Miracle-dz/UniFAR-FTUs huggingface dataset:
Medical Visualization for Orthopedic Applications

This dissertation discusses visualization techniques of a rticular cartilage for both quantitative and qualitative assessment. Articular c artilage is a very thin structure covering the endings of human bones. Thus, even sl ight changes in its thickness and inner structure may indicate an occurrence or prog ess of degeneration. The early detection of these factors is crucial for dia gnosis and treatment of cartilage degeneration. Research to find treatments to stop o r even reverse these degenerative changes is well in progress. Magnetic resonance imaging is currently the best and most us ed non-invasive technique for acquisition of soft tissue structures like ar ticular cartilage. In this work we use two types of data: a high-resolution anatomical s n of the cartilage and aT2 map, which is computed from a set of sequences with varying pa rameters. While the thickness of the data can be precisely assessed from the anatomical scan, theT2 map offers information on the inner structures of the cartil ge. Since the femoral cartilage is a curved thin-wall structure with a relatively small extent in one direction, it is very difficult to estimat e i s thickness from a stack of slices or even from a three-dimensional reconstruc tion of its surface. We discuss inspection of the tissue by unfolding and, thus, rep resenting the tissue as a two-dimensional height field. Such a transformation of the object enables the application of 2D geometrical operations in order to observ e subtle details in the thickness of the tissue. Nowadays scanners allow a quality assessment checking disr uptions in the pattern of theT2 map of the patellar cartilage. The T2 map illustrates the quality of the cartilage and changes in the pattern of T2 map indicate defects before changes in the thickness itself occur. We propose the Profile Flags a n intuitive interface for probing of theT2 maps by browsing the reconstructed surface of the cartilage . The Profile Flag is positioned on the reconstructed surface o f the tissue, and can be moved along it. The Profile Flags can be applied to annotate local as well as global characteristics of the underlying data in a single st i l image. Furthermore, we present a set of extensions to Profile Flags for selection, classification and automatic positioning. Profile Flags can also be used to meas ur time-varying dynamic contrast enhanced magnetic resonance imaging data .
Robust correlation analysis with an application to functional MRI

Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are corrupted by local bursts of noise. This adversely affects the performance of signal recognition algorithms. This paper presents a novel correlation estimator, which is robust to locally corrupted signals. The estimator is generalized to multivariate correlation analysis (general linear model, GLM, and canonical correlation analysis, CCA). Synthetic functional MRI data is used to demonstrate the estimator, and its robustness is shown to increase the performance of signal detection.
Locally-optimized reweighted belief propagation for decoding finite-length LDPC codes

In practice, LDPC codes are decoded using message passing methods. These methods offer good performance but tend to converge slowly and sometimes fail to converge and to decode the desired codewords correctly. Recently, tree-reweighted message passing methods have been modified to improve the convergence speed at little or no additional complexity cost. This paper extends this line of work and proposes a new class of locally optimized reweighting strategies, which are suitable for both regular and irregular LDPC codes. The proposed decoding algorithm first splits the factor graph into subgraphs and subsequently performs a local optimization of reweighting parameters. Simulations show that the proposed decoding algorithm significantly outperforms the standard message passing and existing reweighting techniques.
Analysis and Improvement of Patient Self-controllable Multi-level Privacy-preserving Cooperative Authentication Scheme

In 2014, a patient self-controllable multi-level privacy-preserving cooperative authentication scheme (PSMPA) was proposed for attempting to address the issue of data confidentiality and patients' identity privacy simultaneously when the personal healthcare record (PHR) is shared in the distributed m-healthcare cloud computing system. In this paper, we show the PSMPA scheme fails to achieve the two goals under the collusion attack. Furthermore, the scheme also suffers from forgery attack because of a awed design in the transcript simulation phase. In order to avoid the attacks, we propose an improved PHR sharing scheme by incorporating ciphertext policy attribute-based encryption (CP-ABE) and attribute-based signature (ABS) as a possible solution.
Interference avoidance mechanisms in the hybrid cellular and device-to-device systems

A hybrid system of cellular mode and device-to-device (D2D) mode is considered in this paper, where the cellular uplink resource is reused by the D2D transmission. In order to maximize the overall system performance, the mutual interference between cellular and D2D sub-systems has to be addressed. Here, two mechanisms are proposed to solve the problem: One is mitigating the interference from cellular transmission to D2D communication by an interference tracing approach. The other one is aiming to reduce the interference from D2D transmission to cellular communication by a tolerable interference broadcasting approach. Both mechanisms can work independently or jointly to synergy the transmission in the hybrid system for the efficient resource utilization. In the end, simulation is conducted to study the performance of the proposed schemes, which shows satisfying results.
Proposed Systems for Knowledge Sharing Between the Faculty Concentrating in the Middle East and Gulf Region Area

Knowledge sharing is an imperative aspect of every higher education institution. Among the different parties that require information to be shared among them are faculties because knowledge gained in different faculties are also applicable to others. The motivation for understanding the knowledge sharing behavior in faculties in the Middle East and Gulf Region area is because of the current weaknesses in the current knowledge sharing systems and thus, solutions for these weaknesses are recommended. The aim of this study is to provide an understanding of knowledge sharing behavior in the middle-east region and particular in the gulf region. The level of usage of knowledge sharing systems in the university institutions is the main focus while also determining the factors influencing the same. The advantages and disadvantages associated with knowledge sharing are discussed. Effects of knowledge sharing on students’ performance were also assessed. Qualitative research design was used to gather data for this study where secondary sources were used. These included books, scholarly journal articles, reports and credible sources from the internet. From the findings of the research, it was established that factors that some of the main factors that affect the knowledge sharing in universities in the Middle East include inadequate capital, technology resources and cultural differences in the region. Several recommendations have been made to enhance knowledge sharing and these include more commitment by the government to support it by allowing more collaboration between the local universities and foreign academicians who are well experienced in terms of information sharing.
Modeling Distributed Computing System Reliability with DRBD

Nowadays the great part of devices or systems we commonly use are often driven or managed by microchips and computers: cars, music players, phones, trains, planes, . A consolidated trend of technology is to substitute mechanical with electronic parts, analogical with digital devices or controls, and so on. In this context, features like security, availability and reliability, usually summarized under the concept of dependability, are receiving higher attention. The dependability analysis, especially for what regards critical parts as computing systems or subsystems, is becoming more strategic: specific requirements and explicit or tighter constraints have to be satisfied. Even though this fact, there is a lack of suitable tools to properly model and analyze these aspects, with particular reference to reliability. To fill this gap, we propose the dynamic reliability block diagram (DRBD) modeling tool derived from the reliability block diagram (RBD) formalism. The DRBD permits to model the dynamic reliability behavior of a system through dependence models, exploited to represent dynamics behaviors as redundancy, load sharing, multiple, probabilistic and common failure mode. In this paper, the DRBD expressiveness and other capabilities, are illustrated through the analysis of a complex distributed computing system taken as example
Performance analysis and comparison of two 1T/1MTJ-based logic gates

A performance analysis and comparison of two one-transistor/one-magnetic tunnel junction (1T/1MTJ)-based logic gates is presented. The energy consumption as well as the reliability of different Boolean logic functions utilizing the two circuit topologies are studied and the adequacy of their employment for specific non-volatile logic applications is discussed. It has been shown that the implication logic design exhibits a more reliable behavior compared to the reprogrammable logic design featuring conventional Boolean logic operations like (N)AND and (N)OR. Although the comparison between the two error optimized logic gate types shows that the fundamental logic operations with the reprogrammable gates require less energy than with the IMP gates, the situation reverses for more complex Boolean logic operations.
Galaxy groups and low mass clusters at z<0.6: A perspective from the XMM Large Scale Structure survey

Galaxy groups and low-mass clusters provide important laboratories in which to study X-ray gas physics and the interplay between galaxy evolution and environmental effects. The X-ray Multi-Mirror (XMM) Large Scale Structure (LSS) survey has currently imaged 5 deg2 to a nominal extended source flux limit of order 10e-14 ergs/s/cm2 and is dominated numerically by low-mass groups and clusters at redshifts 0.2 < z < 0.6. We discuss the generation of the XMM-LSS cluster sample, initial results on the physics of groups and low-mass clusters and the prospects for detailed follow-up studies of these systems.
Performance outcomes and their associations with network measures during FIFA World Cup 2018

ABSTRACT This study aimed to test the relationships between performance outcomes and network measures of the teams that participated in FIFA World Cup 2018 matches. It was also an aim to compare the performance outcomes and network measures of winners and losers and to compare them for both close and unbalanced scores. A total of 64 matches and 32 teams were observed and coded during this study. Matches were observed and coded, based on the following performance outcomes: scored goals, goals against and shots. Passing distribution was also collected. The network measures were calculated based on passing distribution matrices. Clear large correlations were found between shots and total arcs (0.53, [0.16;0.77]) and density (0.54, [0.18;0.77]) in drawn matches. Clear moderate correlations were found between shots and total arcs (0.42, [0.22;0.59]) and density (0.43, [0.23;0.60]) in won matches. Comparisons between winners and losers during unbalanced scores revealed very likely moderate increases in scored goals (88.7%, [13.8;213.0]; ES: 1.15, [0.23;2.07]) and dyad reciprocity (36.5%, [9.5;70.1]; ES: 0.69, [0.20;1.18]) in winners during unbalanced matches. This study concludes that general network measures studied are not too sensitive to variations in final score. However, some of them can be associated with performance outcomes, such as shots.
Detecting Deep Divergence in Seventeen Populations of Tea Geometrid (Ectropis obliqua Prout) in China by COI mtDNA and Cross-Breeding

The tea geometrid (Ectropis obliqua Prout, Lepidoptera: Geometridae) is a dominant chewing insect endemic in most tea-growing areas in China. Recently some E. obliqua populations have been found to be resistant to the nucleopolyhedrovirus (EoNPV), a host-specific virus that has so far been found only in E. obliqua. Although the resistant populations are morphologically indistinguishable from susceptible populations, we conducted a nationwide collection and examined the genetic divergence in the COI region of the mtDNA in E. obliqua. Phylogenetic analyses of mtDNA in 17 populations revealed two divergent clades with genetic distance greater than 3.7% between clades and less than 0.7% within clades. Therefore, we suggest that E. obliqua falls into two distinct groups. Further inheritance analyses using reciprocal single-pair mating showed an abnormal F1 generation with an unbalanced sex ratio and the inability to produce fertile eggs (or any eggs) through F1 self-crossing. These data revealed a potential cryptic species complex with deep divergence and reproductive isolation within E. obliqua. Uneven distribution of the groups suggests a possible geographic effect on the divergence. Future investigations will be conducted to examine whether EoNPV selection or other factors prompted the evolution of resistance.
Conspecific and Heterospecific Information Use in Bumblebees

Heterospecific social learning has been understudied in comparison to interactions between members of the same species. However, the learning mechanisms behind such information use can allow animals to be flexible in the cues that are used. This raises the question of whether conspecific cues are inherently more influential than cues provided by heterospecifics, or whether animals can simply use any cue that predicts fitness enhancing conditions, including those provided by heterospecifics. To determine how freely social information travels across species boundaries, we trained bumblebees (Bombus terrestris) to learn to use cues provided by conspecifics and heterospecific honey bees (Apis mellifera) to locate valuable floral resources. We found that heterospecific demonstrators did not differ from conspecifics in the extent to which they guided observers' choices, whereas various types of inorganic visual cues were consistently less effective than conspecifics. This was also true in a transfer test where bees were confronted with a novel flower type. However, in the transfer test, conspecifics were slightly more effective than heterospecific demonstrators. We then repeated the experiment with entirely naïve bees that had never foraged alongside conspecifics before. In this case, heterospecific demonstrators were equally efficient as conspecifics both in the initial learning task and the transfer test. Our findings demonstrate that social learning is not a unique process limited to conspecifics and that through associative learning, interspecifically sourced information can be just as valuable as that provided by conspecific individuals. Furthermore the results of this study highlight potential implications for understanding competition within natural pollinator communities.
Flow-sensitive alias analysis with referred-set representation for Java

Alias analysis refers to the determination of objects that may be accessed by two or more names. Many alias analysis algorithms have been proposed for several programming languages. In this paper, we propose a flow-sensitive alias analysis algorithm for Java, an object-oriented language. This algorithm is more efficient and precise than previous algorithms for C++, another object-oriented language. For the efficiency, we define a referred-set representation of an alias that is proper for Java, while the conventional representations in C++ cause an inefficient analysis for Java. We also present a data-flow equation based on propagation rules for the referred-set. The equation computes alias information more efficiently and precisely by removing redundant alias information at a call statement. Finally, we propose our algorithm that uses an iterative looping method for an alias analysis with a structural traverse of a CFG to improve its efficiency.
Differential Evolution based Multi-objective Optimization of a Deregulated Power Network under Contingent State

This paper proposes a methodology to limit the variations in generation cost in a power system under normal and contingent state using Differential Evolution (DE) optimization technique keeping consumer welfare in view. The aim of this proposed methodology is to minimize the deviations of generation cost, during contingency, from a preferred value by rescheduling of the generation with a controlled load curtailment technique and hence relieving the lines from overloading for congestion management. A comparative study between rescheduling with and without load curtailment has also been presented in this paper. Numerical results on test system, namely IEEE 30 Bus System, are presented for illustration purpose and the same has been verified by a well esteemed optimization technique, Particle Swarm Optimization (PSO). The comprehensive simulation results establish that the developed method not only reduces the economic variations of the market but also ensures the voltage stability of the system during contingency.
Web Page Classification Based on Uncorrelated Semi-Supervised Intra-View and Inter-View Manifold Discriminant Feature Extraction

Web page classification has attracted increasing research interest. It is intrinsically a multi-view and semi-supervised application, since web pages usually contain two or more types of data, such as text, hyperlinks and images, and unlabeled pages are generally much more than labeled ones. Web page data is commonly high-dimensional. Thus, how to extract useful features from this kind of data in the multi-view semi-supervised scenario is important for web page classification. To our knowledge, only one method is specially presented for this topic. And with respect to a few semi-supervised multi-view feature extraction methods on other applications, there still exists much room for improvement. In this paper, we firstly design a feature extraction schema called semi-supervised intra-view and inter-view manifold discriminant (SI2MD) learning, which sufficiently utilizes the intra-view and inter-view discriminant information of labeled samples and the local neighborhood structures of unlabeled samples. We then design a semi-supervised uncorrelation constraint for the SI2MD schema to remove the multi-view correlation in the semi-supervised scenario. By combining the SI2MD schema with the constraint, we propose an uncorrelated semi-supervised intra-view and inter-view manifold discriminant (USI2MD) learning approach for web page classification. Experiments on public web page databases validate the proposed approach.
Stochastic Modelling and Analysis of Image Acquisition and Sub-pixel Edge Detection

Stochastic analysis of edge detectors can be made either by theoretical modeling of the image formation process and the edge detectors or by empirical stochastic analysis of the edge locations. In this paper we study and model the image formation process in detail. In particular the much neglected discretisation process is modelled and taken into account. One of the major results of the paper is that after scale-space smoothing the image can be modelled as the sum of a deterministic intensity image and a stationary random eld. Furthermore the scale space smoothing almost completely eliminates the eeect of the discretisation process. This newly gained insight makes it possible to analyse feature detectors. In this paper we apply this model to the stochastic analysis of edge detectors. The location of the edge is essentially detected as the maximum of the directional derivative perpendicular to the line after a smoothing operation. This is also done in detail. The results are then veriied both through stochastic analysis of simulated and real image data. The reened analysis gives both the location of the edge and a conndence interval around the located edge. The purpose is not just to locate the edge but also to have some measure of how accurate the actual detection is. We propose that such estimates of the accuracy of the detection should propagate through further algorithms such as reconstruction, recognition, calculation of epipolar structure and so on. Furthermore we show that the estimated location of the edge can be regarded as a random process along the edge.
Constructing Isogenies between Elliptic Curves Over Finite Fields

Let E1 and E2 be ordinary elliptic curves over a finite field Fp such that #E1.Fp/ D #E2.Fp/. Tate’s isogeny theorem states that there is an isogeny fromE1 toE2 which is defined over Fp. The goal of this paper is to describe a probabilistic algorithm for constructing such an isogeny. The algorithm proposed in this paper has exponential complexity in the worst case. Nevertheless, it is efficient in certain situations (that is, when the class number of the endomorphism ring is small). The significance of these results to elliptic curve cryptography is discussed.
Sequence Analysis with Stata

We describe a general strategy to analyze sequence data and introduce SQ-Ados, a bundle of Stata programs implementing the proposed strategy. The programs include several tools for describing and visualizing sequences as well as a Mata library to perform optimal matching using the Needleman–Wunsch algorithm. With these programs Stata becomes the first statistical package to offer a complete set of tools for sequence analysis.
IRIS RECOGNITION BASED ON KERNELS OF SUPPORT VECTOR MACHINE

Ensuring security biometrically is essential in most of the authentication and identification scenario. Recognition based on iris patterns is a thrust area of research cause to provide reliable, simple and rapid identification system. Machine learning classification algorithm of support vector machine [SVM] is applied in this work for personal identification. The profuse as well as unique patterns of iris are acquired and stored in the form of matrix template which contains 4800 elements for each iris. The row vectors of 2400 elements are passed as inputs to SVM classifier. The SVM generates separate classes for each user and performs matching based on the template’s unique spectral features of iris. The experimental results of this proposed work illustrate a better performance of 98.5% compared to the existing methods such as hamming distance, local binary pattern and various kernels of SVM. The popular CASIA (Chinese Academy of Sciences – Institute of Automation) iris database with fifty users’ eye image samples are experimented to prove, that the least Square method of Quadratic kernel based SVM is comparatively better with minimal true rejection rate.