Search

Deploying ad-hoc learning environments to use and represent data from multiple sources and networks and to dynamically respond to user demands could be very expensive and ineffective in the long run. Moreover, most of the available data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards. It is preferable to focus on data availability to choose and develop interoperability strategies suitable for smart learning systems based on open standards and allowing seamless integration of third-party data and custom applications. This paper highlights the opportunity to take advantage of emerging technologies, like the linked open data platforms and automatic reasoning to effectively handle the vast amount of information and to use data linked queries in the domain of cognitive smart learning systems., Journal of e-Learning and Knowledge Society, Vol 16 No 1 (2020): Je-LKS - Special Issue on "Smart Learning in Smart Cities"

Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular-oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Peer assessment mechanism is becoming increasingly important in different computer programming courses to give students opportunities to learn from each other, to improve the learning experience and to reach efficient learning outcomes. Nevertheless, new methodological approaches developed to help students in computer programming courses have had to deal with a thorough study of the aspects related to the gender difference in this context. The paper presents a web-based system, which improved students' program skills by reviewing peers' source codes and delivering feedback to peers. The purpose was to provide evidence that peer assessment in computer programming has a positive instructive effect. Moreover, we want to investigate if there is a gender gap in the traditional introductory programming course and how can we use peer assessment principles to reduce it., Journal of e-Learning and Knowledge Society, Vol 13 No 3 (2017): EMEMITALIA Conference 2016

Recommender systems have become an important research area since the emergence of the first research paper on collaborative filtering in the mid-1990s. In general, recommender systems directly help users to select content, products, or services by aggregating and analysing historical data including suggestions from other users, and turning them into predictions of users’ possible future preferences. Recommender systems combine ideas from user profiling, information filtering, data mining, machine learning and social networking to provide personalized and meaningful recommendations. For example, while standard search engines are very likely to generate the same results to the same search queries entering from different users, recommender systems are able to generate results that are personalized taking into account the individual user’s profile.

Il processo di sommarizzazione delle informazioni diventa sempre più importante alla luce dei recenti miglioramenti nella creazione e distribuzione delle risorse e nella diffusione delle stesse nella quotidianità. Questi progressi rappresentano una sfida anche nel mondo dei sistemi di e-learning verso l’adozione delle opportunità disponibili nell’utilizzo di sistemi di condivisione e comunicazione di conoscenza distribuita, ad esempio all’interno di reti sociali di comunicazione. In questo scenario devono essere progettate e sviluppate nuove tecniche e nuovi modelli di estrazione di conoscenza anche basata sul contenuto delle risorse analizzate.

The paper presents an ontological approach for enabling semantic-aware information retrieval and browsing framework facilitating the user access to its preferred contents. Through the ontologies the system will express key entities and relationships describing learning material in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and concept recognition, for reasoning processes and for enabling user-friendly and intelligent multimedia content search and retrieval.

In this paper we propose ontology-based educational video annotation and recommendation tools. Our system is able to perform automatic shot detection and supports users during the annotation phase in a collaborative framework by providing suggestions on the basis of actual user needs as well as modifiable user behaviour and interests. Annotations are based on domain ontologies expressing hierarchical links between entities and guarantying interoperability of resources. Examples to verify the effectiveness of both the shot detection and the frame matching modules are analyzed

In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources.

Motivation: The scientific literature embeds an enormous amount of relational knowledge, encompassing interactions between biomedical entities, like proteins, drugs, and symptoms. To cope with the ever-increasing number of publications, researchers are experiencing a surge of interest in extracting valuable, structured, concise, and unambiguous information from plain texts. With the development of deep learning, the granularity of information extraction is evolving from entities and pairwise relations to events. Events can model complex interactions involving multiple participants having a specific semantic role, also handling nested and overlapping definitions. After being studied for years, automatic event extraction is on the road to significantly impact biology in a wide range of applications, from knowledge base enrichment to the formulation of new research hypotheses. Results: This paper provides a comprehensive and up-to-date survey on the link between event extraction and natural language understanding, focusing on the biomedical domain. First, we establish a flexible event definition, summarizing the terminological efforts conducted in various areas. Second, we present the event extraction task, the related challenges, and the available annotated corpora. Third, we deeply explore the most representative methods and present an analysis of the current state-of-the-art, accompanied by performance discussion. To help researchers navigate the avalanche of event extraction works, we provide a detailed taxonomy for classifying the contributions proposed by the community. Fourth, we compare solutions applied in biomedicine with those evaluated in other domains, identifying research opportunities and providing insights for strategies not yet explored. Finally, we discuss applications and our envisions about future perspectives, moving the needle on explainability and knowledge injection.

Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by mobile platforms. In this article, we report on some exploratory experience in this area. We illustrate some practical, real-world cases where running optimization programs on mobile or embedded devices can be useful, with particular emphasis on matheuristics approaches. Then, we discuss a practical use case involving the feasibility version of the generalized assignment problem (GAP). We present a JavaScript implementation of a GAP solver that can be executed inside an ordinary browser supporting ECMAScript. We tested the code on different smartphones of varying age and power, as well as on desktop PCs and other embedded devices. Our experiments confirm the viability of mobile devices for computational intensive tasks.

The huge volume of data gathered from wearable fitness devices and wellness appliances, if effectively analysed and integrated, can be exploited to improve clinical decision making and to stimulate promising applications, as they can provide good measures of everyday patient behaviour and lifestyle. However, several obstacles currently limit the true exploitation of these opportunities. In particular, the healthcare landscape is characterised by a pervasive presence of data silos which prevent users and healthcare professionals from obtaining an overall view of the knowledge, mainly due to the lack of device interoperability and data representation format heterogeneity. This work focuses on current, important needs in self-tracked health data modelling, and summarises challenges and opportunities that will characterise the community in the upcoming years. The paper describes a virtually integrated approach using standard Web Semantic technologies and Linked Open Data to cope with heterogeneous health data integration. The proposed approach is verified using data collected from several IoT fitness vendors to form a standard context-aware resource graph, and linking other health ontologies and open projects. We developed a web portal for integrating, sharing and analysing through a customisable dashboard heterogeneous IoT health and fitness data. In this way, we are able to map information onto an integrated domain model by providing support for logical reasoning.

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods.

In the last few years, several initiatives based on extracurricular activities have been organized in many countries around the world, with the aim to reduce the digital gender gap in STEM (Science, Technology, Engineering, Math) fields. Among them, the Digital Girls summer camp, organized every year since 2014 by two Italian universities with the aim to attract female students to ICT (Information and Communication Technologies) disciplines, represents quite a unique initiative for its characteristics of long-duration (3–4 entire weeks) and complete gratuitousness for the participants. The COVID-19 emergency imposed severe changes to such activities, that had to be modified and carried out in the online mode as a consequence of social distancing. However, on one hand, the general lack of high-quality evaluations of these initiatives hinders the possibility to understand the actual impact of extracurricular activities on the future academic choices of the participants. On the other hand, the availability of data collected over different editions of Digital Girls has allowed us to analyze the summer camp impact and to evaluate the pros and cons of in-presence and online activities. The main contribution of this paper is twofold. First, we present an overview of existing experiences, at the national (Italian) and international levels, to increase female participation in integrated STEM and ICT fields. Second, we analyze how summer camp participation can influence girls’ future academic choices, with specific attention to ICT-related disciplines. In particular, the collection of a significant amount of data through anonymous surveys conducted before and after the camp activities over the two editions allowed us to evidence the different impacts of in-presence and online extracurricular activities.

In knowledge intensive nutrition-related contexts, such as personalised dietary, diet-sensitive diseases management and sport supplementation, ontologies play an important role. In this paper, we propose an ontology-based consultation system which aims to improve the life quality of both healthy people and individuals affected by chronic diet-related diseases. We developed a system which is capable of transferring human dietary and nutrition expertise into machine understandable knowledge through a set of semantic rules in order to better assist users in making the correct nutritional choices for their particular health status, age, lifestyle and food preferences. Our system makes use of open data, published ontologies, domain knowledge and IoT data to construct a domain representation consisting of unified concepts and instances suitable for reasoning processes. We described how several knowledge bases in knowledge-intensive contexts can be integrated to provide a unified structured and precise representation of heterogeneous information to provide better diet recommendation to individuals.

A variety of technology enhanced teaching strategies and learning activities have been applied in education, including assessment mechanisms. In this paper, we aim to examine the extent to which peer assessment promotes deep learning and favours the incremental learning of the concepts presented throughout a traditional introductory programming course. In particular, we want to examine how the enactment of the peer-assessor and peer-assessee roles is associated with students’ learning improvements, after enacting recip-rocal peer assessment. We illustrate a case study based on a novel web-based peer assessment tool to improve engagement and learning outcomes in a pro-gramming course. We want to assess whether programming skills are growing through peer review and whether exposure to different programming tech-niques is helpful to students. One of the most remarkable findings of our ex-perience was that students reported that assessing others’ work was an ex-tremely valuable learning activity.

Nowadays, the enormous volume of health and fitness data gathered from IoT wearable devices offers favourable opportunities to the research community. For instance, it can be exploited using sophisticated data analysis techniques, such as automatic reasoning, to find patterns and, extract information and new knowledge in order to enhance decision-making and deliver better healthcare. However, due to the high heterogeneity of data representation formats, the IoT healthcare landscape is characterised by an ubiquitous presence of data silos which prevents users and clinicians from obtaining a consistent representation of the whole knowledge. Semantic web technologies, such as ontologies and inference rules, have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (1) consistently represent health and fitness data from heterogeneous IoT sources; (2) integrate and exchange them; and (3) enable automatic reasoning by inference engines.

With the blooming of data created for example by IoT devices, the possibility to handle all information coming from healthcare applications is becoming increasingly challenging. Cognitive computing systems can be used to analyse large information volume by providing insights and recommendations to represent, access, integrate, and investigate data in order to improve outcomes across many domains, including healthcare. This paper presents an ontology-based system for the eHealth domain. It provides semantic interoperability among heterogeneous IoT devices and facilitates data integration and sharing. The novelty of the proposed approach lies in exploiting semantic web technologies to explicitly describe the meaning of sensor data and define a common communication strategy for information representation and exchange.