Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcomplete Dictionary and Deep Learning Approachesto Image and Video Analysis

Extracting useful information while ignoring others (e.g. noise, occlusion, lighting) is an essential and challenging data analyzing step for many computer vision tasks such as facial recognition, scene reconstruction, event detection, image restoration, etc. Data analyzing of those tasks can be formulated as a form of matrix decomposition or factorization to separate useful and/or fill in missing information based on sparsity and/or low-rankness of the data. There has been an increasing number of non-convex approaches including conventional matrix norm optimizing and emerging deep learning models. However, it is hard to optimize the ideal l0-norm or learn the deep models directly and efficiently. Motivated from this challenging process, this thesis proposes two sets of approaches: conventional and deep learning based. For conventional approaches, this thesis proposes a novel online non-convex lp-norm based Robust PCA (OLP-RPCA) approach for matrix decomposition, where 0 < p < 1. OLP-RPCA is developed from the offline version LP-RPCA. A robust face recognition framework is also developed from Robust PCA and sparse coding approaches. More importantly, OLP-RPCA method can achieve real-time performance on large-scale data without parallelizing or implementing on a graphics processing unit. We mathematically and empirically show that our OLP-RPCA algorithm is linear in both the sample dimension and the number of samples. The proposed OLP-RPCA and LP-RPCA approaches are evaluated in various applications including Gaussian/non-Gaussian image denoising, face modeling, real-time background subtraction and video inpainting and compared against numerous state-of-the-art methods to demonstrate the robustness of the algorithms. In addition, this thesis proposes a novel Robust lp-norm Singular Value Decomposition (RP-SVD) method for analyzing two-way functional data. The proposed RP-SVD is formulated as an lp-norm based penalized loss minimization problem. The proposed RP-SVD method is evaluated in four applications, i.e. noise and outlier removal, estimation of missing values, structure from motion reconstruction and facial image reconstruction. For deep learning based approaches, this thesis explores the idea of matrix decomposition via Robust Deep Boltzmann Machines (RDBM), an alternative form of Robust Boltzmann Machines, which aiming at dealing with noise and occlusion for face-related applications, particularly. This thesis proposes an extension to texture modeling in the Deep Appearance Models (DAMs) by using RDBM to enhance its robustness against noise and occlusion. The extended model can cope with occlusion and extreme poses when modeling human faces in 2D image reconstruction. This thesis also introduces new fitting algorithms with occlusion awareness through the mask obtained from the RDBM reconstruction. The proposed approach is evaluated in various applications by using challenging face datasets, i.e. Labeled Face Parts in the Wild (LFPW), Helen, EURECOM and AR databases, to demonstrate its robustness and capabilities.
Instrumentos para evaluar ansiedad al tratamiento estomatológico en el adulto

Introduccion : La ansiedad al tratamiento estomatologico puede ser obtenida haciendo uso de instrumentos de evaluacion para obtener medidas objetivas, eliminando el componente subjetivo del evaluador. Objetivo : Caracterizar los instrumentos de evaluacion para ansiedad al tratamiento estomatologico en adultos. Metodos : Se realizo una revision bibliografica sobre instrumentos de evaluacion para ansiedad al tratamiento estomatologico, considerando la bibliografia publicada en Google academico, SciELO y Medline. Se obtuvo como resultado final un total de 44 articulos en el periodo revisado, 40 de ellos en revistas internacionales, 1 publicacion de la Organizacion Mundial de la Salud y 3 libros. Analisis e integracion de la informacion : Se obtuvieron un total de 6 instrumentos con protagnismo en la literatura cientifica. Estos se caracterizaron por tener como objetivo fundamental diagnosticar y clasificar al sujeto ansioso enmarcandolo en una categoria determinada, presentandose fundamentalmente en escalas e inventarios, originarios del idioma ingles. El Modifield Dental Anxiety Scale (MDAS) constituye el instrumento de evaluacion mas utilizado en investigaciones y en la practica clinica. No son reportados instrumentos disenados por investigadores en paises hispanohablantes, sino validaciones al idioma espanol de los ya existentes quedando poco representado este fenomeno psicologico en poblaciones hispanohablantes considerandose deprimido el desarrollo de la evaluacion psicologica en el ambito estomatologico. Conclusiones : Los instrumentos de evaluacion son muy poco validados al idioma y contexto cultural de paises hispanohablantes, por lo que esta limitado su uso en poblaciones de este continente, a excepcion de la poblacion mexicana.
Vergleichsmessungen zwischen Durchfluss-Normalmessanlagen

Zusammenfassung Im Oktober 1999 wurde durch das “International Committee of Weights and Measures” (CIPM) im Rahmen der Meterkonvention die Vereinbarung zur gegenseitigen Anerkennung (“Mutual Recognition Arrangement”, MRA) der nationalen Normale und der zugehörigen Messmöglichkeiten der Nationalen Metrologieinstitute (NMI) getroffen. Ziele dieser Übereinkunft sind die Bestimmung des Grades der Äquivalenz (“Degree of Equivalence”, DoE) zwischen den einzelnen nationalen Normalen, die gegenseitige Anerkennung von Zertifikaten der NMI und eine Erhöhung der Transparenz von Messergebnissen. Zu den vertrauensbildenden Maßnahmen bei der Umsetzung des MRA spielen internationale Ringvergleiche (CIPM “Key Comparisons”, KC) zwischen den metrologischen Staatsinstituten eine elementare Rolle. Im Rahmen dieses Beitrags werden Vergleichsmessungen der kohärenten SI-Einheit Volumen, respektive ‘Volumen von Wasser} mithilfe von konventionellen Durchflusssensoren (DFS) zwischen den weltweit renommierten Durchfluss-Normalmessanlagen der PTB — Institut Berlin (“Wärmezählerprüfstrecke”, WZP), der PTB Braunschweig (“Hydrodynamisches Prüffeld”, HDP) und dem Durchflusslabor des qutBundesamts für Eich- und Vermessungswesen’ (BEV) in Wien dargelegt. Ferner werden grundsätzliche Vorgehensweisen bei diesen Vergleichsmessungen vorgestellt und exemplarisch Ergebnisse bei 20°C präsentiert. Abstract In October 1999, the “International Committee of Weights and Measures” (CIPM) reached — within the scope of the Metre Convention — the agreement on the shape “Mutual Recognition Arrangement” (MRA) of the national standards and of the associated measurement capabilities of the “National Metrology Institutes” (NMI). Objectives of this agreement are the determination of the “Degree of Equivalence” (DoE) between the individual national standards, the mutual recognition of certificates of the NMIs and an increase in the transparency of measurement results. Among the confidence-building measures for the implementation of the MRA, international interlaboratory comparisons (CIPM “Key Comparisons”, KC) between the NMIs play an elementary role. Within the scope of this contribution, comparison measurements of the coherent SI unit volume — especially of ‘volumes of water’ — between the worldwide renowned flowrate standard measuring system of PTB´s Berlin Institute (“Heat Meter Test Section”, WZP), of PTB Braunschweig (“Hydrodynamic Test Field”, HDP) and of the flowrate laboratory of the Federal Office for Metrology and Survey (“Bundesamt für Eich- und Vermessungswesen”, BEV) in Vienna are presented. In addition, fundamental procedures for comparison measurements between flowrate standard measuring facilities are shown in this contribution and results of these measurements for 20°C are presented as examples.
Cooperative Base Station Caching and X2 Link Traffic Offloading System for Video Streaming Over SDN-Enabled 5G Networks

This paper presents a cooperative base station caching and X2 link traffic offloading system for video streaming services over software-defined networking (SDN)-enabled 5G networks. The goal of the proposed system is to provide seamless video streaming services to users while alleviating the backhaul traffic in the cellular network by effectively caching contents at the base stations and appropriately offloading parts of the backhaul traffic to X2 links. In the proposed system, the main functionalities of content-centric networking (CCN) are deployed at the base stations to take advantage of the attributes offered by CCN such as in-network caching and name-based forwarding. Furthermore, SDN technology is adopted not only to solve the practical issues of CCN, but also to control network resources efficiently. The proposed system is designed to determine near optimal caching positions and routes for all content in two phases to reduce the computational complexity for the real-time processing. The proposed system is fully implemented using well-known open sources, e.g., CCNx and ONOS, and ${\mathrm{C/ C}}+ +$C/C++. Finally, the system is examined in real wireless network environments. During the experiment, the proposed system can reduce cellular traffic by more than 13 percent over existing traffic offloading algorithms.
Physical Activity Recognition using Wearable Accelerometers in Controlled and Free-Living Environments

Physical activity recognition through wearables has enabled the development of novel applications in healthcare. Most of the existing studies focus on predicting activities using wearable sensors, either in a controlled or uncontrolled environment. However, there is not a clear distinction between these two environments. Hence, this thesis aimed to answer the research question “How accurately can we classify physical activity based on wearable accelerometers placed on the wrist and chest in a controlled and in a free-living environment? ". For the data collection phase, two experiments were conducted in the working environment of imec. 40 participants were recruited and were asked to participate in the Controlled and Free-Living Study. The subjects wore two imec wearables, a wrist-worn and chest-worn accelerometer sensor and performed everyday activities. These activities include sitting, dynamic sitting, lying with face up and face down, lying to the left and right, standing, dynamic standing, walking upstairs, walking downstairs, walking, running, and cycling. The Controlled Study showed that most of these activities could be detected accurately using accelerometer data from both sensors with 91.83% F1-score. Similarly, the combination of these two sensors achieved the best performance for the Free-Living Study with 86.98% F1-score. Finally, this work proved that between the two environments a correlation could be possible only for the activity cycling. Consequently, this research concludes that the activity recognition should be explicitly investigated in free-living environments, focusing on real-time activity detection.
O que fazer para sobreviver politicamente?: padrões de carreira dos deputados estaduais no Brasil

Esta tese tem como tema as carreiras politicas dos deputados estaduais no Brasil, e seu objetivo e analisar os efeitos dos padroes espaciais de votacao dos deputados estaduais sobre as estrategias de sobrevivencia na carreira politica. O objetivo e responder em que medida as formas com que os individuos se relacionam com suas bases eleitorais (conexoes eleitorais) condicionam padroes identificaveis de carreira politica entre os cargos eletivos disponiveis na estrutura de oportunidades de carreira (caracterizada pelos custos e beneficios envolvidos em cada escolha) no Brasil. Utilizou-se como fonte o Repositorio de Dados Eleitorais do TSE. A analise dos padroes de carreira dos deputados estaduais eleitos em 2010 a partir da ambicao predominante de carreira (se progressiva nacional, progressiva estadual, estatica, pseudoestatica, progressiva local, ou discreta) definida pela combinacao das trajetorias de origem e de destino dos deputados estaduais ao longo de cinco eleicoes, indicou um certo equilibrio em torno da preferencia manifesta pela ambicao estatica. Em seguida, identificou-se que os padroes espaciais de votacao (construidos a partir de uma combinacao de indicadores de concentracao e dominância), possuem alguma relacao com os padroes de carreira dos deputados, sustentando a hipotese de que esses padroes espaciais condicionam as preferencias por cargos disponiveis na estrutura de oportunidades. Contudo, ainda de acordo com a Teoria da Ambicao Politica, nao sao so os beneficios que condicionam a decisao sobre qual cargo disputar, mas tambem os riscos envolvidos nessa decisao. Com base nessa hipotese, a analise empirica da relacao dos atributos individuais das votacoes dos candidatos com o grau de incerteza inerente as disputas por cargos mostrou que, em alguns casos, o calculo do risco torna necessaria uma adaptacao da ambicao politica manifesta de modo a adequar a ambicao de curto prazo a um cenario mais factivel de acordo com as caracteristicas da extracao eleitoral do deputado.
Broadcast Language Identification & Subtitling System (BLISS)

Jinling Wang Karla Muñoz Esquivel James Connolly Kevin Curran Paul Mc Kevitt Faculty of Computing, Engineering & Built Environment, Ulster University (Jordanstown), Newtownabbey, BT37 0QB, Ulster University (Magee), Derry/Londonderry, BT48 7JL, Northern Ireland {j.wang, kc.munoz-esquivel, kj.curran} @ulster.ac. uk Department of Computing, Letterkenny Institute of Technology (LYIT), Port Road, Letterkenny, IRLF92 FC93, Co. Donegal, Ireland James.Connolly@lyit.ie Faculty of Arts, Humanities & Social Sciences, Ulster University (Magee), Derry/Londonderry, BT48 7JL, Northern Ireland p.mckevitt@ulster.ac. uk
A Study on Mining User-Aware Uncommon Consecutive Topic Patterns in Report Streams

Textual documents created and distributed on the Internet are constantly changing in different structures. The greater part of existing works is given to subject demonstrating and the development of individual topics, while sequential relations of topics in progressive reports distributed by a particular client are disregarded. In this paper, keeping in mind the end goal to describe and identify customized and anomalous practices of Internet clients, we propose Sequential Topic Patterns (STPs) and figure the issue of mining User-mindful Rare Sequential Topic Patterns (URSTPs) in archive streams on the Internet. They are uncommon all in all however moderately visit for particular clients, so can be connected in some genuine situations, for example, real-time monitoring on abnormal user behaviors. We show a gathering of calculations to tackle this inventive mining issue through three stages: preprocessing to remove probabilistic themes and recognize sessions for various clients, producing all the STP applicants with (expected) bolster values for every client by example development, and selecting URSTPs by making client mindful irregularity investigation on determined STPs. Probes both genuine (Twitter) and manufactured datasets demonstrate that our approach can without a doubt find extraordinary clients and interpretable URSTPs successfully and effectively, which significantly reflect users’ characteristics.
A New Quantum Tunneling Particle Swarm Optimization Algorithm for Training Feedforward Neural Networks

—In this paper a new Quantum Tunneling Particle Swarm Optimization (QTPSO) algorithm is proposed and applied to the training of feedforward Artificial Neural Networks (ANNs). In the classical Particle Swarm Optimization (PSO) algorithm the value of the cost function at the location of the personal best solution found by each particle cannot increase. This can significantly reduce the explorative ability of the entire swarm. In this paper a new PSO algorithm in which the personal best solution of each particle is allowed to tunnel through hills in the cost function analogous to the Tunneling effect in Quantum Physics is proposed. In quantum tunneling a particle which has insufficient energy to cross a potential barrier can still cross the barrier with a small probability that exponentially decreases with the barrier length. The introduction of the quantum tunneling effect allows particles in the PSO algorithm to escape from local minima thereby increasing the explorative ability of the PSO algorithm and preventing premature convergence to local minima. The proposed algorithm significantly outperforms three state-of-the-art PSO variants on a majority of benchmark neural network training problems.
De la coréférence exacte à la coréférence complexe : une typologie et sa mise en œuvre en corpus

L'annotation de la coreference est une tâche delicate des lors que l'on rencontre des expressions qui semblent referer a la meme entite sans etre exactement coreferentes. Il arrive parfois que le referent d'une expression soit difficile a identifier de maniere precise. Dans ce cas, la coreference avec une autre expression referentielle dont on a identifie clairement le referent ne peut pas etre stricte. C'est le cas par exemple des anaphores a antecedent flou. Nous proposons ici une typologie des corerefences non exactes, avec comme objectif d'en tenir compte d'une part lors de l'annotation de corpus en (co)references, d'autre part pour des applications de traitement automatique des langues.
Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring

We propose a novel mathematical semantic embedding for problem retrieval in adaptive tutoring. The goal is to retrieve problems with similar mathematical concepts. There are two challenges: First, problems conducive to tutoring are never exactly the same in terms of underlying concepts: those problems often mix concepts in innovative ways. Second, it is difficult for human to determine a consistent similarity score across a large enough training set. To address these two challenges, we develop a hierarchical problem embedding algorithm, Prob2Vec, which consists of abstraction and embedding steps. Prob2Vec is able to distinguish very finegrained differences among problems, an ability humans need time and effort to acquire. In addition, the associated concept labeling is a multi-label problem with imbalanced training data set suffering from dimensionality explosion. Robust concept labeling is achieved with a novel negative pre-training algorithm that dramatically reduces false negative and positive ratios for classification. Experimental results show that Prob2Vec achieves 96.88% accuracy on a problem similarity test, in contrast to 75% from directly applying state-of-the-art sentence embedding methods.
Topic extraction using local graph centrality and semantic similarity

Topic extraction is a challenging task under Natural Language Processing and Text Mining. Topic extraction is useful in natural language processing tasks such as automated summarization, question answering, and personalized search. In this paper, we propose an unsupervised topic extraction method using semantic similarity, keyword significance, and graph centrality. First, we select semantically similar words from text documents. Next, we perform disambiguation to find the correct senses of selected words. Then, we build a weighted graph using semantic relationships and significance of words in the text. Finally, we identify topic keywords using a novel concurrent local weighted centrality (LWWC) from words represented as nodes in a graph. Using standard annotated CiteULike and standard Brown datasets, we evaluated the results with precision, recall, and F‐measure. We show that the proposed method yields results comparable with the state of the art LDA (Latent Dirichlet Allocation) and S‐LDA (Sparse‐LDA) topic extraction techniques. We also show that the proposed concurrent LWWC algorithm is more effective than the existing generic centrality measures in networks of words. We verified the statistical significance of improved effectiveness of our approach, using one‐way analysis of variance (ANOVA) and Tukey‐Honest Significant Difference (Tukey‐HSD) post‐hoc methods.
Segmentation of Speech

Spoken language is typically produced in a continuous stream, with the realization of phonemes influenced by adjacent sounds (coarticulation) and few silent boundaries between words. Despite these problems of variability and continuity, listeners generally perceive speech as a coherent sequence of discrete words. Understanding the segmentation process has entailed a search for fundamental units of speech perception, notably the syllable, and led to an understanding of the critical importance of word recognition, with segmentation often achieved implicitly when hearing our native languages in clear listening conditions. However, in first and second language acquisition, or when speech quality is compromised, a range of general and language-specific cues to the location of word boundaries are exploited by listeners, including intonation, timing, allophony, vowel harmony, lexical stress, and phonotactic regularities. Indeed, for infants learning their first languages, such cues may be hyperarticulated in adults’ infant-directed speech, thereby facilitating segmentation and hence vocabulary development.
Filter Design for Suppressing Sidelobe Interference in NC-OFDM System

Abstract. OFDM technology plays an important role in multicarrier modulation. Combining with cognitive radio technology, the traditional OFDM transform into NC-OFDM. The two main drawback of the NC-OFDM system are the secondary users’ sidelobe interference to primary users and the high PAPR (peak to average power ratio). One way to suppress sidelobe interference of primary users is to design the filter in the time domain and the PTS method can effectively reduce the PAPR of the NC-OFDM system. According to the distribution of subcarriers in NC-OFDM system, this paper proposes a model of secondary user’s interference to primary users. The average interference power density is defined for this model, which is used to measure the interference level of different filters applied in NC-OFDM system. After filtering in time domain, it will cause PAPR to pick up. In this paper, we still use the PTS method to suppress PAPR, and can obtain better system performance.
A Strategy for Elimination of Data Redundancy in Internet of Things (IoT) Based Wireless Sensor Network (WSN)

In order to give a complete description of an environment or to make a robust decision, a number of observations must be collected and combined from multiple sensor nodes. In these large collections of data, only some are useful, whereas others are redundant. This redundancy decreases performance in terms of computing overhead, excessive transmission, and covering a large space. The process of selecting and analyzing the useful information from the collection of sensed data is called mining. Mining is used to produce more consistent, accurate, and useful information than that provided by any individual sensor node. Data mining has been widely applied in many areas, such as object recognition, wireless sensor networks (WSNs), image processing, environment mapping, and localization. Nowadays, Internet of Things utilizes WSN as a necessary platform for sensing and communication of the data. For efficiency, mining of spatial and temporal data is performed on the sensed sample collected by sensor nodes. Therefore, in this paper, a redundancy removal strategy is proposed, which performs mining on collected data to select the appropriate information before forwarding to a base station or a cluster head in the WSN. Extensive simulations were conducted, and the related results showed that the proposed scheme had better performance compared to other schemes in our simulated scenarios.