Miracle-dz/UniFAR-FTUs huggingface dataset:
Support Vector Machine for Predicting Candlestick Chart Movement on Foreign Exchange

Foreign Exchange, commonly called Forex, is a form of investment in the non-real sector in great demand. Forex is a marketplace that specializes in foreign exchange trading. Technology advancements have made it easy to monitor investment conditions in real time and present them in an easyto - understand graphical form. As a result, predictions are closely related to investment, starting from market sentiment and economic conditions to technical matters. One of the Artificial Intelligence methods that can be used in classifying is the Support Vector Machine (SVM). SVM is a machine learning classification method based on the Structural Risk Minimization (SRM) principle to find the best hyperplane that separates two classes in the input space that determines the classification decision function by minimizing empirical risk. This study used candlestick patterns to predict foreign exchange chart movements using the Support Vector Machine (SVM) classification method. The purpose of this study was to measure the accuracy of the Support Vector Machine method in making predictions using candlestick patterns so that it can assist traders in making decisions in forex trading. The accuracy level obtained from the data classification results reached 90.72% with a precision of 87.69%. With a relatively good level of accuracy, the Support Vector Machine (SVM) method can be used to predict chart movements in foreign exchange using candlesticks to indicate the current trend’s direction.
The service concept – a missing link in lean for services

This paper aims to contribute to the empirical literature on lean implementation for services by examining how the relation between lean implementation and operational performance improvement is moderated by the degree of service concept diffusion. The effect of explicit service concept diffusion on the outcome of lean implementation processes is tested by a sample of 153 respondents from Dutch service organizations. By means of hierarchical regression analysis this research shows that lean implementation in service organizations with an explicit and widely disseminated service concept positively affects organizational performance. Additionally, it is found that the moderating influence of service concept diffusion on performance improvement is especially apparent in case of explicit presence and a clearly disseminated service concept or an explicitly absent and unclear service concept. These results are corroborated by the finding that lean implementation in service environments without the moderating influence of service concept diffusion does not result in enhanced organizational performance. Finally, the positive sole effect of a disseminated service concept on organizational performance is empirically corroborated. These findings show the importance of clarity on what is expected of employee- and improvement specialist’s involvement in terms of goals, roles, responsibilities and activities with respect to the service concept in service-based organizations. Having such clarity enhances their ability to make a meaningful contribution to improvement initiatives in lean implementations. Thereby, this research is the first to provide empirical evidence for explaining how employee involvement in service-based organizations can yield better lean implementation results.
A Group-Specific Recommender System

ABSTRACT In recent years, there has been a growing demand to develop efficient recommender systems which track users’ preferences and recommend potential items of interest to users. In this article, we propose a group-specific method to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. The new approach is effective for the “cold-start” problem, where, in the testing set, majority responses are obtained from new users or for new items, and their preference information is not available from the training set. One advantage of the proposed model is that we are able to incorporate information from the missing mechanism and group-specific features through clustering based on the numbers of ratings from each user and other variables associated with missing patterns. In addition, since this type of data involves large-scale customer records, traditional algorithms are not computationally scalable. To implement the proposed method, we propose a new algorithm that embeds a back-fitting algorithm into alternating least squares, which avoids large matrices operation and big memory storage, and therefore makes it feasible to achieve scalable computing. Our simulation studies and MovieLens data analysis both indicate that the proposed group-specific method improves prediction accuracy significantly compared to existing competitive recommender system approaches. Supplementary materials for this article are available online.
Body mass index, aerobic fitness, and eating habits for affiliated colleges students to King Fahd University at Hafr AL Batin: cross sectional study.

Objectives : the objectives of this study were 1) determine prevalence of overweight, obesity, aerobic fitness and eating habits among male college students. 2) Find correlation between their body composition status- Body Fat Percent (%BF) and Body Mass Index (BMI)- with their aerobic fitness (VO2max). Methods : a cross-sectional study was conducted in Hafr Albatin Community Colleges (HBCC) Which was affiliated to (KFUPM) in (KSA). A number of (244) male students aged (18-26) years were chosen by the purposive non-random sampling method. Students filled out a self-reported questionnaire on their eating habits. Also (VO2max), (BF%) and (BMI) were measured. Results : the results revealed that half of the college students were above normal weight (49.18%) - overweight students represented (22.95%) and (26.23%) were obese. Moreover, significant and strong correlation (P > 0.05) between (BMI) and (BF%) reached to 0.751. VO2max level among them was good (46.38+7.94 ml/kl/min) and there was a reverse significant correlation between both %BF & BMI with Vo2max (-0.403, -0.401) respectively. Eating habits revealed that most of students have bad eating habits.
CPU Optimization of Particle Deposition in PIC Simulation Code

This thesis discusses how to optimize computational physics software for speed through maximizing the use of novel architectural features of current CPUs. Speciﬁcally, the optimization of the Particle-In-Cell (PIC) algorithm is considered. The PIC algorithm is widely use in the study of plasmas, rariﬁed gases, ﬂuid dynamics, and gravitational dynamics. For this algorithm, the main performance bottleneck is the deposition of charge onto the grid. The goals of the optimizations described in this paper were to maximize cache reuse to overcome memory bandwidth limitations and improve data processing speeds by taking advantage of multithreading and vector instructions. The particular techniques, discussed in detail in the paper, were sorting particles into tiles, operating on tiles in a manner that prevents race conditions, separating out the vectorizable operations (also known as strip mining), and sorting particles based on cells. Performance analysis results are given at each step of the optimization. An overall improvement of a factor of 20 in computational speed was obtained.
Outcomes of pediatric tracheostomy after surgery for congenital heart disease: A 20‐year experience

Children with congenital heart defects (CHD) requiring cardiovascular surgery (CVS) rarely require tracheostomy placement; however the mortality rate remains high. The study aimed to analyze the incidence of tracheostomy in children with CHD, and to determine factors contributing to postoperative outcomes, decannulation rates, and mortality.
Replacement of humans in the classroom by artificial Intelligence: A rhetoric

This study explores the probability if artificial intelligence can replace humans in the classroom. It identifies the limitations of artificial intelligence and the prevalence of human-level intelligence in education. Artificial intelligence is conceptualized and the research gaps in comparing artificial and human-level intelligence are identified. Comparative research method and explorative research design are adopted to guide this study. Secondary data is utilized with the use of a systematic desk research method and content analysis is used to explain the concepts and their relationship to each other. Three research objectives are used to assess human superiority to AI, identify limitations of AI and proffer measures to encourage collaboration between human-level and artificial intelligence. The study concludes and further provides recommendations necessary to support the relevance of human-level intelligence and AI in education.
CRS: A Privacy-Preserving Two-Layered Distributed Machine Learning Framework for IoV

Nowadays, vehicles can provide many valuable data (such as the videos recorded by dashcams) for analytical model building. Integrating vehicular ad hoc networks with the Internet of Things (IoT), the Internet of Vehicles (IoV) has a promising future. In IoV, vehicles maintain their own communication, computing, and learning capabilities. Thus, instead of sending the data to a central server for model training, which leads to a high communication overhead, vehicles can train the data locally. However, it is still a challenge to preserve the privacy while keeping both the communication and computation overheads of vehicles acceptable. In this article, we present a distributed machine learning framework with a two-layered architecture. The architecture uniquely involves vehicle clusters, roadside units, and a central server, which provides a basic guarantee to the vehicle privacy and also limits the overhead. By carefully adopting cryptographic tools and techniques, the framework has the following properties: 1) it preserves the privacy of the local inputs and model weight vectors to all parties; 2) it protects the identities and trajectories of vehicles; 3) packet loss is handled in the application layer; 4) the evaluation shows that it is lightweight for vehicles. Compared with other existing works, the proposed framework is more suitable for IoV.
A Parallel Newton-Krylov-Schur Algorithm for the Reynolds-Averaged Navier-Stokes Equations

A PARALLEL NEWTON-KRYLOV-SCHUR ALGORITHM FOR THE REYNOLDS-AVERAGED NAVIER-STOKES EQUATIONS Michal Osusky Doctor of Philosophy Graduate Department of Aerospace Science and Engineering University of Toronto 2013 Aerodynamic shape optimization and multidisciplinary optimization algorithms have the potential not only to improve conventional aircraft, but also to enable the design of novel configurations. By their very nature, these algorithms generate and analyze a large number of unique shapes, resulting in high computational costs. In order to improve their efficiency and enable their use in the early stages of the design process, a fast and robust flow solution algorithm is necessary. This thesis presents an efficient parallel Newton-Krylov-Schur flow solution algorithm for the three-dimensional Navier-Stokes equations coupled with the Spalart-Allmaras oneequation turbulence model. The algorithm employs second-order summation-by-parts (SBP) operators on multi-block structured grids with simultaneous approximation terms (SATs) to enforce block interface coupling and boundary conditions. The discrete equations are solved iteratively with an inexact-Newton method, while the linear system at each Newton iteration is solved using the flexible Krylov subspace iterative method GMRES with an approximateSchur parallel preconditioner. The algorithm is thoroughly verified and validated, highlighting the correspondence of the current algorithm with several established flow solvers. The solution for a transonic flow over a wing on a mesh of medium density (15 million nodes) shows good agreement with experimental results. Using 128 processors, deep convergence is obtained in under 90 minutes. The solution of transonic flow over the Common Research Model wing-body geometry with grids with up to 150 million nodes exhibits the expected grid convergence ii behavior. This case was completed as part of the Fifth AIAA Drag Prediction Workshop, with the algorithm producing solutions that compare favourably with several widely used flow solvers. The algorithm is shown to scale well on over 6000 processors. The results demonstrate the effectiveness of the SBP-SAT spatial discretization, which can be readily extended to high order, in combination with the Newton-Krylov-Schur iterative method to produce a powerful parallel algorithm for the numerical solution of the Reynolds-averaged Navier-Stokes equations. The algorithm can efficiently solve the flow over a range of clean geometries, making it suitable for use at the core of an optimization algorithm.
Numerical method for solving second-order fuzzy boundary value problems by using the RPSM

The aim of this paper is to present analytic-numeric solutions of two-point, second-order fuzzy boundary value problems under strongly generalized differentiability based on the residual power series (RPS) method. The new approach provides the solution in the form of a rapidly convergent series with easily computable components, using symbolic computation software. The proposed method obtains the expansion of the solutions of the parameterized systems under appropriate guesses approximations. The proposed technique will be applied to a few test examples in order to illustrate the accuracy, the efficiency, and the applicability of the method.
FOR NON-FREE CIRCLE ACTIONS ∗

Let �: S 1 × M → M be a smooth action of the unit circle S 1 on a manifold M. In this work, we compute the minimal model of M in terms of the orbit space B and the fixed point set F ⊂ B, as a dg-module over the Sullivan’s minimal model of B.
Analysis of Parameterized-Chain Networks: The Dependency Graph and Its Full, Consistent Subgraphs

This thesis studies algorithmic aspects of deadlock analysis for parameterized networks of discrete-event systems. A parameterized network consists of a finite, but arbitrarily large, number of interacting finite-state subsystems, each within one of a fixed, finite number of isomorphism classes. While deadlock analysis of such systems is generally undecidable, decidable subproblems have recently been identified. The decision procedure rests on the construction of a finite dependency graph for the network, and the computation of its full, consistent subgraphs . We present a software tool for such computations, and apply it to a train network example that extends beyond the current theoretical framework. The results suggest ways in which the framework could usefully be extended.
A Systematic Review of Economic Evaluations of Fall Prevention Interventions for Community-Dwelling Older People

The purpose of this article was to identify the best cost-effective fall prevention measures for 65-year-old and older community-dwelling older adults. Eligibility criteria included being 65 or older, residing in the community, experiencing primary or recurrent falls, and evaluating the cost-effectiveness of any fall prevention intervention. Web of Science, National Institute for Health and Care Excellence Compliance Database, Cinahl Plus, Ovid (Medline), PubMed, ProQuest, Wiley Online, National Health Services Economic Evaluation Databases, EBSCOhost, and PubMed databases were scanned for this study. The risk of bias in cost-effectiveness study reviews was assessed using the Consensus Health Economics Criteria checklist and the Review Manager software. Thus, twenty-two studies met the inclusion criteria (multifactorial program: 12, exercises program: 6, home assessment program: 3, vitamin D supplement: 1). Analyzing those research results, it was revealed that preventive interventions were cost-effective, cost-saving, or cost-beneficial in 17 of them. The Falls Rehabilitation Program, the Home Hazard Reduction Program, and the Community-Based Interventions Targeting Falls Prevention were identified as net cost savings in studies suggesting the assessment of treatments.
Structured Condition Numbers for the Tikhonov Regularization of Discrete Ill-Posed Problems

The possibly most popular regularization method for solving the least squares problem min x parallel to Ax - b parallel to(2) with a highly ill-conditioned or rank deficient coefficient matrix A is the Tikhonov regularization method. In this paper we present the explicit expressions of the normwise, mixed and component wise condition numbers for the Tikhonov regularization when A has linear structures. The structured condition numbers in the special cases of nonlinear structure i.e. Vandermonde and Cauchy matrices are also considered. Some comparisons between structured condition numbers and unstructured condition numbers are made by numerical experiments. In addition, we also derive the normwise, mixed and componentwise condition numbers for the Tikhonov regularization when the coefficient matrix, regularization matrix and right-hand side vector are all perturbed, which generalize the results obtained by Chu et al.
Extended gray level co-occurrence matrix computation for 3D image volume

Gray Level Co-occurrence Matrix (GLCM) is one of the main techniques for texture analysis that has been widely used in many applications. Conventional GLCMs usually focus on two-dimensional (2D) image texture analysis only. However, a three-dimensional (3D) image volume requires specific texture analysis computation. In this paper, an extended 2D to 3D GLCM approach based on the concept of multiple 2D plane positions and pixel orientation directions in the 3D environment is proposed. The algorithm was implemented by breaking down the 3D image volume into 2D slices based on five different plane positions (coordinate axes and oblique axes) resulting in 13 independent directions, then calculating the GLCMs. The resulted GLCMs were averaged to obtain normalized values, then the 3D texture features were calculated. A preliminary examination was performed on a 3D image volume (64 x 64 x 64 voxels). Our analysis confirmed that the proposed technique is capable of extracting the 3D texture features from the extended GLCMs approach. It is a simple and comprehensive technique that can contribute to the 3D image analysis.
Characteristic Mode Inspired Broadband Circularly Polarized Folded Transmitarray Antenna

The circularly polarized (CP) antennas with high gains have gained growing interest due to their significant advantages, but the existing works usually suffer from narrow bandwidths. In this work, a novel circularly polarized folded transmitarray antenna (CPFTA) inspired by the characteristic mode analysis (CMA) method is proposed to achieve a broadband and high-gain CP radiation. The proposed CPFTA consists of a top-layer multifunctional metasurface (MTS), and a bottom-layer polarization conversion metasurface (PCM) integrated with a linearly polarized (LP) microstrip feed source. Enabled by the CMA, the unit cell etched with an asymmetric U-slot of the MTS is optimized to generate two pairs of CP modes to broaden the bandwidth of the CPFTA. Moreover, a modified broadband E-shaped LP microstrip antenna is integrated with the PCM, resulting in a compact profile. A CPFTA prototype is fabricated and characterized to verify the effectiveness of the design. The measured results show that the fabricated antenna could obtain an impedance bandwidth of 31.7%, a 3-dB axial ratio (AR) bandwidth of 36.5%, and a 3-dB gain bandwidth of 30.1% with a peak gain of 22.35 dBic at 9.55 GHz.
MULTI -VIEW FACE DETECTION BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT VECTOR TECHNIQUES

Detecting faces across multiple views is more challenging than in a frontal view. To address this problem, an efficient approach is presented in this paper using a kernel machine based approach for learning such nonlinear mappings to provide effective view-based representation for multi-view face detection. In this paper Kernel Principal Component Analysis (KPCA) is used to project data into the view-subspaces then computed as view-based features. Multi-view face detection is performed by classifying each input image into face or non-face class, by using a two class Kernel Support Vector Classifier (KSVC). Experimental results demonstrate successful face detection over a wide range of facial variation in color, illumination conditions, position, scale, orientation, 3D pose, and expression in images from several photo collections.
PENERAPAN MODEL PROBLEM BASED LEARNING DALAM PEMBELAJARAN TRIGONOMETRI DI KELAS X IPA SMAN 3 PONTIANAK

Abstract The tendency of Mathematic learning process at present time is still dependent on the teacher. The result is less developed student’s problem solving ability. The application of Problem Based Learning Model was considered as its solution. The purpose of this research was to know the effectiveness of application Problem Based Learning model in learning trigonometry on grade X Science 2 SMAN 3 Pontianak. The form of this research was a quasi- experimental design with pre-experimental design. The sample of this research are X Science 2. The ability of teacher to manage learning is in good category with average from two treatments is 3.13, the student’s activity during learning is in active category with average from two treatments is 3.05 and the students have problem solving ability in high or very high category don’t get 85% but only 50%. It means that application of Problem Based Learning model is enough effective in learning trigonometry on grade X science 2 SMAN 3 Pontianak. Keywords : Problem Based Learning Model, Learning Trigonometry, Problem Solving Ability, Student’s Activity, The Ability to Manage Learning
Practical Implementations Of The Active Set Method For Support Vector Machine Training With Semi-definite Kernels

The Support Vector Machine (SVM) is a popular binary classification model due to its superior generalization performance, relative ease-of-use, and applicability of kernel methods. SVM training entails solving an associated quadratic programming (QP) that presents significant challenges in terms of speed and memory constraints for very large datasets; therefore, research on numerical optimization techniques tailored to SVM training is vast. Slow training times are especially of concern when one considers that re-training is often necessary at several values of the models regularization parameter, C, as well as associated kernel parameters. The active set method is suitable for solving SVM problem and is in general ideal when the Hessian is dense and the solution is sparse–the case for the `1-loss SVM formulation. There has recently been renewed interest in the active set method as a technique for exploring the entire SVM regularization path, which has been shown to solve the SVM solution at all points along the regularization path (all values of C) in not much more time than it takes, on average, to perform training at a single value of C with traditional methods. Unfortunately, the majority of active set implementations used for SVM training require positive definite kernels, and those implementations that do allow semi-definite kernels tend to be complex and can exhibit instability and, worse, lack of convergence. This severely limits applicability since it precludes the use of the linear kernel, can be an issue when duplicate data points exist, and doesn’t allow use of low-rank kernel approximations to improve tractability for large datasets. The difficulty, in the case of a semi-definite kernel, arises when a particular active set results in a singular KKT matrix (or the equality-constrained problem formed using the active set is semidefinite). Typically this is handled by explicitly detecting the rank of the KKT matrix. Unfortunately, this adds significant complexity to the implementation; and, if care is not taken, numerical