Radiomics is an innovative discipline in medical imaging that uses advanced quantitative feature extraction from radiological images to provide a non-invasive method of interpreting the intricate biological panorama of diseases. This discipline takes advantage of the unique characteristics of medical imaging, where radiation or ultrasound combines with biological tissues, to reveal disease features and important biomarkers that are invisible to the human eye. Radiomics plays a crucial role in healthcare, spanning disease diagnosis, prognosis, recurrences, treatment response assessment, and personalized medicine. Radiomics uses a systematic approach that includes image preprocessing, segmentation, feature extraction, feature selection, classification, and evaluation. This survey attempts to shed light on the crucial roles that feature selection and classification play in discovering important biomarkers and forecasting disease directions despite the challenges posed by high dimensionality (i.e., when the data contains a huge number of features). By analyzing 47 relevant research articles, this study has provided several insights into the key techniques used across different stages of the radiology workflow. The findings indicate that 27 articles utilized the SVM classifier, while 23 of the surveyed studies used the LASSO feature selection approach. This demonstrates how these particular methodologies have been widely used in Radiomics research. The assessment did, however, also point out areas that require more research, such as evaluating the stability of feature selection and classification algorithms and adopting novel approaches like ensemble and hybrid selection methods. Additionally, we examine some of the challenges and emerging subfields within the field of radiomics.

The rapid increase of the elderly population and new advances in pervasive computing technologies allow innovative tools and applications to support independent living for frail people and identify early symptoms of health problems, including neurodegenerative disorders. Among several studies reported in the literature, monitoring locomotion traces to detect symp-toms of cognitive impairment has gained increasing attention. Therefore, in this work, we propose a novel technique for the recognition of locomotion patterns related to cognitive decline based on sensor data acquired in smart homes. In particular, we introduce a vision-based method to graphically represent indoor trajectories with random rotation, using different handcrafted features designed for image analysis tasks and combined with features extracted directly from spatio-temporal sequences of movements. Experiments on a real-world dataset acquired in a smart-home test-bed show that the proposed approach achieves promising results.

Mobile sensor-based activity recognition is a growing research field with important applications areas, such as healthcare and well-being. Data collected from multiple sensors, including smartphone sensors that are now ubiquitous, can be exploited to build predictive models capable of recognizing actions and activities performed by humans in their daily life. This involves several processing steps, from the cleansing of raw data to the extraction of suitable features and the induction of proper classifiers. Dimensionality reduction techniques may also be important for the efficiency and the exploitability of the induced models, especially when dealing with multi-sensor data leading to high-dimensional feature vectors. In such a scenario, feature selection algorithms can be very useful to identify and retain only the most informative and predictive features. However, little research has so far investigated which selection approaches may be most appropriate in sensor-based activity recognition tasks. To give a contribution in this direction, our paper compares the performance of different feature selection methods, both univariate and multivariate, on a public domain benchmark containing smartphone sensor data. We performed a comprehensive evaluation considering the extent to which each method effectively identifies the most predictive features and the overall stability of the selection process, i.e., its robustness to changes in the input data. Our results give interesting insight on which methods may be most suited in this domain, showing that it is possible to significantly reduce the data dimensionality without compromising the activity recognition performance.

Selecting a subset of relevant features is crucial to the analysis of high-dimensional datasets coming from a number of application domains, such as biomedical data, document and image analysis. Since no single selection algorithm seems to be capable of ensuring optimal results in terms of both predictive performance and stability (i.e. robustness to changes in the input data), researchers have increasingly explored the effectiveness of “ensemble” approaches involving the combination of different selectors. While interesting proposals have been reported in the literature, most of them have been so far evaluated in a limited number of settings (e.g. with data from a single domain and in conjunction with specific selection approaches), leaving unanswered important questions about the large-scale applicability and utility of ensemble feature selection. To give a contribution to the field, this work presents an empirical study which encompasses different kinds of selection algorithms (filters and embedded methods, univariate and multivariate techniques) and different application domains. Specifically, we consider 18 classification tasks with heterogeneous characteristics (in terms of number of classes and instances-to-features ratio) and experimentally evaluate, for feature subsets of different cardinalities, the extent to which an ensemble approach turns out to be more robust than a single selector, thus providing useful insight for both researchers and practitioners.

Now in its third edition, the Data Exploration in the Web 3.0 Age (DEW) track of the IEEE International WETICE Conference continues to bring together researchers and practitioners from both the Academia and Industry working in the areas related to data exploration, in a very broad sense. Papers accepted for presentation at DEW 2019 are representatives of emerging topics in the fields of data and text mining, machine learning, semantic web and Internet of things.

When dealing with biomedical data, the first and most challenging issue is often the huge dimensionality, i.e. the presence of a very high number of features for each of the problem instances at hand. A vast literature is available on different dimensionality reduction techniques that can be suitable for handling such kind of data, with a special focus on feature selection algorithms that allow to discard uninformative/useless features. In most cases, however, the dimensionality issue is addressed without a joint consideration of other potential problems in the data, including an imbalanced class distribution that may hinder the construction of effective classification models. Class imbalance, in turn, has been mostly treated in literature as an independent problem, especially in application fields where the number of features is not so critical. But several biomedical datasets are both high-dimensional and class-imbalanced, so there is a strong need for designing and evaluating learning strategies that can properly deal with both the issues simultaneously. In this work, we experiment with using feature selection techniques in conjunction with sampling-based class balancing methods and cost-sensitive classification, in order to gain insight into the most effective strategies to use when dealing with such complex data.

Data Exploration in the Web 3.0 Age (DEW) is a track of the IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). DEW aims at bringing together researchers and practitioners from both the Academia and Industry working in the areas related to data exploration, in a very broad sense. The track welcomes contributions from data mining, query languages, data visualization, graph databases and other fields related to the analysis and exploitation of data. This summary report includes a brief description of the papers accepted for presentation at the 2018 edition of DEW.

The goal of the Data Exploration in the Web 3.0 Age (DEW 2017) conference track has been bringing together researchers and practitioners from both the Academia and Industry working in the areas related to data exploration, in a very broad sense. The track welcomes contributions from data mining, query languages, data visualization, graph databases and other fields related to the analysis and exploitation of data. This paper is a Summary Report for the Data Exploration in the Web 3.0 Age (DEW) Track that includes the list of accepted papers.

Feature selection has become a necessary step to the analysis of high-dimensional datasets coming from several application domains (e.g., web data, document and image analysis, biological data). Though well-established methods exist to select highly discriminative features, discarding the ones that may be either redundant or irrelevant to the problem at hand, little attention has been so far given to the stability of these methods, in cases where the composition of the original dataset is perturbed to some extent (e.g., by adding new records or by random sampling). In this work, we highlight the importance of jointly considering both stability and predictive performance when the selection results are used for knowledge discovery and domain understanding. As a case study, we consider five popular feature selection algorithms, representatives of different selection approaches, and experimentally investigate their behaviour across three different domains: Internet advertisements, text categorization and biomedical data classification. Useful insight on the "intrinsic" stability of each algorithm seems to emerge, despite the peculiar characteristics of each dataset.

Going toward a more democratic society involves the release of the so-called "Open Government Data" (OGDs) i.e. Any data and information produced by public administrations (PAs) and made available to citizens in order to increase government transparency. Although a large amount of OGD repositories exist, accessing and analyzing OGDs is far from being user-friendly. Trying to push in this direction, this paper presents a framework to extract open government data from government portals, collect them in a repository and increase data transparency with the introduction of spatial features. A case study is presented that sets out the application of the framework on SIOPE, an Italian administrative portal that provides the receipts and payments made by Italian institutions.

The evolution of web technologies seems to characterize a new scenario for biomedical researchers who take advantage from sophisticated on to logies offered by scientific portals. This paper introduces the concept of BioContext, a software environment which combines services for the fully exploitation of several ontologies whose knowledge is captured, and managed in a local repository. We illustrate our ideas pragmatically by presenting BCnotes, a BioContext which enables the functional annotation of biomedical texts in order to discover important relationships among genes.

With the advent of biological ontologies an increasing amount of methods are emerging for enriching gene information by means of their annotations. However, problems occur in assessing semantic similarity over genetic aspects that are represented independently in different schemas when, in reality, they are not. This paper presents a framework that integrates heterogeneous knowledge from different resources (i.e. ontologies, texts, expert classifications) for capturing information about how a set of genes work together in targeting a biological process. Our approach grounds on the ontological annotation of gene summaries. Given the analogy between these annotations and the representation of documents in information retrieval, we apply techniques used in text mining to evaluate the semantic similarity of summaries within a gene set. To determine if our framework makes sense in a biological context, we conducted experiments on popular gene sets and compared results with what asserted by domain experts. Our approach provides an empirical basis for capturing complementary information about how genes interact and could be used in conjunction with other similarity methods or bioinformatic tools.

Acknowledged as important factors for business environments operating as virtual organizations (VOs), trust and reputation are receiving attention also in Grids devoted to scientific applications where problems of finding suitable models and architectures for flexible security management of heterogeneous resources arise. Being these resources highly heterogeneous (from individual users to whole organizations or experiment tools and workflows), this paper presents a trust and reputation framework that integrates a number of information sources to produce a comprehensive evaluation of trust and reputation by clustering resources having similar capabilities of successfully executing a specific job. Here, trust and reputation are considered as quality of service (QoS) parameters, and are asserted on the operative context of resources, a concept expressing the resources capability of providing trusted services within collaborative scientific applications. Specifically, the framework exploits the use of distributed brokers that support interaction trust and the creation of VOs from existing scientific organizations. A broker is a distributed software module launched at some node of the Grid that makes use of resources and communicates with other brokers to perform specific reputation services. In turn, each broker contributes to maintain a dynamic and adaptive reputation assessment within the Grid in a collaborative and distributed fashion. The proposed framework is empirically implemented by adopting a SOA approach and results show its effectiveness and its possible integration in a scientific Grid.

This paper describes a system to automate university entrance examinations involving several hundreds of candidates. Based on a client-server architecture, the system supports the random generation of multiple choice questionnaires and a large number of question libraries each covering a specific mathematical or logical topic. A user friendly interface is provided with graphical facilities allowing navigation through questions. Details of concrete experiences associated with computer science course entrance examinations are given and the results of a survey on students' perception are also presented.

In order to structure multimedia learning material, a data model is presented that specially addresses scalability and provides a high level of flexibility for interactive distance education environments. The model organizes learning objects in a set of nodes connected to each other through directed edges. Relationships capture didactic aspects and edges allow ordered sequencing of objects. The data organization is searchable, hierarchically structured and navigable according to paths that express the pedagogical strategy of the teacher. Model is mapped in an XML schema that is generic enough to be reused in different contexts and has been usefully applied in a C programming course.