Miracle-dz/UniFAR-FTUs huggingface dataset:
Anthropometric profile, body composition and somatotyping of national Iranian cross-country runners

This study was conducted to determine the anthropometric profile of selected national athletes. The purpose of this study was to find out anthropometric measurements, body composition and somatotyping of Iranian cross-country runners. The participants were 9 male national Iranian cross-country runners. The age of athletes was between 20 to 32 years. Cross-country personal best 36min 55s (47s); training volume: 120-180 km.wk; All subjects were assessed for height, weight, breadths, lengths, girths and skin-fold thickness. Percentage of body fat was calculated from the sum of 7 measurements of skin-fold thickness. The somatotype and height to weight ratio (HWR) as well as the skin-folds extremity to trunk ratio (E:T) were also calculated. The Mean (SD) of LBM (Lean Body Mass), %BF (Body Fat), WHR (Waist Hip Ratio), HWR (Height to Weight Ratio), E:T (Extremity/Trunk) skin-fold ratio, SAD (Somatotype Attitudinal Distance) and Manourrier were 60.36 (8.25), 8.07 (1.71) %, . 82 (.06), 44.01 (1.00), . 77 (.17), 0.90 (0.33) and 1.10 (.63), respectively. The Mean (SD) endomorph, mesomorph and ectomorph were 1.43 (.43), 4.10 (.56), and 3.63 (.73), respectively. National Iranian cross-country runners seem to have mesomorph and ectomorph are equal (or do not differ by more than one-half unit), and endomorph is smaller. Despite population comparisons would be required to identify any connection between specific anthropometric dimensions, these reference data reported in the present study for useful to practitioners and researchers, carry immense practical application and should be useful for future investigation on endurance runners selection, talent identification in cross-country running and training program development.
Time series modelling with MATLAB: the SSpace toolbox

SSpace is a MATLAB toolbox for State Space modelling that provides the user with tools for linear Gaussian, non-linear and nonGaussian systems with the most advanced and up-to-date features available in any State Space framework. Great flexibility is achieved because each model is coded on a standard MATLAB function, thence having absolute control on particular parameterizations, parameter constraints, time variation of parameters or variances, arbitrary non-linear relations with inputs, time aggregation, nested models, system concatenation, etc. The toolbox may be used by specifying State Space systems from scratch or by using ready-to-use templates for standard methods (like VARMAX, exponential smoothing, unobserved components, dynamic linear regression, etc.). The toolbox is freely available via a public code repository with full documentation and help system. This chapter demonstrates the toolbox’s potential with several examples.
Fraud prediction in loan default using support vector machine

The concept of taking loan has been in existence since inception of the human race but it is now taking diverse dimensions. This spans through personal exchange of loans for repayment based on personal track records, enjoying loans as proceeds of daily contribution without collaterals, except for the banking sector that requests collaterals for official loans. The uniform occurrence of being unable to pay the debts and resulting in a default is evident to the level of bank closures and nations’ bankruptcy is experienced across board. With a large volume and variety of data, credit history judgment by man is inefficient; case-based, analogy-based reasoning and statistical approaches have been employed but the 21st century fraudulent attempts cannot be discovered by these approaches, hence; the machine learning approach using the support vector machine. This work employs a supervised learning approach based on machine learning to predict the possibility of a fraud in a loan application through hidden trends in data instead of giving loans which ordinarily should not be approved; past occurrences discovered through machine learning reveals risky loans and a possible fraud by humans in approvals that can result in a default. Machine learning approaches are able to detect fraudulent financial statements to avert business comatose.
RS-HABE: Revocable-Storage and Hierarchical Attribute-Based Access Scheme for Secure Sharing of e-Health Records in Public Cloud

Personal e-health records (EHR) enable medical workers (e.g., doctors and nurses) to conveniently and quickly access each patient’s medical history through the public cloud, which greatly facilitates patients’ visits and makes telemedicine possible. Additionally, since EHR involve patients’ personal privacy information, EHR holders would hesitate to directly outsource their data to cloud servers. A natural and favorite manner of conquering this issue is to encrypt these outsourced EHR such that only authorized medical workers can access them. Specifically, the ciphertext-policy attribute-based encryption (CP-ABE) supports fine-grained access over encrypted data and is considered to be a perfect solution of securely sharing EHR in the public cloud. In this paper, to strengthen the system security and meet the requirement of specific applications, we add functionalities of user revocation, secret key delegation and ciphertext update to the original ABE, and propose a revocable-storage hierarchical attribute-based encryption (RS-HABE) scheme, as the core building of establishing a framework for secure sharing of EHR in public cloud. The proposed RS-HABE scheme features of forward security (a revoked user can no longer access previously encrypted data) and backward security (a revoked user also cannot access subsequently encrypted data) simultaneously, and is proved to be selectively secure under a complexity assumption in bilinear groups, without random oracles. The theoretical analysis indicates that the proposed scheme surpasses existing similar works in terms of functionality and security, at the acceptable cost of computation overhead. Moreover, we implement the proposed scheme and present experiments to demonstrate its practicability.
A Comment on Fitting Pareto Tails to Complex Survey Data

Taking survey data on household wealth as our major example, this short paper discusses some of the issues applied researchers are facing when fitting (type I) Pareto distributions to complex survey data. The major contribution of this paper is twofold: First, we provide a novel take on key aspects of Pareto tail fitting and a new and easy way of implementing the latter. Second, we summarise key results on goodness of fit tests in the context of complex survey data. Taken together we think the paper provides a concise and useful presentation of the fundamentals of Pareto tail fitting with complex survey data.
Automated configuration of a performance management platform in NFV and SDN environments

With 5G set to arrive within the next three years, this next-generation of mobile networks will transform the mobile industry with a profound impact both on its customers as well as on the existing technologies and network architectures. Software-Defined Networking (SDN), together with Network Functions Virtualization (NFV), are going to play key roles for the operators as they prepare the migration from 4G to 5G allowing them to quickly scale their networks. This dissertation will present a research work done on this new paradigm of virtualized and programmable networks focusing on the performance management, supervision and monitoring domains, aiming to address Self-Organizing Networks (SON) scenarios in a NFV/SDN context, with one of the scenarios being the detection and prediction of potential network and service anomalies. The research work itself was done while participating in a R&D project designated SELFNET (A Framework for Self-Organized Network Management in Virtualized and Software Defined Networks) funded by the European Commission under the H2020 5G-PPP programme, with Altice Labs being one of the participating partners of this project. Performance management system advancements in a 5G scenario require aggregation, correlation and analysis of data gathered from these virtualized and programmable network elements. Both opensource monitoring tools and customized catalog-driven tools were either integrated on or developed with this purpose, and the results show that they were able to successfully address these requirements of the SELFNET project. Current performance management platforms of the network operators in production are designed for non virtualized (nonNFV) and non programmable (non-SDN) networks, and the knowledge gathered while doing this research work allowed Altice Labs to understand how its Altaia performance management platform must evolve in order to be prepared for the upcoming 5G next generation mobile networks.
Analysing mesoscale structures in economic and financial networks

Modelling, characterising and detecting the structure of complex networks is of primary importance to understand the dynamics of the systems considered. This is especially true for economic and financial networks, whose structural organisation deeply affects their resilience to shock propagation. Many real world networks are characterised by the presence of mesoscale structures: while a lot of attention has been focused on the community structure, many real-world networks are characterised by core-periphery, bow-tie and bipartite structures, especially so economic and financial networks. In this thesis we present new methods to model and detect these mesoscale structures. We apply these methods to characterise the structure of real-world economic and financial networks. Using maximum entropy networks encoding different levels of information, we model the structure of the international trade network and of national interbank exposures networks. We find that constraining local information is enough to reconstruct the mesoscale structure of these networks: hence, we introduce a new method to detect statistically significant bimodular structures, based on the connectivity within and between network modules. We also apply our method to multiplex networks. In particular, to unravel different types of corporate networks, we construct a new multilayer dataset of company interactions: we find that the disaggregate network describes a small corporate world, but that these different company interactions are characterised by vastly different topological properties.
Understanding the multidimensionality of information systems use: A study of nurses' use of a mandated electronic medical record system

This study outlines the findings of a qualitative study designed to develop an understanding of nurses‘ experiences using an electronic medical record system (EMR) in a mandatory usage context. Drawing upon the Unified Theory of Acceptance and Use of Technology, a combined deductive/inductive research approach was adopted to study nurses working in an urban hospital system. This approach allowed for an in-depth study of the nature and structure of mandated information systems (IS) use in a healthcare context. We found that understanding the relationship between key technology acceptance constructs and system use required a multidimensional conceptualization of usage – something not commonly found in the IS literature. We identified three facets important to gaining a holistic understanding of nurses‘ use of EMR technology: time spent using the system, timing of use, and mode of use. We empirically demonstrate that the dimensions of IS use can be mandated and internalized to varying degrees even within the same organization, and that the predictors of use can be differentially associated with the dimensions of use given the degree of the mandate.
An Engineering Toolbox for the Evaluation of Metallic Flow Field Plates

Metallic flow field plates, also called bipolar plates, are an important component of fuel cell stacks, electrolyzers, hydrogen purification and compression stacks. The manufacturing of these plates by means of stamping or hydroforming is highly suitable for mass production. In this work, a toolbox is created that is suitable for a screening process of different flow field design variants. For this purpose, the geometry and computational mesh are generated in an automated manner. Basic building blocks are combined using the open source software SALOME, and these allow for the construction of a large variant of serpentine-like flow field structures. These geometric variants are evaluated through computational fluid dynamics (CFD) simulations with the open source software OpenFOAM. The overall procedure allows for the screening of more than 100 variants within one week using a standard desktop computer. The performance of the flow fields is evaluated on the basis of two parameters: the overall pressure difference across the plate and the relative difference of the hydrogen concentration at the outlet of the channels. The results of such a screening first provide information about optimum channel geometry and the best choice of the general flow field layout. Such results are important at the beginning of the design process, as the channel geometry has an influence on the selection of the metal for deep drawing or hydroforming processes.
Using Machine Learning Algorithms to Predict First-generation College Students’ Six-year Graduation: A Case Study

This paper studies the forecasting mechanism of the most widely used machine learning algorithms, namely linear discriminant analysis, logistic regression, k-nearest neighbors, random forests, artificial neural network, naive Bayes, classification and regression trees, support vector machines, adaptive boosting, and stacking ensemble model, in forecasting first-generation college students’ six-year graduation using the first college year’s data. Five standard evaluating metrics are used to evaluate these models. The results show that these machine learning models can significantly predict firstgeneration college students’ six-year graduation with mean forecasting accuracy rate spanning from 69.58% to 75.17% and median forecasting accuracy rate spanning from 70.37% to 74.52%. Among these machine learning algorithms, stacking ensemble model, logistic regression model, and linear discriminant analysis are the best three ones in terms of mean forecasting accuracy rate. Furthermore, the results from the repeated ten-fold crossvalidation process reveal that the variations of the five evaluating metrics exhibit remarkably different patterns across the ten machine learning algorithms. Index TermsMachine learning algorithms, firstgeneration college students, six-year graduation, forecasting evaluation.
Blockchain-Based Distributed Software-Defined Vehicular Networks: A Dueling Deep ${Q}$ -Learning Approach

Vehicular ad hoc networks (VANETs) have become an essential part in smart transportation systems of modern cities. However, because of dynamicity and infrastructure-less of VANETs, the ever increasing number of network security issues become obstacles for the realization of smart cities. Software-defined VANETs have provided a reliable way to manage VANETs dynamically and securely. However, the traditionally centralized control plane makes it vulnerable to malicious nodes and results in performance degradation. Therefore, a distributed control plane is necessary. How to reach a consensus among multiple controllers under complex vehicular environment is an essential problem. In this paper, we propose a novel blockchain-based distributed software-defined VANET framework (block-SDV) to establish a secure architecture to overcome the above issues. The trust features of blockchain nodes, the number of consensus nodes, trust features of each vehicle, and the computational capability of the blockchain are considered in a joint optimization problem, which is modeled as a Markov decision process with state space, action space and reward function. Since it is difficult to be solved by traditional methods, we propose a novel dueling deep ${Q}$ -learning (DDQL) with prioritized experience replay approach. Simulation results are presented to show the effectiveness of the proposed block-SDV framework.
Study of a Hirschsprung's Disease Patient Cohort Using Multiplex Ligation-Dependent Probe Amplification Badania wybranych genów u pacjentów z chorobą Hirschsprunga z użyciem metody MLPA

Background. Hirschsprung’s disease is a congenital disorder with an incidence of 1 per 5000 live births, characterized by the absence of intestinal ganglion cells. The etiology of Hirschsprung’s disease is very heterogeneous (chromosomal, monogenic, oligo- and polygenic, as well as multifactorial). Various genes play a role in its etiology, i.e. RET, EDNRB, EDN3, GDNF, SOX10, and ZEB2; new susceptible loci have also been identified. Material and Methods. The characterized mutations in these genes are mostly small deletions and point mutations in both coding and non-coding regions. Also microdeletions of exons of the critical genes have been reported. The goal of this study was to screen 71 patients with Hirschsprung’s disease for micro-rearrangements in four genes (RET, ZEB2, EDN3, and GDNF) using the MLPA method. Results and Conclusions. In one case, deletion of all ZEB2 exons was detected and Mowat-Wilson syndrome was diagnosed. In the rest of the patients, no genomic rearrangements were observed in any of the four genes (Adv Clin Exp Med 2010, 19, 1, 83–88).
Real-Time Depth-Based Hand Tracking for American Sign Language Recognition

There are estimated to be more than a million Deaf and severely hard of hearing individuals living in the United States. For many of these individuals, American Sign Language (ASL) is their primary means of communication. However, for most day-to-day interactions, native-ASL usersmust either get by with a mixture of gestures and written communication in a non-native language or seek the assistance of an interpreter. Whereas advances towards automated translation between many other languages have benefited greatly from decades of research into speech recognition and Statistical Machine Translation, ASLs lack of aural and written components have limited exploration into automated translation of ASL. In this thesis, I focus on work towards recognizing components of American Sign Language in real-time. I first evaluate the suitability of a real-time depth-based generative hand tracking model for estimating ASL handshapes. I then present a study of ASL fingerspelling recognition, in which real-time tracking and classification methods are applied to continuous sign sequences. I will then discuss the future steps needed to expand a real-time fingerspelling recognition to theproblem of general ASL recognition.
Trust correlation of mobile agent nodes with a regular node in a Adhoc network using decision-making strategy

A mobile agent offers discrete advantage both in facilitating better transmission as well as controlling the traffic load in Mobile Adhoc Network (MANET). Hence, such forms of network offers maximized dependencies on mobile agents in terms of its trust worthiness. At present, there are various work being carried out towards resisting security breach in MANET; however approaches using mobile agent based mechanism is few to found. Therefore, the proposed system introduces a novel mathematical model where an extensive decision making system has been constructed for identifying the malicious intention of mobile agents in case they go rogues. By adopting multi-tier communication policy and fairness concept, the proposed system offers the capability to resist any form of malicious activity of mobile agent without even presence of any apriori information of adversary. The outcome shows proposed system outshines existing security scheme in MANET.
Machine Learning For Waveform Spectral Analysis On Signal Seismic With Broadband Vertical Component

Machine learning of seismic signal waveform is core component to realize the characteristics of signal. The processing of waveform signal is broadly used for analysis of real time seismic signal. The numerous wavelet filters are developed by spectral synthesis using machine learning python to realize the signal characteristics. Our paper aims to generate and processing the row data of waveform from seismic sensor by using Continuous Wavelet Transform (CWT). CWT is clearly to identify of spectral amplitudes and frequency-energy from component of signal seismic performed by Broadband Network in Indonesia. Finally, by machine learning python allows good time resolution for identified and performed of seismic signal from broadband which deployed in Indonesia.
Evaluation of Nurses Performance by Data Envelopment Analysis

Employee Performance Assessment is a very process-driven process in the organization. Today, the goal is to evaluate the guidance of the staff for team participation to enhance the effectiveness and efficiency of the organization. In this study, Data Envelopment Analysis (DEA) model was used to evaluate units. Nurses were the sample of this study whose information was collected by self-assessment questionnaires. It was six inputs and nine outputs to measure nurses' performance. The nurses of comprise a community of 130 people who completed the questionnaire at random. For data analysis, the data were initially recorded in SPSS software and calculated with DEA solver software. The output of the software is a list of inefficient individuals. How many people with disabilities need to be modeled and suggestions are made to increase the efficiency of people with disabilities.
Information Processing on Online Review Platforms

Abstract Online reviews represent an important decision aid for consumers. Therefore, the question of whether online reviews reflect the currently available information is of high importance. Nevertheless, previous research neglects information processing on online review platforms. We address this research gap and analyze whether restaurant health inspection results have an impact on the review generation process of online restaurant reviews. We find that while severe health inspection results lead to changes of online review star ratings, information processing depends on the current environment. We find indications for corrective actions after critical health inspections: on the one hand, the restaurant health score improves, which shows an increase in restaurant quality. On the other hand, we observe indications for an increased amount of fake reviews in case of poorly-graded restaurants. We contribute to theory by providing an understanding of the nature of information processing on online review platforms. For practitioners, our findings allow for an understanding of the dynamics of online review generation. Furthermore, we outline the importance of considering the risk of deceptive behavior.