Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining How NCCP Competition-Development Modules Contribute to Coach Learning

The purpose of the present study was to explore the influence of an NCCP coach education module on coaches‟ ongoing learning. Using the theoretical framework of Jarvis (2006) and Moon (2004) to guide the research, this study consisted of three phases: an interview with each of the 10 coaches prior to attending a module to understand their biographies as well as various learning situations they had already experienced; attendance at one of three potential Competition-Development modules, Managing Conflict, Coaching and Leading Effectively, or Psychology of Performance, and an interview with each of the 10 coaches immediately following the module to explore their thoughts, reflections and possible learning within the module; and a third and final interview with each of the 10 coaches three months following the module to explore how they implemented learning from the module, as well as other learning situations that may have occurred within that 3 month timeframe. The findings indicated that the biographies of each of the coaches varied considerably. For example, the coaches‟ athletic experiences ranged from recreational to national level and several of the coaches were still active in sport at a master‟s level. Their formal education levels ranged from high school to completion of a university masters degree, and their ages varied from 21-45. As well, each of the coaches said they learned something from the NCCP coaching module they attended such as a more effective method for communicating with their athletes, strategies to cope better with conflict, and the importance of setting a variety of goals. The findings also indicated that a number of the coaches were open to on-going learning and stated that they would continue to enrol in formal coach education modules.
Missing Data Estimation using Principle Component Analysis and Autoassociative Neural Networks

Three new methods are used for estimating missing data in a database using Neural Networks, Principal Component Analysis and Genetic Algorithms are presented. The proposed methods are tested on a set of data obtained from the South African Antenatal Survey. The data is a collection of demographic properties of patients. The proposed methods use Principal Component Analysis to remove redundancies and reduce the dimensionality in the data. Variations of autoassociative Neural Networks are used to further reduce the dimensionality of the data. A Genetic Algorithm is then used to find the missing data by optimizing the error function of the three variants of the Autoencoder Neural Network. The proposed system was tested on data with 1 to 6 missing fields in a single record of data and the accuracy of the estimated values were calculated and recorded. All methods are as accurate as a conventional feedforward neural network structure however the use of the newly proposed methods employs neural network architectures that have fewer hidden nodes.
Licensed under Creative Commons Attribution Cc by a Survey on Feature Based Product Recommendation System

The Recommender system is an essential part of the data and the e-commerce system. The system provides a highly powerful method for allowing users to filter out large set of information and the product places. The researchers from the same field are still working on how the recommender methodology is implemented in a particular domain. The software product engineering uses Feature Models (FMs) technology in a broad manner for helping and validating each product structure and it also provides support for the domain analysis. The creation of FM is tedious and very time-requiring technology. Now a day, it's popular to buy a product online and online product shopping is a new trade in E-Commerce domain. The customers can choose their preferred products and buy many products online from the Online shopping website or portal. Online shopping websites combine Business-to-Consumers, Business-to-business as well as Consumers-to-Consumers services with each other. Because of the customized, high feature and cost capability products the universality of the software is increases. Model-Driven Software Product Lines (MDSPL) permits the creators to construct a product based on the configuration information and the reusable resources automatically. A product line is constructed from a public set of resources.
ExpExpExplosion: Uniform Interpolation in General EL Terminologies

Although ℇL is a popular logic used in large existing knowledge bases, to the best of our knowledge no procedure has yet been proposed that computes uniform ℇL interpolants of general ℇL terminologies. Up to now, also the bounds on the size of uniform ℇL interpolants remain unknown. In this paper, we propose an approach based on proof theory and the theory of formal tree languages to computing a finite uniform interpolant for a general ℇL terminology if it exists. Further, we show that, if such a finite uniform ℇL interpolant exists, then there exists one that is at most triple exponential in the size of the original TBox, and that, in the worst-case, no shorter interpolants exist, thereby establishing the triple exponential tight bounds on their size.
Action Recognition from Weak Alignment of Body Parts – Supplementary Material

We train a kernel SVM for each action class. The SVM kernel is a convex combination of base kernels, which capture different visual cues: HOG, SIFT, color, pose, object detection scores. Some of these cues are computed at various relative locations of the provided human bounding box, yielding a total of 20 kernels. We optimize the weights for kernel combination using randomized grid search. This section describes these kernels and implementation details.
Modeling Comma Placement in Chinese Text for Better Readability using Linguistic Features and Gaze Information

Comma placements in Chinese text are relatively arbitrary although there are some syntactic guidelines for them. In this research, we attempt to improve the readability of text by optimizing comma placements through integration of linguistic features of text and gaze features of readers. We design a comma predictor for general Chinese text based on conditional random field models with linguistic features. After that, we build a rule-based filter for categorizing commas in text according to their contribution to readability based on the analysis of gazes of people reading text with and without commas. The experimental results show that our predictor reproduces the comma distribution in the Penn Chinese Treebank with 78.41 in F1-score and commas chosen by our filter smoothen certain gaze behaviors.
Direct Discriminant Locality Preserving Projection With Hammerstein Polynomial Expansion

Discriminant locality preserving projection (DLPP) is a linear approach that encodes discriminant information into the objective of locality preserving projection and improves its classification ability. To enhance the nonlinear description ability of DLPP, we can optimize the objective function of DLPP in reproducing kernel Hilbert space to form a kernel-based discriminant locality preserving projection (KDLPP). However, KDLPP suffers the following problems: 1) larger computational burden; 2) no explicit mapping functions in KDLPP, which results in more computational burden when projecting a new sample into the low-dimensional subspace; and 3) KDLPP cannot obtain optimal discriminant vectors, which exceedingly optimize the objective of DLPP. To overcome the weaknesses of KDLPP, in this paper, a direct discriminant locality preserving projection with Hammerstein polynomial expansion (HPDDLPP) is proposed. The proposed HPDDLPP directly implements the objective of DLPP in high-dimensional second-order Hammerstein polynomial space without matrix inverse, which extracts the optimal discriminant vectors for DLPP without larger computational burden. Compared with some other related classical methods, experimental results for face and palmprint recognition problems indicate the effectiveness of the proposed HPDDLPP.
Prenatal and neonatal brain structure and white matter maturation in children at high risk for schizophrenia.

OBJECTIVE Schizophrenia is a neurodevelopmental disorder associated with abnormalities of brain structure and white matter, although little is known about when these abnormalities arise. This study was conducted to identify structural brain abnormalities in the prenatal and neonatal periods associated with genetic risk for schizophrenia. METHOD Prenatal ultrasound scans and neonatal structural magnetic resonance imaging (MRI) and diffusion tensor imaging were prospectively obtained in the offspring of mothers with schizophrenia or schizoaffective disorder (N=26) and matched comparison mothers without psychiatric illness (N=26). Comparisons were made for prenatal lateral ventricle width and head circumference, for neonatal intracranial, CSF, gray matter, white matter, and lateral ventricle volumes, and for neonatal diffusion properties of the genu and splenium of the corpus callosum and corticospinal tracts. RESULTS Relative to the matched comparison subjects, the offspring of mothers with schizophrenia did not differ in prenatal lateral ventricle width or head circumference. Overall, the high-risk neonates had nonsignificantly larger intracranial, CSF, and lateral ventricle volumes. Subgroup analysis revealed that male high-risk infants had significantly larger intracranial, CSF, total gray matter, and lateral ventricle volumes; the female high-risk neonates were similar to the female comparison subjects. There were no group differences in white matter diffusion tensor properties. CONCLUSIONS Male neonates at genetic risk for schizophrenia had several larger than normal brain volumes, while females did not. To the authors' knowledge, this study provides the first evidence, in the context of its limitations, that early neonatal brain development may be abnormal in males at genetic risk for schizophrenia.
ogaraK: a population genetics simulator for malaria

MOTIVATION The evolution of resistance in Plasmodium falciparum malaria against most available treatments is a major global health threat. Population genetics approaches are commonly used to model the spread of drug resistance. Due to uncommon features in malaria biology, existing forward-time population genetics simulators cannot suitably model Plasmodium falciparum malaria. RESULTS Here we present ogaraK, a population genetics simulator for modelling the spread of drug-resistant malaria. OgaraK is designed to make malaria simulation computationally tractable as it models infections, not individual parasites. OgaraK is also able to model the life cycle of the parasite which includes both haploid and diploid phases and sexual and asexual reproduction. We also allow for the simulation of different inbreeding levels, an important difference between high and low transmission areas and a fundamental factor influencing the outcome of strategies to control or eliminate malaria. AVAILABILITY OgaraK is available as free software (GPL) from the address http://popgen.eu/soft/ogaraK.
Multiobjective Evolutionary Algorithm Approach in Modeling Discrete-Time Multivariable Dynamics Systems

Multiobjective evolutionary algorithms are robust tool in solving many optimization problems. Model structure selection is a procedure in system identification procedures. This procedure counters two contradicting objective functions which are minimizing mean square error and complexity of the selected model. This paper investigates the effectiveness and the performance of multiobjective evolutionary algorithm using elitist nondominated sorting genetic algorithm (NSGA-II) in identifying the model structure for discrete-time multivariable dynamic systems. Two simulated multivariable systems and a real multivariable system, which is a jacketed continuous stirred tank reactor, were used to investigate the effectiveness of NSGA-II. The identified model is validated using one-step-ahead prediction. The results indicate that NSGA-II is able to optimize the model structure of the multivariable systems with good predictive accuracy and adequate model structure.
Chapter 4 Behaviometrics for Identifying Smart Home Residents

Smart homes and ambient intelligence show great promise in the fields of medical monitoring, energy efficiency and ubiquitous computing applications. Their ability to adapt and react to the people relying on them positions these systems to be invaluable tools for our aging populations. The most privacy protecting and easy to use smart home technologies often lack any kind of unique tracking technologies for individuals. Without a built-in mechanism to identify which resident is currently triggering events, new tools need to be developed to help determine the identity of the resident(s) in situ. This work proposes and discusses the use of behaviometrics as a strategy for identifying people through behavior. By using behaviometrics-based approaches, the smart home may identify residents without requiring them to carry a tracking device, nor use privacy insensitive recording systems such as cameras and microphones. With the ability to identify the residents through behavior, the smart home may better react to the multitude of inhabitants in the space.
NVX-412, a New Oncology Drug Candidate, Induces S-Phase Arrest and DNA Damage in Cancer Cells in a p53-Independent Manner

The new molecular entity quinoxalinhydrazide derivative NVX-412 was identified as a promising drug candidate for the treatment of various cancer types due to its strong cytotoxic activity and relative specificity. Here, we provide first data about the mechanisms of action of NVX-412. We show that NVX-412 exerts its anti-neoplastic activity in a p53-independent manner and induces S-phase arrest and DNA damage as assessed by γH2AX staining. We suggest a bi-modal (dose-dependent) mode of action of NVX-412, being primarily cytostatic at lower and predominantly cytotoxic at higher concentrations. Based on the broad and consistent anti-neoplastic activity observed, NVX-412 holds promise as an effective drug candidate for the treatment of various cancer types, especially for hematological malignancies with highly unmet medical need.
Anti-Helicobacter pylori activity of crude N-acetylneuraminic acid isolated from glycomacropeptide of whey

Helicobacter pylori colonizes the gastric mucosa of about half of the world's population, causing chronic gastritis and gastric cancer. An increasing emergence of antibiotic-resistant H. pylori arouses demand on alternative non-antibiotic-based therapies. In this study, we freshly prepared crude N-acetylneuraminic acid obtained from glycomacropeptide (G-NANA) of whey through a neuraminidase-mediated reaction and evaluated its antibacterial ability against H. pylori and H. felis. Overnight cultures of the H. pylori were diluted with fresh media and different concentrations (1-150 mg/mL) of crude G-NANA were added directly to the culture tube. Bacterial growth was evaluated by measuring the optical density of the culture medium and the number of viable bacteria was determined by a direct count of the colony forming units (CFU) on agar plates. For the in vivo study, mice were orally infected with 100 µL (5×108 cfu/mL) of H. felis four times at a day's interval, accompanied by a daily administration of crude G-NANA or vehicle. A day after the last infection, the mice were daily administered the crude G-NANA (0, 75, and 300 mg/mL) for 10 days and euthanized. Their stomachs were collected and bacterial colonization was determined by quantitative real-time PCR. Crude G-NANA inhibited H. pylori's growth and reduced the number of viable bacteria in a dose-dependent manner. Furthermore, crude G-NANA inhibited bacterial colonization in the mice. These results showed that crude G-NANA has antibacterial activity against Helicobacter and demonstrated its therapeutic potential for the prevention of chronic gastritis and gastric carcinogenesis induced by Helicobacter infection in humans.
The Doors of the Church are Now Open: Black Clergy, Collective Efficacy, and Neighborhood Violence

Prior research has documented the historical significance of the black church beyond serving parishioners’ religious and spiritual needs. Specifically, several black churches are involved in community organizing, social service activities, and political action. Scholars, however, have paid less attention to its role as a potent social institution in community crime control and prevention efforts. We conducted face–to–face interviews with 30 members of Boston's Ten Point Coalition of activist black clergy to document the motivations for and mechanisms through which ministers became involved in efforts to reduce street violence, the varied methods through which ministers develop strategic coalitions and manage violence reduction initiatives, and the ways ministers address the complex challenges involved in doing this work. Study findings suggest that black churches can serve as sources of collective efficacy that can help mobilize other churches, community organizations, police departments, and neighborhood residents in a coordinated effort to address urban youth violence.
Proactive vs. Reactive Security Investments in the Healthcare Sector Proactive vs. Reactive Security Investments in the Healthcare Sector

Building on organizational learning theory, we seek to identify the performance effects of security investments that arise from previous failures or external regulatory pressure. This study focuses on the healthcare sector where legislation mandates breach disclosure and detailed data on security investments are available. Using a Cox proportional hazard model, we demonstrate that proactive security investments are associated with lower security failure rates than reactive investments. Further, the results show that external pressure improves the security performance of healthcare organizations. However, external pressure decreases the positive effect of proactive investments on security performance. This implies that proactive investments, voluntarily made, have the greatest impact on security performance. Our findings suggest that security managers and policy makers should pay attention to the strategic and regulatory factors influencing security investment decisions. The implications for proactive and reactive learning with external regulatory pressure can likely be generalized to other industries.
Evaluation of Jumboframes feasibility in LTE access networks

Long Term Evolution (LTE) represents the cutting-edge broadband wireless access technology in providing ubiquitous and simultaneous connectivity to many users. This paper evaluates the impact that packets breaking the 1500 bytes legacy value, called Jumboframes, have in LTE networks, by exploiting and extending the network stack in the ns-3 simulator. We first provide an overview about the key features of LTE starting from a physical layer perspective, to logical functions like the adaptive modulation and coding scheme, together with a detailed description of the Radio Link Control (RLC) segmentation capabilities. A comparative evaluation is performed based on diverse network configuration criteria, such as user position, density and mobility. We aim at assessing the benefits and caveats that derive from Jumboframes usage in LTE networks. Moreover, a novel cross-layer approach is proposed to mitigate the effect of rapid buffer saturation, due to the transmission of oversized packets with scarce radio resources. To conclude, we test our framework through the analysis of realistic video traces.
MERC: Match at Edge and Route intra--Cluster for Content-based Publish/Subscribe Systems

Despite suffering from inefficiency and flexibility limitations, the filter-based routing (FBR) algorithm is widely used in content-based publish/subscribe (pub/sub) systems. To address its limitations, we propose a dynamic destination-based routing algorithm called D-DBR, which decomposes pub/sub into two independent parts: Content-based matching and destination-based multicasting. D-DBR exhibits low event matching cost and high efficiency, flexibility, and robustness for event routing in small scale overlays. To boost scalability, we further complement D-DBR with a new routing algorithm called MERC. MERC divides the overlay into interconnected clusters and applies content-based and destination-based mechanisms to route events inter- and intra-cluster, respectively. We implemented all algorithms in the PADRES pub/sub system. Experimental results show that our algorithms outperform FBR in terms of improving event dissemination throughput by up to 700% and reducing the end-to-end latency by up to 55%.
Dynamic quadrant partitioning adaptive routing algorithm for irregular reduced vertical link density topology 3-Dimensional Network-on-Chips

3-Dimensional Networks-on-Chips (3D NoCs) are proposed as the next generation interconnect infrastructure for multi/many core embedded systems due to the high performance characteristics and scalability. However the heat and thermal issues in 3D NoCs are critical. Reducing the vertical links between dies becomes can be one of the proper solutions, but the 3D NoC system performance can be harmed due to the less number of possible vertical links. In order to find the best trade-off point between the vertical links reduction and 3D NoC performance, in this paper, we demonstrate a dynamic quadrant partitioning (DQP) adaptive routing algorithm for 3D NoCs with irregular reduced vertical link density topologies, which can improve the system performance (latency max. 10.9% and energy max. 24.1%) in comparison to deterministic routing algorithm with the same vertical link number configurations. Also our DQP routing algorithm can maintain the system performance in comparison to full vertical link connection running dimension-order deterministic routing algorithm (ZXY) by reducing 40% vertical links number. The comparison results are demonstrated with different benchmark applications and random generated task graphs.