Miracle-dz/UniFAR-FTUs huggingface dataset:
Hog Feature - A Survey

: Detecting a specified object in a given image is a difficult task due to its possible widely variable appearance. An essential requirement for this task is a robust feature set that allows the appearance of the object in the image to be discriminated cleanly, even in complex backgrounds under all possible illumination effects. Study of the existing literature shows that Histogram Oriented Gradient(HOG) feature is one of the most powerful feature for object recognition in scene images. In this report, we made a survey of the use of HOG feature in image classification tasks. This report also covers different variations of HOG features proposed at a later period. Results of the use of HOG feature along with Support Vector Machine (SVM) in scene text recognition tasks on several standard databases like ICDAR2003 robust reading dataset, Street View Text (SVT) etc. are available in the literature and these are comparable with the classification results provided by the state-of-the-art
Anthropogenic threats to high-altitude parnassian diversity

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Anthropogenic threats to high-altitude parnassian diversity Fabien Condamine, Felix Sperling
Reposing Humans by Warping 3D Features

We address the problem of reposing an image of a human into any desired novel pose. This conditional image-generation task requires reasoning about the 3D structure of the human, including self-occluded body parts. Most prior works are either based on 2D representations or require fitting and manipulating an explicit 3D body mesh. Based on the recent success in deep learning-based volumetric representations, we propose to implicitly learn a dense feature volume from human images, which lends itself to simple and intuitive manipulation through explicit geometric warping. Once the latent feature volume is warped according to the desired pose change, the volume is mapped back to RGB space by a convolutional decoder.Our state-of-the-art results on the DeepFashion and the iPER benchmarks indicate that dense volumetric human representations are worth investigating in more detail.
A Survey on Human Action Recognition

: Human Action Recognition is one of the important research area in computer vision and image processing field. A machine can be interacted and controlled by gestures and facial expression using the visual modality. Human action recognition can be seen as a bridge for machines to understand human body language, an intelligent interacting system between machines and humans which limits majority of input devices such as keyboard and mouse. Thus, producing an accurate and meaningful information on human activities and behaviours is one of the important tasks in pervasive computing. In this survey, we tried to focus on advancement of action recognition after yr. 2010.
Pattern of Antibiotic Resistance Bacteria Isolated From Various Clinical Specimens

Introduction: Although progress has been made to eradicate or control many infectious diseases, humankind remains vulnerable to a wide array of new and resurgent organisms. The outcome of any infection depends on the virulence of infectious agents, the number of organisms in infecting inoculum and the response of the immune system of the host. Materials and Methods: This is a retrospective descriptive study done in Lifecare Diagnostics and Research Centre for three years (2015-2017). Specimens of different pattern like blood, pus and urine were studied. Total specimens were 563. Urine specimens were 418, blood specimens were 48 and Pus 97. Before starting this study, we have taken ethical clearance from the institution. Results: This study shows most common organism cultured in urine specimen is E. coliand most sensitive antibiotic is Amikacin. Similarly, in Pus culture is Staphylococcus aureus and most sensitive antibiotic is Vancomycin and in blood culture the organism was Staphylococcus aureus and Enterococcus spp and the most sensitive antibiotic is Vancomycin and Ampicilin. Conclusion: Regular surveillance of different types of organisms and sensitivity to different types of drugs should be carried out for the treatment of the patient scientifically. --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 28-08-2018 Date of acceptance: 10-09-2018 --------------------------------------------------------------------------------------------------------------------------------------
1.16. PREVENTION: OPERATING ROOM, SURGICAL TECHNIQUE

Since infections can have such a devastating effects on total joint arthroplasty, it will always be necessary to search for methods to reduce contamination. The main sources of contamination come from skin and particles in the air of the operating room [1,2]. Controversy remains about the use of separate blades for skin incision and internal use, although this practice has been discredited [3–10]. Preoperative preparation of skin with antiseptics can help reduce the number of microorganisms, but cannot completely eradicate them, especially resident flora. Hypothetically, whenever the skin is incised microorganisms that colonize the deeper layers of skin can contaminate the exposed tissues and lead to surgical site infections (SSIs) [11–13]. A systematic review was conducted on this subject following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the PRISMA statement. A comprehensive search of the literature was carried out in February 2017 using electronic databases PubMed, Medline and the Cochrane Library. The search terms used were “Arthroplasty AND Infection AND Knife OR Blade. ” Only English studies were reviewed. This yielded four results after duplicates were removed. Because of the low numbers of studies done on this subject, there was no limitation on the type of the articles that were reviewed. Cross references revealed four more results. One study was not analyzed as it was not comparative, leaving seven reports for analysis. The contamination rates of skin and deep knives were assessed with the Fisher’s exact test. Seven studies were included in the final analysis (Table 1). None of the studies showed a direct relationship between knife contamination and SSIs. Six studies could not demonstrate a difference in the contamination rates between the skin and deep knives [5,8–12]. In one study, the deep knife was significantly more contaminated then the skin knife [7]. Analysis of all seven studies together shows higher contamination rate for deep knives than skin knives, mostly due to the latter study.
Male and Female Infertility: Causes, And Management

Infertility is a common problem worldwide. Childlessness could be male infertility,femaleinfertility or combined infertility. Infertility has psychological and social impact. Female infertility is due to ovulatory problem, in male semen quality, and idiopathic cause. Sexually transmitted disease (STD), genetic, diabetes, pituitary factors, and toxins also play and importantrole.Diagnosisinclude, semenanalysis, for male, female partner tested for hormonal para meters,e.g.,FSH,TSH, abnormal levels indication of infertility. If the conservative medical treatment fail, physician my advice the patient to undergo in-vitro fertilization(IVF),and assisted reproductive technology (ART). IVF has promising results.
Impaired Ca-Dependent Activation of Large-Conductance Ca-Activated K Channels in the Coronary Artery Smooth Muscle Cells of Zucker Diabetic Fatty Rats

The large-conductance Ca-activated K (BK) channels play an important role in the regulation of cellular excitability in response to changes in intracellular metabolic state and Ca homeostasis. In vascular smooth muscle, BK channels are key determinants of vasoreactivity and vital-organ perfusion. Vascular BK channel functions are impaired in diabetes mellitus, but the mechanisms underlying such changes have not been examined in detail. We examined and compared the activities and kinetics of BK channels in coronary arterial smooth muscle cells from Lean control and Zucker Diabetic Fatty (ZDF) rats, using single-channel recording techniques. We found that BK channels in ZDF rats have impaired Ca sensitivity, including an increased free Ca concentration at half-maximal effect on channel activation, a reduced steepness of Ca dose-dependent curve, altered Ca-dependent gating properties with decreasedmaximal open probability, and a shortenedmean open-time and prolonged mean closed-time durations. In addition, the BK channel b-subunit-mediated activation by dehydrosoyasaponin-1 (DHS-1) was lost in cells from ZDF rats. Immunoblotting analysis confirmed a 2.1-fold decrease in BK channel b1-subunit expression inZDF rats, comparedwith that of Lean rats. Theseabnormalities inBKchannel gating lead to an increase in the energy barrier for channel activation, andmay contribute to the development of vascular dysfunction and complications in type 2 diabetes mellitus.
Enhancement of Osteoconductivity of TNTZ by Hydrothermal Treatment

The surface wettability of implants is a significant factor in their osteoconductivity because it influences the adsorption of cell-attached proteins onto the surface. In this study, a single-step hydrothermal surface treatment using distilled water at a temperature of 180 °C for 3 h was applied Ti–29Nb–13Ta–4.6Zr (TNTZ) mass%) and compared with as-polished TNTZ and with implants produced by anodizing TNTZ in 0.1 M of H3PO4 with applied voltages of 150 V at a scanning rate of 0.1 V s−1. The surface-treated samples were stored in a five time phosphate buffered saline (×5 PBS(−)) solution to prevent increasing the water contact angle (WCA) with time. The surface characteristics were evaluated using scanning electron microscopy, X-ray diffraction, Auger electron spectroscopy, surface roughness, and contact angle measurement using a 2 μL droplet of distilledwater. The relationship betweenWCA and osteoconductivity at various surface modifications was examined using in vivo tests. The results showed that a superhydrophilic surface with aWCA≤10° and a high osteoconductivity (RB–I) ofupto50% in the cortical bone part, about four times higher than the as-polished TNTZ, were provided by the combination of the hydrothermal surface treatment and storage in ×5 of PBS(−). Abstrak. Wettabilitas permukaan merupakan faktor krusial dari osteconductivitas implan karena berpengaruh terhadap penyerapan protein pada pelekatan sel. Pada penelitian ini, perlakuan hydrothermal menggakan air destilasi pada temperatur 180 °C selama 3 h diaplikasikan kepada TNTZ dan dibandingkan dengan TNTZ pada kondisi as-polished dan as-anodized pada larutan 0.1 M of H3PO4 dengan tegangan 150 V pada scanning rate of 0.1 V s−1, Karakteristikpermukaan dievaluasi menggunakan scanning electron microscopy, X-ray diffraction, Auger electron spectroscopy, surface roughness, and pengukuran contact angle menggunakana 2 μL droplet of distilled water. Hubungan antara WCA dan osteoconductivity pada berbagai modifikasi permukaan dilakukan melalui in vivo tes. Hasilnya menunjukkan bahwa permukaan yang superhydrophilic dengan WCA≤10° dang osteoconductivity yang tinggi meningkat sampai 50 % pada cortical bone part, sekitar 4 kali lebih tinggi disbanding dengan as-polished TNTZ diperoleh dari kombiasi antara hydrothermal treatment dan penyimpanan dalam ×5 PBS(−). Kata kunci: TNTZ, wettability, osteoconductivity, hydrophilic, superhydrophilic © 2018. BKSTM-Indonesia. All rights reserved
CYBERBULLYING AMONG FIRST YEAR SECONDARY SCHOOL STUDENTS

The purpose of this study was to investigate the combined effects of two variables, namely, moral disengagement, parental monitoring on one outcome measure, namely, cyberbullying. Moreover, it aimed to investigate the relative contribution of moral disengagement, parental monitoring to cyberbullying among first year secondary school students in Egypt. Additionally, the aim was to find out if there were correlations between and among moral disengagement, parental monitoring and cyberbullying among first year secondary school students. A total of 140 (80 males, and 60 females) students participated in this study. They ranged in age from 15 to 16 years (M = 15.90, SD =1.03). Quantitative survey research was employed. The Cyberbullying-specific Moral Disengagement Questionnaire (CBMDQ-15) (DAY and LAZURAS (2016), Parental Monitoring Scale (Kerr, Stattin and Burk, 2010) and Revised Cyber Bullying Inventory (RCBI, Erdur-Baker and Kavsut 2007) were employed for data collection. Findings indicated that moral disengagement correlated negatively with parental monitoring. On the other hand, moral disengagement was found to be positively correlated with cyberbullying. As predicted, moral disengagement related positively and significantly to students’ cyberbullying. The two independent variables (moral disengagement, parental monitoring), as presented in table 2, when put together yielded a coefficient of multiple regression (R) of 0.631 and a multiple correlation square of 0. 626. This shows that 62.6% of the total variance in cyberbullying of those who participated in the study is accounted for by the combination of moral disengagement, parental monitoring.
Speech Signal Reconstruction using Two-Step Iterative Shrinkage Thresholding Algorithm

The idea behind Compressive Sensing(CS) is the reconstruction of sparse signals from very few samples, by means of solving a convex optimization problem. In this paper we propose a compressive sensing framework using the Two-Step Iterative Shrinkage/ Thresholding Algorithms(TwIST) for reconstructing speech signals. Further, we compare this framework with two other convex optimization algorithms, l1 Magic and Gradient Projection for Sparse Reconstruction(GPSR). The performance of our framework is demonstrated via simulations and exhibits a faster convergence rate and better peak signal-to-noise ratio(PSNR).
Natural Disasters, Aggregate Trade Resilience and Local Disruptions: Evidence From Hurricane Katrina

What drives the resilience of international trade against increasingly destructive natural disasters? In this study, I investigate the dynamics and spatial distribution of Hurricane Katrina's trade effects across U.S. infrastructure and break new ground on the mechanisms underlying the static and dynamic trade resilience. Analyzing port-level data, I find that ports subject to this calamity experience significant and lasting trade reductions, while shipments handled by the adjacent ports increase by as much as 13,000%. Interestingly, the duration of this rerouting effect is persistent for eight years uncovering novel path dependencies and providing important policy insights in light of recent events.
Expressive Languages for Querying the Semantic Web

The problem of querying RDF data is a central issue for the development of the Semantic Web. The query language SPARQL has become the standard language for querying RDF since its W3C standardization in 2008. However, the 2008 version of this language missed some important functionalities: reasoning capabilities to deal with RDFS and OWL vocabularies, navigational capabilities to exploit the graph structure of RDF data, and a general form of recursion much needed to express some natural queries. To overcome these limitations, a new version of SPARQL, called SPARQL 1.1, was released in 2013, which includes entailment regimes for RDFS and OWL vocabularies, and a mechanism to express navigation patterns through regular expressions. Unfortunately, there are a number of useful navigation patterns that cannot be expressed in SPARQL 1.1, and the language lacks a general mechanism to express recursive queries. To the best of our knowledge, no efficient RDF query language that combines the above functionalities is known. It is the aim of this work to fill this gap. To this end, we focus on a core fragment of the OWL 2 QL profile of OWL 2 and show that every SPARQL query enriched with the above features can be naturally translated into a query expressed in a language that is based on an extension of Datalog, which allows for value invention and stratified negation. However, the query evaluation problem for this language is highly intractable, which is not surprising since it is expressive enough to encode some inherently hard queries. We identify a natural fragment of it, and we show it to be tractable and powerful enough to define SPARQL queries enhanced with the desired functionalities.
Design of advanced primitives for secure multiparty computation : special shuffles and integer comparison

In modern cryptography, the problem of secure multiparty computation is about the cooperation between mutually distrusting parties computing a given function. Each party holds some private information that should remain secret as much as possible throughout the computation. A large body of research initiated in the early 1980's has shown that any computable function can be evaluated using secure multiparty computation. Though these feasibility results are general, their applicability in practical situations is rather unsatisfactory. This thesis concerns the study of two particular cryptographic primitives with focus on efficiency. The first primitive studied is a generalization of verifiable shuffles of homomorphic encryptions, where the shuffler is only allowed to apply a permutation from a restricted set of permutations. In this thesis, we consider shuffles using permutations from a k-fragile set, meaning that any k input-output correspondences uniquely identify a permutation within the set. We provide verifiable shuffles restricted to the set of all rotations (1-fragile), affine transformations (2-fragile), and Mobius transformations (3-fragile). Applications of these special shuffles include fragile mixing, electronic elections, secure function evaluation using scrambled circuits, and secure integer comparison. Two approaches for verifiable rotations are presented. On the one hand, we use properties of the Discrete Fourier Transform (DFT) to express in a compact way that a rotation is applied in a shuffle. The solution is efficient, but imposes some mild restrictions on the parameters to allow DFT to work. On the other hand, we present a general solution that does not impose any parameter constraint and works on any homomorphic cryptosystem. These protocols for rotations are used to build efficient shuffling protocols for affine and Mobius transformations. The second primitive is secure integer comparison. In a general scenario, parties are given homomorphic encryptions of the bits of two integers and, after running a protocol, an encryption of a bit is produced, telling the result of the greater-than comparison of the two integers. This is a useful building block for higher-level protocols such as electronic voting, biometrics authentication or electronic auctions. A study of the relationship of other problems to integer comparison is given as well. We present two types of solutions for integer comparison. Firstly, we consider an arithmetic circuit yielding secure protocols within the framework for multiparty computation based on threshold homomorphic cryptosystems. Our circuit achieves a good balance between round and computational complexities, when compared to the similar solutions in the literature. The second type of solutions uses a intricate approach where different building blocks are used. A full analysis is made for the two-party case where efficiency of the resulting protocols compares favorably to other solutions and approaches.
Updating Automata Networks

This thesis is concerned with the events and the organisation of events that take place within networks of abstract predetermined elements called "automata". In these networks, automata incite one another to switch states in agreement with predefined rules which, precisely, define the net-work. When an automaton effectively conforms to the influences it receives from others, its state is said to be updated. The elementary events that are considered here are thus automata state changes. To define an update mode for all the automata of a network allows to select some events among all those that are a priori possible. It also allows to organise and order the events relatively so as to impose, for example, that independent events occur simultaneously or so that simply, they happen close enough to disallow the occurrence of any other events in between. Informally, update modes can be interpreted as the expressions of influences incoming from outside the network, forbidding certain changes, or else, as the formalisation of a relaxed and relative version of time flow. This thesis proposes to study their influences on network behaviours. And to distinguish their influences from that of network structures, it starts by highlighting the role of certain structural motives. After that, it explores in particular the information that is "encoded" in a sequence of updates as well as the general impact of synchronism in updates.
Robust training of recurrent neural networks to handle missing data for disease progression modeling

Disease progression modeling (DPM) using longitudinal data is a challenging task in machine learning for healthcare that can provide clinicians with better tools for diagnosis and monitoring of disease. Existing DPM algorithms neglect temporal dependencies among measurements and make parametric assumptions about biomarker trajectories. In addition, they do not model multiple biomarkers jointly and need to align subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. We, therefore, propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle missing values in both target and predictor variables. This algorithm is applied for modeling the progression of Alzheimer's disease (AD) using magnetic resonance imaging (MRI) biomarkers. The results show that the proposed LSTM algorithm achieves a lower mean absolute error for prediction of measurements across all considered MRI biomarkers compared to using standard LSTM networks with data imputation or using a regression-based DPM method. Moreover, applying linear discriminant analysis to the biomarkers' values predicted by the proposed algorithm results in a larger area under the receiver operating characteristic curve (AUC) for clinical diagnosis of AD compared to the same alternatives, and the AUC is comparable to state-of-the-art AUCs from a recent cross-sectional medical image classification challenge. This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling.
System identification for feedforward and feedback control : with application to semiconductor manufacturing

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