schema entails the redefinition of queries and views to adapt them to the new schema. Although this problem has been mainly raised in the context of traditional information systems, solutions to it are also advocated in other database-related areas, such as Data Integration, Web Data Integration, and Data Warehouses. The problem is a critical one, since industrial organizations often need to adapt their databases and data warehouses to frequent changes in the real world. In this article, we provide a survey of existing approaches and tools to the problem of adapting queries and views upon a database schema evolution; we also propose a classification framework to enable a uniform comparison method among many heterogeneous approaches and tools.

Design patterns are recognized as a means to improve software maintenance by furnishing an explicit specification of class and object interactions and their underlying intent [Gamma et al. 1995]. Only a few empirical investigations have been conducted to assess whether the kind of documentation for design patterns implemented in source code affects its comprehensibility. To investigate this aspect, we conducted a family of four controlled experiments with 88 participants having different experience (i.e., professionals and Bachelor, Master, and PhD students). In each experiment, the participants were divided into three groups and asked to comprehend a nontrivial chunk of an open-source software system. Depending on the group, each participant was, or was not, provided with graphical or textual representations of the design patterns implemented within the source code. We graphically documented design-pattern instances with UML class diagrams. Textually documented instances are directly reported source code as comments. Our results indicate that documenting design-pattern instances yields an improvement in correctness of understanding source code for those participants with an adequate level of experience.

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.

This article introduces a new soft keyboard, named Tap and Slide, specifically designed for mobile devices. The new interaction method, on which the keyboard is based, allows performing text entry operations in a very small space, so minimizing the space required. To evaluate the keyboard from a usability point of view, three studies were performed: the first verifies whether the subjects’ abilities expressed in terms of technological knowledge may specifically provide advantages in performing text entry operations, the second tries to understand the ease of learning of the keyboard considering both accuracy and efficiency in task execution, and the third analyzes the performance of the soft keyboard in comparison with the more common QWERTY soft keyboard.

Data warehouse loading and refreshment is typically performed by means of complex software processes called extraction–transformation–loading (ETL). In this paper, we propose a system based on a suite of visual languages for mastering several aspects of the ETL development process, turning it into a visual programming task. The approach can be easily generalized and applied to other data integration contexts beyond data warehouses. It introduces two new visual languages that are used to specify the ETL process, which can also be represented by means of UML activity diagrams. In particular, the first visual language supports data manipulation activities, whereas the second one provides traceability information of attributes to highlight the impact of potential transformations on integrated schemas depending on them. Once the whole ETL process has been visually specified, the designer might invoke the automatic generation of an activity diagram representing a possible orchestration of it based on its dependencies. The designer can edit such a diagram to modify the proposed orchestration provided that changes do not alter data dependencies. The final specification can be translated into code that is executable on the data sources. Finally, the effectiveness of the proposed approach has been validated through a user study in which we have compared the effort needed to design an ETL process in our approach with respect to the one required with main visual approaches described in the literature.Copyright © 2013 John Wiley & Sons, Ltd.

The visualization of information contained in reports is an important aspect of human-computer interaction, for both the accuracy and the complexity of relationships between data must be preserved. A greater attention has been paid to individual report visualization through different types of standard graphs (Histograms, Pies, etc.). However, this kind of representation provides separate information items and gives no support to visualize their relationships which are extremely important for most decision processes. This paper presents a design methodology exploiting the visual language CoDe based on a logic paradigm. CoDe allows to organize the visualization through the CoDe model which graphically represents relationships between information items and can be considered a conceptual map of the view. The proposed design methodology is composed of four phases: the CoDe Modeling and OLAP Operation pattern definition phases define the CoDe model and underlying metadata information, the OLAP Operation phase physically extracts data from a data warehouse and the Report Visualization phase generates the final visualization. Moreover, a case study on real data is provided.

We carried out a family of experiments to investigate whether the use of UML models produced in the requirements analysis process helps in the comprehensibility and modifiability of source code. The family consists of a controlled experiment and 3 external replications carried out with students and professionals from Italy and Spain. 86 participants with different abilities and levels of experience with UML took part. The results of the experiments were integrated through the use of meta-analysis. The results of both the individual experiments and meta-analysis indicate that UML models produced in the requirements analysis process influence neither the comprehensibility of source code nor its modifiability.

The widespread use of mobile devices in basic map navigation tasks has recently attracted researchers on usability problems arising from the reduced visualization area and the limited interaction modes allowed by small screens. In this paper we analyze the most common approaches suggested in the literature and present the results of a usability study carried out on one of them, an interactive technique, named FRAMY, which was conceived to cover a wider range of spatial data visualization tasks, possibly involving different geographic layers. A comparative usability study has been carried out on Framy and a traditional map application in order to evaluate the inoact of the novel approach on usability, in terms of efficiency and efficacy.

Modeling is a fundamental activity within the requirements engineering process and concerns the construction of abstract descriptions of requirements that are amenable to interpretation and validation. The choice of a modeling technique is critical whenever it is necessary to discuss the interpretation and validation of requirements. This is particularly true in the case of functional requirements and stakeholders with divergent goals and different backgrounds and experience. This paper presents the results of a family of experiments conducted with students and professionals to investigate whether the comprehension of functional requirements is influenced by the use of dynamic models that are represented by means of the UML sequence diagrams. The family contains five experiments performed in different locations and with 112 participants of different abilities and levels of experience with UML. The results show that sequence diagrams improve the comprehension of the modeled functional requirements in the case of high ability and more experienced participants.

With the invention and rapid improvement of data-capturing devices, such as satellite imagery and digital cameras, the information that archaeologists must manage in their everyday's activities has rapidly grown in complexity and amount. In this work we present Indiana Finder, an interactive visualization system that supports archaeologists in the examination of large repositories of documents and drawings. In particular, the system provides visual analytic support for investigative analysis such as the interpretation of new archaeological findings, the detection of interpretation anomalies, and the discovery of new insights. We illustrate the potential of Indiana Finder in the context of the digital protection and conservation of rock art natural and cultural heritage sites. In this domain, Indiana Finder provides an integrated environment that archaeologists can exploit to investigate, discover, and learn from textual documents, pictures, and drawings related to rock carvings. This goal is accomplished through novel visualization methods including visual similarity ring charts that may help archaeologists in the hard task of dating a symbol in a rock engraving based on its shape and on the surrounding symbols.

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 In this paper we present an approach to automate the architecture recovery process of software systems. The approach is built on information retrieval and clustering techniques, and, in particular, uses Latent Semantic Indexing (LSI) to get similarities among software entities (e.g., programs or classes) and the k-means clustering algorithm to form groups of software entities that implement similar functionality. In order to improve computational time in the context of the software evolution and then reduce energy waste, the architecture recovery process can be also applied by using fold-in and fold-out mechanisms that, respectively, add and remove software entities to the LSI representation of the understudy software system. The approach has been implemented in a prototype of a supporting software system as an Eclipse plug-in. Finally, to assess the approach and the plug-in, we have conducted an empirical investigation on five open source software systems implemented using the programming languages Java and C/C++. In the investigation special emphasis has been also given to the effect of using the fold-in and fold-out mechanisms.

Research on biometrics has noticeably increased. However, no single bodily or behavioral feature is able to satisfy acceptability, speed, and reliability constraints of authentication in real applications. The present trend is therefore toward multimodal systems. In this paper, we deal with some core issues related to the design of these systems and propose a novel modular framework, namely, novel approaches for biometric systems (NABS) that we have implemented to address them. NABS proposal encompasses two possible architectures based on the comparative speeds of the involved biometries. It also provides a novel solution for the data normalization problem, with the new quasi-linear sigmoid (QLS) normalization function. This function can overcome a number of common limitations, according to the presented experimental comparisons. A further contribution is the system response reliability (SRR) index to measure response confidence. Its theoretical definition allows to take into account the gallery composition at hand in assigning a system reliability measure on a single-response basis. The unified experimental setting aims at evaluating such aspects both separately and together, using face, ear, and fingerprint as test biometries. The results provide a positive feedback for the overall theoretical framework developed herein. Since NABS is designed to allow both a flexible choice of the adopted architecture, and a variable compositions and/or substitution of its optional modules, i.e., QLS and SRR, it can support different operational settings.

Virtual simulators have been used in the last twenty years for applications ranging from flight simulation to computer-based training, just to name a few. More recently a new level of simulation has been introduced thanks to haptic interfaces able to reproduce kinesthetic and/or tactile feedback, typically experimented during interaction with real-world objects. In this paper visual simulation and haptic interfaces are integrated in a novel training system for Intra Cytoplasmic Sperm Injection (ICSI), an in-vitro fertilization technique which is now a standard for the treatment of human infertility. We describe a virtual micromanipulation simulator made by two hand-based Cyberforce haptic devices (a synthetic replica of the actual manipulation gear) and a visual-haptic engine simulating the shape and the dynamic behavior of the main components in the artificial fertilization process: the human egg, the selected sperm and the micro needles required to inject the latter into the egg's cytoplasm. Our first tests, conducted so far, are encouraging.

In this paper we present the preliminary results of a controlled experiment to assess the contribution provided by the design patterns on the maintenance of source code. In particular, the study aimed at assessing the effort and the efficiency to perform maintenance operations in case design pattern instances are properly documented and provided to the maintainer. The context of the experiment is constituted of Master Students in Computer Science at the University of Basilicata. The preliminary analysis conducted on the gathered data revealed that the effort is significantly reduced in case design pattern instances are properly documented and provided to the subjects. Similarly, the efficiency is significantly better in case the documentation of design pattern instances is used to accomplish maintenance operations.

In this paper we present a recognizer of sketched symbols based on Latent-Dynamic Conditional Random Fields (LDCRF), a discriminative model for sequence classification. The LDCRF model classifies unsegmented sequences of strokes into domain symbols by taking into account contextual and temporal information. In particular, LDCRFs learn the extrinsic dynamics among strokes by modeling a continuous stream of symbol labels, and learn internal stroke sub-structure by using intermediate hidden states. The performance of our work is evaluated in the electric circuit domain.

A number of clustering based approaches and tools have been proposed in the past to partition a software system into subsystems. The greater part of these approaches is semiautomatic, thus requiring human decision to identify the best partition of software entities into clusters among the possible partitions. In addition, some approaches are conceived for software systems implemented using a particular programming language (e.g., C and C++). In this paper we present an approach to automate the partitioning of a given software system into subsystems. In particular, the approach first analyzes the software entities (e.g., programs or classes) and then using Latent Semantic Indexing the dissimilarity between these entities is computed. Finally, software entities are grouped using iteratively the k-means clustering algorithm. The approach has been implemented in a prototype of a supporting software system as an Eclipse plug-in. Finally, to assess the approach and the plug-in, we have conducted an empirical investigation on three open source software systems implemented using the programming languages Java and C/C++.

Spreadsheets are intensively used for crucial management activities by several organizations. Collaboration through spreadsheet is recognized to be a powerful means to enhance crucial activities carried out in Small and Medium sized Enterprises (SME). However, the main obstacles to its realization appears to be the small size and the limited human and financial resources, which usually characterize SMEs themselves. We present a collaborative environment for spreadsheet-based activities, that overcomes such limitation allowing a smooth shift from single user to multiple user spreadsheet application deployment.

Abstract We present ADvanced Artefact Management System (ADAMS), a web‐based system that integrates project management features, such as work‐breakdown structure definition, resource allocation, and schedule management as well as artefact management features, such as artefact versioning, traceability management, and artefact quality management. In this article we focus on the fine‐grained artefact management approach adopted in ADAMS, which is a valuable support to high‐level documentation and traceability management. In particular, the traceability layer in ADAMS is used to propagate events concerning changes to an artefact to the dependent artefacts, thus also increasing the context‐awareness in the project. We also present the results of experimenting with the system in software projects developed at the University of Salerno. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract In this paper, we describe theit Search Tree visual language. It is a novel methodology able to support users to build up complex queries to be run on given search engines. For using this visual language, neither parentheses nor precedence rules are needed, nor the specific ability to perform advanced search tasks. The language is proven to have the same expressive power as the expressions in Sum Of Product form. In order to prove the appropriateness of our proposal, we measured the usability of the proposed querying approach against the traditional Yahoo TM web search query language. Results show that, even if both the approaches fully support users in terms of efficacy, the Search Tree visual language significantly improves task efficiency, both in terms of the number of actions performed and the time requested with respect to the advanced search interface. Copyright © 2010 John Wiley & Sons, Ltd.

Rationale is the justification behind decisions taken during the software development process. The usefulness of rationale pervades the entire software lifecycle. However, it is during maintenance that the benefits of rationale management are most evident, as it provides an insight into the motivations and the reasoning behind decisions taken during the original design and implementation. One of the strongest limitation to the capturing of rationale information during development concerns its time-consuming and disruptive nature that cause many organizations to consider rationale management costs excessive. A possible solution is to extract and capture rationale information when it is needed. This can be done by analyzing documents shared or exchanged among software engineers during the development process. In this paper, we propose to supports the software engineer during the rationale capturing by automatically identifying candidate rationale information extracted from email repositories. Besides this, we also support the designer during the rationale retrieval by identifying possible rational information within a document repository starting from a query represented by a source document.

This paper presents a two-steps process aiming at improving the tracing performances of the software engineer when using an IR-based traceability recovery tool. In the first step the software engineer performs an incremental coarse-grained traceability recovery between a set of source artefacts and a set of target artefacts. During this step he/she traces as many links as possible keeping low the effort to discard false positives. In the second step he/she uses a coverage link analysis aiming at identifying source artefacts poorly traced and guiding focused fine-grained traceability recovery sessions to recover links missed in the first step. The results achieved in a reported controlled experiment demonstrate that the proposed approach significantly increases the amount of correct links traced by the software engineer with respect to a tradition process.

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.

In this demonstration we present the traceability recovery tool developed in ADAMS, a fine-grained artefact management system. The tool is based on an Information Retrieval technique, namely Latent Semantic Indexing, and aims at supporting the software engineer in the identification of traceability links between artefacts of different types. The tool has also been integrated in the Eclipse-based client of ADAMS.

Concurrent versioning of source code is a common and well-established practice to manage concurrency and consistency within source code repository. Similarly to source code, software models are often the result of cooperative work by different software engineers, that need to update them even concurrently. Unfortunately, modeling tools rarely provide support for concurrency and consistency. On the other hand, the available concurrent versioning tools do not provide an adequate support for software models. In this paper we present COMOVER (COncurrent MOdel VERsioning), a tool that integrates software modeling features with versioning and concurrency management as well as model elements sharing and exchanging.

We present the results of two controlled experiments to compare ER and UML class diagrams, in order to find out which of the models provides better support during the comprehension of data models. The experiment involved Master and Bachelor students performing comprehension tasks on data models represented by ER or UML class diagrams. The achieved results show that UML class diagrams significantly improve the comprehension level achieved by subjects. Moreover, having different subjects with different levels of ability and experience allowed us to also make some considerations on the influence of such factors on the comprehension performances.

We present the results of a controlled experiment aiming at analysing the role played by the approach adopted during an IR-based traceability recovery process. In particular, we compare the tracing performances achieved by subjects using the "one-shot" process, where the full ranked list of candidate links is proposed, and the incremental process, where a similarity threshold is used to cut the ranked list and the links are classified step-by-step. The analysis of the achieved results shows that, in general, the incremental process improves the tracing accuracy and reduces the effort to analyse the proposed links.

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.

In the present paper, we propose an approach for the development of Web GIS based on WebML, a high-level, formal visual language specifically conceived to design data-intensive Web applications. The proposal is motivated by the observation that Web GIS can be considered as a particular class of data-intensive Web applications. In the paper, we describe the extension of the visual formalism for modeling relevant interaction and navigation operations typical of Web GIS.

We propose a distributed inspection method that tries to minimise the synchronous collaboration among team members to identify defects in software artefacts. The approach consists of identifying conflicts on the potential defects and then resolving them using an asynchronous discussion before performing a traditional synchronous meeting. This approach has been implemented in a Web based tool and assessed through a controlled experiment with master students in Computer Science at the University of Salerno. The tool presented provides automatic merge and conflict highlighting functionalities to support the inspectors during the pre-meeting refinement phase and provides the moderator with information about the inspection progress as a decision support. The tool also supports a synchronous inspection meeting to discuss about unsolved conflicts. However, by analysing the data collected during a controlled experiment we found that this phase can often be skipped due to the fact that asynchronous discussion resolved most of the conflicts.

During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. However, it is necessary to deal with occlusions when the subject is wearing sunglasses, scarves and such. In the same way, ear recognition is arising as a new promising biometric for people recognition, even if the related literature appears to be somewhat underdeveloped. In this paper, several hybrid face/ear recognition systems are investigated. The system is based on IFS (Iterated Function Systems) theory that are applied on both face and ear resulting in a bimodal architecture. One advantage is that the information used for the indexing and recognition task of face/ear can be made local, and this makes the method more robust to possible occlusions. The distribution of similarities in the input images is exploited as a signature for the identity of the subject. The amount of information provided by each component of the face and the ear image has been assessed, first independently and then jointly. At last, results underline that the system significantly outperforms the existing approaches in the state of the art.

Abstract We present an approach based on Winner Takes All (WTA), a competitive clustering algorithm, to support the comprehension of static and dynamic Web applications during Web application reengineering. This approach adopts a process that first computes the distance between Web pages and then identifies and groups similar pages using the considered clustering algorithm. We present an instance of application of the clustering process to identify similar pages at the structural level. The page structure is encoded into a string of HTML tags and then the distance between Web pages at the structural level is computed using the Levenshtein string edit distance algorithm. A prototype to automate the clustering process has been implemented that can be extended to other instances of the process, such as the identification of groups of similar pages at content level. The approach and the tool have been evaluated in two case studies. The results have shown that the WTA clustering algorithm suggests heuristics to easily identify the best partition of Web pages into clusters among the possible partitions. Copyright © 2007 John Wiley & Sons, Ltd.

The main drawback of existing software artifact management systems is the lack of automatic or semi-automatic traceability link generation and maintenance. We have improved an artifact management system with a traceability recovery tool based on Latent Semantic Indexing (LSI), an information retrieval technique. We have assessed LSI to identify strengths and limitations of using information retrieval techniques for traceability recovery and devised the need for an incremental approach. The method and the tool have been evaluated during the development of seventeen software projects involving about 150 students. We observed that although tools based on information retrieval provide a useful support for the identification of traceability links during software development, they are still far to support a complete semi-automatic recovery of all links. The results of our experience have also shown that such tools can help to identify quality problems in the textual description of traced artifacts.

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.

Distributed software development is increasingly becoming a common practice in the software industry. The increased complexity of software systems also reflects in the complexity of design documentation, thus requiring a specific tool support for change and configuration management in distributed development settings. We present the fine-grained versioning management approach adopted in the ADAMS artefact management system, focusing on support to high level documentation versioning. We also present the results of experimenting the tool in software development projects developed at the University of Salerno

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

A reliable effort estimation is crucial for a successful web application development planning. Several approaches exist to address this issue. Among them, the algorithmic approach is one of the most widely used and investigated methods. It is based on suitable effort prediction models which relate the development effort with project characteristics. The size represents one of the most interesting characteristics of software products and several measures can be defined in order to estimate the size of web systems. Moreover, several techniques have been proposed in the literature to build the effort prediction models. Thus, of special interest should be to establish the most effective size measures to be employed in effort prediction models and the most suitable techniques for the model construction. To this aim some empirical studies have been undertaken so far. Since it is widely recognized that several investigations should be performed to verify/confirm empirical results, in the paper we will report on an empirical analysis we have carried out by exploiting data coming from 15 web projects developed by a software company. In particular, for the analysis we have considered two sets of size measures: Length Measures (e.g. number of pages, number of medias, number of client and server side scripts) and Functional Measures (e.g. external input, external output, external query). Moreover, we have employed different techniques, such as Linear Regression, Regression Tree, and Analogy-Based Estimation, in order to determine the one that provides the best prediction.

Applying information retrieval (IR) techniques to retrieve all correct links between software artefacts is in general impractical, as usually this means producing a high effort for discarding too many false positives. We show that the only way to recover traceability links using IR methods is to identify an "optimal" threshold that achieves an acceptable balance between traced links and false positives. Unfortunately, such threshold is not known a priori. For this reason we have devised the need to use an incremental traceability recovery approach to gradually identify the threshold where it is more convenient to stop the traceability recovery process, and provide evidence of this in a case study. We also report the experience of using the incremental traceability recovery during the development of software projects

Nowadays face recognition is gaining great attention from the researcher respect to other biometrics, because it represents a good compromise between reliability and people acceptance. While this growth largely is driven by growing application demands, such as identification for law enforcement and authentication, for banking and security system access. The recognition task is difficult because of image variation in terms of position, size, expression, and pose. In this paper a new IFS based recognition method is presented. It exploits the IFS theory, largely studied in still image compression and indexing, but not enough for the face recognition task.

In this paper, we present an FP-like approach, named class point, which was conceived to estimate the size of object-oriented products. In particular, two measures are proposed, which are theoretically validated showing that they satisfy well-known properties necessary for size measures. An initial, empirical validation is also performed, meant to assess the usefulness and effectiveness of the proposed measures to predict the development effort of object-oriented systems. Moreover, a comparative analysis is carried out, taking into account several other size measures.

Recently, much attention has been devoted to the management of continuous fields, which describe geographic phenomena, such as temperature, electromagnetism and pressure. While objects are distinguished by their dimensions, and can be associated with points, lines, or areas, such phenomena can be measurable at any point of their domain by distinguishing what varies, and how smoothly. Thus, when dealing with continuous fields, a basic requirement is represented by users' capability to capture some features of a scenario, by selecting an area of interest and handling the involved phenomena.The aim of our research is to provide GIS users with a visual environment where they can manage both continuous fields and discrete objects, by posing spatial queries which capture the heterogeneous nature of phenomena. In particular, in this paper we propose a visual query language Phenomena, which provides users with a uniform style of interaction with the world, which is conceptually modeled as a composition of continuous fields and discrete objects. The intuitiveness of the underlying operators as well as of the query formulation process is ensured by the choice of suitable metaphors and by the adoption of the paradigm of direct manipulation.A prototype of a visual environment running Phenomena has been realized, which allows users to query experimental data by following a SQL-like SELECT-FROM-WHERE scheme.

We present a traceability recovery method and tool based on latent semantic indexing (LSI) in the context of an artefact management system. The tool highlights the candidate links not identified yet by the software engineer and the links identified but missed by the tool, probably due to inconsistencies in the usage of domain terms in the traced software artefacts. We also present a case study of using the traceability recovery tool on software artefacts belonging to different categories of documents, including requirement, design, and testing documents, as well as code components.

International audience

The aim of our research is to provide GIS users with a visual environment where they can formulate spatial queries which implicitly capture the double nature of geographical data. In particular, in this paper we propose an extension to the MGISQL visual environment, where users may pose 3D queries about those phenomena where the third dimension is a relevant feature for data retrieving. The interaction between users and the visual environment is performed by manipulating 3D geometaphors. The underlying algebra for spatial operators is enriched accordingly. Visual queries are composed in a 3D environment, called the Sensitive Cube, characterized by the 3D geometaphors, visualized as 'floating objects'.

We present a decision support system based on data mining algorithms to be used for tactical decisions. The system has been developed and engineered to solve a typical problem involving strategic decisions: supporting a trainer of a basketball team in making technical/tactical decisions. The experiments conducted on this application domain proved the effectiveness of the system and its underlying algorithms. The basketball domain stressed several aspects of decision making, proving that the used approach is suitable for other domains involving tactical decisions.

Many authoring systems have been realized so far in order to allow users to build advanced presentation of documents. These systems represent the result of a requirement analysis performed in specific application domains and their functionality is meaningful for the domain experts. Consequently, in many cases there exists a barrier between system functionality and users, who have a limited knowledge of the domain: in order to use the application, the content expert end-user has to interact with the application expert. Such a barrier limits the effectiveness and the efficiency of the authoring system, because the development of a multimedia presentation forces the user to acquire experience far from his/her background. In this paper we introduce a new approach to educational authoring, based on a multilevel development methodology. It allows and supports teachers to create their own multimedia learning environment. The proposed approach has been experimented on an initial prototype which allows users to manage multimedia authoring of units of study.

We introduce the virtual image, an iconic index suited for pictorial information access in a pictorial database, and a similarity retrieval approach based on virtual images to perform content-based retrieval. A virtual image represents the spatial information contained in a real image in explicit form by means of a set of spatial relations. This is useful to efficiently compute the similarity between a query and an image in the database. We also show that virtual images support real-world applications that require translation, reflection, and/or rotation invariance of image representation.

In order to achieve quality products with reliable cost and effort estimations, one of the main tasks for planning software project development is size estimation. This is especially true when dealing with interactive applications which represent critical components in a software project. In the paper, we address the problem of the size estimation of interactive graphical applications developed using the object-oriented methodology. In particular, we define and validate a metric, the Class Point metric, for estimating the size of object-oriented GUIs. The method is based on the idea of quantifying classes in a program analogous to function counting performed by the function point metric. Theoretical validation has proven the consistency of the Class Point metric as size measure. Empirical validation provides evidence that the Class Point metric is a useful measure for OO software size.

This paper presents the software development workbench WSDW (Web structure-oriented Software Development Workbench) together with the tool development language TDL. WSDW is an integrated structure-oriented software environment which contains several tools for software evolution. The integration of tools is achieved by sharing a program representation which is based upon the mathematical concept of relation: the web structure is the basic high level representation of programs within the environment. The TDL language is a structure-oriented language that supports the creation of a wide variety of tools both for software development and maintenance. The elementary statements in a TDL program are web rewriting rules and manipulations of programs are expressed as web transformations. Moreover, to make program transformations more intuitive to the tool programmer, web rewriting rules are expressed graphically. Each tool in WSDW performs a sequence of web transformations and new software tools can be implemented as TDL programs and integrated into WSDW.

The Visual Language Compiler-Compiler (VLCC) is a grammar-based graphical system for the automatic generation of visual programming environments. In this paper the theoretical and algorithmic issues of VLCC are discussed in detail. The parsing methodology we present is based on the "positional grammar" model. Positional grammars naturally extend context-free grammars by considering new relations in addition to string concatenation. Thanks to this, most of the results from LR parsing can be extended to the positional grammars inheriting the well known LR technique efficiency. In particular, we provide algorithms to implement a YACC-like tool embedded in the VLCC system for automatic compiler generation of visual languages described by positional grammars.

The paper presents a grammatical inference methodology for the generation of visual languages, that benefits from the availability of semantic information about the sample sentences. Several well-known syntactic inference algorithms are shown to obey a general inference scheme, which the authors call the Gen-Inf scheme. Then, all the algorithms of the Gen-Inf scheme are modified in agreement with the introduced semantics-based inference methodology. The use of grammatical inference techniques in the design of adaptive user interfaces was previously experimented with the VLG system for visual language generation. The system is a powerful tool for specifying, designing, and interpreting customized visual languages for different applications. They enhance the adaptivity of the VLG system to any visual environment by exploiting the proposed semantics-based inference methodology. As a matter of fact, a more general model of visual language generation is achieved, based on the Gen-Inf scheme, where the end-user is allowed to choose the algorithm which best fits his/her requirements within the particular application environment.

Introduces a variation of the 2D string representation for symbolic pictures, the non-redundant 2D string, and analyze it with respect to compactness and non-ambiguity. It results that the non-redundant 2D string is a more compact representation than the 2D string, and that the class of unambiguous pictures under the non-redundant 2D string is almost equal to the class of unambiguous pictures under the reduced 2D string, up to a special case. Moreover, we show that the compactness of the new index does not affect the time complexity of picture retrieval.>

This paper presents the architecture and some tools of the software development workbench WSDW. The authors propose a structure-oriented workbench, in which interactive software tools are integrated through sharing a unique high level program representation, satisfying the request of independence from the source language. The data structure representing programs, the web structure, is based upon the mathematical concept of relation and it is easily implemented as a Prolog data base. Program transformations, given as web transformations, can be expressed as rewriting rules, so that software tools can be implemented as sets of rewriting rules and then added to the WSDW.>

Refining a specification S1 means to provide another specification S2 which contains all the information given in S1 but with more detail. In this paper, we use logical implication from lower to higher levels of logic specifications to give a definition of refinement between these levels. This guarantees that any property of the higher level is also verified at the lower one. The definition of the relation "is refinement of" is given for specifications which are general first-order theories and it is proved to be transitive. A relevant aspect is that the different levels of logic specifications are in general not immediately comparable, because they can use different vocabularies. For this reason, the concept of transcription is introduced formally in our definition. Then the particular case of Horn specifications is considered. Horn specification semantics can be given by the methodology of least models. This may suggest definitions of the concept of refinement different from the one based on logical implication from lower to higher levels. However, conceptual problems can arise depending on the kind of the refinement definition chosen. Perhaps the most interesting effect is that the property of refinement transitivity may be lost. A possible way to restore the transitivity is provided.

Several iconic indexes for representing three-dimensional scenes are presented. The approach extends previous work in iconic indexing of two-dimensional scenes in a unified manner. Good characteristics for iconic indexes are also pointed out. OPP2 and OPP3, two-dimensional iconic indexes for three-dimensional scenes, are introduced. The problem of ambiguity in the OPP2 and OPP3 representations of three-dimensional scenes is studied in detail and a class of images for which they are unambiguous is identified. The Genstring, a linear iconic index which can be used to represent two-, three-, or higher-dimensional scenes, is introduced. It provides a compact, unambiguous representation of a three-dimensional scene. The Genstring takes advantage of previous work, thus providing fast pattern matching for higher-dimensional scenes. In fact, the pattern matching algorithm given for k-dimensional scenes is as fast as that previously given for two-dimensional scenes.>

A system to generate and interpret customized visual languages in given application areas is presented. The generation is highly automated. The user presents a set of sample visual sentences to the generator. The generator uses inference grammar techniques to produce a grammar that generalizes the initial set of sample sentences, and exploits general semantic information about the application area to determine the meaning of the visual sentences in the inferred language. The interpreter is modeled on an attribute grammar. A knowledge base, constructed during the generation of the system, is then consulted to construct the meaning of the visual sentence. The architecture of the system and its use in the application environment of visual text editing (inspired by the Heidelberg icon set) enhanced with file management features are reported.>

The authors introduce web structures and their transformations and develop their theory in the framework of category theory. Once a program has been represented as a web structure, software tools, such as a high-level data flow analyzer or other general program transformers, can be written as sets of web structure production rules. An implementation of web structure transformations is in progress. The mathematical theory of web structure transformations allows form proofs of properties both at the metatheoretical and theoretical levels.>

In this paper, we give purity-preserving conditions for iconic operators, and present a formal definition of degree of icon purity by extending an icon system to a fuzzy icon system, based upon the theory of fuzzy sets. We also show an icon system can be modeled by means of an attribute grammar.

This paper describes a generic image processing language IPL, and a programming environment supporting the language primitives for an image information system. The central notion of IPL is that it allows the user to navigate through the image database and manipulate images using generalized icons. The image processing language IPL consists of three subsets: the logical image processing language LIPL, the interactive image processing language IIPL, and the physical image processing language PIPL. This paper presents the main concepts of this generic language, some examples, and a scenario.