Abstract Disinformation and conspiracy theories represent a growing social threat, often amplified within echo chambers where emotions strongly influence content creation and diffusion. This paper introduces the concept of Emotional Signatures , aggregated affective/emotional profiles designed to capture the dominant emotions expressed on social media. The approach has been tested on Reddit communities, where we processed users’ posts through an emotion recognition model that scores 27 emotions. We compare the resulting emotional profiles of conspiracy and non-conspiracy subreddits groups using similarity measures, dimensionality reduction, and supervised classifiers. Experimental results show that emotions provide a powerful discriminative signal. Logistic Regression achieved an overall accuracy of 0.874, macro-precision of 0.904, and F1 of 0.870, outperforming a Multi-Layer Perceptron, which reached an accuracy of 0.785. Further tests confirmed that conspiracy groups show greater emotional homogeneity and stronger alignment with emotions such as optimism and curiosity, which means cohesion in terms of how the topic is perceived. In contrast, non-conspiracy communities show different emotional patterns, such as excitement, nervousness and confusion, linked to the different ideas expressed during discussions. We have also compared our models with the state of art ConspEmoLLM , an LLM trained for Conspiracy topic detection. The comparison shows promising results. The proposed framework not only detects potentially conspiratorial subreddits but also highlights the emotional drivers behind their discourse, providing interpretability and insights for understanding the affective mechanisms of online misinformation. These findings demonstrate that embedding emotions into computational models is a promising direction to identify, explain, and mitigate disinformation dynamics in online communities.

Multiple Sequence Alignment (MSA) is a fundamental NP-hard problem in Bioinformatics, central to numerous sequence analysis tasks. Despite extensive research, existing approaches still struggle to achieve optimal alignment accuracy. Recently, Deep Reinforcement Learning (DRL) approaches have shown promise in addressing these limitations. However, their scalability remains limited by the high computational cost and training time required for large-scale MSA tasks. The proposed MSA approach integrates bio-inspired optimization with adaptive learning. A Genetic Algorithm (GA) serves as a high-level “orchestrator”, reformulating alignment as an evolution-driven optimization process. At each evolutionary step, multiple localized reinforcement learning agents generate highfidelity sub-alignments that are then merged into a globally consistent solution. This hybridization of stochastic evolutionary search and policy-driven learning prevents the need to retrain DRL models on extensive datasets, while simultaneously enhancing alignment precision and computational scalability. Preliminary experimental results confirm the effectiveness of the proposed approach, which achieves higher pairwise sum scores across multiple benchmark datasets, highlighting its robustness and competitive advantage in sequence alignment.

The paper explores the integration of graph-based inferences and visualizations to feed novel, experimental approaches to the critical exploration of gig economy workers’ conditions. The analysis builds upon an ongoing research project that has already turned into the design of GigAdvisor, a cross-platform (web and mobile) application for collecting and displaying worker evaluations of digital labor platforms. After a brief introduction to the project, we explore how networks can serve as tools for knowledge discovery and civic engagement: how, on the one hand, they allow us to uncover relational patterns and discover structural dynamics often concealed in tabular or numerical formats and how, on the other, they provide new, intuitive ways to reflect on platform labor and its regulation. The paper outlines the theoretical underpinnings and design choices underlying the approach and illustrates a key use case drawn from the food delivery sector. The concluding section summarizes the implications of our approach, sketching future research directions with a special focus on the integration of graph neural networks in the discovery process.

By 2030, the World Health Organization estimates that over 2.5 billion people will need assistive technologies, yet nearly one billion will lack access, posing significant barriers to inclusion. While often considered non-essential, creative technologies, particularly in music, have demonstrated therapeutic value and support cognitive well-being. However, physical and cognitive barriers continue to restrict access to active music-making.This work addresses the challenge of democratizing musical creativity by designing Digital Musical Interfaces (DMIs) that are inclusive, adaptable, and capable of supporting both per-formative and therapeutic goals. We propose a comprehensive design framework centered on accessibility, usability, and creative freedom. A key case study illustrates this framework through the development of a gesture-based music system, enabling users to create music solely through hand movements. The framework emerged through a structured series of technological milestones, incorporating AI, IoT, Virtual (VR), and Augmented Reality (AR). Each stage introduced and evaluated specific innovation, such as deep reinforcement learning for gesture recognition, low-latency VR/AR environments, multiplayer interaction, and integration with a full-featured digital audio workstation—via experimental prototypes and iterative user testing.Results of a user evaluation involving educators, musicians, and users, indicate that this interdisciplinary and user-centered approach effectively supports motor-impaired users while remaining accessible and engaging for broader populations.

BACKGROUND: Cancer is a complex disease influenced by numerous concurrent genetic factors that result in diverse tumor microenvironments (TMEs) across different cancer types. Large-scale genomic projects, such as The Cancer Genome Atlas, have underscored the need for molecular classification of cancer to enable more precise therapeutic strategies. Yet, traditional machine learning (ML) approaches currently face several limitations. First, while effective, they predominantly rely on gene expression data and often overlook critical genomic alterations such as copy number alterations, single nucleotide polymorphisms, and other mutational profiles, limiting the scope of biomarker discovery. Most importantly, they are usually limited by the need of large sample sizes. RESULTS: Building on the hypothesis that type-agnostic representations integrating gene expression with genomic mutations can comprehensively characterize TMEs and capture the similarity or dissimilarity between samples of the same or different types, we propose a novel ML-based method for cancer detection using a one-shot learning framework implemented through Siamese Neural Networks. Our method redefines cancer detection as a similarity-based classification task, allowing the model to generalize to unseen cancer types, a critical advantage in genomics where data scarcity and frequent updates pose significant challenges. To enhance interpretability, we introduce a robust explainability technique founded on SHapley Additive exPlanations (SHAP) values, to provide clear insights into the contributions of gene expression and mutational data, enabling a deeper understanding of the key factors driving cancer detection decisions. CONCLUSIONS: Our experimental results show that integrating mutational profiles with gene expression data allows for more accurate cancer type detection and reveals significant mutation patterns. These findings indicate that the proposed method has the potential to significantly enhance cancer type detection by leveraging a more comprehensive understanding of TMEs. Beyond merely classifying cancer types, the proposed SHAP-based explainability technique enables the identification and the analysis of key biomarkers relevant for immunotherapy success, thereby addressing limitations of existing approaches.

Machine learning (ML) has shown significant success in real-world scenarios where data is represented in the Euclidean domain. However, in the biomedical field, complex relational information between biological entities is often encapsulated in non-Euclidean structures, such as biomedical graphs, which are difficult to learn by traditional ML methods. Graph representation learning aims to embed graphs into a lowdimensional space while preserving topology and properties. This approach, generally organized into graph embedding techniques and graph neural networks (GNNs), bridges the gap between complex biomedical graphs and modern ML methods. Recently, it has garnered widespread interest as it offers a powerful framework for leveraging relational information inherent in biomedical data. In this context, it becomes challenging to navigate the complexities of graph-based biological problems, since the intricate relational data and the challenge of preserving graph structure during embedding pose substantial obstacles. The goal of this paper is to clarify which of these two main approaches-graph embedding techniques or GNNs-is more suitable for one of the most successful applications of graph representation learning: molecular property prediction. Molecules contain many types of substructures that may affect their properties, and recognizing substructures and relations embedded in a molecular structure representation is crucial for structureactivity relationship and structure-property relationship studies. By examining the effectiveness of different graph representation learning techniques in this context, this paper aims to provide valuable insights and guidelines for researchers and practitioners. To this aim, we carried out experiments on 4 well-known benchmarking datasets for molecular property prediction tasks, showing that GNNs are more effective. We also developed a platform for experiments in molecular property prediction with GNNs, which integrates an attention mechanism to highlight the atoms within a molecule that most significantly impact its biological function. The result of this preliminary study is not only the demonstration of the advantages of GNNs in terms of effectiveness for these tasks but also the validation of their suitability for an “explainable” AI approach. This advancement makes GNNs a powerful tool in the realm of molecular machine learning, facilitating both accurate predictions and enhanced understanding of the underlying molecular mechanisms.

The design of digital musical instruments is based on the perceptions, especially visual, that they can generate in users during their use. Given the multifaceted nature of musical expression, this aspect plays a crucial role in shaping their playability, requiring careful selection of the information to integrate into the instrument's interface. In this context, the music visualization techniques can offer substantial assistance, aiding in the pedagogical process of mastering these tools. In this work, we introduce Pulsate, an Android application engineered to enable real-time music composition by leveraging visual perceptions generated through the collision dynamics of geometric shapes resulting from the user's tactile interactions at specific points on the screen. The development of Pulsate involved the integration of various features: (i) provision for polytonality and different musical scales, (ii) internal playback modes which include percussive, harmonic, and melodic functionalities, (iii) incorporation of the MIDI protocol to facilitate control over external instruments. Through these features, Pulsate offers an intuitive platform for real-time music production. To realize this objective, we capitalized on graphics-oriented programming language processing capabilities. An evaluation study was conducted to assess the efficacy of how music can be produced expressively, involving a heterogeneous cohort of participants with varied musical backgrounds and degrees of proficiency in music theory. A further usability study was conducted to analyze the overall user satisfaction. The results of these studies provided us with positive feedback regarding the effectiveness of the concept, the aesthetic appeal of the graphical interface, and user satisfaction concerning the usability and utility of the provided tool.

The notion of Lyndon word and Lyndon factorization has shown to have unexpected applications in theory as well in developing novel algorithms on words. A counterpart to these notions are those of inverse Lyndon word and inverse Lyndon factorization. Differently from the Lyndon words, the inverse Lyndon words may be bordered. The relationship between the two factorizations is related to the inverse lexicographic ordering, and has only been recently explored. More precisely, a main open question is how to get an inverse Lyndon factorization from a classical Lyndon factorization under the inverse lexicographic ordering, named CFLin. In this paper we reveal a strong connection between these two factorizations where the border plays a relevant role. More precisely, we show two main results. We say that a factorization has the border property if a nonempty border of a factor cannot be a prefix of the next factor. First we show that there exists a unique inverse Lyndon factorization having the border property. Then we show that this unique factorization with the border property is the so-called canonical inverse Lyndon factorization, named ICFL. By showing that ICFL is obtained by compacting factors of the Lyndon factorization over the inverse lexicographic ordering, we provide a linear time algorithm for computing ICFL from CFLin.

Abstract The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of “never seen” shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages.

Abstract Optimal portfolio selection—composing a set of stocks/assets that provide high yields/returns with a reasonable risk—has attracted investors and researchers for a long time. As a consequence, a variety of methods and techniques have been developed, spanning from purely mathematics ones to computational intelligence ones. In this paper, we introduce a method for optimal portfolio selection based on multi-objective evolutionary algorithms, specificallyNondominated Sorting Genetic Algorithm-II (NSGA-II), which tries tomaximizethe yield andminimizethe risk, simultaneously. The system, namedEvoFolio, has been experimented on stock datasets in a three-years time-frame and varying the configurations/specifics of NSGA-II operators.EvoFoliois aninteractivegenetic algorithm, i.e., users can provide their own insights and suggestions to the algorithm such that it takes into account users’ preferences for some stocks. We have performed tests with optimizations occurring quarterly and monthly. The results show howEvoFoliocan significantly reduce the risk of portfolios consisting only of stocks and obtain very high performance (in terms of return). Furthermore, considering the investor’s preferences has proved to be very effective in the portfolio’s composition and made it more attractive for end-users. We argue thatEvoFoliocan be effectively used by investors as a support tool for portfolio formation.

Visual Knowledge Discovery (VKD), the emerging research field exploring the integration between Artificial Intelligence and Visual Analytics is impacting a growing number of contexts. This paper dwells on the potential intersections between the VKD perspective and the legal world, a scenario for many reasons drawn by the idea of combining empirical insights offered by computation with intuitive, visually-enhanced forms of data mining. We will focus on criminal justice, taking the cue from an ongoing experimental research project that investigates novel applications of computational social science methods - complex network analysis - to analyze criminal organizations for both scientific and investigative purposes. The work will describe the VKD solutions devised to allow public prosecutors to visually browse the content of phone calls and environmental tapping gathered during preliminary inquiries. We will discuss how given visualizations - graph-based visualizations in the first place - can enhance text mining techniques helping to capture in-a-glance communication flows, discussions' topics and both structural and functional features of criminal networks at hand.

Mobile devices are nowadays ubiquitous. They are equipped with a variety of sensors, each designed for capturing specific signals which can be exploited to acquire discriminating user’s traits, thus allowing to recognize, for instance, the authorized users. In this regard, we focus on capturing soft biometric traits from smartphones. Soft biometric information extracted from a human body (e.g., gender and age) is ancillary information proved to improve the performance of biometric authentication systems, and it has drawn a great deal of attention for its applications in healthcare, smart spaces and digital world as a whole. This paper presents an approach to gender and age-group recognition, namely TGSB, leveraging a transfer learning strategy applied to state-of-the-art Convolutional Neural Networks (CNNs) fed with image-based representations of touch gestures performed by users on mobile devices. We perform experiments considering, one at a time, touch gestures of the same kind, and combinations thereof, with intermediate and late fusion learning strategies. Experiments prove that TGSB is a promising approach, with up to 94% accuracy for the gender recognition and up to 99% for the age-group recognition. We highlight the most useful touch gesture for gender and age-group recognition, that is Scroll with 81% and 96% accuracy, respectively. We show that combining multiple touch gestures (intermediate fusion) with a joint latent subspace learning mechanism in CNNs improves the TGSB performance, up to 99% accuracy when considering a combination of two Scroll. Compared to previous works, TGSB exhibits much better performance in both gender and age-group recognition.

We define and implement a novel side-channel attack that exploits a smartphone’s accelerometer to eavesdrop entire words that the device itself is reproducing through its loudspeakers. The proposed approach consists of two modules: (i) a deep learning-based system that, using a Convolutional Neural Network (CNN), learns to recognize a set of significant speech units, using the spectrogram representation of the corresponding acceleration signals; (ii) an evolutionary-based segmentation method that, given the accelerometer measurements corresponding to an input speech, finds the best way to split it so that the proposed CNN maintains a high classification performance on each of the segments obtained, guarantying the recognition of a significant percentage of words from the original speech. Results of experiments performed to assess the effectiveness of the proposed attack, show its ability to recognize a percentage of words which is higher for short speeches and diminishes as the speeches get longer. We experimented with speeches of lengths ranging from 5 to 60 s, obtaining a recognition percentage going from about 80% for the shortest speeches, down to about 54% for the longest ones.

The digital evolution of economies and markets brings changes that go largely beyond growth and efficiency. In the gig economy, also fueled by algorithmic management solutions, digital labour platforms (DPLs) raise significant issues that include power asymmetries, new forms of workers' abuse and discrimination, algorithms' opacity and over-control. In such a scenario, while normative frameworks evolve novel safeguards, a crucial challenge is that of feeding public (social, institutional) oversight on the dynamics that, at various levels, affect the gig work world. In this paper, we show how the combination of visual analytics and data-driven heuristics can be used to offer new insights on and promote higher levels of transparency and awareness about the fairness of DPLs' activity seen, in the first place, from the workers' perspective. Solutions will be presented as part of GigAdvisor, an experimental cross-platform application developed within an ongoing research that draws on computational social science methods to enable new critical approaches to the digital economy.

Plagiarism is a debated and controversial topic in different fields. For example, in Law, where the subjectivity of the judges that have to pronounce a suspicious case usually lead to long and often unsolved cases, and in Music, where huge amounts of money are invested every year to face and try to solve suspicious cases. In this scenario, the automatic detection of music plagiarism is fundamental by representing useful support for judges during their pronouncements and an important result to avoid musicians spending more time in court than on composing music. This paper shows how the combination of visual analytics and the employment of adaptive meta-heuristics can assist domain experts in judging suspicious cases. Solutions will be presented as part of PlagiarismDetection, a cross-platform tool that leverages text-similarity algorithms, computational intelligence, optimization methods, and visualization techniques to enable new critical approaches to music plagiarism analysis.

Abstract Plagiarism is a controversial and debated topic in different fields, especially in the Music one, where the commercial market generates a huge amount of money. The lack of objective metrics to decide whether a song is a plagiarism, makes music plagiarism detection a very complex task: often decisions have to be based on subjective argumentations. Automated music analysis methods that identify music similarities can be of help. In this work, we first propose two novel such methods: a text similarity-based method and a clustering-based method. Then, we show how to combine them to get an improved (hybrid) method. The result is a novel adaptive meta-heuristic for music plagiarism detection. To assess the effectiveness of the proposed methods, considered both singularly and in the combined meta-heuristic, we performed tests on a large dataset of ascertained plagiarism and non-plagiarism cases. Results show that the meta-heuristic outperforms existing methods. Finally, we deployed the meta-heuristic into a tool, accessible as a Web application, and assessed the effectiveness, usefulness, and overall user acceptance of the tool by means of a study involving 20 people, divided into two groups, one of which with access to the tool. The study consisted in having people decide which pair of songs, in a predefined set of pairs, should be considered plagiarisms and which not. The study shows that the group supported by our tool successfully identified all plagiarism cases, performing all tasks with no errors. The whole sample agreed about the usefulness of an automatic tool that provides a measure of similarity between two songs.

Abstract Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles in the last decade, which in turn have had spillovers on all the markets and real economies of countries. These kinds of markets and their unique characteristics are of great interest to researchers. Generally, investors and financial operators study market trends to understand when bubbles might occur using technical analysis tools. Such tools, which have been historically used, resulted in being precious allies at the basis of more advanced systems. In this regard, different autonomous, adaptive and automated trading agents have been introduced in the literature to study several kinds of markets. Among these, we can distinguish between agents withZero/Minimal Intelligence (ZI/MI)andComputational Intelligence (CI)-based agents. The first ones typically trade on the market without resorting to complex learning strategies; the second ones usually use (deep) reinforcement learning mechanisms. However, these trading agents have never been tested on the cryptocurrencies market and related financial bubbles, which are still mostly overlooked in the literature. It is unclear how these agents can make profits/losses before, during, and after a bubble to adjust their strategy and avoid critical situations. This paper compares a broad set of trading agents (betweenZI/MIandCIones) and evaluates them with well-known financial indicators (e.g., volatility, returnsSharpe ratio, drawdown,SortinoandOmega ratio). Among the experiment’s outcomes,ZI/MIagents were more explainable thanCIones. Based on the results obtained above, we introduceGGSMZ, a trading agent relying on a neuro-fuzzy mechanism. The neuro-fuzzy system is able to learn from the trades performed by the agents adopted in the previous stage.GGSMZ’s performances overcome those of other tested agents. We argue thatGGSMZcould be used by investors as a decision support tool.

Abstract Gender classification of mobile devices’ users has drawn a great deal of attention for its applications in healthcare, smart spaces, biometric-based access control systems and customization of user interface (UI). Previous works have shown that authentication systems can be more effective when considering soft biometric traits such as the gender, while others highlighted the significance of this trait for enhancing UIs. This paper presents a novel machine learning-based approach to gender classification leveraging the only touch gestures information derived from smartphones’ APIs. To identify the most useful gesture and combination thereof for gender classification, we have considered two strategies:single-viewlearning, analyzing, one at a time, datasets relating to a single type of gesture, andmulti-viewlearning, analyzing together datasets describing different types of gestures. This is one of the first works to apply such a strategy for gender recognition via gestures analysis on mobile devices. The methods have been evaluated on a large dataset of gestures collected through a mobile application, which includes not only scrolls, swipes, and taps but also pinch-to-zooms and drag-and-drops which are mostly overlooked in the literature. Conversely to the previous literature, we have also provided experiments of the solution in different scenarios, thus proposing a more comprehensive evaluation. The experimental results show thatscroll downis the most useful gesture andrandom forestis the most convenient classifier for gender classification. Based on the (combination of) gestures taken into account, we have obtained F1-score up to 0.89 in validation and 0.85 in testing phase. Furthermore, the multi-view approach is recommended when dealing with unknown devices and combinations of gestures can be effectively adopted, building on the requirements of the system our solution is built-into. Solutions proposed turn out to be both an opportunity for gender-aware technologies and a potential risk deriving from unwanted gender classification.

Abstract Splicing systems are a form of DNA computing as they mimic the recombination process among DNA molecules. This work discusses the use of splicing systems to build automatic tools for reproducing human beings’ creativity, in the context of automatic music composition. More specifically, this work describes three general splicing system approaches for automatic music composition, and their application to two specific cases, namely composing 4-voice music and composing Jazz solos in a given style. Examples of music composed by the systems are presented.

One of the challenges in the emerging field of computational crime analysis is that of extracting actionable knowledge from heterogeneous (both legal and empirical) information hidden into criminal proceedings. Public prosecutors generally deal with information systems that do not provide advanced information extraction functionalities and that boil down to databases containing complaints, criminal records or police reports. In this paper, we dwell on how information visualization can support knowledge mining in criminal investigations by playing a three-fold role: (a) depicting the structural and qualitative features of both criminal organizations and their members; (b) showing the evolution of criminal networks over time; (c) enhance the interaction between the domain expert and computational heuristics in the knowledge construction process. We present three visualizations designed to support knowledge mining in criminal investigations that have been tested with real data and evaluated by legal scholars and public prosecutors within a computational crime analysis project.

Several works on representation learning for graph-structured data have been proposed in recent literature. However, most of such techniques have several downsides. On the one hand, graph kernels which use handcrafted features (e.g., shortest paths) are hampered by poor generalization problems. On the other hand, methods for learning representations of whole graphs deal with unattributed or single-attributed graphs.In this work, we propose a novel technique for graph embedding learning able to take into account multi-attribute graphs (from 1 to an arbitrary number). Given a multi-attribute graph, the proposed method generates an embedding vector as follows: (i) the graph is split into several single-attribute graphs; for each of these, one numeric vector is generated by using state-of-the-art graph embedding techniques; (ii) the obtained vectors are concatenated in one representative vector using a multi-view learning integration technique; (iii) the size of such a vector is reduced through deep autoencoders.Experiments have been conducted on the music style recognition problem. We focus on the corpus of 4-voice J. S. Bach’ compositions. First, such a corpus has been decomposed and translated into graph-based structures corresponding to the music scores. Then, the proposed method is applied to generate the embedding vectors from the obtained graphs. Finally, a Random Forest model trained on such obtained vectors is used for generating novels music compositions in the learned style. Results obtained show the effectiveness of the proposed approach.

Abstract The growth of Internet and the pervasiveness of ICT have led to a radical change in social relationships. One of the drawbacks of this change is the exposure of individuals to threats during online activities. In this context, thetechno-regulationparadigm is inspiring new ways to safeguard legally interests by means of tools allowing to hamper breaches of law. In this paper, we focus on the exposure of individuals to specific online threats when interacting with smartphones. We propose a novel techno-regulatory approach exploiting machine learning techniques to provide safeguards against threats online. Specifically, we study a set of touch-based gestures to distinguish between underages or adults who is accessing a smartphone, and so to guarantee protection. To evaluate the proposed approach’s effectiveness, we developed an Android app to build a dataset consisting of more than 9000 touch-gestures from 147 participants. We experimented bothsingle-viewandmulti-viewlearning techniques to find the best combination of touch-gestures able of distinguishing between adults and underages. Results show that the multi-view learning combining scrolls, swipes, and pinch-to-zoom gestures, achieves the best ROC AUC (0.92) and accuracy (88%) scores.

Abstract Terms of Service (ToS) are fundamental factors in the creation of physical as well as online legally relevant relationships. They not only define mutual rights and obligations but also inform users about contract key issues that, in online settings, span from liability limitations to data management and processing conditions. Despite their crucial role, however, ToS are often neglected by users that frequently accept without even reading what they agree upon, representing a critical issue when there exist potentially unfair clauses. To enhance users’ awareness and uphold legal safeguards, we first propose a definition of ToS unfairness based on a novel unfairness measure computed counting the unfair clauses contained in a ToS, and therefore, weighted according to their direct impact on the customers concrete interests. Secondly, we introduce a novel machine learning-based approach to classify ToS clauses, represented by using sentence embedding, in different categories classes and fairness levels. Results of a test involving well-known machine learning models show that Support Vector Machine is able to classify clauses into categories with a F1-score of 86% outperforming state-of-the-art methods, while Random Forest is able to classify clauses into fairness levels with a F1-score of 81%. With the final goal of making terms of service more readable and understandable, we embedded this approach into ToSware, a prototype of a Google Chrome extension. An evaluation study was performed to measure ToSware effectiveness, efficiency, and the overall users’ satisfaction when interacting with it.

During the traditional music analysis process, stylistic rules usually have to be deduced directly from examples of compositions or past performance. In such cases, musicians create external representations of a music style domain as source for reflection, inspiration and collaboration. However, due to the large number of music examples, creating such representations can be essential, but at the same time, slow and costly.In this paper, we show that interactive visualization and machine learning could aid in supporting and enhancing musician cognition and team-based collaboration. Specifically, we propose an approach to this problem which: (1) allows musicians to visually externalize their evolving mental models of a music domain, in the form of thematically organized anchored pairs. i.e., (narrative, graph), each one corresponding to a specific music pattern, and (2) uses such pairs to develop a music style classification system based on machine learning, as support for musicians during their activities (composition, performance). To this end, we introduce a novel graph representation of music stylistic patterns and discuss the advantages of linking such a representation to machine learning. Results of a preliminary study involving 10 musicians provided us with overall positive feedback about the effectiveness of our approach as well as further directions to explore.

Over the last few years, biometrics has emerged as an increasingly reliable solution to recognize people using their physiological or behavioural characteristics. Despite their advantages, biometric systems raise many practical, ethical and legal issues. While, understandably, main concerns involve privacy and the risk of covert surveillance, profiling, and social control, another relevant question is the potential exclusion of individuals that, due to injuries, disability or genetic defects, may not meet the physical requirements used for the identification. In such situations, the risk comes out from the limits of current biometrics systems, which could exclude entire classes of individuals with negative spillovers on the possibility of access services and even exercise rights. In this paper, we focus on the recognition of iris suffering from Coloboma, a congenital abnormality of membranes of the eye. We first show how this pathological state impacts on the performance of the Daugman's algorithm, which represents the most widespread method used for the iris localization step in eye-based biometrics. Second, we designed and tested a classifier based on Convolutional Neural Network able to detect the presence of Coloboma with 95.45% accuracy. This result opens up new perspectives towards the definition of more sophisticated "diversity-aware" biometric systems.

Sifting through the flood of documents today available and making sense of them is an ever-growing challenge of our time. Visualization is offering new ways to navigate through large sets of documents, mainly through graphs that intuitively depict textual entities (documents or parts thereof) and relations tying them. A relevant issue is that graphs can be smoothly used, especially in desktop environments. The small size of tablets' and smartphones' screens makes it difficult to simply port graph visualizations from desktop to mobile devices, just as the use of the latter is growing for browsing and reading texts. In the light of the above, a challenge is seamlessly merging abstraction power offered by visualization, information retrieval, and the access to texts. This paper presents a Human-Computer Interaction strategy integrating mobile devices (for visualization-based, high-level and intuitive interaction with data and their relations) and desktop computers (for full-text reading). We focus on Sliding Treemap, a visualization designed to render graph-like (hierarchical, relational) structures on mobile touch devices. The solution, suitable to be applied also in other domains, has been designed based on the needs of the legal domain, which is "highly textual" and still rather unfamiliar with visualization techniques. Sliding Treemap has been developed in the prototype of a cross-platform app using as testbed the catalogue of the European Court of Justice Library, and sets of heterogeneous legal sources from the Italian legal system.

Evolutionary algorithms mimic evolutionary behaviors in order to solve problems. They have been successfully applied in many areas and appear to have a special relationship with creative problems; such a relationship, over the last two decades, has resulted in a long list of applications, including several in the field of music. In this article, we provide an evolutionary algorithm able to compose music. More specifically we consider the following 4-voice harmonization problem: one of the 4 voices (which are bass, tenor, alto, and soprano) is given as input and the composer has to write the other 3 voices in order to have a complete 4-voice piece of music with a 4-note chord for each input note. Solving such a problem means finding appropriate chords to use for each input note and also finding a placement of the notes within each chord so that melodic concerns are addressed. Such a problem is known as the unfigured harmonization problem. The proposed algorithm for the unfigured harmonization problem, named EvoComposer, uses a novel representation of the solutions in terms of chromosomes (that allows to handle both harmonic and nonharmonic tones), specialized operators (that exploit musical information to improve the quality of the produced individuals), and a novel hybrid multiobjective evaluation function (based on an original statistical analysis of a large corpus of Bach's music). Moreover EvoComposer is the first evolutionary algorithm for this specific problem. EvoComposer is a multiobjective evolutionary algorithm, based on the well-known NSGA-II strategy, and takes into consideration two objectives: the harmonic objective, that is finding appropriate chords, and the melodic objective, that is finding appropriate melodic lines. The composing process is totally automatic, without any human intervention. We also provide an evaluation study showing that EvoComposer outperforms other metaheuristics by producing better solutions in terms of both well-known measures of performance, such as hypervolume, [Formula: see text] index, coverage of two sets, and standard measures of music creativity. We conjecture that a similar approach can be useful also for similar musical problems.

Circular splicing systems are a mathematical model, inspired by a recombinant behaviour of circular DNA. They are defined by a finite alphabet A, an initial set I of circular words, and a set R of rules. A circular splicing language is a language generated by a circular splicing system. An open pro blem is to characterize regular circular splicing languages and the corresponding circular splicing systems. In this framework an important role is played by unavoidable sets. These sets have been considered in several contexts. In particular, Ehrenfeucht, Haussler and Rozenberg (1983) proved the following generalization of a famous Higman’s theorem: the quasi-order induced by insertions of words from a fixed finite set is a well-quasi-order if and only if the finite set is unavoidable. In this paper we survey the known relations between unavoidable sets and regular circular languages. Motivated by these connections we give an alternative and simpler proof of the Ehrenfeucht, Haussler and Rozenberg result. Our proof is strongly based on a known characterization of unavoidable sets in terms of graphs associated with them.

The paper presents ongoing research aiming to ease the interaction with large amounts of public sector data. The project focuses on the development of a modular online platform for both desktop and mobile devices exploiting Visual Analytics to offer citizens, researchers and policymakers crosscutting reading of heterogeneous information. We experiment new ways to integrate, analyze, and visualize administrative, legal, and economic data as they appear to be a powerful ally not only to increase the transparency of government activities but also to enhance evidence-based policy making and agenda setting.

Visual languages are studied in many different disciplines including Formal Logic. Several diagram methods have been proposed for the visual representation of the logical relations and in particular for First Order Predicate Logic (FOPL) formulas. Among these, logical symbolism, Euler diagrams, semantic networks, conceptual grids, conceptual spaces and so on. It is shown that these representations are formally equivalent and can be inter-translated algorithmically, but provide different and complementary visualizations such that the use of multiple representations may provide greater insight than any alone. We present a new visual language, V-Logic, which support different visual representation schemes: spatial, graphical and symbolic notations. It specifies rules for mapping FOPL formulas in special semantically equivalent diagrams, named V-diagrams. Logical inference based on the interpretation of a V-diagram is essentially a translation of the diagram into a logical formalism. Such a translation is natural and could be used to teach FOPL. We performed a preliminary study with 10 students. Results provided us with overall positive feedback about the effectiveness of our approach as well as further directions to explore.

Music expertise is the ability to understand the structural elements of music compositions by reading musical scores or simply listening to music performance. Although the most common way to learn music is through the study of musical scores, this approach is demanding in terms of learning ability, given the required implicit knowledge of music theoretical concepts. Learning musical rules is hard, especially for classical music. To simplify this task, visualization is one of the most promising approaches, also thanks to the human visual cognition ability (i.e., visual memory, visual attention, and so on). This work aims at building a visual tool, named VisualHarmony, to help people in composing music pieces in a quick and efficient way (i.e., avoiding specific errors as dictated by classical music theory rules). More specifically, a visualization technique able to represent harmonic structures has been evaluated by teachers of Conservatory classes and from domain experts in order to collect requirements used to define graphical features needed to facilitate the study of the rules used in classical music, and to implement VisualHarmony. We have focused our attention on a specific type of music compositions, i.e., the chorale style (4-voice music). VisualHarmony was tested in order to analyze system usability and user satisfaction. Results of these studies provided us with positive feedback about the effectiveness of the idea, the pleasantness of the graphical choices, the satisfaction of the users with regard to the easiness and the usefulness of the provided tool.

In order to master the harmonic analysis of musical compositions, a musician needs to profoundly understand the music theory, have an extensive training, and put a considerable effort in the task. For learners it can be a time-consuming and tedious task due to the steep learning curve. The idea throughout this paper is to visually annotate musical compositions with the objective to support users in performing the harmonic analysis, in which the task is mainly based on the identification of similar tonalities and relevant degrees. The paper proposes two visualisations that use rectangles to represent tonalities and the degree and exploit colours to represent similarities. The design of visualisations is based on guidelines drawn from informal interviews with teachers of the Conservatorio G. Martucci, a conservatory in Salerno, and from literature. The evaluation study by involving 30 participants showed that overall the 30 participants of the evaluation study achieved better results performing the harmonic analysis using the musical composition enhanced with visualisations compared to the standard musical composition; it is a promising result that encourages further investigation in the field.

Plagiarism, i.e., copying the work of others and trying to pass it off as one own, is a debated topic in different fields. In particular, in music field, the plagiarism is a controversial and debated phenomenon that has to do with the huge amount of money that music is able to generate. However, the existing mechanisms for plagiarism detection mainly apply superficial and brute-force string matching techniques. Such well-known metrics, widely used to discover similarities in text documents, cannot work well in discovering similarities in music compositions. Despite the wide-spread belief that few notes in common between two songs is enough to decide whether a plagiarism exists, the analysis of similarities is a very complex process. In this work, we provide novel perspectives in the field of automatic music plagiarism detection, and specifically, we propose an approach based on a fuzzy vectorial-based similarity. Given a suspicious melody, our approach envisions three steps: (1) its transformation in a vectorial representation, (2) retrieving of a list of similar melodies, (3) analysis and comparison with this subset of associated similar scores by using a fuzzy degree of similarity, that varies in a range between 0 for melodies that are fully musically different, and 1 for identical melodies. To assess the effectiveness of our system we performed tests on a large dataset of ascertained plagiarisms. Results show that it is able to reach an accuracy of 93%.

The plagiarism is a debated topic in different fields and in particular in music, given the huge amount of money that music is able to generate. Moreover, it is controversial aspect in the law's field given the subjectivity of the judges that have to pronounce on a suspicious case. Automatic detection of music plagiarism is fundamental to overcome these limits by representing an useful support for judges during their pronouncements and an important result to avoid musicians to spend more time in court than on composing and playing music. In this paper we address this issue by defining a new metric to discover pop music similarity and we study whether visualization can assist domain experts in judging suspicious cases. We describe a user study in which subjects performed different tasks on a song collection using different visual representations to investigate which one is best in terms of intuitiveness and accuracy. Results provided us with positive feedback about our choices and some useful suggestions for future directions.

Today's technology is redefining the way individuals can work, communicate, share experiences, constructively debate, and actively participate to any aspect of the daily life, ranging from business to education, from political and intellectual to social, and so on. Enabling access to technology by any individual, reducing obstacles, avoiding discrimination, and making the overall experience easier and enjoyable is an important objective of both research and industry.

Nowadays plagiarism is an interesting and debated topic in different fields. In music, the plagiarism is a very common phenomenon which touch the vast amounts of money that music melodies are able to generate in today's pop music market. In a music composition, the melody is assumed to be the most significant factor in a court's decision about whether a new music composition is an illegitimate version of a pre-existing composition. Despite the wide-spread belief that there is a fixed and trivial number of corresponding notes between two melodies, the similarity analysis is a very complex process. In this paper we address the plagiarism in pop music, and specifically, we study whether visualization can facilitate the task of discovering melodic similarities among musical songs. To investigate this, we defined three representations to show the melodic relations among songs. We performed a user study in which subjects performed different tasks on a song collection using these representations to investigate which one is best in terms of intuitiveness and accuracy. Results of the study provided us with positive feedback as well as further directions to explore.

Music expertise is the ability to understand the structural elements of music compositions by reading musical scores or even by simply listening to music performance. Although the most common way to learn music is through the study of musical scores, this approach is demanding in terms of learning ability, given the required implicit knowledge of music theoretical notations and concepts. In this work we define a two-level color-based approach, that exploits graphical visualization techniques to represent data structures of classical music, and to perform harmonic analysis of musical compositions. Our main goal is to make easier and very quick the study of classical notations (recognized as a tedious and difficult task in the field), by providing individuals with a mechanism that clarifies complex relationships in music using visual clues. We performed a preliminary study to evaluate the effectiveness of our approach as well as participants' perceptions about its usefulness and pleasantness. The results of the study provided us with overall positive feedback about the effectiveness of our approach as well as further directions to explore.

Music education for blind people heavily relies on Braille. The use of Braille for music causes difficulties for the blind student: new meanings for the Braille symbols have to be learned and the reading of the music is not immediate. More-over, in the majority of the cases, music teachers don't know Braille. Although Braille remains the primary means for music education for blind people, alternative methods can help. We propose a new methodology that helps the reading of music scores by means of a software that sings the name of the notes. Singing the name of the notes provides to a blind user a direct perception of the score. Moreover the information is directly conveyed to the student through the ear. Although the method has several limitations we believe that it is effective. The methodology is not intended to "replace" Braille, but only to offer a different approach to the study of music.

The use of computers in the production of artifacts has drawn the attention of both artists and computer scientists. Among the different art disciplines, music is one of the arts that most benefited from the use of computers. There are many works which demonstrate the great synergy between these two fields. In this paper we will focus on a specific music composition problem: the figured bass problem, in which we have to automatically generate a 4 voice piece of music, starting from an input the bass line. To solve this problem we use a hybrid strategy, in which different metaheuristics cooperate to find high quality solutions. The cooperation is controlled by means of the combination of fuzzy control and knowledge obtained through Data Mining. As will be shown in the experimental results section, this hybrid strategy is capable of finding musical solutions with an acceptable quality and never discordant which, according to experts, are sound and adhere to scholastic rule.

In this paper we consider the musical problem called unfigured bass harmonization: a bass line is given and the composer has to write other 3 voices to have a complete 4-voice piece of music with a 4-note chord for each bass note. Solving such a problem means finding appropriate chords to use for each bass note and also find a placement of the four notes within each chord so that melodic concerns are addressed, especially for the highest voice (soprano).We present a multi-objective genetic algorithm that automatically composes music when provided with a bass line input. The objectives considered are two: the harmonic objective (finding appropriate chords) and the melodic objective (find good melodic lines).