Self-assess momentary mood in mobile devices: a case study with mature female participants Senette C., Buzzi M. C., Paratore M. T. Starting from the assumption that mood has a central role in domain-specific persuasion systems for well-being, the main goal of this study was to investigate the feasibility and acceptability of single-input methods to assess momentary mood as a medium for further interventions in health-related mobile apps destined for mature women. To this aim, we designed a very simple android App providing four user interfaces, each one showing one interactive widget to self-assess mood. Two widgets report a hint about the momentary mood they represent; the last two do not have the hints but were previously refined through questionnaires administered to 63 women (age 45-65) in order to reduce their expressive ambiguity. Next, fifteen women (age 45-65 years) were recruited to use the app for 15 days. Participants were polled about their mood four times a day and data were saved in a remote database. Moreover, users were asked to fill out a preliminary questionnaire, at the first access to the app, and a feedback questionnaire at the end of the testing period. Results appear to prove the feasibility and acceptability of this approach to self-assess momentary mood in the target population and provides some potential input methods to be used in this context.Source: ICCT 2023, Jaipur, India, 9-12/10/2023
Efficient adaptive ensembling for image classification Bruno A., Moroni D., Martinelli M. In recent times, except for sporadic cases, the trend in Computer Vision is to achieve minor improvements over considerable increases in complexity.
To reverse this tendency, we propose a novel method to boost image classification performances without an increase in complexity.
To this end, we revisited ensembling, a powerful approach, not often adequately used due to its nature of increased complexity and training time, making it viable by specific design choices. First, we trained end-to-end two EfficientNet-b0 models (known to be the architecture with the best overall accuracy/complexity trade-off in image classification) on disjoint subsets of data (i.e. bagging). Then, we made an efficient adaptive ensemble by performing fine-tuning of a trainable combination layer. In this way, we were able to outperform the state-of-the-art by an average of 0.5\% on the accuracy with restrained complexity both in terms of number of parameters (by 5-60 times), and FLoating point Operations Per Second (by 10-100 times) on several major benchmark datasets, fully embracing the green AI.Source: Expert systems (Online) (2023). doi:10.1111/exsy.13424 DOI: 10.1111/exsy.13424 Metrics:
Revisiting ensembling for improving the performance of deep learning models Bruno A., Moroni D., Martinelli M. Ensembling is a very well-known strategy consisting in fusing several different models to achieve a new model for classification or regression tasks. Over the years, ensembling has been proven to provide superior performance in various contexts related to pattern recognition and artificial intelligence. Moreover, the basic ideas that are at the basis of ensembling have been a source of inspiration for the design of the most recent deep learning architectures. Indeed, a close analysis of those architectures shows that some connections among layers and groups of layers achieve effects similar to those obtainable by bagging, boosting and stacking, which are the well-known three basic approaches to ensembling. However, we argue that research has not fully leveraged the potential offered by ensembling. Indeed, this paper investigates some possible approaches to the combination of weak learners, or sub-components of weak learners, in the context of bagging. Based on previous results obtained in specific domains, we extend the approach to a reference dataset obtaining encouraging results.Source: ICPR 2022 - International Conference on Pattern Recognition, pp. 445–452, Montreal, Canada, 21-25/08/2022 DOI: 10.1007/978-3-031-37742-6_34 Metrics:
Mesoscale events classification in sea surface temperature imagery Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G. Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G., Ojha V., La Malfa E., La Malfa G., Pardalos P., Di Fatta G., Giuffrida G., Umeton R., pp. 516–527, 2023 DOI: 10.1007/978-3-031-25599-1_38 Project(s): NAUTILOS Metrics:
On the applicability of prototypical part learning in medical images: breast masses classification using ProtoPNet Carloni G., Berti A., Iacconi C., Pascali M. A., Colantonio S. Deep learning models have become state-of-the-art in many areas, ranging from computer vision to agriculture research.
However, concerns have been raised with respect to the transparency of their decisions, especially in the image domain.
In this regard, Explainable Artificial Intelligence has been gaining popularity in recent years.
The ProtoPNet model, which breaks down an image into prototypes and uses evidence gathered from the prototypes to classify an image, represents an appealing approach.
Still, questions regarding its effectiveness arise when the application domain changes from real-world natural images to gray-scale medical images.
This work explores the applicability of prototypical part learning in medical imaging by experimenting with ProtoPNet on a breast masses classification task.
The two considered aspects were the classification capabilities and the validity of explanations.
We looked for the optimal model's hyperparameter configuration via a random search.
We trained the model in a five-fold CV supervised framework, with mammogram images cropped around the lesions and ground-truth labels of benign/malignant masses.
Then, we compared the performance metrics of ProtoPNet to that of the corresponding base architecture, which was ResNet18, trained under the same framework.
In addition, an experienced radiologist provided a clinical viewpoint on the quality of the learned prototypes, the patch activations, and the global explanations.
We achieved a Recall of 0.769 and an area under the receiver operating characteristic curve of 0.719 in our experiments. Even though our findings are non-optimal for entering the clinical practice yet, the radiologist found ProtoPNet's explanations very intuitive, reporting a high level of satisfaction.
Therefore, we believe that prototypical part learning offers a reasonable and promising trade-off between classification performance and the quality of the related explanation.Source: ICPR 2022 - International Conference on Pattern Recognition - ICPR 2022 International Workshops and Challenges, pp. 539–557, Montreal, Canada, 21-25/08/2022 DOI: 10.1007/978-3-031-37660-3_38 Metrics:
GAM Forest explanation Lucchese C., Orlando S., Perego R., Veneri A. Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In thisworkwe focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests.We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions.We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset.Source: EDBT 2022 - 26th International Conference on Extending Database Technology, pp. 171–182, Ioannina, Greece, 28-31/03/2023 DOI: 10.48786/edbt.2023.14 Metrics:
A case study in formal analysis of system requirements Belli D., Mazzanti F. One of the goals of the 4SECURail project has been to demonstrate the benefits, limits, and costs of introducing formal meth- ods in the system requirements definition process. This has been done, on an experimental basis, by applying a specific set of tools and method- ologies to a case study from the railway sector. The paper describes the approach adopted in the project and some considerations resulting from the experience.Source: Software Engineering and Formal Methods. SEFM 2022 Collocated Workshops, edited by Masci P., Bernardeschi C., Graziani P., Koddenbrock M., Palmieri M., pp. 164–173, 2023 DOI: 10.1007/978-3-031-26236-4_14 Project(s): 4SECURAIL Metrics:
Automated image processing for remote sensing data classification Reggiannini M., Papini O., Pieri G. Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: ICPR 2022 - International Workshops and Challenges, pp. 553–560, Montreal, Canada, 21-25/08/2022 DOI: 10.1007/978-3-031-37742-6_43 Project(s): NAUTILOS Metrics:
Haptic-based cognitive mapping to support shopping malls exploration Paratore M. T., Leporini B. This paper describes a study, which is currently underway, whose aim is to investigate how the haptic channel can be effectively exploited by visually impaired users in a mobile app for the preliminary exploration of an indoor environment, namely a shopping mall. Our goal was to use haptics to convey knowledge of how the points of interest (POIs) are distributed within the physical space, and at the same time provide information about the function of each POI, so that users can get a perception of how functional areas are distributed in the environment "at a glance". Shopping malls are typical indoor environments in which orientation aids are highly appreciated by customers, and many different functional areas persist. We identified seven typical categories of POIs which can be encountered in a mall, and then associated a different vibration pattern each. In order to validate our approach, we designed and developed a prototype for preliminary testing, based on the Android platform. The prototype was periodically debugged with the aid of two visually impaired experienced users, who gave us precious advice throughout the development process. We will describe how this app was conceived, the issues emerged during its development and the positive outcomes produced by a very early testing stage. Finally, we will show that the proposed approach is promising and is worthy of further investigation.Source: EAI GOODTECHS 2022 - 8th EAI International Conference on Smart Objects and Technologies for Social Good, pp. 54–62, Online event, 16-18/11/2022 DOI: 10.1007/978-3-031-28813-5_4 Metrics:
A primer on open science-driven repository platforms Bardi A., Manghi P., Mannocci A., Ottonello E., Pavone G. Following Open Science mandates, institutions and communities increasingly demand repositories with native support for publishing scientific literature together with research data, software, and other research products. Such repositories may be thematic or general-purpose and are deeply integrated with the scholarly communication ecosystem to ensure versioning, persistent identifiers, data curation, usage stats, and so on. Identifying the most suitable off-the-shelf repository platform is often a non-trivial task as the choice depends on functional requirements, programming and technical skills, and infrastructure resources. This work analyses four state-of-the-art Open Source repository platforms, namely Dryad, Dataverse, DSpace, and InvenioRDM, from both a functional and a software perspective. This work intends to provide an overview serving as a primer for choosing repository platform solutions in different application scenarios. Moreover, this paper highlights how these platforms reacted to some key Open Science demands, moving away from the original and old-fashioned concept of a repository serving as a static container of files and metadata.Source: Metadata and Semantic Research, edited by Garoufallou E., Vlachidis A., pp. 222–234, 2023 DOI: 10.1007/978-3-031-39141-5_19 Project(s): OpenAIRE Nexus Metrics:
Brain metastases from NSCLC treated with stereotactic radiotherapy: prediction mismatch between two different radiomic platforms Carloni G., Garibaldi C., Marvaso G., Volpe S., Zaffaroni M., Pepa M., Isaksson L. J., Colombo F., Durante S., Lo Presti G., Raimondi S., Spaggiari L. J., De Marinis F., Piperno G., Vigorito S., Gandini S., Cremonesi M., Positano V., Jereczek-Fossa B. A. Background and purpose. Radiomics enables the mining of quantitative features from medical images. The influence of the radiomic feature extraction software on the final performance of models is still a poorly understood topic. This study aimed to investigate the ability of radiomic features extracted by two different radiomic platforms to predict clinical outcomes in patients treated with radiosurgery for brain metastases from non-small cell lung cancer. We developed models integrating pre-treatment magnetic resonance imaging (MRI)-derived radiomic features and clinical data.
Materials and methods. Pre-radiotherapy gadolinium enhanced axial T1-weighted MRI scans were used. MRI images were re-sampled, intensity-shifted, and histogram-matched before radiomic extraction by means of two different platforms (PyRadiomics and SOPHiA Radiomics). We adopted LASSO Cox regression models for multivariable analyses by creating radiomic, clinical, and combined models using three survival clinical endpoints (local control, distant progression, and overall survival). The statistical analysis was repeated 50 times with different random seeds and the median concordance index was used as performance metric of the models.
Results. We analysed 276 metastases from 148 patients. The use of the two platforms resulted in differences in both the quality and the number of extractable features. That led to mismatches in terms of end-to-end performance, statistical significance of radiomic scores, and clinical covariates found significant in combined models.
Conclusion. This study shed new light on how extracting radiomic features from the same images using two different platforms could yield several discrepancies. That may lead to acute consequences on drawing conclusions, comparing results across the literature, and translating radiomics into clinical practice.Source: Radiotherapy and oncology 178 (2023). doi:10.1016/j.radonc.2022.11.013 DOI: 10.1016/j.radonc.2022.11.013 Metrics:
Privacy by design in systems for assisted living, personalized care and well-being: a stakeholder analysis Carboni A., Russo D., Moroni D., Barsocchi P. The concept of privacy-by-design within a system for assisted living, personalized care and well-being is crucial to protect users from misuse of the data collected about their health. Especially if the information is collected through audio-video devices, the question is even more delicate due to the nature of this data. In fact, in addition to guaranteeing a high level of privacy, it is necessary to reassure end-users about the correct use of these streams. The evolution of data analysis techniques began to take In review on an important role and increasingly defined characteristics in recent years. In this article, with reference to European projects in the AHA/AAL domain, we will see a differentiation of the concept of privacy-by-design according to different dimensions (Technical, Contextual, Business) and to the Stakeholders involved. The analysis is intended to cover technical aspects, legislative and policies-related aspects also regarding the point of view of the municipalities and aspects related to the acceptance and, therefore, to the perception of the safety of these technologies by the final end-users.Source: Frontiers in digital health (2023). doi:10.3389/fdgth.2022.934609 DOI: 10.3389/fdgth.2022.934609 Project(s): PlatformUptake.eu Metrics:
A self-training automatic infant-cry detector Coro G., Bardelli S., Cuttano A., Scaramuzzo R. T., Ciantelli M. Infant cry is one of the first distinctive and informative life signals observed after birth. Neonatologists and automatic assistive systems can analyse infant cry to early-detect pathologies. These analyses extensively use reference expert-curated databases containing annotated infant-cry audio samples. However, these databases are not publicly accessible because of their sensitive data. Moreover, the recorded data can under-represent specific phenomena or the operational conditions required by other medical teams. Additionally, building these databases requires significant investments that few hospitals can afford. This paper describes an open-source workflow for infant-cry detection, which identifies audio segments containing high-quality infant-cry samples with no other overlapping audio events (e.g. machine noise or adult speech). It requires minimal training because it trains an LSTM-with-self-attention model on infant-cry samples automatically detected from the recorded audio through cluster analysis and HMM classification. The audio signal processing uses energy and intonation acoustic features from 100-ms segments to improve spectral robustness to noise. The workflow annotates the input audio with intervals containing infant-cry samples suited for populating a database for neonatological and early diagnosis studies. On 16 min of hospital phone-audio recordings, it reached sufficient infant-cry detection accuracy in 3 neonatal care environments (nursery--69%, sub-intensive--82%, intensive--77%) involving 20 infants subject to heterogeneous cry stimuli, and had substantial agreement with an expert's annotation. Our workflow is a cost-effective solution, particularly suited for a sub-intensive care environment, scalable to monitor from one to many infants. It allows a hospital to build and populate an extensive high-quality infant-cry database with a minimal investment.Source: Neural computing & applications (Print) (2023). doi:10.1007/s00521-022-08129-w DOI: 10.1007/s00521-022-08129-w Project(s): EcoScope Metrics:
Global-scale parameters for ecological models Coro G., Bove P., Kesner-Reyes K. This paper presents a collection of environmental, geophysical, and other marine-related data for marine ecological models and ecological-niche models. It consists of 2132 raster data for 58 distinct parameters at regional and global scales in the ESRI-GRID ASCII format. Most data originally belonged to open data owned by the authors of this article but residing on heterogeneous repositories with different formats and resolutions. Other data were specifically created for the present publication. The collection includes 565 data with global scale range; 154 at 0.5° resolution and 411 at 0.1° resolution; 196 data with annual temporal aggregation over ~10 key years between 1950 and 2100; 369 data with monthly aggregation at 0.1° resolution from January 2017 to ~May 2021 continuously. Data were also cut out on 8 European marine regions. The collection also includes forecasts for different future scenarios such as the Representative Concentration Pathways 2.6 (63 data), 4.5 (162 data), and 8.5 (162 data), and the A2 scenario of the Intergovernmental Panel on Climate Change (180 data).Source: Scientific data 10 (2023). doi:10.1038/s41597-022-01904-3 DOI: 10.1038/s41597-022-01904-3 Project(s): EcoScope Metrics:
Towards adaptation of humanoid robot behaviour in serious game scenarios using reinforcement learning Zedda E., Manca M., Paternò F. Repetitive cognitive training can be seen as tedious by older adults and cause participants to drop out. Humanoid robots can be exploited to reduce boredom and the cognitive burden in playing serious games as part of cognitive training. In this paper, an adaptive technique to select the best actions for a robot is proposed to maintain the attention level of elderly users during a serious game. The goal is to create a strategy to adapt the robot's behaviour to stimulate the user to remain attentive through reinforcement learning. Specifically, a learning algorithm
(QL) has been applied to obtain the best adaptation strategy for the selection of the robot's
actions. The robot's actions consist of a combination of verbal and nonverbal interaction
aspects. We have applied this approach to the behaviour of a Pepper robot for which two
possible personalities have been defined. Each personality is exhibited by performing specific
actions in the various modalities supported. Simulation results indicate learning convergence
and seem promising to validate the effectiveness of the obtained strategy. Preliminary test
results with three participants suggest that the adaption in the robot is perceived.Source: ALTRUIST 2022 - 2nd Workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, pp. 93–99, Florence, Italy, 16/12/2022
From unstructured texts to semantic story maps Bartalesi V., Coro G., Lenzi E., Pagano P., Pratelli N. Digital maps greatly support storytelling about territories, especially when enriched with data describing cultural, societal, and ecological aspects, conveying emotional messages that describe the territory as a whole. Story maps are interactive online digital narratives that can describe a territory beyond its map by enriching the map with text, pictures, videos, and other multimedia information. This paper presents a semi-automatic workflow to produce story maps from textual documents containing territory data. An expert first assembles one territory-contextual document containing text and images. Then, automatic processes use natural language processing and Wikidata services to (i) extract key concepts (entities) and geospatial coordinates associated with the territory, (ii) assemble a logically-ordered sequence of enriched story-map events, and (iii) openly publish online story maps and an interoperable Linked Open Data semantic knowledge base for event exploration and inter-story correlation analyses. Our workflow uses an Open Science-oriented methodology to publish all processes and data. Through our workflow, we produced story maps for the value chains and territories of 23 rural European areas of 16 countries. Through numerical evaluation, we demonstrated that territory experts considered the story maps effective in describing their territories, and appropriate for communicating with citizens and stakeholders.Source: International journal of digital earth (Online) 16 (2023): 234–250. doi:10.1080/17538947.2023.2168774 DOI: 10.1080/17538947.2023.2168774 Project(s): MOVING Metrics:
The impact of noise on evaluation complexity: the deterministic trust-region case Bellavia S., Gurioli G., Morini B., Toint P. L. Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation into account are presented in the framework of a deterministic trust-region method. The results show that the presence of intrinsic noise may dominate these bounds, in contrast with what is known for methods in which the inexactness in function and derivatives' evaluations is fully controllable. Moreover, the new analysis provides estimates of the optimality level achievable, should noise cause early termination. Numerical experiments are reported that support the theory. The analysis finally sheds some light on the impact of inexact computer arithmetic on evaluation complexity.Source: Journal of optimization theory and applications (2023). doi:10.1007/s10957-022-02153-5 DOI: 10.1007/s10957-022-02153-5 Metrics:
An environment to collect personal memories of older adults and use them to personalise serious games with humanoid robots Catricalà B., Ledda M., Manca M., Paternò F., Santoro C., Zedda E. One of the goals of Ambient Assisted Living (AAL) solutions is to be able to stimulate the cognitive resources of older adults. An innovative way to address such stimulation is the use of serious games delivered through humanoid robots, as they can provide an engaging way to perform exercises useful for training human memory, attention, processing, and planning activities. This paper presents an approach to supporting cognitive stimulation based on personal memories. The humanoid robot can exhibit different behaviours using various modalities, and propose the games in a way personalised to specific individuals' requirements, preferences, abilities, and motivations, which can vary among older adults, and even dynamically evolve over time for the same person depending on changing user needs and health conditions. Using personal memories associated with facts and events that
occurred in older adults life in the serious games can increase their engagement, and thus potentially reduce the cognitive training drop-out.Source: ALTRUIST 2022 - 2nd Workshop on sociAL roboTs for peRsonalized, continUous and adaptIve aSsisTance, pp. 44–54, Florence, Italy, 16/12/2022
Implementation of a drug discovery pipeline on the D4Science platform Orro A., D'Ursi P., Fossa P., Candela L., Panichi G. This report documents the implementation of drug discovery pipeline in the D4Science platform realised in the context of the EOSC-Pillar project.
In particular, it documents the pipeline and its constituents. Moreover, it describes how this pipeline has been integrated into the D4Science platform and exploited to create a dedicated Virtual Research Environment facilitating its exploitation and promoting a collaborative oriented approach for screening activities.Source: ISTI Technical Report, ISTI-2023-TR/001, 2023 DOI: 10.32079/isti-tr-2023/001 Project(s): EOSC-Pillar Metrics:
The application of SWAT model and remotely sensed products to characterize the dynamic of streamflow and snow in a mountainous watershed in the High Atlas Taia S., Erraioui L., Arjdal Y., Chao J., El Mansouri B., Scozzari A. Snowfall, snowpack, and snowmelt are among the processes with the greatest influence on the water cycle in mountainous watersheds. Hydrological models may be significantly biased if snow estimations are inaccurate. However, the unavailability of in situ snow data with enough spatiotemporal resolution limits the application of spatially distributed models in snow-fed watersheds. This obliges numerous modellers to reduce their attention to the snowpack and its effect on water distribution, particularly when a portion of the watershed is predominately covered by snow. This research demonstrates the added value of remotely sensed snow cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) in evaluating the performance of hydrological models to estimate seasonal snow dynamics and discharge. The Soil and Water Assessment Tool (SWAT) model was used in this work to simulate discharge and snow processes in the Oued El Abid snow-dominated watershed. The model was calibrated and validated on a daily basis, for a long period (1981-2015), using four discharge-gauging stations. A spatially varied approach (snow parameters are varied spatially) and a lumped approach (snow parameters are unique across the whole watershed) have been compared. Remote sensing data provided by MODIS enabled the evaluation of the snow processes simulated by the SWAT model. Results illustrate that SWAT model discharge simulations were satisfactory to good according to the statistical criteria. In addition, the model was able to reasonably estimate the snow-covered area when comparing it to the MODIS daily snow cover product. When allowing snow parameters to vary spatially, SWAT model results were more consistent with the observed streamflow and the MODIS snow-covered area (MODIS-SCA). This paper provides an example of how hydrological modelling using SWAT and snow coverage products by remote sensing may be used together to examine seasonal snow cover and snow dynamics in the High Atlas watershed.Source: Sensors (Basel) 23 (2023). doi:10.3390/s23031246 DOI: 10.3390/s23031246 Metrics: