Miracle-dz/UniFAR-FTUs huggingface dataset:
Systems theoretic process analysis of information security: the case of aadhaar

ABSTRACT A new way of thinking about cybersecurity is much needed to deal with the complex and dynamic cyber-ecosystem. In this paper, we introduce a systems thinking based approach for solving problems related to cybersecurity. We adapt the powerful safety-hazard analysis method, Systems Theoretic Process Analysis (STPA) based on systems theory to analyze the cybersecurity related features of India’s massive digital identity program, Aadhaar. Our findings produce important insights. On one hand, it helps identify the security gaps of the Aadhaar system, and on the other hand, it provides controls using systems thinking to overcome these gaps. We contribute to understanding the world of cybersecurity practices and develop risk mitigation strategies that can benefit the Aadhaar.
Association of chicken MC 1 R gene polymorphism with coat colour trait in Iraqi native chicken

MC1R gene is mainly involved in melanogenesis and has crucial key in hair and skin colour in humans and coat colour in animals. The aim of this study was to investigate the genetic diversity of the partial coding regions of the MC1R gene and its association with coat colour in Iraqi native chicken. Blood samples from 95 Iraqi native chickens were collected to extract DNA and the 681-bp fragment of the MC1R gene was amplified and DNA sequencing method was adopted for single nucleotide polymorphism (SNP) discovering and genotyping. Sequence analysis showed that there were four SNPs in chicken MC1R gene, two of them were synonymous (D78272:g.1094A>G and D78272:g.1292C>T) and others were nonsynonymous (D78272:g.915A>G and D78272:g.1095C>T). The allele frequency of these mutations is 0.92/0.08, 0.95/0.05, 0.94/0.06 and 0.96/0.04 for D78272:g.915A>G, D78272:g.1094A>G, D78272:g.1095C>T, and D78272:g.1292C>T mutations, respectively. The association analysis showed that no significant differences between different genotypes and coat colour (p> 0.05). However, our finding identified four novel SNPs for MC1R gene in Iraq indigenous chicken and it is useful for considering theirs association with other biophysical and biochemical indexes. [Salah Mahdi Alsudany, Hossain Moradi Shahrbabak, Seyed Reza Miraee Ashtiani, Mostafa Sadeghi. Association of chicken MC1R gene polymorphism with coat colour trait in Iraqi native chicken. Life Sci J 2017;14(12):7175]. ISSN: 1097-8135 (Print) / ISSN: 2372-613X (Online). http://www.lifesciencesite. com. 10. doi:10.7537/marslsj141217.10.
TEXTURE MAPPING USING TILED TEXTURES

Texture Mapping Using Tiled Textures. (May 2003) Avneet Kaur, B.Arch., Birla Institute of Technology Chair of Advisory Committee: Dr. Ergun Akleman This thesis work presents a simple and practical technique for seamlessly texturing quadrilateral meshes. Using this technique, an isotropic homogeneous texture can be mapped to any quadrilateral mesh without any discontinuity or singularity in the resultant texturing. The method involves organizing a set of square texture tiles that satisfy specific boundary conditions into one texture image file which is called a tiled texture. Based on the tiled textures, we have developed an extremely simple texture mapping algorithm that randomly assigns one tile to every patch in any given quadrilateral mesh. The mapping technique developed yields singularity free textures, regardless of the singularities existing in the quadrilateral mesh, gives seamless and continuous boundaries across textures, and provides an aperiodic and interesting look to the entire textured surface.
Genetic variability in Aegorhinus superciliosus (Coleoptera: Curculionidae) populations in Chilean Maytenus boaria (Celastrales: Celastraceae)

The raspberry weevil (Aegorhinus superciliosus) is a curculionid native of Chile and part of Argentina. This insect is considered to be a pest to both European hazel (Corylus avellana) and blueberry (Vaccinium corymbosum) fruit plantations in south-central Chile and has caused considerable economic damage to the local fruit industry. The aim of this study was to evaluate the genetic variability within A. superciliosus populations associated with Maytenus boaria. Adult A. superciliosus specimens were collected from Máfil, Futrono, La Unión and Frutillar populations. Their classification and storage was carried out at the Animal Biotechnology Research Laboratory (LINBA) of the Universidad de La Frontera, Temuco, Chile. Molecular analysis was assessed through ISSR (Inter Simple Sequence Repeats). Results showed high genetic similarities across all studied populations. A positive correlation was observed between genetic variability and geographic distribution. The southernmost populations (Futrono, La Unión and Frutillar) expressed high interspecific genetic similarities, associated with the abundance of M. boaria, which allows for genetic flow throughout the studied area. The low level of genetic variability in A. superciliosus populations in southern Chile is likely to be correlated with the large number of hosts found in agricultural and forest ecosystems which have the capacity to colonize extensive areas in this part of the country. These conditions contribute to an increase in the genetic flux of A. superciliosus, thus transforming this insect into one of the most significant and most harmful blueberry pests in Chile.
Comparing Statistical and Data Mining Techniques for Enrichment Ontology with Instances

Enriching instances into an ontology is an important task because the process extends knowledge in ontology to cover more extensively the domain of interest, so that greater benefits can be obtained. There are many techniques to classify instances of concepts with two popular techniques being the statistical and data mining methods. The paper compares the use of the two methods to classify instances to enrich ontology having greater domain knowledge, and selects a conditional random field for the statistical method and feature-weight k-nearest neighbor classification for the data mining method. The experiments are conducted on tourism ontology. The results show that conditional random fields methods provide greater precision and recall value than the other, specifically, F1-measure is 74.09% for conditional random fields and 60.04% for feature-weight k-nearest neighbor classification.
Model Counting for Conjunctive Queries Without Self-Joins

We study the problem of model counting for Boolean Conjunctive Queries (CQs): given a database, how many of its subsets satisfy the CQ? This problem is computationally equivalent to the evaluation of a CQ over a tuple-independent probabilistic database (i.e., determining the probability of satisfying the CQ) when the probability of every tuple is 1/2. In particular, it follows from the work of Dalvi and Suciu that this problem is solvable in polynomial time (data complexity) for every hierarchical CQ without self-joins. However, while probabilistic query evaluation is intractable for non-hierarchical CQs, it has been open whether this hardness applies already for model counting. We prove that, indeed, model counting is #P-complete for every non-hierarchical CQ without self-joins. Hence, we establish that the dichotomy in the complexity probabilistic query evaluation also holds for model counting.
Efficiently Improving and Quantifying Robot Accuracy In Situ

The advancement of simulation-assisted robot programming, automation of high-tolerance assembly operations, and improvement of real-world performance engender a need for positionally accurate robots. Despite tight machining tolerances, good mechanical design, and careful assembly, robotic arms typically exhibit average Cartesian positioning errors of several millimeters. Fortunately, the vast majority of this error can be removed in software by proper calibration of the so-called "zero-offsets" of a robot's joints. This research developed an automated, inexpensive, highly portable, in situ calibration method that fine tunes these kinematic parameters, thereby, improving a robot's average positioning accuracy four-fold throughout its workspace. In particular, a prospective low-cost motion capture system and a benchmark laser tracker were used as reference sensors for robot calibration. Bayesian inference produced optimized zero-offset parameters alongside their uncertainty for data from both reference sensors. Relative and absolute accuracy metrics were proposed and applied for quantifying robot positioning accuracy. Uncertainty analysis of a validated, probabilistic robot model quantified the absolute positioning accuracy throughout its entire workspace. Altogether, three measures of accuracy conclusively revealed multi-fold improvement in the positioning accuracy of the robotic arm. Bayesian inference on motion capture data yielded zero-offsets and accuracy calculations comparable to those derived from laser tracker data, ultimately proving this method's viability towards robot calibration.
Synthetic Extreme-ultraviolet Emissions Modulated by Leaky Fast Sausage Modes in Solar Active Region Loops

We study the extreme-ultraviolet (EUV) emissions modulated by leaky fast sausage modes (FSMs) in solar active region (AR) loops and examine their observational signatures via spectrometers like the EUV imaging spectrometer (EIS). After computing fluid variables of leaky FSMs with magnetohydrodynamic (MHD) simulations, we forward-model the intensity and spectral properties of the Fe x 185 Å and Fe xii 195 Å lines by incorporating nonequilibrium ionization (NEI) in the computations of the relevant ionic fractions. The damping times derived from the intensity variations are then compared with the wave values, namely, the damping times directly found from our MHD simulations. Our results show that in the equilibrium ionization cases, the density variations and the intensity variations can be either in phase or in antiphase, depending on the loop temperature. NEI considerably impacts the intensity variations but has only marginal effects on the derived Doppler velocity or Doppler width. We find that the damping time derived from the intensity can largely reflect the wave damping time if the loop temperature is not drastically different from the nominal formation temperature of the corresponding emission line. These results are helpful for understanding the modulations to the EUV emissions by leaky FSMs and hence helpful for identifying FSMs in solar AR loops.
Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memory

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions have been of interest to engineers, mathematicians and physicists over the last several decades. With the motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.
SYNCHRONIZATION OF OVULATION BASED ON STAGES OF FOLLICULAR AND CORPORA LUTEA DEVELOPMENT IN LACTATING DAIRY COWS DIAGNOSED NOT-PREGNANT By

SYNCHRONIZATION OF OVULATION BASED ON STAGES OF FOLLICULAR AND CORPORA LUTEA DEVELOPMENT IN LACTATING DAIRY COWS DIAGNOSED NOT-PREGNANT By Megan Kron Cows that fail to conceive to 1st or subsequent inseminations are a major reproductive problem on dairy farms and commonly used re-synchronization programs are not as effective as presynch/Ovsynch programs for 1st AI. Ovsynch appears to be most effective when initiated during the 1st follicular wave of an estrous cycle when the dominant follicle is responsive to the 1st GnRH allowing the PGF2α to be administered prior to natural luteolysis. Yet, most cows are in other stages of follicle and CL development at time of a not-pregnant diagnosis. Presynchronization programs are designed to synchronize cows that are in these other stages of follicle and CL development to the 1st follicular wave. The objective of this thesis was to test 3 re-synchronization programs (Ovsynch, G6G, and GGPG) tailored to 4 different stages of follicular and corpora lutea development in cows (n = 515) that did not become pregnant at 1st AI in order to synchronize cows to the 1st follicular wave at the start of Ovsynch. Conception rates were 27.8, 36.6, and 45.3% for Ovsynch, G6G, and GGPG, respectively, and were not statistically different. Heart girth measurements indicated that smaller cows had greater rates of luteolysis following the final PGF2α of Ovsynch and a tendency for higher conception rates in cows with a mature CL. This could potentially be an artifact of incorrect dosage of PGF2α and GnRH used in synchronization protocols. In Chapter 4, the relationship between linear type traits and reproduction in 2299 cows was examined. Cows with increased body volume traits (strength and body depth) consistently had poorer fertility, which agrees with our findings in Chapter 3.
A new record of insect pathogen on larva of Rosaceous longhorn beetle , Osphranteria coerulescens ( Coleoptera : Cerambycidae )

The Rosaceous longhorn beetle (RLHB), Osphranteria coerulescens Redtenbacher (Coleoptera: Cerambycidae) is a serious wood borer insect that commonly occurs on fruit trees in Iran. During an ongoing project on natural enemies of O. coerulescens in Mashhad region, North East of Iran, two isolates of entomopathogenic fungi (EPF) were isolated as natural pathogens on the larvae. The initial species identity of the isolated EPFs was determined based on classic methods. For the molecular, ITS gene sequences were used for inferring phylogenetic relationship. The results confirmed the species identity as Beauveria bassiana sensu lato (Balsamo) Vuillemin (Hypocreales: Clavicipitaceae) and Metarhizium anisopliae sensu lato (Metschnikoff) Sorokin (Hypocreales: Clavicipitaceae). This is the first report about natural incidence of fungal infection on the O. coerulescens.
Quadrotor Upset Recovery after Rotor Failure Master of Science Thesis

A fault-tolerant controller is presented that is able to recover a quadrotor from an upset condition in the case of a single rotor failure. With only 3 rotors the quadrotor is not fully controllable anymore, however by using precession the quadrotor can be made controllable again in roll and pitch. A step-by-step recovery strategy is proposed in which controllability is actively recovered before recovery of attitude and altitude. Control allocation is based on the Incremental Non-Linear Dynamic Inversion (INDI) principle and takes rotor saturation into account by solving a constrained quadratic optimisation problem. The controller is validated in a real life test environment where the quadrotor is thrown into the air with only 3 propellers from which it has to recover using the techniques presented in this paper.
Edinburgh Research Explorer Interaction between Record Matching and Data Repairing Interaction between Record Matching and Data Repairing Algorithm eRepair Interaction between Record Matching and Data Repairing

Central to a data cleaning system are record matching and data repairing. Matching aims to identify tuples that refer to the same real-world object, and repairing is to make a database consistent by ﬁxing errors in the data by using integrity constraints. These are typically treated as separate processes in current data cleaning systems, based on heuristic solutions. This paper studies a new problem in connection with data cleaning, namely, the interaction between record matching and data repairing. We show that repairing can effectively help us identify matches, and vice versa. To capture the interaction, we provide a uniform framework that seamlessly uniﬁes repairing and matching operations, to clean a database based on integrity constraints, matching rules and master data. We give a full treatment of fundamental problems associated with data cleaning via matching and repairing, including the static analyses of constraints and rules taken together, and the complexity, termination and determinism analyses of data cleaning. We show that these problems are hard, ranging from NP -complete or co NP -complete, to PSPACE -complete. Nevertheless, we propose efﬁcient algorithms to clean data via both matching and repairing. The algorithms ﬁnd deterministic ﬁxes and reliable ﬁxes based on conﬁdence and entropy analyses, respectively, which are more accurate than ﬁxes generated by heuristics. Heuristic ﬁxes are produced only when deterministic or reliable ﬁxes are unavailable. We experimentally verify that our techniques can signiﬁcantly improve the accuracy of record matching and data repairing that are taken as separate processes, using real-life and synthetic data.
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image

We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image. Several approaches in this direction have been investigated that explore different shape representations and suitable learning architectures. We focus instead on the underlying probabilistic mechanisms involved and contribute a more principled probabilistic inference-based reconstruction framework, which we coin Probabilistic Reconstruction Networks. This framework expresses image conditioned 3D shape inference through a family of latent variable models, and naturally decouples the choice of shape representations from the inference itself. Moreover, it suggests different options for the image conditioning and allows training in two regimes, using either Monte Carlo or variational approximation of the marginal likelihood. Using our Probabilistic Reconstruction Networks we obtain single image 3D reconstruction results that set a new state of the art on the ShapeNet dataset in terms of the intersection over union and earth mover's distance evaluation metrics. Interestingly, we obtain these results using a basic voxel grid representation, improving over recent work based on finer point cloud or mesh based representations.
A ug 2 01 9 DETERMINING WAVENUMBERS FOR THE INCOMPRESSIBLE HALL-MAGNETOHYDRODYNAMICS

The above system describes the evolution of a system consisting of a magnetic field b, electrons and ions, whose collective motion under b can be approximated as an electrically conducting fluid with velocity field u. The focus here is primarily the visco-resistive case, corresponding to positive fluid viscosity ν and magnetic resistivity μ. The external forcing term f , which one assumes to have zero mean, renders system (1.1)-(1.3) inhomogeneous. The Hall-MHD system is derived using generalized Ohm’s law which takes into account the effect of the electric current on the Lorentz force, neglected in the derivation of the MHD equations. The resulting extra Hall term η(∇ × ((∇ × b) × b)), distinguishing system (1.1)-(1.3) from the conventional MHD system, becomes significant in the case of large magnetic shear. The coefficient η here is proportional to the ion skin length. The Hall-MHD system has a wide range of applications including modelling solar winds, designing magnetic confinement devices for fusion reactors and interpreting the origin of the geomagnetic field. It is believed to be an essential model for magnetic reconnection, an intriguing phenomenon frequently observed in space plasmas. Over the past decade, the Hall-MHD system has received more attentions from the mathematical community. Acheritogaray, Degond, Frouvelle and Liu [1] rigorously derived the system and established global existence of weak solutions on periodic domains. Chae, Degond and Liu [5] proved existence of global weak solutions in
Secrecy Preserving in Stochastic Resource Orchestration for Multi-Tenancy Network Slicing

Network slicing is a proposing technology to support diverse services from mobile users (MUs) over a common physical network infrastructure. In this paper, we consider radio access network (RAN)-only slicing, where the physical RAN is tailored to accommodate both computation and communication functionalities. Multiple service providers (SPs, i.e., multiple tenants) compete with each other to bid for a limited number of channels across the scheduling slots, aiming to provide their subscribed MUs the opportunities to access the RAN slices. An eavesdropper overhears data transmissions from the MUs. We model the interactions among the non-cooperative SPs as a stochastic game, in which the objective of a SP is to optimize its own expected long-term payoff performance. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game using the channel auction outcomes. Then we linearly decompose the per-SP Markov decision process to simplify the decision- makings and derive a deep reinforcement learning based scheme to approach the optimal abstract control policies. TensorFlow-based experiments verify that the proposed scheme outperforms the three baselines and yields the best performance in average utility per MU per scheduling slot.
The low-FODMAP diet and the gluten-free diet in the management of functional abdominal bloating and distension

This review summarizes current knowledge on the role of low-FODMAP diet and gluten-free diet in functional abdominal bloating and distension, an emerging disorder of gut-brain interaction characterized by remarkable costs for healthcare systems and a significant impact on the patient’s quality of life. Ingested food plays a key role in the pathophysiology of disorders of gut-brain interaction as up to 84% of patients with irritable bowel syndrome (IBS) report food-triggered symptoms. Potential pathogenetic mechanisms of food-related symptoms in these patients are discussed, focusing on bloating and abdominal distension. These mechanisms provide the rationale for dietary treatment in patients with functional abdominal bloating and distension. The role of fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs) and gluten in functional abdominal bloating and distension is examined. Current literature evaluating the efficacy of the low-FODMAP diet and the gluten-free diet in abdominal bloating and distension is analyzed. Available evidence originates mainly from studies on patients with IBS, since clinical studies on selected cohorts of patients with only functional abdominal bloating and distension have been missing to date. Promising evidence on the potential efficacy of the low-FODMAP diet in functional abdominal bloating and distension is provided by the reduction of the bloating observed in patients with IBS. Regarding the gluten-free diet, there is insufficient evidence to recommend it to reduce bloating and abdominal distension. In conclusion, this review asserts the need for a close collaboration with experts in nutrition to optimize the management of these patients and reduce the risks associated with elimination diets.