Miracle-dz/UniFAR-FTUs huggingface dataset:
RISE: A RelIable and SEcure scheme for wireless Machine to Machine communications

Wireless Machine to Machine (M2M) communications enable ubiquitous sensing, controlling, and acting via sensors, actuators, and actors. Reliability and security are of foremost importance in wireless M2M systems. A simple, target distinguishing attack can result in M2M’s failure. This paper presents a RelIable and SEcure scheme, RISE, which is a package of policies that guarantee the reliability of data (including sensor reports and actuator instructions) and devices (including sensors, actuators, and actors). The data reliability is improved by four algorithms, ChooseMedian, ChooseMost, ChooseNearest, and Trust-based Enhancement. Report attainability is improved via m repeat-sending and n multiple-reporting algorithms. Device reliability is guaranteed by device-indistinguishability, which comprises data-indistinguishability and behavior-indistinguishability. The security requirements are formally defined, and the security analysis proves the soundness and completeness of the scheme.
An Adaptive Neighborhood Graph for LLE Algorithm without Free-Parameter

Locally Linear Embedding (LLE) algorithm is the first classic nonlinear manifold learning algorithm based on the local structure information about the data set, which aims at finding the low-dimension intrinsic structure lie in high dimensional data space for the purpose of dimensionality reduction. One deficiency appeared in this algorithm is that it requires users to give a free parameter k which indicates the number of nearest neighbors and closely relates to the success of unfolding the true intrinsic structure. Here, we present an adaptive neighborhood graph with respect to LLE algorithm for learning an adaptive local infrastructure in order to avoid the problem of how to automatically choosing nearest neighbors existed in manifold learning by making use of a novel concept: natural nearest neighbor (3N). Experiment results show that LLE algorithm without free parameter performs more practical and simple algorithm than LLE.
Image Captioning Based on Deep Neural Networks

With the development of deep learning, the combination of computer vision and natural language process has aroused great attention in the past few years. Image captioning is a representative of this filed, which makes the computer learn to use one or more sentences to understand the visual content of an image. The meaningful description generation process of high level image semantics requires not only the recognition of the object and the scene, but the ability of analyzing the state, the attributes and the relationship among these objects. Though image captioning is a complicated and difficult task, a lot of researchers have achieved significant improvements. In this paper, we mainly describe three image captioning methods using the deep neural networks: CNN-RNN based, CNN-CNN based and Reinforcement-based framework. Then we introduce the representative work of these three top methods respectively, describe the evaluation metrics and summarize the benefits and major challenges.
Moving object recognition and guidance of robots using neural networks

The design of a robust guidance system for a robot is discussed. The two major tasks for this guidance system are the online recognition of a moving object invariant to rotation and translation, and tracking the moving object using a neural-network-driven vision system. This system included computer software ported to the IBM PC and interfaced with an IBM 7535 robot. The operation of this guidance system involved recognition of a moving object and the ability to track it till the robot and effector was in close proximity of the object. It was found that the robot was able to track a moving object as long as it did not leave the region visible to the camera. Recognition was successfully demonstrated for objects of arbitrary shapes. Even similarly shaped objects of different sizes were correctly recognized. <<ETX>>
Surgery of acquired canalicular stenosis

Between January 1984 and May 1991 sixty eyes of 58 patients underwent canalicular surgery. According to the intraoperative findings, the canaliculi were either dilated and intubated (group I: 27 cases) or the medial canthus was opened, the stenosis excised and the canaliculi intubated (group II: 33 cases). Two patients were lost for follow-up. The results were good in group I in 20 cases (74.1%) and in group II in 17 cases (54.9%). After revision surgery and including moderate results, the final success rate in group I was 24 (88.9%) and in group II 23 (75.5%).
A wide-dynamic-range and extremely high-sensitivity CMOS optical receiver IC using feed-forward auto-bias adjustment

This is the first report of a CMOS receiver IC that has high transimpedance gain of 150 dB/spl Omega/ and minimum detectable input current signal range from 0.05 /spl mu/A to 17 /spl mu/A with stable operation for FET's Vth and power supply voltage deviations. To implement wide dynamic range, a new multi-staged feed-forward auto-bias adjustment provides a wide dynamic range without any external elements and adjustments. Using this design technique, an optical receiver IC was fabricated by standard 0.8-/spl mu/m CMOS technology and it has a wide dynamic range of >25 dB for burst-mode optical input at 29 Mb/s. <<ETX>>
Towards Detecting Social Events by Mining Geographical Patterns with VGI Data

Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.
Le personnage de marque sur le packaging, catalyseur de prescription enfantine: l'effet modérateur de la mise en scène du personnage, de la familiarité de l'enfant envers lui et du niveau scolaire

Cet article étudie l'influence sur la prescription enfantine du personnage de marque apposé sur le packaging. Trois variables modératrices de cette relation sont testées à l'aide de deux expérimentations: la familiarité de l'enfant envers le personnage, la mise en scène du personnage et le niveau scolaire de l'enfant. Cette recherche montre que sur le packaging un personnage de marque favorise la prescription enfantine uniquement s'il est familier du jeune consommateur. De même, lorsque le personnage est introduit dans un décor narratif sur le packaging, son influence sur la prescription est accrue dans certains cas; cela dépend du type de mise en scène et de la catégorie de produits.
DISCUSSING OBSTETRIC VIOLENCE THROUGH THE VOICES OF WOMEN AND HEALTH PROFESSIONALS

Objetivo: analisar os discursos de mulheres e profissionais de saude sobre a assistencia ao parto, considerando as situacoes vivenciadas e as interacoes construidas entre eles durante o trabalho de parto e parto. Metodo: trata-se de um estudo interpretativo, com abordagem qualitativa. Utilizou-se a Analise de Discurso como metodo de pesquisa. Os cenarios de investigacao foram sete maternidades, pertencentes a rede publica da Regiao Centro-Oeste de Minas Gerais. Foram realizadas entrevistas com 36 parturientes, dez enfermeiros obstetras e 14 medicos obstetras. Os dados coletados foram submetidos a analise de discurso. Resultados: os dados foram organizados em tres categorias: 1) A violencia obstetrica presenciada no discurso da enfermeira obstetra: que discute que mesmo reconhecendo a presenca desta, falam da dificuldade de garantir os direitos das parturientes na cena do parto; 2) Hoje tudo e violencia obstetrica: mostra a negacao da existencia desse fenomeno na relacao profissional-paciente; 3) Aqui a gente nao tem voz: ha presenca da violencia obstetrica, porem ha certo consentimento por parte das mulheres que, na presenca do nascimento, esquecem a forma da assistencia recebida. Conclusao: o tratamento hostil constitui um dos obstaculos a humanizacao da assistencia ao parto, interferindo na escolha da via de parto, sendo necessario rever o conceito de violencia obstetrica, considerando todas as suas especificidades e nuances.
Simulation of Precipitation Phenomenon Based on Particle System Method

Precipitation phenomenon in liquid is one of general phenomenon in nature or in research.It has very important significance for reappearing precipitation course and addressing precipitation disciplinarians by simulation.Based on particle system method,the mathematics model for precipitation phenomenon in liquid was brought out,and the implementation of precipitation phenomenon in liquid was particularly described,more over,the technologies for real-time and reality were discussed.It is demonstrated by experiment that the method is not only simply,easy,and satisfying for requirements of real-time and reality,but also has preferable commonality,it's fit for other occasion by little modify.
Estudio del esqueleto de las dos cinturas y el miembro superior de los homínidos de la Sima de los Huesos: Sierra de Atapuerca, Burgos

LOS HOMINIDOS DE LA SIMA DE LOS HUESOS REPRESENTAN UNA DE LAS EVIDENCIAS FOSILES MAS IMPORTANTES DEL MUNDO PARA EL ESTUDIO DE LA EVOLUCION HUMANA EN EUROPA DURANTE EL PLEISTOCENO MEDIO. EN LA PRESENTE TESIS DOCTORAL, SE HA REALIZADO EL INVENTARIO DE TODOS LOS RESTOS HUMANOS RECUPERADOS EN LA SIMA DE LOS HUESOS HASTA EL ANO 1993 Y PERTENECIENTES A LOS HUESOS DE AMBAS CINTURAS Y AL MIEMBRO SUPERIOR. TAMBIEN SE HA REALIZADO SU DESCRIPCION ANATOMICA, LA DETERMINACION DEL NUMERO MINIMO DE INDIVIDUOS REPRESENTADOS POR CADA HUESO, LA DETERMINACION DE LA EDAD DE MUERTE CUANDO HA SIDO POSIBLE Y EL ESTUDIO COMPARATIVO DE TODOS ELLOS. EL ESTUDIO COMPARATIVO SE HA CENTRADO EN AQUELLOS RASGOS MAS RELEVANTES DESDE EL PUNTO DE VISTA FILOGENETICO. BASANDONOS EN LA EVIDENCIA DE ATAPUERCA, Y APLICANDO LOS PRESUPUESTOS DE LA SISTEMATICA FILOGENETICA O CLADISTICA, SE HA PROPUESTO POR PRIMERA VEZ LA POLARIDAD DE MUCHOS DE LOS RASGOS POSTCRANELAES UTILIZADOS NORMALMENTE EN ESTE TIPO DE ESTUDIOS. ASIMISMO, SE APORTAN INTERESANTES DATOS SOBRE LA VARIABILIDAD Y EL DIMORFISMO SEXUAL DE LA CLAVICULA, LA ESCAPULA, EL HUMERO Y EL COXAL DEL HOMBRE MODERNO, BASADOS EN EL ESTUDIO DE LA IMPORTANTE COLECCION DE RESTOS HUMANOS DE FILIACION CONOCIDA DE LA UNIVERSIDAD DE COIMBRA. TAMBIEN SE HAN EXPLORADO LAS POSIBILIDADES QUE OFRECE, PARA EL ESTUDIO DE LA VARIABILIDAD MORFOLOGICA, EL ANALISIS MULTIVARIANTE DE VARIABLES QUE HEMOS DENOMINADO DE FORMA (LIBRE DEL FACTOR TAMANO), Y SU APLICACION AL ESTUDIO DE LOS HOMINIDOS FOSILES.
Optical flow based model for scene cut detection

Cut detection is a fundamental operation for video sequence segmentation. This paper presents an optical flow based model with linear prediction to realize cut detection in various transition conditions. Each frame of a video sequence is divided into 4/spl times/4 non-overlapping blocks. This model initiates the cut detection by backward block searching for a match. Three continuous frames with the same matched block and the fourth predicted frame are grouped as a subsequence. The linearly predicted non-overlapping block locations in the fourth frame are defined as a cut detectable region. The forward search for a match is performed between the third and fourth frames in the cut detectable region. Mismatched blocks indicate a scene change in these block locations, namely, a cut. This algorithm has a better performance for detecting cuts in quick scene changes, static scenes, and scenes with slow motion compared to techniques reported in the literature.
NooJ, un outil TAL pour l'enseignement des langues. Application pour l'étude de la morphologie lexicale en FLE

NooJ est un systeme de traitement de corpus – reprenant et ameliorant les fonctionnalites d'INTEX – concu pour l'enseignement des langues et de la linguistique. NooJ integre des outils de traitement automatique du langage qui offrent a l'enseignant des possibilites de traiter un corpus, et des procedures de recherche, de test, et d'entrainement pour l'etudiant. Nous presentons ici un exemple d'application de NooJ a l'enseignement du francais langue etrangere, qui reprend quelques activites sur l'etude de la morphologie lexicale.
A Robotic System Employing Deep Learning for Visual Recognition and Detection of Weeds in Grasslands

In this paper, we describe the vision system of a robot prototype that operates in dairy farm grasslands and detects the presence of the harmful Broad-leaved dock (Rumex obtusifolius L.). Image data were collected using the prototype robot from 3 fields in 2 different countries under real conditions. The proposed recognition and detection system is using solely 2D visual input and is based on state-of-the-art Convolutional Neural Networks (CNNs), which were used for high level feature extraction in combination with various examined classifiers. Gathered data were used to experimentally show that the proposed system yields state-of-the-art detection and recognition performance, while being able to keep low false-positive rates under challenging operation conditions.
Generative deep deconvolutional neural network for increasing and diversifying training data

Large amount of annotated images with rich variations are needed to train a deep network for detecting instance object in unstructured environment. Addressing the problem that the artificial acquisition and manual annotation is time-consuming, the generative deep deconvolutional neural network (GDDNE) to increase and diversify training data through the supervised learning strategy is created in this paper. Specifically, our network can not only generate with different styles such as shift, zoom, brightness and other superimposed transformations, but also interpolate generate the new ones between given viewpoints images in training samples. With 180 viewpoints realistic images in training samples: 30 rotation angles in plane and 6 angles of depression, our network can finally generated 1000 diversified viewpoint images and 21 kinds of data transformations for each instance object. Abundant experiments demonstrate that the remarkable performance of our generative network used in the generation task of large magnitude.
A Deep Learning Approach for Analyzing Video and Skeletal Features in Sign Language Recognition

Sign language recognition (SLR) refers to the classification of signs with a specific meaning performed by the deaf and/or hearing-impaired people in their everyday communication. In this work, we propose a deep learning based framework, in which we examine and analyze the contribution of video (image and optical flow) and skeletal (body, hand and face) features in the challenging task of isolated SLR, in which each signed video corresponds to a single word. Moreover, we employ various fusion schemes in order to identify the optimal way to combine the information obtained from the various feature representations and propose a robust SLR methodology. Our experimentation on two sign language datasets and the comparison with state-of-the-art SLR methods reveals the superiority of optimally combining skeletal and video features for SLR tasks.
A novel method to explore intrarenal arterial tree using micro-CT

The minimally invasive approach for renal surgeries, among others, requires preoperative determination of intrarenal arteries. Their proper identification based on standard preoperative CT images is still not a fully solved problem. Therefore, we decided to resolve this issue by providing a tree topology model using post-mortem kidneys. The number of vessels that compose intrarenal tree is so large, that it cannot by analyzed manually without the aid of image processing techniques. So that, the vessels has to be first depicted using an imaging technique that enables to provide superior resolution in comparison to standard CT scan. In this paper, we present a research on various injection materials enabling to fill the vascular beds in order to scan them using micro-CT and further reconstruct as a 3D model. The evaluated materials were chosen so that they are widely available and affordable. We compare them in terms of their ability to absorb ionizing radiation and penetrate vascular beds (density, viscosity), homogeneity, solidification rate, resistance to solvents and durability. We also present the technique of injecting kidney arteries with the use of the selected material two-part epoxy adhesive with 10% iodine. In contrast to standard corrosive endocast preparation, in our case there is no need to remove soft parenchymal tissue which takes about two weeks using corrosive materials such as strong acids and bases. The proposed filling material enables to enhance vascular tree to such extend that micro-CT scans of the whole kidneys can be performed. This enables instantaneous substance injection and imaging without permanently destroying the soft tissue material. This approach can be used in various scenarios in which a filling material with the ability to increase radiation absorption is required and there is a need to maintain the integrity of the structure. To the best of our knowledge, this is the first such attempt. The obtained vascular trees by the means of micro-CT confirm the validity of the presented approach.
Intra-Operative 3D Registration of MIS Reconstructed Surfaces to Pre-Operative Models

This work focuses on the challenges of 3D surface registration in MIS applications. A novel dataset has been created based on CT scans of a knee phantom and pre-operative 3D models of the meniscus are extracted. Four state-of-the-art 3D registration methods are evaluated on the registration of noisy patches segmented from the models' surface to the model itself in an attempt to objectively compare their performance using ground-truth correspondences. The results of this analysis provide useful insight on the problem at hand and indicate where our focus should be steered in order to successfully tackle it.
Assessment of a Markerless Optical Tracking Technique for Computer-Assisted Hip Surgery

This article presents an evaluation of a prototype optical tracking system intended for use in computer-assisted orthopedic surgery. The device is based on a smartphone with a built-in video camera, and an external miniature time-of-flight camera. It is able to automatically recognize a surgical instrument based on its shape and appearance, and to determine its orientation relative to an array of light-reflective markers. The experiments were done in a measurement configuration and with the instruments that are specific to computer-assisted implantation of the acetabular component in total hip replacement surgery. The evaluation included the recognition rate of the instruments, and the angular accuracy of the system. The results show, that the accuracy is comparable to that obtained by conventional tracking devices in clinical settings, and partially meets the requirements specified in the literature for hip surgery.
Compensation for fringe effect of electrical resistance tomography sensor by multiple-plane sensor scheme

Fringe effect exists in conventional ERT sensors, i.e., severe distortions of electric field on both ends of sensor electrodes. This leads to a 3D sensing region for a 2D ERT sensor. As a result, the objects outside the ERT sensor plane can still be sensed and imaged, which is undesired and deteriorates the image accuracy. To tackle with this issue, a three-plane ERT sensor scheme is proposed. Simulation results show that the three-plane sensor scheme can compensate for the fringe effect induced by the object outside the measurement sensor plane regarding a distribution with three non-conductive bars at different cross-sectional and axial positions. Meanwhile, it is found that for axially uniform distributions, the proposed compensation method can reduce the size overestimation of non-conductive objects to be imaged by linear reconstruction method such as the Landweber iteration. Finally, an experimental ERT system is established to verify the findings from simulation.
An OCTA Based Diagnosis System Based on a Comprehensive Local Features Analysis for Early Diabetic Retinopathy Detection

In this work, we propose a comprehensive diagnosis system for detecting early signs of diabetic retinopathy (DR). The proposed system is based on extracting features that are describing both shape and appearance of the retinal vascular system from optical coherence tomography angiography (OCTA) scans. First, two various OCTA plexuses are segmented, which are retinal superficial and deep plexuses, to extract the retinal blood vessels from the other background tissues. Then, the developed system calculates the blood vessels density, blood vessels caliber, and distance map of the foveal avascular zone (FAZ) from both two segmented OCTA plexuses. Also, the large blood vessels are extracted from the segmented superficial plexus to retrieve the skeleton of the blood vessels. The skeleton of the large blood vessels is used to calculate bifurcation, crossover, and branch points. Finally, the extracted four features that are capturing shape and appearance of the segmented vessels and FAZ, which are blood vessels density, blood vessel caliber, the width of the FAZ, and different types of vascular bifurcation points are used to diagnosis the OCTA images by using two-stage random forest (RF) classifier. To measure the performance of the proposed system, 133 OCTA scans for different patients are used for training and testing based on k-fold cross-validation technique. The performance of the proposed system is measured by using five various metrics. A promising average result of overall accuracy (ACC) of 97% is obtained that can differentiate normal from mild DR cases.
Elitism in intensity-based image registration

Elitism is a variant of genetic algorithms which enables quicker convergence to the global optimum by preserving the best solutions of the current generation and passing them either unchanged or slightly changed to the next one. This guarantees that in each generation the best elements will be, at least, as good as those of the previous one. As it happens with many stochastic algorithms, Elitism has been used in image registration software and systems. Yet, to the best of our knowledge, no one has investigated its optimization potential in intensity-based image registration with respect to the number of the elites that is allowed to be reserved in the next generation and its connection to the mutation rate. In this paper, a series of experiments are conducted with respect to the number of the best solutions that are preserved unchanged into the next generation and the mutation rate with the purpose to study its effect on convergence, and more specifically whether it converges to the global optimum as well as its stability. Our results indicate that increasing the number of elites as well as the mutation rate will most likely improve the convergence of the registration method to the global optimum.