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The prevalence of hate speech leads to an increase in hate crimes, online violence, and serious harm to social safety, physical security, and cyberspace. To address this issue, several studies have been conducted on hate speech detection in European languages, whereas little attention has been paid to low-resource South Asian languages, making social media vulnerable for millions of users. Due to the scarcity of the datasets and the samples available, there is a need to apply some strategies to increase the data samples. In this paper, we improved the performance of the already fine-tuned m-Bert model by applying data augmentation techniques to one of the datasets on hate speech on tweets in Roman Urdu language. F1-score and accuracy matrix have been used to compare the results. We also experiment to determine the optimal percentage of augmented data to be included and the percentage of words augmented in each instance of data. The new RUHSOLD++ Dataset containing the augmented data has also been published publicly. The improvement in hate speech detection of the model proved that the performance of the models can be improved by applying data augmentation techniques to the dataset with a limited number of instances.

This research focuses on the identification and causality analysis of coherent structures that arise in turbulent flows in square and rectangular ducts. Coherent structures are first identified from direct numerical simulation data via proper orthogonal decomposition (POD), both by using all velocity components, and after separating the streamwise and secondary components of the flow. The causal relations between the mode coefficients are analysed using pairwise-conditional Granger causality analysis. We also formulate a nonlinear Granger causality analysis that can account for nonlinear interactions between modes. Focusing on streamwise-constant structures within a duct of short streamwise extent, we show that the causal relationships are highly sensitive to whether the mode coefficients or their squared values are considered, whether nonlinear effects are explicitly accounted for, and whether streamwise and secondary flow structures are separated prior to causality analyses. We leverage these sensitivities to determine that linear mechanisms underpin causal relationships between modes that share the same symmetry or anti-symmetry properties about the corner bisector, while nonlinear effects govern the causal interactions between symmetric and antisymmetric modes. In all cases, we find that the secondary flow fluctuations (manifesting as streamwise vorticial structures) are the primary cause of both the presence and movement of near-wall streaks towards and away from the duct corners., QC 20240527

Contains fulltext : 296500.pdf (Publisher’s version ) (Open Access), Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices. MATERIAL AND METHODS: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology. RESULTS: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data. DISCUSSION: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries.

Lo studio si occupa della digitalizzazione dell'amministrazione giudiziaria civile, con l'obiettivo di migliorare l'efficienza del sistema. Tuttavia, si delinea anche una prospettiva più ampia a livello europeo, dove la digitalizzazione è vista come un mezzo per migliorare l'accesso alla giustizia. Questo concetto, ampiamente discusso nella letteratura e a livello istituzionale in UE come in Italia, mira a soddisfare i bisogni dei destinatari delle decisioni giudiziarie, garantendo loro un accesso equo e efficace al sistema giudiziario. Questo studio esplora, dunque, il connubio tra digitalizzazione e accesso alla giustizia, evidenziando la necessità di un equilibrio tra l'efficienza del sistema e la soddisfazione dei bisogni dei cittadini.

Item does not contain fulltext, Exa-scale volumes of medical data have been produced for decades. In most cases, the diagnosis is reported in free text, encoding medical knowledge that is still largely unexploited. In order to allow decoding medical knowledge included in reports, we propose an unsupervised knowledge extraction system combining a rule-based expert system with pre-trained Machine Learning (ML) models, namely the Semantic Knowledge Extractor Tool (SKET). Combining rule-based techniques and pre-trained ML models provides high accuracy results for knowledge extraction. This work demonstrates the viability of unsupervised Natural Language Processing (NLP) techniques to extract critical information from cancer reports, opening opportunities such as data mining for knowledge extraction purposes, precision medicine applications, structured report creation, and multimodal learning. SKET is a practical and unsupervised approach to extracting knowledge from pathology reports, which opens up unprecedented opportunities to exploit textual and multimodal medical information in clinical practice. We also propose SKET eXplained (SKET X), a web-based system providing visual explanations about the algorithmic decisions taken by SKET. SKET X is designed/developed to support pathologists and domain experts in understanding SKET predictions, possibly driving further improvements to the system.

We examine the effects of three basic but effective control strategies, namely uniform blowing, uniform suction, and body-force damping, on the intense Reynolds-stress events in the turbulent boundary layer (TBL) developing on the suction side of a NACA4412 airfoil. This flow is subjected to a non-uniform adverse pressure gradient (APG), which substantially modifies its turbulence statistics with respect to a zero-pressure-gradient (ZPG) boundary layer, and it also changes how control strategies affect the flow. The strong APG results in intense events that are shorter and more often detached from the wall than in ZPG TBLs. In a quadrant analysis, ejections remain the most relevant structures, but sweeps become more important than in ZPG TBLs, a fact that results in a lower contribution to the wall-normal velocity from intense Reynolds-stress events. Control effects are relatively less important on intense events than on the turbulent statistics. Uniform blowing has an impact similar to that of an even more intense APG, while uniform suction has more complex effects, most likely due to the particular behavior of the wall-normal velocity component near the wall. Body-force damping also reduces the probability of occurrence of very-large attached structures and that of intense events in the proximity of the actuation region. Our results show that intense Reynolds-stress events are robust features of the flow. If control strategies do not target directly these structures, their effects on the strong events is less pronounced than the effects on the mean flow., QC 20230510

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future. This phenomenon is mostly due to the daily collection of telecommunication data from mobile phones and other location-aware devices and is expected to enable novel classes of applications based on the extraction of behavioral patterns from mobility data. Such patterns could be used for instance in traffic and sustainable mobility management (e.g., to study the accessibility to services), urban planning, environmental monitoring, and collaborative location-based services. Clearly, in these applications privacy is a concern, since some knowledge may be sensitive, or an over-specific pattern may reveal the behaviour of groups of few individual. In this paper we focus on automated privacy-preserving methods we developed for extracting and sharing user-consumable forms of knowledge from large amounts of raw data referenced in space and in time. ©2007 IEEE., IEEE Computer Society;Informatik-Forum Stuttgart;NEC Europe Ltd. Network Laboratories

Spatio-temporal traces left behind by moving individuals are increasingly available. On the one hand, mining this kind of data is expected to produce interesting behavioral knowledge enabling novel classes of mobility applications; but on the other hand, due to the peculiar nature of position data, mining it creates important privacy concerns. Thus, studying privacy preserving data mining methods for moving object data is interesting and challenging. In this paper, we address the problem of hiding sensitive trajectory patterns from moving objects databases. The aim is to modify the database such that a given set of sensitive trajectory patterns can no longer be extracted, while the others are preserved as much as possible. We provide the formal problem statement and show that it is NP-hard; so we devise heuristics and a polynomial sanitization algorithm. We discuss a possible attack to our model, that exploits the knowledge of the underlying road network, and we enhance our model to protect from this kind of attacks. Experimental results show the effectiveness of our proposal. © 2007 IEEE.

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future. This phenomenon is mostly due to the daily collection of telecommunication data from mobile phones and other location-aware devices and is expected to enable novel classes of applications based on the extraction of behavioral patterns from mobility data. Such patterns could be used for instance in traffic and sustainable mobility management (e.g., to study the accessibility to services), urban planning, environmental monitoring, and collaborative location-based services. Clearly, in these applications privacy is a concern, since some knowledge may be sensitive, or an over-specific pattern may reveal the behaviour of groups of few individual. In this paper we focus on automated privacy-preserving methods we developed for extracting and sharing user-consumable forms of knowledge from large amounts of raw data referenced in space and in time. ©2007 IEEE., IEEE Computer Society;Informatik-Forum Stuttgart;NEC Europe Ltd. Network Laboratories

Spatio-temporal traces left behind by moving individuals are increasingly available. On the one hand, mining this kind of data is expected to produce interesting behavioral knowledge enabling novel classes of mobility applications; but on the other hand, due to the peculiar nature of position data, mining it creates important privacy concerns. Thus, studying privacy preserving data mining methods for moving object data is interesting and challenging. In this paper, we address the problem of hiding sensitive trajectory patterns from moving objects databases. The aim is to modify the database such that a given set of sensitive trajectory patterns can no longer be extracted, while the others are preserved as much as possible. We provide the formal problem statement and show that it is NP-hard; so we devise heuristics and a polynomial sanitization algorithm. We discuss a possible attack to our model, that exploits the knowledge of the underlying road network, and we enhance our model to protect from this kind of attacks. Experimental results show the effectiveness of our proposal. © 2007 IEEE.

Automatic music classification has been of interest since digital data about music became available within the Web. For this task, different automatic classification approaches have been proposed but all existing approaches are based on the analysis of sounds. To the best of our knowledge, there is no automatic solution that considers only the sheet music for classification. Therefore, within the following study, we introduce a machine-learning based approach in order to assign an author to new sheet music. Different features, that best represent the style of a writer has been extracted, and are given in input for training to a kNN algorithm. In addition, the article discusses the results and cases when the classifier fails to assign the right author.

Reynolds-number effects in the adverse-pressure-gradient (APG) turbulent boundary layer (TBL) developing on the suction side of a NACA4412 wing section are assessed in the present work. To this end, we analyze four cases at Reynolds numbers based on freestream velocity and chord length ranging from Re-c = 100, 000 to 1,000,000, all of them with 5 degrees angle of attack. The results of four well-resolved large-eddy simulations (LESs) are used to characterize the effect of Reynolds number on APG TBLs subjected to approximately the same pressure-gradient distribution (defined by the Clauser pressure-gradient parameter beta). Comparisons of the wing profiles with zero pressure-gradient (ZPG) data at matched friction Reynolds numbers reveal that, for approximately the same beta distribution, the lower-Reynolds-number boundary layers are more sensitive to pressure-gradient effects. This is reflected in the values of the inner-scaled edge velocity U-e(+), the shape factor H, the components of the Reynolds-stress tensor in the outer region and the outer-region production of turbulent kinetic energy. This conclusion is supported by the larger wall-normal velocities and outer-scaled fluctuations observed in the lower-Re-c cases. Thus, our results suggest that two complementing mechanisms contribute to the development of the outer region in TBLs and the formation of large-scale energetic structures: one mechanism associated with the increase in Reynolds number, and another one connected to the APG. Future extensions of the present work will be aimed at studying the differences in the outer-region energizing mechanisms due to APGs and increasing Reynolds number., QC 20180911

A switchable interaction between pairs of highly coherent qubits is a crucial ingredient for the physical realization of quantum information processing. One promising route to enable quantum logic operations involves the use of nuclear spins as protected elementary units of information, qubits. Here we propose a simple way to use fast electronic spin excitations to switch the effective interaction between nuclear spin qubits and the realization of a two-qubit molecular architecture based on highly coherent vanadyl moieties to implement quantum logic operations. Controlled generation of entanglement between qubits is possible here through chemically tuned magnetic coupling between electronic spins, which is clearly evidenced by the splitting of the vanadium(iv) hyperfine lines in the continuous-wave electron paramagnetic resonance spectrum. The system has been further characterized by pulsed electron paramagnetic resonance spectroscopy, evidencing remarkably long coherence times. The experimentally derived spin Hamiltonian parameters have been used to simulate the system dynamics under the sequence of pulses required to implement quantum gates in a realistic description that includes also the harmful effect of decoherence. This demonstrates the possibility of using this molecular complex to implement a control-Z (CZ) gate and simple quantum simulations. Indeed, we also propose a proof-of-principle experiment based on the simulation of the quantum tunneling of the magnetization in a S = 1 spin system.