Cognitive impairment is a growing public health concern, with early detection playing a crucial role in improving patient outcomes. The Montreal Cognitive Assessment (MoCA) is widely used for screening mild cognitive impairment (MCI) and early-stage dementia. However, traditional MoCA assessments require manual scoring by trained professionals, making the process labor-intensive, time-consuming, and susceptible to human error. To overcome these limitations, we propose an automated pipeline for MoCA score estimation using eye-gaze data and Vision Transformers (ViTs). Our approach leverages gaze-tracking technology to capture spatial and temporal eyemovement patterns during structured cognitive tasks, identifying subtle cognitive impairments that may otherwise go unnoticed. The raw gaze data is preprocessed and mapped onto taskrelevant image regions, where a pretrained ViT extracts highdimensional feature representations. To address inconsistencies in gaze sampling and improve temporal modeling, we introduce a time-aware positional embedding mechanism that enhances the model's ability to infer cognitive performance. These extracted features are then processed by a transformer-based classification model to predict MoCA scores with high accuracy. We validate our approach using a dataset collected from seven cognitive gaming sessions, demonstrating its effectiveness in automated cognitive assessment. The experimental results indicate that our method provides a reliable and efficient alternative to traditional MoCA evaluations, reducing dependency on human intervention while maintaining diagnostic accuracy.

As our understanding of emotions continues to evolve, the ability of machines to accurately interpret and respond to emotional cues is more important than ever. Traditional methods of emotion recognition often fall short, particularly when it comes to the subtle and complex responses elicited by music. The EEG-Music Emotion Recognition Challenge aims to leverage electroencephalography (EEG) to decode emotional states from brain signals while subjects listen to music. This initiative seeks to uncover the intricate relationship between neural activity and emotional responses, offering insights for advancing adaptive user interfaces. We propose two tracks: (1) Person Identification aims to identify the subject from whom the EEG was recorded, while (2) Emotion Recognition targets the decoding of emotional state of the subject while listening to a musical stimulus.

We introduce SeeingSounds, a lightweight and modular framework for audio-to-image generation that leverages the interplay between audio, language, and vision—without requiring any paired audio-visual data or training on visual generative models. Rather than treating audio as a substitute for text or relying solely on audio-to-text mappings, our method performs dual alignment: audio is projected into a semantic language space via a frozen language encoder, and, contextually grounded into the visual domain using a vision-language model. This approach, inspired by cognitive neuroscience, reflects the natural cross-modal associations observed in human perception. The model operates on frozen diffusion backbones and trains only lightweight adapters, enabling efficient and scalable learning. Moreover, it supports fine-grained and interpretable control through procedural text prompt generation, where audio transformations (e.g., volume or pitch shifts) translate into descriptive prompts (e.g., “a distant thunder”) that guide visual outputs. Extensive experiments across standard benchmarks confirm that SeeingSounds outperforms existing methods in both zero-shot and supervised settings, establishing a new state of the art in controllable audio-to-visual generation.

Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework that employs diffusion models and large language models to generate global, textual explanations of visual classifiers. DEXTER operates by optimizing text prompts to synthesize class-conditional images that strongly activate a target classifier. These synthetic samples are then used to elicit detailed natural language reports that describe class-specific decision patterns and biases. Unlike prior work, DEXTER enables natural language explanation about a classifier's decision process without access to training data or ground-truth labels. We demonstrate DEXTER's flexibility across three tasks—activation maximization, slice discovery and debiasing, and bias explanation—each illustrating its ability to uncover the internal mechanisms of visual classifiers. Quantitative and qualitative evaluations, including a user study, show that DEXTER produces accurate, interpretable outputs. Experiments on ImageNet, Waterbirds, CelebA, and FairFaces confirm that DEXTER outperforms existing approaches in global model explanation and class-level bias reporting. Code is available at https://github.com/perceivelab/dexter.

Bharadwaj et al. (2023) present a comments paper evaluating the classification accuracy of several state-of-the-art methods using EEG data averaged over random class samples. According to the results, some of the methods achieve above-chance accuracy, while the method proposed in (Palazzo et al. 2020), that is the target of their analysis, does not. In this rebuttal, we address these claims and explain why they are not grounded in the cognitive neuroscience literature, and why the evaluation procedure is ineffective and unfair.

The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion's geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer's self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.

In this paper, we propose a deep model based on graph convolutional networks for emotion recognition using EEG data. The model encodes spatial and temporal features of EEG channels and learns relationships between nodes through a self-attention mechanism, capturing spatio-temporal synchrony in brain regions. Experimental results show that our model outperforms existing approaches, with the attention mechanism contributing significantly to classification accuracy. In particular, the attention scores provide insights into how EEG channels influence each other at different times, revealing spatio-temporal patterns of brain connectivity related to emotions.

In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We specifically design an ensemble scheme of six models - five transformer-based ones and a CNN model - for the identification of subjects from wearable devices, while a personalized - one for each subject - scheme is used for relapse detection in psychotic disorder. Our final submitted solutions yield top performance on both tracks of the challenge: we ranked 2ndon the subject identification task (with an accuracy of 93.85%) and 1ston the relapse detection task (with a ROC-AUC and PR-AUC of about 0.65). Code and details are available at https://github.com/perceivelab/e-prevention-icassp-2023.

Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervised-trained models. However, most research on this subject has tackled continual learning in simple image classification scenarios. In this paper, we present a benchmark of state-of-the-art continual learning methods on video action recognition. Besides the increased complexity due to the temporal dimension, the video setting imposes stronger requirements on computing resources for top-performing rehearsal methods. To counteract the increased memory requirements, we present two method-agnostic variants for rehearsal methods, exploiting measures of either model confidence or data information to select memorable samples. Our experiments show that, as expected from the literature, rehearsal methods outperform other approaches; moreover, the proposed memory-efficient variants are shown to be effective at retaining a certain level of performance with a smaller buffer size.

Abstract In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity maps) generated using features extracted at different abstraction levels. We provide the base hierarchical learning mechanism with two techniques for domain adaptation and domain-specific learning. For the former, we encourage the model to unsupervisedly learn hierarchical general features using gradient reversal at multiple scales, to enhance generalization capabilities on datasets for which no annotations are provided during training. As for domain specialization, we employ domain-specific operations (namely, priors, smoothing and batch normalization) by specializing the learned features on individual datasets in order to maximize performance. The results of our experiments show that the proposed model yields state-of-the-art accuracy on supervised saliency prediction. When the base hierarchical model is empowered with domain-specific modules, performance improves, outperforming state-of-the-art models on three out of five metrics on the DHF1K benchmark and reaching the second-best results on the other two. When, instead, we test it in an unsupervised domain adaptation setting, by enabling hierarchical gradient reversal layers, we obtain performance comparable to supervised state-of-the-art. Source code, trained models and example outputs are publicly available at https://github.com/perceivelab/hd2s .

This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that maximizes a compatibility measure between visual features and brain representations. We then carry out image classification and saliency detection on the learned manifold. Performance analyses show that our approach satisfactorily decodes visual information from neural signals. This, in turn, can be used to effectively supervise the training of deep learning models, as demonstrated by the high performance of image classification and saliency detection on out-of-training classes. The obtained results show that the learned brain-visual features lead to improved performance and simultaneously bring deep models more in line with cognitive neuroscience work related to visual perception and attention.

Saliency detection is a fundamental process of human visual perception, since it allows us to identify the most important parts of a scene, directing our analysis and interpretation capabilities on a reduced set of information and reducing reaction times. However, current approaches for automatic saliency detection either attempt to mimic human capabilities by building attention maps from hand-crafted feature analysis, or employ convolutional neural networks trained as black boxes, without any architectural or information prior from human biology. In this paper, we present an approach for saliency detection that combines the success of deep learning in identifying representations for visual data with a training paradigm aimed at matching neural activity provided directly by brain signals recorded while subjects look at images. We show that our approach is able to capture correspondences between visual elements and neural activities, successfully generalizing to unseen images to identify their most salient regions.

In this paper we define a deep learning architecture for automated segmentation of anatomical structures in Craniomaxillofacial (CMF) CT scans that leverages the recent success of encoder-decoder models for semantic segmentation of natural images. In particular, we propose a fully convolutional deep network that combines the advantages of recent fully convolutional models, such as Tiramisu, with squeeze-and-excitation blocks for feature recalibration, integrated with convolutional LSTMs to model spatio-temporal correlations between consecutive slices. The proposed segmentation network shows superior performance and generalization capabilities (to different structures and imaging modalities) than state of the art methods on automated segmentation of CMF structures (e.g., mandibles and airways) in several standard benchmarks (e.g., MICCAI datasets) and on new datasets proposed herein, effectively facing shape variability.

In this paper we define a deep multi-stage architecture for automated landmarking of craniomaxillofacial (CMF) CT images. Our model is composed of three subnetworks that first localize, on reduced-resolution images, areas where landmarks may be found and then refine the search, at full-resolution scale, through a hierarchical structure aiming at increasing the granularity of the investigated region. The multi-stage pipeline is designed to deal with full resolution data and does not require any additional pre-processing step to reduce search space, as opposed to existing methods that can be only adopted for searching landmarks located in well-defined anatomical structures (e.g., mandibles). The automated landmarking system is tested on identifying landmarks located in several CMF regions, achieving an average error of 0.8 mm, significantly lower than expert readings. The proposed model also outperforms baselines and is on par with existing models that employ additional upstream segmentation, on state-of-the-art benchmarks.

Being able to estimate the traversability of the area surrounding a mobile robot is a fundamental task in the design of a navigation algorithm. However, the task is often complex, since it requires evaluating distances from obstacles, type and slope of terrain, and dealing with non-obvious discontinuities in detected distances due to perspective. In this paper, we present an approach based on deep learning to estimate and anticipate the traversing score of different routes in the field of view of an on-board RGB camera. The backbone of the proposed model is based on a state-of-the-art deep segmentation model, which is fine-tuned on the task of predicting route traversability. We then enhance the model's capabilities by a) addressing domain shifts through gradient-reversal unsupervised adaptation, and b) accounting for the specific safety requirements of a mobile robot, by encouraging the model to err on the safe side, i.e., penalizing errors that would cause collisions with obstacles more than those that would cause the robot to stop in advance. Experimental results show that our approach is able to satisfactorily identify traversable areas and to generalize to unseen locations.

BACKGROUND: De novo RNA-Seq assembly is a powerful method for analysing transcriptomes when the reference genome is not available or poorly annotated. However, due to the short length of Illumina reads it is usually impossible to reconstruct complete sequences of complex genes and alternative isoforms. Recently emerged possibility to generate long RNA reads, such as PacBio and Oxford Nanopores, may dramatically improve the assembly quality, and thus the consecutive analysis. While reference-based tools for analysing long RNA reads were recently developed, there is no established pipeline for de novo assembly of such data. RESULTS: In this work we present a novel method that allows to perform high-quality de novo transcriptome assemblies by combining accuracy and reliability of short reads with exon structure information carried out from long error-prone reads. The algorithm is designed by incorporating existing hybridSPAdes approach into rnaSPAdes pipeline and adapting it for transcriptomic data. CONCLUSION: To evaluate the benefit of using long RNA reads we selected several datasets containing both Illumina and Iso-seq or Oxford Nanopore Technologies (ONT) reads. Using an existing quality assessment software, we show that hybrid assemblies performed with rnaSPAdes contain more full-length genes and alternative isoforms comparing to the case when only short-read data is used.

Integrating human-provided location priors into video object segmentation has been shown to be an effective strategy to enhance performance, but their application at large scale is unfeasible. Gamification can help reduce the annotation burden, but it still requires user involvement. We propose a video object segmentation framework that leverages the combined advantages of user feedback for segmentation and gamification strategy by simulating multiple game players through a reinforcement learning (RL) model that reproduces human ability to pinpoint moving objects and using the simulated feedback to drive the decisions of a fully convolutional deep segmentation network. Experimental results on the DAVIS-17 benchmark show that: 1) including user-provided prior, even if not precise, yields high performance; 2) our RL agent replicates satisfactorily the same variability of humans in identifying spatiotemporal salient objects; and 3) employing artificially generated priors in an unsupervised video object segmentation model reaches state-of-the-art performance.

The papers in this special section focus on the challenges faced in the digital healthcare market. Recent advances in information and communication technologies (ICT), as well as biomedical engineering, sensor technology and data science, have acted as catalysts for significant developments in the sector of health care, strongly affecting medical diagnosis, patient and healthcare management, disease treatment and health education. In fact, small wearable, disposable sensors, implantable devices or medical devices, as well as elementary services are being featured as keys for monitoring health and facilitating well-being.

Eye tracking is a non-invasive procedure to acquire eye-gaze data. The accuracy offered by the new eye tracking technologies gives to physicians and scientists a great opportunity to employ eye trackers to perform quantitative assessment of eye movements for diagnostic and rehabilitation purposes. However, eye trackers do not support physicians in their analysis, as they typically lack specific software solutions tailored to the diseases under investigation. For instance, ophthalmologists need to use eye trackers in tests such as visually guided saccades or smooth pursuit eye movements, while neuropsychiatrists need them in psychological tests to measure attention deficit. The development of a software tool for a specific test cannot be done by physicians (even using the high-level programming frameworks bundled with eye tracker devices) and needs to be carried out by expert computer programmers. Thus, in this paper we propose a general computer system for eye tracker - based investigation, which allows physicians to easily customize experiments in different scenarios without the need of expert computer programmers. To demonstrate the generalization capabilities of the proposed system, we show how it was employed to support the investigation of eye movements for patients affected by a) glycogen storage disease, b) idiopathic congenital nystagmus and c) neurodevelopmental diseases such as autism spectrum disorders.

xWhat if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories in a reading the mind effort. Afterward, we transfer the learned capabilities to machines by training a Convolutional Neural Network (CNN)-based regressor to project images onto the learned manifold, thus allowing machines to employ human brain-based features for automated visual classification. We use a 128-channel EEG with active electrodes to record brain activity of several subjects while looking at images of 40 ImageNet object classes. The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain-driven approach obtains competitive performance, comparable to those achieved by powerful CNN models and it is also able to generalize over different visual datasets.

Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have supported the development of image generation techniques able to reach satisfactory levels of realism. Further improvements have been proposed to condition GANs to generate images matching a specific object category or a short text description. In this work, we build on the latter class of approaches and investigate the possibility of driving and conditioning the image generation process by means of brain signals recorded, through an electroencephalograph (EEG), while users look at images from a set of 40 ImageNet object categories with the objective of generating the seen images. To accomplish this task, we first demonstrate that brain activity EEG signals encode visually-related information that allows us to accurately discriminate between visual object categories and, accordingly, we extract a more compact class-dependent representation of EEG data using recurrent neural networks. Afterwards, we use the learned EEG manifold to condition image generation employing GANs, which, during inference, will read EEG signals and convert them into images. We tested our generative approach using EEG signals recorded from six subjects while looking at images of the aforementioned 40 visual classes. The results show that for classes represented by well-defined visual patterns (e.g., pandas, airplane, etc.), the generated images are realistic and highly resemble those evoking the EEG signals used for conditioning GANs, resulting in an actual reading-the-mind process.

Reading the human mind has been a hot topic in the last decades, and recent research in neuroscience has found evidence on the possibility of decoding, from neuroimaging data, how the human brain works. At the same time, the recent rediscovery of deep learning combined to the large interest of scientific community on generative methods has enabled the generation of realistic images by learning a data distribution from noise. The quality of generated images increases when the input data conveys information on visual content of images. Leveraging on these recent trends, in this paper we present an approach for generating images using visually-evoked brain signals recorded through an electroencephalograph (EEG). More specifically, we recorded EEG data from several subjects while observing images on a screen and tried to regenerate the seen images. To achieve this goal, we developed a deep-learning framework consisting of an LSTM stacked with a generative method, which learns a more compact and noise-free representation of EEG data and employs it to generate the visual stimuli evoking specific brain responses.

Video object segmentation can be considered as one of the most challenging computer vision problems. Indeed, so far, no existing solution is able to effectively deal with the peculiarities of real-world videos, especially in cases of articulated motion and object occlusions; limitations that appear more evident when we compare the performance of automated methods with the human one. However, manually segmenting objects in videos is largely impractical as it requires a lot of time and concentration. To address this problem, in this paper we propose an interactive video object segmentation method, which exploits, on one hand, the capability of humans to identify correctly objects in visual scenes, and on the other hand, the collective human brainpower to solve challenging and large-scale tasks. In particular, our method relies on a game with a purpose to collect human inputs on object locations, followed by an accurate segmentation phase achieved by optimizing an energy function encoding spatial and temporal constraints between object regions as well as human-provided location priors. Performance analysis carried out on complex video benchmarks, and exploiting data provided by over 60 users, demonstrated that our method shows a better trade-off between annotation times and segmentation accuracy than interactive video annotation and automated video object segmentation approaches.

In recent decades, people, especially the older ones, try to live at home in autonomous way. For this purpose it is useful to monitor their vital signs and the home environment to activate suitable environment regulation and to send alarms to family members, medical, or hospitals, according to the criticality of the subject. The paper aims at proposing a flexible and reliable monitoring system based on embedded systems and wearable devices. The system main feature is that it allows the doctor and family members to monitor the patients at distance using their mobiles. A suitable communication with the first aid center is foreseen for a fast rescue of the patients in case of critical situations. A user model ontology is adopted so that patient and context data may be used by any diagnostic and first aid software, thus envisaging an open and interoperable health monitoring system for elderly people living at home. A diagnostic system based on fuzzy rules is proposed to support the choice of the best course of actions in case of critical health conditions.

Aim of the paper is to illustrate the methodology used to implement an ubiquitous city platform called Wi-City-Plus provided with mobile and centralized Decision Support Systems (DSSs) taking advantage of all the data of city interest, including social data, and those sensed by networked ambient Cyber-Physical-Systems (CPSs). The paper proposes to model such data by a suitable RDF ontology specifically designed to support the main user scenarios in smart cities and to implement both the technical and the service interoperability envisaged by the CPS ideal model. This ontology is obtained by reusing existing ontologies and allows the DSSs to access by SPARQL queries all the relevant data independent of the proprietary technology of the CPSs and of the DBMS adopted by the data owners. Examples derived from the pilot version of the proposed platform allows us to point out advantages and perspectives of our approach.

Aim of the paper is to give a demonstration of the main Cyber-Physical-Systems (CPSs) and social networking facilities used to implement an ubiquitous city platform called Wi-City-Plus provided with mobile and centralized decision support systems (DSSs) taking advantage of all the data of city interest, including social data, and those sensed by networked monitoring devices. In particular, the paper adopts a semantic web approach that allow us to model such data by a suitable ontology specifically designed to support the main user scenarios in smart cities and to implement both the technical interoperability and the service integration envisaged by the CPS ideal model.

In computer vision and machine learning, the availability of annotated datasets is of crucial importance for both learning and performance evaluation. However, annotating visual datasets is a tedious and error-prone task and computer vision researchers usually dedicate a large amount of their time for collecting and generating annotations, which most of the time cannot be re-used in other scenarios. In this paper, we propose a simple, but effective, interactive video object segmentation method exploiting large noisy data gathered from crowd of users while playing a web game. Experimental results, carried out on two challenging video benchmarks, show how it is possible to generate reliable object segmentations in videos with a small human effort, achieving an accuracy comparable to the one obtained with manually-labeled annotations and also outperforming state-of-the-art video object segmentation approaches.

Dysgraphia is a learning disability that affects the performance of children's handwriting, which can seriously impact their school performance and their willingness to write their thoughts, and can also adversely affect their social life and can lead to low self-esteem. In this work a mobile web-based serious game for improving children's handwriting is presented. The design principles of the game are presented as well as the software tools and frameworks used for its implementation, including the results of an initial informal testing with children which are presented and discussed.

In this paper we present an approach for segmenting objects in videos taken in complex scenes with multiple and different targets. The method does not make any specific assumptions about the videos and relies on how objects are perceived by humans according to Gestalt laws. Initially, we rapidly generate a coarse foreground segmentation, which provides predictions about motion regions by analyzing how superpixel segmentation changes in consecutive frames. We then exploit these location priors to refine the initial segmentation by optimizing an energy function based on appearance and perceptual organization, only on regions where motion is observed. We evaluated our method on complex and challenging video sequences and it showed significant performance improvements over recent state-of-the-art methods, being also fast enough to be used for “on-the-fly” processing.

There is a strong movement asserting the importance of quality education all over the world and for students of all ages. Many educators believe that in order to achieve this 21stcentury skills must be taught and that digital literacy should be coupled with rigorous Computer Science principles and computational thinking. Accordingly this work will describe a didactic experience in an introductory programming course by describing the context, pedagogical approach, content of the course based on a procedure-first approach, technologies used, research questions addressed, experimental design adopted, data collection and analysis and the main conclusion supported by qualitative and quantitative data. The research questions focus on understanding which is the best medium to design algorithms by comparing flow chart and the Scratch programming language and by evaluating whether using textual language is worth the effort of the syntactic burden imposed by these languages. An analysis of quantitative and qualitative data revealed that both a visual programming and a flow-chart approach are suitable for algorithm design with no statistical difference in terms of number of errors and time taken to write the corresponding code in a textual language. However, the high number of errors suggest that using visual programming allows the student to focus on the problem solving activities.

This paper investigates how to exploit eye gaze data for understanding visual content. In particular, we propose a human-in-the-loop approach for object segmentation in videos, where humans provide significant cues on spatiotemporal relations between object parts (i.e. superpixels in our approach) by simply looking at video sequences. Such constraints, together with object appearance properties, are encoded into an energy function so as to tackle the segmentation problem as a labeling one. The proposed method uses gaze data from only two people and was tested on two challenging visual benchmarks: 1) SegTrack v2 and 2) FBMS-59. The achieved performance showed how our method outperformed more complex video object segmentation approaches, while reducing the effort needed for collecting human feedback

The fast growing importance of the semantic web and semantic web applications is demonstrated by the exponentially growing amount of semantic data produced on the web and by the rise of the linked open data movement. Besides this strong interest there is a request both from academia and industry for well-prepared students in order to minimize the training effort needed to prepare them for productive work. This paper describes the teaching experience of a Master's course entitled “Laboratory in software design and development – semantic technologies”. A detailed description of the curriculum, the rationale underlying the choice of content and the software tools used, as well as the main lessons learnt from the experience, are presented.

Programming skills are an important component of an engineering curriculum, not only because they enable the customization of software tools to be used in the profession, but also (and perhaps more crucially) because of the "computational thinking" and problem solving capabilities that are ideally developed by young students who learn to program for the first time. The necessity to expand the computing curriculum across a wider range of schools and university courses for students who are not majoring in Computer Science (CS) ) it is well-documented in literature [1], as is the difficulty of teaching 21st century skills (www.p21.org. This work presents an educational approach to teaching initial programming based on the development of fundamental and transversal skills and computer science skills, including creative and computational thinking as well as problem solving and critical thinking. The approach is based on cutting-edge educational tools, namely the visual programming frameworks Scratch, AppInventor, BYOB, and the well-known C/C++ language; curriculum material is drawn from CSPrinciples pilot courses, CS unplugged, school level preparation material for the International Olympiad in Informatics, and are complemented by supplementary information. The pedagogical approaches used in the course are based on constructivist learning theory, experiential learning and guided inquiry. This paper presents a year-long teaching experience in a 10th/13thgrade high school with 14 to 16-year-old students. Ways to extend the experience to a university course are also presented. An initial analysis of the course results, both qualitative (based on two student surveys) and quantitative (based on formal written examinations) is presented and discussed. Results are encouraging, showing how visual programming languages help students to improve their problems solving skills and reasoning practices. Exposing the younger generation to computational concepts is fundamental in order to improve the mastering of these concepts and increase the success rate in university studies.

The importance of teaching a solid design methodology is well-recognized and is the goal of many software development courses. There is an ongoing debate concerning how to approach the learning and teaching of this skill, i.e., by focusing on “design first” by means of the UML formalism or by “OO programming first”, deferring the development of UML specifications. This work presents a teaching experience and curriculum content where a “design-first” approach was used to teach Object Oriented Design, incrementally interleaved with Object Oriented Programming aimed at implementing the modeled software through laboratory activities. Working in groups allowed the students to improve communication and collaboration skills, and the use of web 2.0 technologies, such as a wiki, allowed for better course management and for the deployment of a project involving all the students. This approach was used in two year-long courses with students who had slightly different backgrounds and dispositions. Also presented are a preliminary analysis of the written examinations and laboratory exercises; an analysis of common errors and student misconceptions and a preliminary quantitative measure of the results.

The use of low-cost devices for depth estimation, such as Microsoft Kinect, is becoming more and more popular in computer vision research. In this paper, we propose an algorithm for background modeling which exploits this kind of devices to make the background and foreground models more robust to effects such as camouflage and illumination changes. Our algorithm, after a preprocessing stage for aligning color and depth data and for filtering/filling noisy depth measurements, explicitly models the scene's background and foreground with a Kernel Density Estimation approach in a quantized x-y-hue-saturation-depth space. The results in three different indoor environments, with different lighting conditions, showed that our approach is able to achieve an accuracy in foreground segmentation over 90% and that the combination of depth data and illumination-independent color space proved to be very robust against noise and illumination changes.

Ecology is, nowadays, an interdisciplinary, collabo- rative and data-intensive science, therefore, discovering, integrat- ing and analysing daily-produced data is necessary to support researchers to investigate complex questions, ranging from single particles to animals to the biosphere [1]. As a consequence, ecology-related multimedia content has been produced massively in recent years: for example, the Xeno-canto project1 and the Pl@ntNet project2 respectively collected 140,000 audio records of 8,700 bird species and about 60,000 thousand images covering thousand of plant species, to be used by scientists or professionals. Unfortunately, a manual analysis of such amount of generated data is impossible: automatic analysis tools combined with high- performance computing (HPC) solutions are therefore heavily demanded for making sense of such big ecological data. In this paper we present a case study of large-scale video processing on HPC facilities for underwater fish monitoring in the context of the Fish4Knowledge project 3, where a system to analyse long-term underwater camera footage has been developed. The paper is meant to report on the employed hardware/software architecture, the design and deployment of the parallel job manager, and the problems encountered during the whole process, from load balancing to job submission policies to bottlenecks.

SUMMARY In this paper, we present a bioinformatics knowledge discovery tool for extracting and validating associations between biological entities. By mining specialized scientific literature, the tool not only generates biological hypotheses in the form of associations between genes, proteins, miRNA and diseases but also validates the plausibility of such associations against high‐throughput biological data (e.g. microarray) and annotated databases (e.g. Gene Ontology). Both the knowledge discovery system and its validation are carried out by exploiting the advantages and the potentialities of the Cloud, which allowed us to derive and check the validity of thousands of biological associations in a reasonable amount of time. The system was tested on a dataset containing more than 1000 gene–disease associations achieving an average recall of about 71%, outperforming existing approaches. The results also showed that porting a data‐intensive application in an Infrastructure as a Service cloud environment boosts significantly the application's efficiency. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we present BioWizard, a bioinformatics knowledge discovery tool for extracting and validating implicit associations between biological entities. By mining specialized scientific literature, BioWizard not only generates biological hypotheses in the form of associations between genes, proteins and diseases, but also validates the plausibility of such associations against high-throughput biological data (microarrays) and annotated databases. The main novelties of the proposed approach are that: (1) it infers associations between biological entities by mining full text papers instead of only abstracts as usually performed by the existing tools, (2) a named entity recognition that improves the precision of the derived associations by enriching the vocabularies used in the mining loop with terms extracted directly from the text and, (3) the inferred associations are filtered according to their evidence in experimental data. We tested the precision and the recall of our system in retrieving known-associations (which did not appear in the same document) from gold standards and the results shown the ability of BioWizard in retrieving valid associations, thus providing a valuable tool for the use of biomedical researchers to speed up scientific progress.

In this work, we present a proactive content based recommender system that employs web document clustering performed by using eye gaze data. Generally, recommender systems are used in commercial applications, where information about the user's habits and interests are of crucial importance in order to plan marketing strategies, or in information retrieval systems in order to suggest similar resources a user is interested in. Commonly, these systems use explicit relevance feedback techniques (e.g. mouse or keyboard) to improve their performance and to recommend products. In contrast, the proposed system permits to capture user's interest by using implicit relevance feedback, based on data acquired by an eye tracker Tobii T60. The purpose of the system is to collect eye gaze data during web navigation and, by employing clustering techniques, to suggest web documents similar to those that the user, implicitly, expressed greater interest. Performance evaluation was carried out on 30 users and the results show that the proposed system enhanced navigation experience in about 73% of the cases.

Visual tracking is a topic on which a lot of scientific work has been carried out in the last years. An important aspect of tracking algorithms is the performance evaluation, which has been carried out typically through hand-labeled ground-truth data. Since the manual generation of ground truth is a time-consuming, error-prone and tedious task, recently many researchers have focused their attention on self-evaluation techniques for performance analysis. In this paper we propose a novel tool that enables image processing researchers to test the performance of tracking algorithms without resorting to hand-labeled ground truth data. The proposed approach consists of computing a set of features describing shape, appearance and motion of the tracked objects and combining them through a naive Bayesian classifier, in order to obtain a probability score representing the overall evaluation of each tracking decision. The method was tested on three different targets (vehicles, humans and fish) with three different tracking algorithms and the results show how this approach is able to reflect the quality of the performed tracking.

A huge amount of important biomedical information is hidden in the bulk of research articles in biomedical fields. At the same time, the publication of databases of biological information and of experimental datasets generated by high-throughput methods is in great expansion, and a wealth of annotated gene databases, chemical, genomic (including microarray datasets), clinical and other types of data repositories are now available on the Web. Thus a current challenge of bioinformatics is to develop targeted methods and tools that integrate scientific literature, biological databases and experimental data for reducing the time of database curation and for accessing evidence, either in the literature or in the datasets, useful for the analysis at hand. Under this scenario, this article reviews the knowledge discovery systems that fuse information from the literature, gathered by text mining, with microarray data for enriching the lists of down and upregulated genes with elements for biological understanding and for generating and validating new biological hypothesis. Finally, an easy to use and freely accessible tool, GeneWizard, that exploits text mining and microarray data fusion for supporting researchers in discovering gene-disease relationships is described.

The assessment of skeletal bone age is an important step both in diagnostic and in therapeutic investigations of en-docrinological problems and growth disorders of children. Currently, there are two main approaches for skeletal bone age estimation that use X-Ray images: 1) the Greulich and Pyle (G&P) method and 2) the Tanner and Whitehouse (TW2 or TW3) methods. Although the G&P method is the most widely used for its simplicity, the TW2/TW3 method is the most accurate one, but it requires intensive training, especially for novice radiologists. The method is complex since it involves the simultaneous assessment of shapes and relative distances among several bones of the hand and with the further complication of the interaction between sex, age and race. In this paper we propose a computer-based system for assisting radiologists in the training with the Tan-ner&Whitehouse method; the system is based on an engine for automated assessment of skeletal bone age from X-Rays. In detail, after evaluating the user level, the system automatically proposes personalized training sessions by drawing from a wide repository of hand X-rays, and it suggests remedial sessions upon identification the features of the cases where the users demonstrated misconceptions. Finally, all the X-Rays that show combinations of features provenly difficult, are treated as teaching cases and are converted into Medical Imaging Resource Center (MIRC) format to ease their integration into more comprehensive digital educational resources.

In this paper we propose a method for quantitative assessment of pathological nystagmus by using eye gaze data recorded with an eye tracker (Tobii T60). In detail, we use data acquired while patients perform two tests, the smooth pursuit and the saccadic movement test (implemented on the Tobii T60 using its API), that may indicate altered ophthalmic functions typical of the pathological nystagmus. Afterwards, data is analyzed by using statistical, spectral and chaotic methods in order to provide the physicians with a useful tool for assessing quantitatively the severity (or the presence) of the pathological nystagmus. A pilot study carried out on a set of 15 patients is here reported for a preliminary investigation of the suitability of the proposed eye tracker method.

This paper presents a novel vehicle detection and tracking system with stationary camera that relies on a recursive background-modeling approach, i.e., the adaptive Poisson mixture model, which is integrated with a hardware module consisting of luminosity sensors. The luminosity information side channel allows the system to effectively handle rapid changes in illumination, which is typical of outdoor applications and bottleneck of the existing background pixel classification methods. A novel algorithm for detecting and removing partial and full occlusions among blobs is also proposed. Partial occlusions are detected by evaluating the ratio between the area of the vehicle and the area of the vehicle's convex hull and are suppressed by identifying a cutting line using curvature analysis. A predictive model of the shape and motion features of the vehicles over consecutive frames instead corrects the error of the previous levels when full occlusions or background-vehicle occlusions occur in the scene. Quantitative evaluation and comparisons on some real-world scenarios demonstrate that the proposed approach outperforms state-of-the-art methods in terms of both vehicle detection and processing time, particularly due to the robustness and the efficiency of the background-modeling algorithm.

Digital resource sharing is a contemporary trend in Medical Education. State-of-the-art digital resources are incrementally appearing in the majority of curriculums worldwide. Institutional repositories have been initiated and metadata descriptions of digital resources have been turned into standards, however sharing, retrieval and re-use across different institutions are still at infant stage, due to the lack of appropriate tools and the cost of metadata creation. To this extend the aim of this paper is to propose a user-friendly editor for the creation of Healthcare Learning Object Metadata (H-LOM), an ANSI standard by MedBiquitous, to enhance the educational sharing and retrieval of digital health resources.

In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users' eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his/her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and "pyramid" like keyword, shows that about the 87% of the users is more satisfied of the output images when the re-raking is applied.

A challenging goal today is the use of computer networking and advanced monitoring technologies to extend human intellectual capabilities in medical decision making. Modern commercial eye trackers are used in many of research fields, but the improvement of eye tracking technology, in terms of precision on the eye movements capture, has led to consider the eye tracker as a tool for vision analysis, so that its application in medical research, e.g. in ophthalmology, cognitive psychology and in neuroscience has grown considerably. The improvements of the human eye tracker interface become more and more important to allow medical doctors to increase their diagnosis capacity, especially if the interface allows them to remotely administer the clinical tests more appropriate for the problem at hand. In this paper, we propose a client/server eye tracking system that provides an interactive system for monitoring patients eye movements depending on the clinical test administered by the medical doctors. The system supports the retrieval of the gaze information and provides statistics to both medical research and disease diagnosis.

This paper proposes a novel method for text mining on the Grid, aimed at pointing out hidden relationships for hypothesis generation and suitable for semi-interactive querying. The method is based on unsupervised clustering and the outputs are visualized with contextual information. Grid implementation is crucial for feasibility. We demonstrate it with a mining run for discovering genes-diseases associations from bibliographic sources and annotated databases. The proposed methodology is in view of a Grid architecture specialized in bioinformatics mining tasks. Some performance considerations are provided.

This paper proposes a traffic monitoring architecture based on a high-speed communication network whose nodes are equipped with fuzzy processors and cellular neural network (CNN) embedded systems. It implements a real-time mobility information system where visual human perceptions sent by people working on the territory and video-sequences of traffic taken from webcams are jointly processed to evaluate the fundamental traffic parameters for every street of a metropolitan area. This paper presents the whole methodology for data collection and analysis and compares the accuracy and the processing time of the proposed soft computing techniques with other existing algorithms. Moreover, this paper discusses when and why it is recommended to fuse the visual perceptions of the traffic with the automated measurements taken from the webcams to compute the maximum traveling time that is likely needed to reach any destination in the traffic network.

A knowledge base consisting of design cases has been structured into an organizational memory, which has been supporting a community of novices to learn information system (IS) for six years. The paper discusses why the organizational learning process, which is taking place, can be described as a genetic system that evolves towards the best quality attainable, and by means of a simulation examines the role of some components of this socio-technical system (i.e. features of the organizational memory, teacher ability and memetic processes in the community) in steering this evolution. Some indications for the design of socio-technical systems supported by design memories are pointed out.

Aim of the paper is to illustrate an ontology based approach to the design of mobility information systems which, through multiple distribution channels, provide companies and individual consumers with the information that facilitate their mobility in a metropolitan area. Main advantages of the proposed approach are user queries whose semantics is independent of the system implementation, and the possibility of delegating user tasks to mobile software agents by means of either PCs or PDAs. The ontology framework also facilitates data collection and control of remote devices such as semaphores and signs. The outlined architecture is under experimentation for managing mobility in the Catania metropolitan area.

Scenario-based design is a significant informal approach to systems specification and verification, but it does not ensure correct user-centered systems. This paper shows how scenario-based design can be augmented with a theory of interactions and scenes that allows the designer to elicit user requirements within a formal framework that does not excessively constrain the user narration, and to derive a system implementation satisfying the basic software engineering requisites. The paper discusses also how the approach facilitates the formal proof of the safety and liveness properties of interactive systems.