Miracle-dz/UniFAR-FTUs huggingface dataset:
Classificação de conteúdo malicioso baseado em Floresta de Caminhos Ótimos

The advent of Internet has brought widespread benefits in the areas of communication, entertainment, shopping, social relations, among others. However, several threats began to emerge in this scenario, leading researchers to create tools to deal with them. Spam, malware, malicious content, phishing, fraud and false URLs are some examples of these threats. In contrast, anti-virus systems, firewalls and intrusion detection and prevention systems are examples of tools to combat such threats. Especially since 2010, headed by the Stuxnet malware, threats have become more complex and persistent, making the tools previously used became obsolete. The reason is that such tools based on signatures and anomalies can not follow both the speed of development of the threats and their complexity. Since then, researchers have turned their attention to more effective methods to combat cyber threats. In this context, machine learning algorithms are being exploited in the search for solutions to analyze real-time threats from the internet. Therefore, this study aims to analyze the performance of classifiers based on Optimum-path Forest, OPF, comparing them with the other state-of-the-art classifiers. To do so, two features extraction methods will be analyzed: one based on tokens and other based on Ngrams, considering N equal 3. Overall, OPF stood out in not blocking legitimate messages and training time. In some bases the amount of spam classified correctly was high as well. The version that uses complete graph was better, although in some cases the version that makes use of knn graph outperformed it. Due to the current demands on security issues, OPF, considering its fast training time, can be improved in its effectiveness aiming at a real application. In relation to feature extraction methods, 3gram was better, improving OPF’s results.
Computational models for first language acquisition

This work investigates a computational model of first language acquisition; the Categorial Grammar Learner or CGL. The model builds on the work of Villavicenio, who created a parametric Categorial Grammar learner that organises its parameters into an inheritance hierarchy, and also on the work of Buszkowski and Kanazawa, who demonstrated the learnability of a k-valued Classic Categorial Grammar (which uses only the rules of function application) from strings. The CGL is able to learn a k-valued General Categorial Grammar (which uses the rules of function application, function composition and Generalised Weak Permutation). The novel concept of Sentence Objects (simple strings, augmented strings, unlabelled structures and functor-argument structures) are presented as potential points from which learning may commence. Augmented strings (which are strings augmented with some basic syntactic information) are suggested as a sensible input to the CGL as they are cognitively plausible objects and have greater information content than strings alone. Building on the work of Siskind, a method for constructing augmented strings from unordered logic forms is detailed and it is suggested that augmented strings are simply a representation of the constraints placed on the space of possible parses due to a string’s associated semantic content. The CGL makes crucial use of a statistical Memory Module (constructed from a Type Memory and Word Order Memory) that is used to both constrain hypotheses and handle data which is noisy or parametrically ambiguous. A consequence of the Memory Module is that the CGL learns in an incremental fashion. This echoes real child learning as documented in Brown’s Stages of Language Development and also as alluded to by an included corpus study of child speech. Furthermore, the CGL learns faster when initially presented with simpler linguistic data; a further corpus study of child-directed speech suggests that this echos the input provided to children. The CGL is demonstrated to learn from real data. It is evaluated against previous parametric learners (the Triggering Learning Algorithm of Gibson and Wexler and the Structural Triggers Learner of Fodor and Sakas) and is found to be more efficient.
SEMANTIC QUANTIFICATION OF TEXT USING HETEROGENEOUS INFORMATION GRAPHS

Machines are one of the communication media, when we start communicate with the system it leads to the improper communication because, systems can understand only the syntactic structure of the text but it fails to understand the meaning of the text. Having an automated system for making the systems to understand the text language semantics role is very desirable because natural languages are not free from uncertainty so, it leads to the human being to understand the same meaning text in different manner. To over come this problem we are going to propose a new framework for making the machine to understand text meaning by semantically quantifying the text using heterogeneous information graphs, because it will connect the text semantically in multiple way so, we can extract meaningful text more efficiently. The quantification of text using HIG has attracted in the field of artificial intelligence, text mining, machine learning system, social - media, semantic search engines.
Research on feature extraction method based on clustering and PCA fusion

In allusion to the limitation of Principal Component Analysis (PCA) in solving the multiple correlations between variables,this paper presents an improved PCA feature extraction method based on K-maxmin clustering,which combines with RelieF algorithm to remove non-correlative features for improving efficiency and accuracy farther.Experimental results show that this method is better than traditional PCA method in feature extraction.
Use of a sonification system for science learning by people who are blind

Purpose The purpose of this paper is to examine a central need of students who are blind: the ability to access science curriculum content. Design/methodology/approach Agent-based modeling is a relatively new computational modeling paradigm that models complex dynamic systems. NetLogo is a widely used agent-based modeling language that enables exploration and construction of models of complex systems by programming and running the rules and behaviors. Sonification of variables and events in an agent-based NetLogo computer model of gas in a container is used to convey phenomena information. This study examined mainly two research topics: the scientific conceptual knowledge and systems reasoning that were learned as a result of interaction with the listen-to-complexity (L2C) environment as appeared in answers to the pre- and post-tests and the learning topics of kinetic molecular theory of gas in chemistry that was learned as a result of interaction with the L2C environment. The case study research focused on A. , a woman who is adventitiously blind, for eight sessions. Findings The participant successfully completed all curricular assignments; her scientific conceptual knowledge and systems reasoning became more specific and aligned with scientific knowledge. Practical implications A practical implication of further studies is that they are likely to have an impact on the accessibility of learning materials, especially in science education for students who are blind, as equal access to low-cost learning environments that are equivalent to those used by sighted users would support their inclusion in the K-12 academic curriculum. Originality/value The innovative and low-cost learning system that is used in this research is based on transmittal of visual information of dynamic and complex systems, providing perceptual compensation by harnessing auditory feedback. For the first time the L2C system is based on sound that represents a dynamic rather than a static array. In this study, the authors explore how a combination of several auditory representations may affect cognitive learning ability.
Classic and convex non-negative matrix visualization in clustering two benchmark data

Both the classic and the convex NMF (Nonnegative Matrix Factorization) yield a parsimonious, lower rank representation of the data. They may yield also an indication on a soft clustering of the data vectors, We analyze two sets of diagnostic data, wine and sonar, for which the classic and convex nonnegative matrix factorization (NMF) behave differently when indicating group membership of the data vectors. The data are given as mxn matrices, with columns denoting objects, and rows their attributes. We assess the clustering by multivariate graphical visualization methods. Streszczenie. Dla wybranych danych ’wine’ i ’sonar’ znajdujemy – za pomoca̧ NMF (nieujemna faktoryzacja macierzy) – ukryta̧ strukturȩ tych macierzy oraz wskazania co do klasteryzacji obiektów przedstawianych w kolumnach danych. Otrzymana̧ klasteryzacjȩ potwierdzamy trzema metodami wielozmiennej wizualizacji wektorów danych. (Klasteryzacja przy użyciu klasycznej i typu convex nieujemnej faktoryzacji macierzy na przykładzie dwóch zbiorów danych)
Social User Mining: User Profiling of Social Media Network Based on Multimedia Data Mining

In recent years, the pervasive use of social media has generated extraordinary amounts of data that has started to gain an increasing amount of attention. Each social media source utilizes different data types such as textual and visual. For example, Twitter is used to transmit short text messages, whereas Flickr is used to convey images and videos. Moreover, Facebook uses all of these data types. From the social media users’ standpoint, it is highly desirable to find patterns from different data formats. The result of the huge amount of data from different sources or types has provided many opportunities for researchers in the fields of data mining and data analytics. Not only the methods and tools to organize and manage such data have become extremely important, but also methods and tools to discover hidden knowledge from such data, which can be used for a variety of applications. For example, the mining of a user's profile on social media could help to discover any missing information, including the user's location or gender information. However, the task of developing such methods and tools is very challenging. Social media data is
Bayesian analysis of heterogeneous reliability data for vulnerability assessment

Abstract Bayesian models are already present in almost every branch of reliability theory and its applications. This class of models provides an easy and intuitive way to infer about the issue at hand with a clear handling of data uncertainty. It is often the case that data used for the systems reliability assessment come from more than one information source. Whether it is power plants at different geographical locations, gas transmission pipelines operating in different environment or power transmission networks deployed within various areas. Therefore, different operating conditions, varying maintenance programs and efficiencies have their share in influencing the vulnerability and variability of reliability data. However, in practice, it is usually the case that this heterogeneity is neglected leading to the underestimation of underlying uncertainty of the data. Bayesian models are capable of dealing with this kind of uncertainty as opposed to the frequentists’ statistical methods. Hierarchical Bayesian modelling technique provides means to quantify not only within-source, but also between-source uncertainties. Even in the case of small data samples, it performs well, unlike for example the classical likelihood method which may provide degenerate estimates. In this paper, authors investigate the possibility to incorporate this kind of uncertainty into the systems reliability and vulnerability assessment through the Bayesian framework in several cases: instrumentation and control components of nuclear power plants, gas transmission networks and power transmission grids.
Analyse de la Voix au Stade Débutant de la Maladie de Parkinson et Corrélations avec Analyse clinique et Neuroimagerie

Les modifications de la voix, connues sous le terme de dysarthrie hypokinetique,sontun des premiers symptomesa apparaitredansla maladie de Parkinson. Les correlations entre des parametres vocaux etles atteintes du systeme dopaminergiquemises en evidence parla 123-IIoflupane tomoscintigraphie d'emission monophotonique (DAT) oul'IRM sensible a la neuromelaninen'ont a notre connaissancejamais ete etudiees. Dans ce but nous avons enregistre et analyse la voix de 103 sujets atteints de maladie de Parkinson a un stade debutant et 39 sujets sains. Nous avons ensuite etudie les correlations entre d'une part les parametres vocaux et d'autre part des donnees cliniques (des scores issus de tests moteurs)etdes donnees de neuroimagerie(DATet IRM sensible a la neuromelanine). Nous avons au final pu identifier un set de parametres vocaux permettant de predire lineairement de facon significative les resultats moteurs et d'imagerie.Ces parametres vocaux pourraient a terme etre utilises comme biomarqueurs, parmi d'autres, de detection precoce et de suivi de la maladie de Parkinson
RUMBA: Runtime Monitoring and Behavioral Analysis Framework for Java Software Systems

A goal of runtime monitoring is to observe software execution to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing prevention activities against catastrophic failure. Although runtime monitoring has been in use for so many years, there is renewed interest in its application largely because of the increasing complexity and ubiquitous nature of software systems. To address such a demand for runtime monitoring and behavioral analysis of software systems, we present RUMBA framework. It utilizes a synergy between static and dynamic analyses to evaluate whether a program behavior complies with specified properties during its execution. The framework is comprised of three steps, namely: i) Extracting Architecture where reverse engineering techniques are used to extract two meta-models of a Java system by utilizing UML-compliant and graph representations of the system model, ii) Seeding Objectives in which information required for filtering runtime events is obtained based on properties that are defined in OCL (Object Constraint Language) as specifications for the behavioral analysis, and iii) Runtime Monitoring and Analysis where behavior of the system is monitored according to the output of the previous stages, and then is analyzed based on the objective properties. The first and the second stages are static while the third one is dynamic. A prototype of our framework has been developed in Java programming language. We have performed a set of empirical studies on the proposed framework to assess the techniques introduced in this thesis. We have also evaluated the efficiency of the RUMBA framework in terms of processor and memory utilization for the case study applications.
Active Learning and Collaborative Filtering

Choosing the right product to consume or purchase is nowadays a challenging problem due to the growing variety of eCommerce services and the informational globalization. Recommender Systems (RSs) aim at addressing this problem providing personalized suggestions for digital content, products or services. In this paper we are concerned with collaborative filtering (CF) RSs (Koren, 2008); they use item ratings provided by a population of users to predict unknown ratings. In fact, the rating prediction accuracy and consequently the quality of the recommendations (i.e., items with the largest predicted ratings) depends not only on the characteristics of the prediction algorithm, but also on the available ratings. The more ratings are available and the more information they bring, the higher the recommendation accuracy is. Therefore, it is very important for a RS to keep acquiring new and useful ratings in order to maintain and even improve the quality of the recommendations.
Morphological Disambiguation using Probabilistic Sequence Models

Amorphological tagger is a computer program that provides complete morphological descriptions of sentences. Morphological taggers find applications in many NLP fields. For example, they can be used as a pre-processing step for syntactic parsers, in information retrieval and machine translation. The task of morphological tagging is closely related to POS tagging but morphological taggers provide more finegrained morphological information than POS taggers. Therefore, they are often applied to morphologically complex languages, which extensively utilize inflection, derivation and compounding for encoding structural and semantic information. This thesis presents work on data-driven morphological tagging for Finnish and other morphologically complex languages. There exists a very limited amount of previous work on data-driven morphological tagging for Finnish because of the lack of freely available manually prepared morphologically tagged corpora. The work presented in this thesis is made possible by the recently published Finnish dependency treebanks FinnTreeBank and Turku Dependency Treebank. Additionally, the Finnish open-source morphological analyzer OMorFi is extensively utilized in the experiments presented in the thesis. The thesis presents methods for improving tagging accuracy, estimation speed and tagging speed in presence of large structured morphological label sets that are typical for morphologically complex languages. More specifically, it presents a novel formulation of generative morphological taggers using weighted finite-state machines and applies finite-state taggers to context sensitive spelling correction of Finnish. The thesis also explores discriminative morphological tagging. It presents structured sub-label dependencies that can be used for improving tagging accuracy. Additionally, the thesis presents a cascaded variant of the averaged perceptron tagger. In presence of large label sets, a cascaded design results in substantial reduction of estimation speed compared to a standard perceptron tagger. Moreover, the thesis explores pruning strategies for perceptron taggers. Finally, the thesis presents the FinnPos toolkit for morphological tagging. FinnPos is an open-source state-of-the-art averaged perceptron tagger implemented by the author.
Mobile Autonomous School and Its Application

Firstly,to study the development of principles and main features,discusses the system architecture,including the development environment,the directory structure,the reference framework,database architecture.Secondly,to explore ways of teaching and learning mode mobile autonomous school with the support of "four classes" model.Third,based on a database generated by the system is running,study the association rules based on multi-valued attributes associated with the dig,according to data analysis model,and the actual database as the basis for the experiment.
Multi-label Classification of Croatian Legal Documents Using EuroVoc Thesaurus

The automatic indexing of legal documents can improve access to legislation. EuroVoc thesaurus has been used to index documents of the European Parliament as well as national legislative. A number of studies exists that address the task of automatic EuroVoc indexing. In this paper we describe the work on EuroVoc indexing of Croatian legislative documents. We focus on the machine learning aspect of the problem. First, we describe the manually indexed Croatian legislative documents collection, which we make freely available. Secondly, we describe the multi-label classification experiments on this collection. A challenge of EuroVoc indexing is class sparsity, and we discuss some strategies to address it. Our best model achieves 79.6% precision, 60.2% recall, and 68.6% F1-score.
Analysis Of Preventive Intervention Data Using Mixture Modeling In Mplus

2 • Inefficient dissemination of statistical methods: – Many good methods contributions from biostatistics, psychometrics, etc are underutilized in practice • Fragmented presentation of methods: – Technical descriptions in many different journals – Many different pieces of limited software • Mplus: Integration of methods in one framework – Easy to use: Simple, non-technical language, graphics – Powerful: General modeling capabilities Mplus Background
Race Differences In Intelligence

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Scalable Query Evaluation over Complex Probabilistic Databases

The age of Big Data has brought with itself datasets which are not just big, but also much more complicated. These datasets are constructed from disparate, unreliable and noisy sources, many times in an ad-hoc way because careful data cleaning and integration is too time consuming and not always necessary anymore. Representing the uncertainty hidden in these datasets is necessary to get meaningful query answers and Probabilistic Databases have come up as arguably the most popular solution to this problem. Their application to practical problems though has been held back because (i) the common models they use are not rich enough to capture the dependencies in these problems, and (ii) unlike traditional databases, query evaluation for probabilistic databases can be very expensive and unpredictable. This dissertation addresses these challenges by first proposing a new model for probabilistic databases that is rich enough to capture the dependencies found in most practical applications, while still allowing for a translation to considerably simpler and well-studied models. Our model leverages existing models from AI literature that combine probability theory with logic. The main challenge of query evaluation over probabilistic databases is that it requires solving probabilistic inference which is a notoriously hard problem. This dissertation studies this problem via both (i) foundational results that give new theoretical insights about existing probabilistic inference algorithms, like Read-Once Formulas, Tree-Decompositions, Binary Decision Diagrams, Negation Normal Forms, when applied to the setting of probabilistic databases, which as we will see have their own distinct challenges and expectations, and (ii) building a robust system where the above ideas are leveraged for efficient and reliable query evaluation.
The Interactive Mechanism of Static and Dynamic Analysis in the Reverse Analysis of Embedded Software

Because the software reverse analysis method which combined the dynamic and static analyses lacks normative interactive mode, the work of the software reverse analysis is inefficient, and its reusability is poor. Based on dynamic and static analysis process of the embedded software, three kinds of interactive mechanism are proposed, including Static To Dynamic (STD), Dynamic To Static (DTS), Static and Dynamic simultaneous (SDM), and has also presented the method of realizing these three interaction mechanisms in detail. The test results show that interactive mechanisms of STD, DTS and SDM are suitable for correction of abnormal nodes in the results of static analysis, optimization of dynamic information extraction, identification of hidden codes and so on. It can greatly improve work efficiency of the embedded software reverse analysis.