Miracle-dz/UniFAR-FTUs huggingface dataset:
An evaluation of the existing practice of primary school leadership in Oman: ensuring the effective implementation of the current reform of the General Education System

In 1998 the current reform of the General Education System (GES) started in Oman. Redefining the existing or finding a suitable new school leadership model has become important to enable Omani primary headteachers to redefine and transform the existing practice of school leadership and to implement and integrate the new educational and leadership tasks which were introduced by the current reform. Studies indicate that educational reforms generally require a powerful mechanism to get things done. Thus, Omani educators (policy makers and headteachers) face a challenge in redefining the present model or choosing a suitable new one. One of the two is, however, urgently required to reform the practice of school leadership in Omani primary schools. Therefore, this research focuses on both redefining and transforming the existing practice and implementing the new tasks (in possibly a new form) of school leadership. This research focuses mainly on three major themes, as follows: (1) The historical background which underlies primary school leadership in Oman. (2) The current practice of primary school leadership. (3) Redefining the existing school leadership model or choosing a new one to develop more effective primary school leadership and ensure the success of the current development of the GES in Oman. Therefore, this research project concentrates on an examination and evaluation of the current practice of primary school management and leadership in the country’s primary schools. Furthermore, this research evaluates the practice of leadership in schools by collecting the necessary data (in a subjective approach) and by using a specially constructed scale (in an objective approach) based on transactional and transformational theories of school leadership. This provides a way of assessing to what extent the current practice of primary school leadership is effective or not in terms of these theories. Overall, this research seeks to ensure the successful and effective implementation of the current reform of the GES.
The department of defense contractor investment policy model

The primary aim of this system dynamics study was to understand aerospace defense contractor investment behavior, and evaluate government policies designed to motivate investment in capital within the current fighter/attack aircraft manufacturing market structure. The approach taken analyzes investment projects and capital equipment expenditure flows that arise as a result of the overall decision framework of the firm. The continuous corporate computer simulation model addresses several of the determinants which enter into the capital investment decision. The experimental design includes analysis of various tax and profit policies. Simulation results suggest that increased capital investment is closely linked to the amount of aircraft orders. Consequently, the short-term motivator of increased capital investment is a higher market demand. Tax and profit policy changes, as those suggested by Carlucci Initiative #5, have minor and only long-term effects on the rate of capital investment.
Performance analysis of Modified Deficit Round Robin schedulers

Deficit Round Robin (DRR) is a scheduling algorithm which provides fair queuing at O(1) complexity. However, due to its round robin structure, its latency properties are not adequate for latency-critical applications, such as voice. For this reason, router manufacturers implement variants of the DRR algorithm which guarantee lower latencies to one (or a subset of) queue(s). In this paper we evaluate the performance of two such variants, both of which are known as Modified Deficit Round Robin, currently implemented in commercial routers. The comparison is carried out analytically, by deriving the latency and bandwidth guarantees of both algorithms, and by simulation.
Identification of the part of soccer court from video signal by neural networks

Recently, it is becoming a popular practice to use character information in broadcasting video programs. This is useful for handicapped people with ears. In some cases, the characters are typed by typists. In that respect, it should be an important technology to produce the abstracted character information from the video information. The abstracted information will be useful for the normal audience, CG representation of the program, and store the game abstract in databases. In this paper, we will deal with the video processing part to identify the part of the court.
Interval-valued symbolic representation based method for off-line signature verification

The objective of this investigation is to present an interval-symbolic representation based method for offline signature verification. In the feature extraction stage, Connected Components (CC), Enclosed Regions (ER), Basic Features (BF) and Curvelet Feature (CF)-based approaches are used to characterize signatures. Considering the extracted feature vectors, an interval data value is created for each feature extracted from every individual's signatures as an interval-valued symbolic data. This process results in a signature model for each individual that consists of a set of interval values. A similarity measure is proposed as the classifier in this paper. The interval-valued symbolic representation based method has never been used for signature verification considering Indian script signatures. Therefore, to evaluate the proposed method, a Hindi signature database consisting of 2400 (100×24) genuine signatures and 3000 (100×30) skilled forgeries is employed for experimentation. Concerning this large Hindi signature dataset, the highest verification accuracy of 91.83% was obtained on a joint feature set considering all four sets of features, while 2.5%, 13.84% and 8.17% of FAR (False Acceptance Rate), FRR (False Rejection Rate), and AER (Average Error Rate) were achieved, respectively.
An inter mode decision method with motion correlation adaptation for H.264/AVC

This paper presents a high efficiency method to decide the inter mode of the H.264/AVC encoder. Although rate-distortion optimization (RDO) improving the video quality in video compression largely, its high computational complexity brings huge time cost. The key idea of this paper is to reduce the candidate modes by the underlying relation between motions and macroblock partition types. Thus, a motion vector related evaluation approach with adaptive multilevel thresholds has been proposed together with SKIP mode early determination strategy. The simulation results show that the proposed method achieves 33.4% time reduction in average with about 0.06db PSNR loss and 1.45% bit-rate increase.
Efficient Conservative Numerical Schemes for 1D Nonlinear Spherical Diffusion Equations with Applications in Battery Modeling

Mathematical models of batteries which make use of the intercalation of a species into a solid phase need to solve the corresponding mass transfer problem. Because solving this equation can significantly add to the computational cost of a model, various methods have been devised to reduce the computational time. In this paper we focus on a comparison of the formulation, accuracy, and order of the accuracy for two numerical methods of solving the spherical diffusion problem with a constant or non-constant diffusion coefficient: the finite volume method and the control volume method. Both methods provide perfect mass conservation and second order accuracy in mesh spacing, but the control volume method provides the surface concentration directly, has a higher accuracy for a given numbers of mesh points and can also be easily extended to variable mesh spacing. Variable mesh spacing can significantly reduce the number of points that are required to achieve a given degree of accuracy in the surface concentration (which is typically coupled to the other battery equations) by locating more points where the concentration gradients are highest. (C) 2013 The Electrochemical Society. All rights reserved.
Managing Knowledge about Knowledge Management: ‘Practising What We Teach’

Abstract There is a view that knowledge management (KM) is primarily a business concept that can bring increased profitability for commercial organisations. However, the management of processes such as knowledge creation, capture, sharing and use are not exclusive to business; good KM practices can bring benefits to all organisations. Given higher education’s (HE) priority of developing knowledge assets, a culture that prioritises KM can be used to underpin a university’s organisational processes including, not least, its teaching activities. This paper illustrates how attention to a particular part of the KM cycle, the knowledge creation (KC) process, can inform the delivery of a KM module; in turn, it shows how the author attempts to practice what she teaches. Evaluation of the module from various perspectives has provided very positive feedback. Lessons for the design and delivery of other modules (KM-based or otherwise) can be drawn from this paper.
Recognition Memory is Improved by a Structured Temporal Framework During Encoding

In order to function optimally within our environment, we continuously extract temporal patterns from our experiences and formulate expectations that facilitate adaptive behavior. Given that our memories are embedded within spatiotemporal contexts, an intriguing possibility is that mnemonic processes are sensitive to the temporal structure of events. To test this hypothesis, in a series of behavioral experiments we manipulated the regularity of interval durations at encoding to create temporally structured and unstructured frameworks. Our findings revealed enhanced recognition memory (d′) for stimuli that were explicitly encoded within a temporally structured vs. unstructured framework. Encoding information within a temporally structured framework was also associated with a reduction in the negative effects of proactive interference and was linked to greater recollective recognition memory. Furthermore, rhythmic temporal structure was found to enhance recognition memory for incidentally encoded information. Collectively, these results support the possibility that we possess a greater capacity to learn and subsequently remember temporally structured information.
Clustering and Synchronization in a One-Dimensional Model for the CA3 Region of the Hippocampus

The behavior of a system of coupled ordinary differential equations is studied in order to characterize the CA3 region of the hippocampus. Clustering and synchronization behavior in a one-dimensional array of cells modeled by a modified Morris–Lecar model is analyzed in terms of a time delay included in the model. The random formation of phase dislocations whose number increases with the time delay seems to be responsible for complex spatiotemporal patterns that have been observed. Alterations to the transmission time between cells have been simulated by adding some noise to the system.
Incremental Development of a Domain-Specific Language That Supports Multiple Application Styles

Domain-Specific Languages (DSLs) are typically built top-down by domain experts for a class of applications (rather than a specific application) that are anticipated in a domain. This paper investigates taking the opposite route: Building a DSL incrementally based on a series of example applications in a domain. The investigated domain of CRUD applications (create, retrieve, update, delete) imposes challenges including independence from application styles and platforms. The major advantages of an incremental approach are that the language is available for use much sooner, there is less upfront cost and the language and domain boundaries are defined organically. Our initial experiments suggest that this could be a viable approach provided certain careful design decisions are made upfront.
BER Analysis in Wavelet Based SC-FDMA for LTE Uplink Transmission

Long Term Evolution (LTE) has adopted Single Carrier Frequency Division Multiple Access (SCFDMA) technique for uplink and Orthogonal Frequency Division Multiple Access (OFDMA) for downlink. SCFDMA is often called as Discrete Fourier Transform (DFT) spread OFDMA. In this paper, Wavelet based SCFDMA is proposed for analyzing Bit Error Rate (BER) performance. Analysis is carried out using different wavelets and different modulation schemes under AWGN channel. This analysis will show that the reduction in BER takes place by using wavelet transform in SCFDMA. Thus wavelet based SCFDMA provides better BER performance than that of DFT based SCFDMA.
“Omerta” in Organized Sport: Bullying and Harassment as Determinants of Threats of Social Sustainability at the Individual Level

The interpretation of the evolutionary theory prevailing in sport, based on the approach that the strongest survive, varies with the rules inherent in the criminal world, forbidding to “take out” negative information about interrelationships, in this case, outside the team or group. Such traditionally established culture puts pressure on athletes to suffer from bullying and follow the “silence law”. In the long run, this turns into a precondition for unsafe environment for athletes, which, due to negative consequences for the individual, can be identified as threats to social sustainability at the individual level. Such a situation is also indirectly approved by the heads and coaches of organizations. The latter, who lack competencies to resolve conflict situations, are left to solve arising problems on their own and do not receive any support from the management of organizations. Therefore, the aim of this research is to reveal the factors determining the specificity, emergence, and development of bullying and harassment in sport as threats to social sustainability at the individual level. The research was conducted using a semi-structured interview method with eight coaches representing team, individual, and duel sport branches. Inductive content analysis served as the basis for the data analysis. Research results disclosed factors determining the specificity of emergence of bullying and harassment in sport and hindering the entrenchment of social sustainability in sport at the individual level. The results revealed the euphemisms distinguished by coaches, which, in sport, have a positive connotation, such as “combativeness” and “sports anger” instead of the term “aggression”. It was identified that coaches named aggressors as “harder players”, “confident players”, “active persons”, “players-torpedoes”, “emotional players”, and “competitive players”. Sports competition is justified in various forms. The emergence of bullying and harassment is grounded on “mastery differences”, it is stated that abusive actions are experienced by “physically weak” players, while physical actions used by athletes are called “mischief”; verbal actions are “raillery”. In addition, inadequate preparation of Lithuanian coaches to respond to bullying and harassment in their trained teams or groups on time and appropriately was identified. It turned out that sports organizations are not particularly interested in organizing trainings for prevention of destructive interrelations and intervention. Such kind of in-service training is left to the coach’s initiative.
The Mario AI Championship 2009-2012

We give a brief overview of the Mario AI Championship, a series of competitions based on an open source clone of the seminal platform game Super Mario Bros. The competition has four tracks. The gameplay and learning tracks resemble traditional reinforcement learning competitions, the Level generation track focuses on the generation of entertaining game levels, and the Turing Test track focuses on humanlike game-playing behavior. We also outline some lessons learned from the competition and its future. The article is written by the four organizers of the competition.
Guided Navigation Using Query Log Mining through Query Expansion

We introduce a method to help people to refine their search queries based on other people’s similar queries. We compared two different methods of collaborative filtering on search logs of a popular e-commerce site to do query expansion. In item-based method, by identifying similarities between different items, and in user-based method, by identifying similarities between different users, we compute recommendations for users. The experimental results show that item-based query expansion method provides better performance than the user-based method. While the user-based method improved the system performance 20%, the item-based method provided success of 73%. Also, item-based method recommended better expansion terms than the user-based method, which is important in helping web users to easily access information needs by formulating qualified queries. This document is a comparison of these two methods, and their effects on system performance.
Experimental evaluation of a soft-switching DC/DC converter for fuel cell vehicle applications

A soft-switched, isolated bi-directional DC/DC converter has been developed for fuel cell powered electric vehicles (FCPEV), in which the 12 V battery for the vehicle accessory loads is also used to start up the fuel cells and to store the energy captured during regenerative braking. The DC/DC converter interfaces the low voltage battery to the fuel cell powered higher voltage DC bus system (255 V - 425 V). Dual half-bridges interconnected through a transformer are employed to minimize the number of switching devices and their associated gate drive components. The transformer provides voltage level matching and galvanic isolation for safety requirements. Snubber capacitors and the transformer leakage inductance are utilized to achieve zero-voltage-switching (ZVS). Therefore, no extra resonant components are required for ZVS, further reducing component count. The inherent soft-switching capability and the low component count of the converter allows efficient power conversion and compact packaging. A prototype was built and successfully tested. This paper presents design considerations and testing data to evaluate the prototype's performance against the requirements for FCPEV applications.
Unsupervised Texture Image Classification using Self-Organizing Maps

- In this paper we report results from unsupervised texture image classification on a data set of images collected with our intelligent machine vision system for pattern recognition (assuming no a priori human vision expert knowledge is available for the image classes). The simulation of the investigated system includes four main phases: data collection and feature extraction, feature analysis, classifier training, and classifier testing and evaluation. Self-Organizing Maps (SOM) are used for classification of the collection of images into several classes, based on their features and texture characteristics. Three main experiments are conducted during this research: in the first one, all extracted features are used for training the classifiers without any statistical pre-processing of the dataset; in the second simulation, the classifiers are trained after normalization of the available data; and in the last experiment, the trained SOMs use linear transformations of the original features, received after pre-processing with principal component analysis (PCA). Each test is performed 50 times and the classification results are assessed using three commonly applied metrics, namely: accuracy rate, sensitivity and specificity. Finally, the findings of this investigation are compared with results from other authors.
Tracking dynamics of topic trends using a finite mixture model

In a wide range of business areas dealing with text data streams, including CRM, knowledge management, and Web monitoring services, it is an important issue to discover topic trends and analyze their dynamics in real-time. Specifically we consider the following three tasks in topic trend analysis: 1)Topic Structure Identification; identifying what kinds of main topics exist and how important they are, 2)Topic Emergence Detection; detecting the emergence of a new topic and recognizing how it grows, 3)Topic Characterization; identifying the characteristics for each of main topics. For real topic analysis systems, we may require that these three tasks be performed in an on-line fashion rather than in a retrospective way, and be dealt with in a single framework. This paper proposes a new topic analysis framework which satisfies this requirement from a unifying viewpoint that a topic structure is modeled using a finite mixture model and that any change of a topic trend is tracked by learning the finite mixture model dynamically. In this framework we propose the usage of a time-stamp based discounting learning algorithm in order to realize real-time topic structure identification. This enables tracking the topic structure adaptively by forgetting out-of-date statistics. Further we apply the theory of dynamic model selection to detecting changes of main components in the finite mixture model in order to realize topic emergence detection. We demonstrate the effectiveness of our framework using real data collected at a help desk to show that we are able to track dynamics of topic trends in a timely fashion.
ViTeNA: An SDN-based virtual network embedding algorithm for multi-tenant data centers

Data centers offer computational resources with various levels of guaranteed performance to the tenants, through differentiated Service Level Agreements (SLA). Typically, data center and cloud providers do not extend these guarantees to the networking layer. Since communication is carried over a network shared by all the tenants, the performance that a tenant application can achieve is unpredictable and depends on factors often beyond the tenant's control. We propose ViTeNA, a Software-Defined Networking-based virtual network embedding algorithm and approach that aims to solve these problems by using the abstraction of virtual networks. Virtual Tenant Networks (VTN) are isolated from each other, offering virtual networks to each of the tenants, with bandwidth guarantees. Deployed along with a scalable OpenFlow controller, ViTeNA allocates virtual tenant networks in a work-conservative system. Preliminary evaluations on data centers with tree and fat-tree topologies indicate that ViTeNA achieves both high consolidation on the allocation of virtual networks and high data center resource utilization.