Miracle-dz/UniFAR-FTUs huggingface dataset:
Video and Image Processing based on Kernel Representations

En este trabajo se proponen diferentes esquemas de representacion de caracteristicas basados en Kernels. El objetivo principal es revelar de forma apropiada la informacion mas relevante a partir de informacion de alta dimensionalidad, para mejorar el desempeno de dos procedimientos de vision por computador: segmentacion de imagenes y video. Con este fin, se propone usar Representaciones Multi-Kernel (MKR) para incorporar multiples caracteristicas de imagenes, como: representaciones de color, informacion espacial de pixeles e informacion basada en flujo optico. Particularmente, para la segmentacion de imagenes, se propone una nueva metodologia de segmentacion de imagenes basada en agrupamiento, la cual usando una metodologia basada en MKR, incorpora multiples caracteristicas de imagenes. Con respecto a la segmentacion de video, diferentes metodologias de modelado de fondo usando representaciones Gausianas son estudiadas con el fin de enfrentarse a los artefactos tipicos de video-vigilancia capturados por camaras estaticas. En este sentido, diferentes funciones de costo son analizadas, buscando aprender y modelar la evolucion temporal de la dinamica de los pixeles. Ademas, con el fin de incorporar relaciones espaciales entre pixeles, una etapa de analisis de movimiento de objetos es desarrollada, la cual usando una metodologia basada en flujo optico, es capaz de detectar, modelar y seguir objetos en movimiento en una secuencia de video dada. Como resultado final, una nueva metodologia de segmentacion de video llamada STAL es desarrollada. STAL acopla ambas fuentes de informacion (temporal y espacial), generando asi un esquema de aprendizaje adaptativo capaz de actualizar adecuadamente los parametros del modelo Gaussiano del fondo. Los esquemas de segmentacion de imagenes y video son probados empleando bases de datos del mundo real y los resultados obtenidos son medidos usando medidas supervisadas mientras se compara contra conjuntos de ground-truth. A partir de los resultados obtenidos, se muestra que el uso de MKR para la elaboracion de espacios de representacion mejora la precision de las segmentaciones resultantes. Ademas, se demuestra que los esquemas propuestos son eﬁcientes y competitivos comparando contra algoritmos del estado del arte
Learning Cross-domain Dictionary Pairs for Human Action Recognition

This paper present a cross domain dictionary learning way, via the introduction of auxiliary domain,as the extra knowledge, the intra class diversity of the original training set (also known as the targetdomain) is effectively enhanced.Firstly,use local motion pattern feature as a low-level feature descriptor, and then through a cross domain reconstructive dictionary pairlearning, which brings the original target data and the auxiliary domain data into the same feature space to get correspondingsparse codes of each human action categories.Finally, classification and recognition is carried on thehuman action representation. Usingthe UCF YouTubedataset as theoriginal training setand the HMDB51 data set asthe auxiliary data set,the recognition rate of the proposed framework is significantly improvedon the UCF YouTube dataset.
Multiple Target Detection and Tracking in aMultiple Camera Network

Given synchronized video sequences from a number of cameras withoverlapping fields of view, the detection and tracking of a priori unknownnumber of individuals entering a determined area is considered, showingthat a generative model can accurately follow the individuals and handleeffectively such problems as occlusions in each view independently. Theaim of this thesis is to implement the exchange of information betweenthe cameras where the detection and tracking processes take place. Theinputs are obtained from synchronized videos and the frames are takenindividually to treat them as independent images. The proposed algo-rithm was implemented in MATLAB and results obtained on a personalcomputer are presented. The results show that the algorithm achievesgood tracking accuracy, has relatively low computational complexity, andat the same time it allows to observe the communication requirementsbetween the cameras and the processing node.
Digital Musical Interactions: Performer–system relationships and their perception by spectators

This article adopts an ecological view of digital musical interactions, considering first the relationship between performers and digital systems, and then spectators’ perception of these interactions. We provide evidence that the relationships between performers and digital music systems are not necessarily instrumental in the same was as they are with acoustic systems, and nor should they always strive to be. Furthermore, we report results of a study indicating that spectators may not perceive such interactions in the same way as performances with acoustic musical instruments. We present implications for the design of digital musical interactions, suggesting that designers should embrace the reality that digital systems are malleable and dynamic, and may engage performers and spectators in different modalities, sometimes simultaneously.
Activating Qualied Thesaurus Terms for Automatic Indexing with taxonomy-based WSD

Many thesauri contain a number of descriptors consisting of the term proper plus a sux in brackets meant to explain the term’s intended interpretation. For instance, the MeSH thesaurus contains a term Polymorphism (Genetics). For dierent thesauri, these terms account for 1%-5% of all descriptors. For automatic indexing based on recognizing term occurrences in free text, these terms are practically useless |free text never or very rarely contains term references of this form. A naive text annotation method, matching these terms with their bracketed qualiers stripped o (the ‘bare’ terms) results in frequently wrong interpretations. We investigated to what extent short forms of qualied terms (viz. Polymorphism) can be disambiguated by looking for concepts in their textual environment that are ontologically related to the represented concepts (in casu, Genetic Polymorphism), or to the concepts used to qualify (Genetics). Using the NLP framework of the Elsevier Fingerprint Engine r we created a set-up to test
A PERSISTENT SCATTERER INTERFEROMETRY PROCEDURE TO MONITOR URBAN SUBSIDENCE

Abstract. This paper describes a Persistent Scatterer Interferometry procedure for deformation monitoring. Its more original part concerns an approach to estimate the atmospheric phase component. The procedure can be used to monitor deformation areas that are relatively small and are surrounded by stable areas. The proposed procedure is described step by step. The procedure can be applied using SAR data coming from different sensors. However, in this work we discuss results obtained using Sentinel-1 data. A case study is described, where the deformation is caused by water pumping associated with construction works. In this case study, a stack of 78 Sentinel-1 images were analysed. The main part of the paper concerns the analysis of the atmospheric component. A comprehensive characterization of this component is first described, considering the original non-filtered phases. This is followed by the characterization of the residual filtered phases. This analysis highlights the goodness of the proposed procedure. This is further confirmed by the analysis of two deformation time series. The procedure can work with any type of deformation phenomena, provided that its spatial extension is sufficiently small.
The relationship between interference control and sense of presence in virtual environments

Background. The sense of presence is an important aspect of interaction with virtual reality applications. Earlier we suggested that presence can depend on cognitive control. The latter is a set of meta-cognitive processes which are responsible for configuring the cognitive system for the accomplishment of specific tasks with respect to a given context. In particular, cognitive control helps in preventing interference from the task-irrelevant variables. objective. This study aimed at investigation of the possible relationship between interference control and aspects of presence. Design. Thirty-nine subjects (32 female and 7 male, aged 18 to 27 years) participated in the study. The subjects were assessed via a battery of interference control tasks (Flanker Task, Go/No Go task, antisaccade task) and performed a virtual scenario (navigating within an array of randomly placed virtual digits in correct numerical order) in high-immersion (CAvE) and low-immersion (standard computer display) virtual environments. Afterwards, the subjects completed a Russian version of the ITC-Sense of Presence inventory. Results. We found that interference control is generally related to the sense of presence, especially in the CAvE (high-immersion) environment. Sensory interference control was most strongly associated with various aspects of presence (overall presence score, spatial presence, and emotional involvement). Motor interference control was associated with spatial presence and emotional involvement, but this relationship was weaker than was the case with sensory interference control. Low-immersion virtual environments attenuate some of these links between interference control and presence so that only sensory interference control remains a notable predictor of presence. conclusion. Interference control is positively associated with presence in virtual environments with varying immersion levels. This may reflect a more general cause-and-effect relationship between cognitive control and the feeling of presence in virtual reality.
Sentiment Analysis of Text Guided by Semantics and Structure

textabstractAs moods and opinions play a pivotal role in various business and economic processes, keeping track of one's stakeholders' sentiment can be of crucial importance to decision makers. Today's abundance of user-generated content allows for the automated monitoring of the opinions of many stakeholders, like consumers. One challenge for such automated sentiment analysis systems is to identify whether pieces of natural language text are positive or negative. Typical methods of identifying this polarity involve low-level linguistic analysis. Existing systems predominantly use morphological, lexical, and syntactic cues for polarity, like a text's words, their parts-of-speech, and negation or amplification of the conveyed sentiment. This dissertation argues that the polarity of text can be analysed more accurately when additionally accounting for semantics and structure. Polarity classification performance can benefit from exploiting the interactions that emoticons have on a semantic level with words – emoticons can express, stress, or disambiguate sentiment. Furthermore, semantic relations between and within languages can help identify meaningful cues for sentiment in multi-lingual polarity classification. An even better understanding of a text's conveyed sentiment can be obtained by guiding automated sentiment analysis by the rhetorical structure of the text, or at least of its most sentiment-carrying segments. Thus, the sentiment in, e.g., conclusions can be treated differently from the sentiment in background information. The findings of this dissertation suggest that the polarity of natural language text should not be determined solely based on what is said. Instead, one should account for how this message is conveyed as well.
Effect of sub-surface hydrogen on intrinsic crack tip plasticity in aluminium

ABSTRACT The effects of sub-surface hydrogen and mixed mode loading on dislocation emission in aluminium are studied using a combination of techniques including crack simulations with an empirical interatomic potential, generalised stacking fault energy (GSF) calculations, with empirical interactions and Density Functional Theory, and the model by Rice which links the critical stress intensity factor to the unstable stacking energy. The crack orientation is and the loading is composed of a moderate traction along and a shear along , such that Shockley partials are emitted along the crack plane. The role of the relaxations around the H atoms and of the concentration of H in the glide plane, in the GSF calculation, is revealed by comparing Rice's model to the results of brute force simulations. The enhanced GSF is then calculated ab initio. The conclusion is a large decrease of the critical load to emit a dislocation, due to the displacement transverse to the glide direction. The effect of sub-surface hydrogen is negligible with respect to the mechanical one.
Uniform propagation of chaos and creation of chaos for a class of nonlinear diffusions

Abstract We are interested in nonlinear diffusions in which the own law intervenes in the drift. This kind of diffusions corresponds to the hydrodynamical limit of some particle system. One also talks about propagation of chaos. It is well known, for McKean-Vlasov diffusions, that such a propagation of chaos holds on finite-time interval. We here aim to establish a uniform propagation of chaos even if the external force is not convex, with a diffusion coefficient sufficiently large. The idea consists in combining the propagation of chaos on a finite-time interval with a functional inequality, already used by Bolley, Gentil and Guillin. Here, we also deal with a case in which the system at time t = 0 is not chaotic and we show under easily checked assumptions that the system becomes chaotic as the number of particles goes to infinity together with the time. This yields the first result of this type for mean field particle diffusion models as far as we know.
Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.
Real-time context-based gesture recognition using HMM and automaton

HMMs are often used for gesture recognition because of the robustness. However, the computational cost and accuracy of recognition are important for real applications such as gesture recognition, speech recognition or virtual reality. In this paper, we propose methods for performance improvement of gesture recognition using HMMs. For the computational cost, we use KL transform to compress the input information and propose a recursive calculation method for the HMMs' probabilities. For the accuracy of recognition, we use an automaton layered up on HMMs to deal with context information of gestures. We also show experimental results to make the efficiency of our methods clear.
Is Economics Research Replicable? Sixty Published Papers from Thirteen Journals Say 'Usually Not'

We attempt to replicate 67 papers published in 13 well-regarded economics journals using author-provided replication files that include both data and code. Some journals in our sample require data and code replication files, and other journals do not require such files. Aside from 6 papers that use confidential data, we obtain data and code replication files for 29 of 35 papers (83%) that are required to provide such files as a condition of publication, compared to 11 of 26 papers (42%) that are not required to provide data and code replication files. We successfully replicate the key qualitative result of 22 of 67 papers (33%) without contacting the authors. Excluding the 6 papers that use confidential data and the 2 papers that use software we do not possess, we replicate 29 of 59 papers (49%) with assistance from the authors. Because we are able to replicate less than half of the papers in our sample even with help from the authors, we assert that economics research is usually not replicable. We conclude with recommendations on improving replication of economics research.
A neural network approach to nondestructive evaluation of complex structures, with application to highway bridges

Many methods have been proposed for nondestructive evaluation (NDE) of structures such as highway bridges, skyscrapers, and pipelines. The analysis of acoustic emission (AE) signals produced during cracking in concrete or steel is a promising approach for nondestructive monitoring to detect degradation in the integrity of a structure. Because of their central role in the highway infrastructure, bridge analysis is a particularly important application area for NDE. We discuss the advantages and disadvantages of AE testing, and describe some of the difficulties in applying classical signal processing (deconvolution) techniques to AE analysis of a bridge. We present instead a neural network approach that has the potential to overcome many of these difficulties.
Validating Families of

With increasing clock frequencies, the signal delay on some interconnects in a System on Chip (SoC) often exceeds the clock period, which necessitates latency insensitive protocols (LIPs). The correctness of a system composed of synchronous blocks communicating via LIPs is established by showing latency equivalence between a completely synchronous composition of the blocks, and the LIP-based composition. Every time a new LIP is conceived, it needs to be debugged and then proven correct. Mathematical theorems to establish correctness, though elegant, are error prone, and tedious to create for every new variant of LIPs. In this work, we present validation frameworks for families of LIPs, both for dynamic validation, useful for early debug cycles, and formal verification for formal proof of correctness. This can be a useful framework in the hands of designers trying to create new LIPs or to optimize existing ones for design convergence. Index terms—Simulation, formal verification, long interconnects, latency insensitive protocols, relay station, splitter, merger, verification framework.
Decentralized Coordination via Task Decomposition and R eward Shaping (Extended Abstract)

In this work, we introduce a method for decentralized coordination in cooperative multiagent multi-task problems where the subtasks and agents are homogeneous. Using the method proposed, the agents cooperate at the high level task selection using the knowledge they gather by learning subtasks. We introduce a subtask selection method for single agent multi-task MDPs and we extend the work to multiagent multi-task MDPs by using reward shaping at the subtask level to coordinate the agents. Our results on a multi-rover problem show that agents which use the combination of task decomposition and subtask based difference rewards result in significant improvement both in terms of learning speed, and converged policies.
AUTOMATED TEXT SUMMARIZATION AND KEYPHRASE EXTRACTION

AUTOMATED TEXT SUMMARIZATION AND KEYPHRASE EXTRACTION Gönenç Ercan M.S. in Computer Engineering Supervisor: Asst. Prof. Dr. İlyas Çiçekli September, 2006 As the number of electronic documents increase rapidly, the need for faster techniques to asses the relevance of documents emerges. A summary can be considered as a concise representation of the underlying text. To form an ideal summary, a full understanding of the document is essential. For computers, full understanding is difficult, if not impossible. Thus, selecting important sentences from the original text and presenting these sentences as a summary is a common technique in automated text summarization research. The lexical cohesion structure of the text can be exploited to determine the importance of a sentence/phrase. Lexical chains are useful tools to analyze the lexical cohesion structure in a text. This thesis discusses our research on automated text summarization and keyphrase extraction using lexical chains. We investigate the effect of the use of lexical cohesion features in keyphrase extraction, with a supervised machine learning algorithm. Our summarization algorithm constructs the lexical chains, detects topics roughly from lexical chains, segments the text with respect to the topics and selects the most important sentences. Our experiments show that lexical cohesion based features improve keyphrase extraction. Our summarization algorithm has achieved good results, compared to some other lexical cohesion based algorithms.
Efectos de la alienación laboral sobre la satisfacción laboral y el empoderamiento psicológico: Caso del sector automotriz de Ciudad Juárez, Chihuahua.

La alienacion laboral es un fenomeno complejo que hace referencia a una condicion cognitiva y social en la que una persona se desconecta y se separa de su yo interno dentro de su entorno laboral. En particular, los trabajadores que se convierten en victimas de la alienacion les otorgan mas importancia y atencion a las recompensas externas (el salario, por ejemplo) que a su desempeno laboral, por lo que es probable que estos renuncien a su trabajo en un corto plazo. Fue a traves de la busqueda y el analisis de la evidencia cientifica que versa sobre la alienacion laboral, lo que permitio el identificar dos variables notorias que han sido asociadas con la tematica ya mencionada: la satisfaccion laboral y el empoderamiento psicologico individual. De ahi que el objetivo de la presente investigacion sea el de definir, valorar e interpretar la asociacion de la alienacion laboral con la satisfaccion laboral y el empoderamiento psicologico individual. El diseno de la investigacion es no experimental, descriptivo, correlacional y exploratorio; su dimension temporal es transversal con un enfoque cuantitativo. Se recolectaron 406 cuestionarios de empleados de mandos medios y superiores de empresas que pertenecen al sector automotriz en Ciudad Juarez, Chihuahua. Los datos se analizaron mediante la tecnica estadistica de modelado estructural basado en minimos cuadrados parciales [PLS]. Los resultados revelan que la alienacion laboral se asocia estadistica y significativamente con la satisfaccion laboral y empoderamiento psicologico individual. Adicionalmente, los hallazgos indican que el empoderamiento psicologico individual se asocia estadistica y significativamente con la satisfaccion laboral.
Development and application of virtual experiments in experimental teaching of information optics

In this paper, the simulation experiments both of Abbe-Porter spatial filtering and of optical processing of image addition and subtraction with a grating filter are designed and performed. We realize the design and operation of optical information processing simulation experiments based on information optics theory and the experimental principle by using MATLAB programing language. The spatial filtering of Fourier spectrum, one of the main concepts in information optics, is intuitively described via the simulation experiments, and the experiment process is demonstrated step by step. The results show that the simulation experiments are really helpful for the filter's design and the image processing. These developed virtual experiments have been used in experimental teaching for undergraduate students majored in optics or optical engineering, which effectively assist students to understand concept and principle of optical information processing.