Miracle-dz/UniFAR-FTUs huggingface dataset:
Path Planning of Mobile Robot with Growing Neural Gas and Ant Colony Optimization

This paper presents a new path planning strategy obtained from the combination between Growing Neural Gas (GNG) and Ant Colony Optimization (ACO) algorithms. The proposed strategy was tested in a real mobile robot for two diferent scenarios and compared with the APF approach. Positive and negative points of the proposed strategy are highlighted throughout the text. As one positive point, the proposed path planning strategy presented a better end positioning of the robot in comparison with APF in the performed tests - an aspect of great interest for practical applications in industry and medical area.
Saliency Detection via Multi-Scale Global Cues

The saliency detection technologies are very useful to analyze and extract important information from given multimedia data, and have already been extensively used in many multimedia applications. Past studies have revealed that utilizing the global cues is effective in saliency detection. Nevertheless, most of prior works mainly considered the single-scale segmentation when the global cues are employed. In this paper, we attempt to incorporate the multi-scale global cues for saliency detection problem. Achieving this proposal is interesting and also challenging (e.g., How to obtain appropriate foreground and background seeds effectively? How to merge rough saliency results into the final saliency map efficiently? ). To alleviate the challenges, we present a three-phase solution that integrates several targeted strategies, first, a self-adaptive strategy for obtaining appropriate filter parameters; second, a cross-validation scheme for selecting appropriate background and foreground seeds; and third, a weight-based approach for merging the rough saliency maps. Our solution is easy to understand and implement, but without loss of effectiveness. Extensive experimental results based on benchmark datasets demonstrate the feasibility and competitiveness of our proposed solution.
Object-oriented data modeling

The object-oriented paradigm models the local behavior, and to a lesser extent, the structure of an application. Semantic data models describe structure and semantics. This paper unifies the behavioral aspects of the object-oriented paradigm with the structural and semantic aspects of semantic data models. The modeling approach contains expressive abstractions to model static and derived data, semantics, and behavior. The abstractions keep the data model closer to the problem domain than the entity-relationship or relational models, and in a form that can readily be translated into a relational (or other) implementation. The paper makes six principal contributions. First, a comprehensive set of data structuring abstractions originating from research into semantic data models, (AI) knowledge representation, CAD/CAM applications, and object-oriented programming languages are described. Second, the abstractions are compared to the entity-relationship and relational models. Third, semantic information inherent in the functional representation of the structuring abstractions is identified. Fourth, a set of behavioral abstractions originating from semantic data models, CAD/CAM applications, and object-oriented programming languages are described. Fifth, an algorithm that describes the dynamics between mathematically derived attributes of cooperating objects is presented. The functional dependencies between derived attributes themselves are modeled using a transform-centered approach. Sixth, some weaknesses of object-oriented programming languages with respect to supporting the structuring abstractions, semantic constructs, and behavioral abstractions are identified.
Mobile application for find alumni using social media application programming interface

The purpose of this research is to build a system that can determine the educational information of a Facebook user account even though the school information is hidden. Facebook as a social media that provides features for users to display user data in detail including the school or college a person is coming from. But the problem occurs when user privacy is enabled so that it cannot be found by other users and also search results that are still too broad in looking for someone. To determine the alumni then made an assessment of the peoples based on existing data derived from social media users. The attribute used to determine the calculation of the alumni information is school name, friend status, user hometown, user current location, user event page, and user like page. The calculation result show that if more number of event page and like the same page between user, then about more than 80% chance of both user have ever or is studying in same school. Furthermore, the results of this study indicate that the attributes of current location and hometown play an important role to determine between users coming from the same school.
Action recognition based on feature-level fusion

In this paper, we propose a new effective and robust framework to recognize human actions from depth map sequence. Firstly, 3D motion trail model (3DMTM) is extracted to represent the temporal motion information. Then, two effective heterogeneous features are proposed to descried actions more comprehensive based on 3DMTM. By computing Multilayer Histograms of Oriented Gradient (MHOG) on 3DMTM, 3DMTM-MHOG is obtained to describe local detail information of different actions. Combining Gist and 3DMTM, we can get 3DMTM-Gist to model holistic structural feature of actions. The feature-level fusion method is utilized to merge two descriptors to form the final feature. Lastly, support vector machine (SVM) classification is used for multi-class action recognition. Experimental results on public depth action dataset (MSR Action3D dataset) show that our method is superior to the state-of-the-art methods.
Dynamic threshold location algorithm based on fingerprinting method

The weighted K‐nearest neighbor (WKNN) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm (DH‐KNN) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points (RPs). We compare its performance with the WKNN and Enhanced K‐Nearest Neighbor (EKNN) algorithms in test spaces of networks with dimensions of 20 m × 20 m, 30 m × 30 m, 40 m × 40 m and 50 m × 50 m. Simulation results show that the maximum position accuracy of DH‐KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well‐known algorithms.
Research on the Stability of Multi PV Inverters Connected to Weak Grid Based on Impedance Analysis

Abstract. In paralleled grid connected PV inverters system, when the design of the inverter control system is connected with the weak grid, the control performance will become poor, and even the operation is not stable. In this paper, a method of impedance analysis is presented, and the equivalent impedance of inverter is derived by using the small signal model of the inverter, which is used to analyze the stability of inverter parallel system. And this paper proposes that system can be stabilized by changing the phase locked loop design of the inverter. Simulation results verify the validity and feasibility of the theoretical analysis and the proposed method.
Design of active power filter for narrow-band power line communications

In power line communication (PLC), couplers such as coupling transformers and band-pass matching coupling circuits are usually required for coupling, band-pass filtering, and impedance matching. However, the cost and size of transformers prevent them from being an economic and compact solution for PLC couplers. In addition, passive band-pass matching coupling circuits need accurate impedance matching and possibly incur power losses. In this paper, a 6th order multiple feedback (MFB) active power filter with the minimum number of components was designed for narrow-band PLC, which has high input impedance and low output impedance, allowing outstanding performance in main voltage isolation, suppressing the current-harmonics and compensating the reactive power simultaneously. Finally, simulations were conducted in the range of 95 kHz-125 kHz (CENELEC “B-band”), which confirmed that the new filter met the CENELEC requirements for transmission and disturbance levels.
Scalable Wavelet-Based Coding of Irregular Meshes With Interactive Region-of-Interest Support

This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 106 ~ 107 vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices.
A robust deep convolutional neural network model for text categorization

Categorizacao de textos e uma das tarefas mais importantes nas aplicacoes do dominio do Processamento de Linguagem Natural (PLN), a qual consiste em associar automaticamente categorias pre-definidas a documentos escritos em linguagem natural. Tecnicas tradicionais de aprendizado de maquina utilizam caracteristicas elaboradas manualmente para a construcao dos modelos, tais como, n-gramas, palavras de negacao, sinais de pontuacao, simbolos representando emocoes, palavras alongadas e dicionarios lexicos. Esta abordagem, chamada de engenharia de caracteristicas, alem de requerer um trabalho arduo, resulta geralmente em modelos que apresentam uma performance ruim em tarefas para os quais nao foram especificamente criados. Neste trabalho, propomos um modelo robusto baseado em uma Rede Neural de Convolucao (RNC) profunda para aprendizado chamado de PLN profundo. Nosso modelo utiliza uma abordagem composicional, na qual o projeto da arquitetura da RNC profunda induz a criacao de uma representacao hierarquica para o texto atraves da descoberta de representacoes intermediarias para as palavras e sentencas do texto. As representacoes iniciais para as palavras, chamadas de incorporacao de palavras, sao obtidas de um modelo de linguagem neural treinado previamente de forma nao supervisionada, as quais sao ajustadas para o contexto da tarefa para o qual o modelo esta sendo treinado. O nosso modelo foi avaliado comparando sua acuracia com os resultados publicados para alguns modelos tradicionais e de aprendizado profundo em tarefas de categorizacao de textos utilizando seis conjuntos de dados de larga escala. Os resultados mostram que nosso modelo e robusto no sentido de que, mesmo quando nos utilizamos os mesmos parâmetros globais, ele supera a acuracia dos modelos considerados estados da arte em diferentes tarefas de categorizacao de textos. Os resultados tambem mostram que a utilizacao de um dicionario de sinonimos semânticos juntamente com as representacoes iniciais de palavras ajuda na generalizacao das representacoes aprendidas pelo modelo, aumentando sua acuracia.
Segmentation algorithms of subcortical brain structures on MRI : a review

Abstract This work covers the current state of the art with regard to approaches to segment subcortical brain structures. A huge range of diverse methods have been presented in the literature during the last decade to segment not only one or a constrained number of structures, but also a complete set of these subcortical regions. Special attention has been paid to atlas based segmentation methods, statistical models and deformable models for this purpose. More recently, the introduction of machine learning techniques, such as artificial neural networks or support vector machines, has helped the researchers to optimize the classification problem. These methods are presented in this work, and their advantages and drawbacks are further discussed. Although these methods have proved to perform well, their use is often limited to those situations where either there are no lesions in the brain or the presence of lesions does not highly vary the brain anatomy. Consequently, the development of segmentation algorithms that can deal with such lesions in the brain and still provide a good performance when segmenting subcortical structures is highly required in practice by some clinical applications, such as radiotherapy or radiosurgery.
Function Embedding Generation Using Program Dependency Graph Based Neural Network

Author(s): Srivastava, Abhishek Kumar | Advisor(s): Yin, Heng | Abstract: Analyzing software binaries can be helpful in tackling important problems such as plagiarism, malware or vulnerability detection. Detecting similarity between two binary functions coming from different sources can be done using binary code similarity detection. Existing approaches use Control-Flow graph information of binaries in some way or another i.e either graph matching or control-block embedding which is either slow or does not utilize all the information. In this work we propose novel way to use program dependency graph of functions to extract control and data dependency information and generate its embedding with help of Neural Network using this information. Measuring the distance between embedding of different binary functions can evaluate their similarity. Since this method does not rely on internal flow structure of the function it can be applied to more generally and is resilient to different compiler optimizations and heavy obfuscation techniques.
Uma abordagem de detecção automática de vandalismo na Wikipédia utilizando aprendizado associativo ativo

A Wikipedia e outros servicos gratuitos cujo conteudo e gerado colaborativamente tem crescido rapidamente em popularidade. No entanto, a falta de controle da edicao tem feito com que esses servicos sejam vulneraveis a varios tipos de acoes maliciosas como o vandalismo. Metodos de deteccao de vandalismo de estado-de-arte sao baseados em tecnicas supervisionadas, e portanto, dependem de colecoes de treinamento geralmente grandes e representativas. A construcao de tais colecoes depende, muitas vezes, de um esforco conjunto (crowdsourcing), sendo ssim caras de construir. Mais ainda, no caso especifico de vandalismo, as colecoes disponiveis tendem a ser muito desbalanceadas com muito poucos exemplos de vandalismo, o que afeta o processo da classificacao. Visando diminuir o custo da construcao das colecoes representativas para esse problema, apresentamos uma nova tecnica de selecao ativa juntamente com um algoritmo de classificacao associativa sob-demanda para a deteccao de vandalismo na Wikipedia. Primeiro mostramos que a classificacao associativa reforcada por uma tecnica simples de balanceamento para a construcao do conjunto de treinamento supera classificadores de estado-de-arte como SVM e kNN, e e competitivo com os melhores resultados da competicao CLEF em deteccao de vandalismo na Wikipedia. Alem disso, atraves da aplicacao da abordagem de selecao ativa, fomos capazes de reduzir a necessidade de treinamento em quase 96% com apenas um pequeno impacto sobre os resultados dadeteccao, tornando assim a nossa solucao muito pratica para cenarios reais.
Noyaux de réécriture de phrases munis de types lexico-sémantiques

De nombreux problèmes en traitement automatique des langues requièrent de déterminer si deux phrases sont des réécritures l’une de l’autre. Une solution efficace consiste à apprendre les réécritures en se fondant sur des méthodes à noyau qui mesurent la similarité entre deux réécritures de paires de phrases. Toutefois, ces méthodes ne permettent généralement pas de prendre en compte des variations sémantiques entre mots, qui permettraient de capturer un plus grand nombre de règles de réécriture. Dans cet article, nous proposons la définition et l’implémentation d’une nouvelle classe de fonction noyau, fondée sur la réécriture de phrases enrichie par un typage pour combler ce manque. Nous l’évaluons sur deux tâches, la reconnaissance de paraphrases et d’implications textuelles.
Hardware Design and RF Performance Evaluation of a Long Range 2.4 GHz Radio Module

Low–power, long–range wireless solutions are needed for transmission of environmental and industrial monitoring data from remote, battery–driven sensors. Chirp spread spectrum (CSS) modulation techniques are able to meet these requirements. A CSS–based radio module was designed to operate in the 2.4 GHz frequency band, whose advantages are worldwide availability, lack of duty–cycle restrictions and a broad spectrum. The module’s power consumption is satisfactory, but its range has to be improved. The accuracy of time-of-flight (ToF) distance estimation, an extra feature of the module, has not been evaluated. Impedance matching, transmission lines and printed circuit board design are among the practical aspects discussed, while ToF ranging and orthogonality of CSS signals are considered from a theoretical point of view.
Des humains dans la machine : la conception d’un algorithme de classification sémantique au prisme du concept d’objectivité

Un algorithme est le resultat de la formalisation d’une procedure qui, une fois implementee dans un programme informatique, peut alors etre rejouee indefiniment sans intervention. La materialite sociotechnique des programmes les inscrit dans des systemes de contingences, de normes et d’habitudes, qui laissent la capacite d’action humaine au cœur du processus. Ni le caractere mecanique des programmes, ni la coherence structurale de leurs fondements mathematiques, ne leur permettent de produire de l’objectivite par eux-memes. Elle provient de l’expertise de leurs concepteurs qui travaillent, soit par echange direct, soit par le biais d’outils d’evaluation, en interaction avec les utilisateurs beneficiaires dont l’appreciation valide pragmatiquement les produits des algorithmes. C’est, en somme, le design des programmes par succession de choix humains qui fait d’eux des machines a produire des connaissances.
Aplicación de técnicas de NLP y mapeo ontológico en el ámbito del diagnóstico clínico

Currently hospital services include high throughput sequencing for the analysis and the study of genetic background in patient disease. Next generation sequencing techniques have made it possible by improving the efficiency and reducing time needed to diagnose rare diseases. WES analysis produces a series of genetic variants with different typology that can be the cause of disease or syndrome, where analyst have to identify the one that really produces the patient phenotype. To make the process faster, patient medical history must contain a precise and standardized description of patient phenotype. Clinical reports use to be written in human natural language, what makes more difficult the algorithm performance. In this context, Human Phenotype Ontology (HPO) provides a standardized vocabulary of phenotypic abnormalities encountered in human disease, which can be combined with inference models as exomiser. This algorithm is able to make a variant list prioritization using VCF file and HPO terms. Each genetic variant included in this list is associated to a score that exomiser attaches according to its probability of being the patient phenotype cause. In this work it was designed a complete pipeline for extracting medical concepts from clinical reports with their subsequently association to the corresponding HPO terms, by manually or automatic mapping. Exomiser performance allowed to make a comparative between the variant list prioritized by manual association and automatic mapping of HPO terms, including the testing of Health29 functionality. The results obtained allowed to confirm that developed procedures can be used as an analytic support for genetic variant identification and validation in the field of clinical diagnosis.
Third Language Acquisition and Linguistic Transfer

1. Setting the context 2. Theoretical approaches to sequential multilingualism 3. Multilingual lexis (acquisition and processing) and phonology 4. Transfer in multilingual morphosyntax 5. A review of published work 6. Moving on and going forward in L3/Ln acquisition.
Multi-hop communication in cognitive radio networks: An experimental evaluation.

IV TABLE OF CONTENTS . V LIST OF PUBLICATIONS . IX AWARDS AND MEDALS . X LIST OF TABLES . XI LIST OF FIGURES . XII LIST OF ABBREVIATIONS . XV 1.0 INTRODUCTION.1 1.1 Motivation . 1 1.2 Research objectives . 4 1.3 Problem statement . 5 1.4 Research questions . 5 1.5 Research contributions . 6 1.6 Thesis outline . 6 2.0 LITERATURE REVIEW . 8 2.1 Cognitive radio . 8 2.1.
QUALIDADE DE VIDA NO TRABALHO DOS EMPREENDEDORES DO MUNICÍPIO DE PARELHAS – RN

A qualidade de vida no trabalho deve estar atrelada a um plano de negocio porque as atividades de uma empresa nao estao completas se seus empregados nao estiverem bem. Partindo desse pressuposto, o presente trabalho teve como principal objetivo analisar a Qualidade de Vida dos empreendedores do municipio de Parelhas-RN. A metodologia consistiu em uma pesquisa descritiva, qualitativa basica, cuja coleta dos dados ocorreu por meio de entrevista estruturada. Para a analise dos dados foi utilizado o SPSS que auxiliou no processamento da estatistica descritiva e da analise do nucleo de sentidos do corpus levantado. Com relacao aos resultados concluiu-se que: os empreendedores do municipio de Parelhas-RN possuem um nivel de Qualidade de Vida no Trabalho adequado que reflete em estrategias empreendedoras para lidar com o risco do mercado que atuam.
Factores motivacionales para el trabajo de los diferentes grupos generacionales

Esta investigacion, permitio presentar algunos elementos importantes en la organizacion, como la motivacion y los grupos generacionales; los cuales adquirieron fuerza por su valor en la atraccion, mantenimiento y retencion del talento, por facilitar la identificacion de consecuencias favorables a la hora de incrementar y mejorar los resultados. Para la obtencion de informacion, se utilizo el CMT el cual se aplico a 116 personas que componian las generaciones Baby Boomers, X y Millennials, activas laboralmente en una empresa manufacturera del Oriente Antioqueno. Esta herramienta evaluo quince factores motivacionales en el trabajo, los cuales se clasificaron en aquellos de mayor y menor importancia por grupo generacional. Los resultados obtenidos demuestran que los factores motivacionales tanto internos, como externos y los medios preferidos para obtener retribucion, se relacionan entre si y son significativos para cada una de las generaciones, en las cuales se identificaron patrones de conducta que se vieron reflejados a nivel laboral en esta organizacion.