Miracle-dz/UniFAR-FTUs huggingface dataset:
The Unifying Frameworks of Information Measures

Information measures are capable of providing us with fundamental methodologies to analyze uncertainty and unveiling the substantive characteristics of random variables. In this paper, we address the issues of different types of entropies through -generalized Kolmogorov-Nagumo averages, which lead to the propositions of the survival Renyi entropy and survival Tsallis entropy. Therefore, we make an inventory of eight types of entropies and then classify them into two categories: the density entropy that is defined on density functions and survival entropy that is defined on survival functions. This study demonstrates that, for each type of the density entropy, there exists a kind of the survival entropy corresponding to it. Furthermore, the similarity measures and normalized similarity measures are, respectively, proposed for each type of entropies. Generally, functionals of different types of information-theoretic metrics are equally diverse, while, simultaneously, they also exhibit some unifying features in all their manifestations. We present the unifying frameworks for entropies, similarity measures, and normalized similarity measures, which helps us deal with the available information measures as a whole and move from one functional to another in harmony with various applications.
Towards optimal treatment for chronic hepatitis C infection

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Customers: Identifying the needs in higher education

Many institutions of higher education are hesitant to consider themselves as customer-driven entities. It is common to view the student as the customer but this notion is not universally accepted. This paper reviews the debate in the education and marketing literature about students as customers and reveals the difficulty in using the word customer to describe the student/university relationship. The author argues that the debate must move away from identifying the customer and focus on the university as a service provider. An emerging perspective on market orientation suggest that strategic insights may be gained when firms take into account their customers’ view on the organization’s level of market orientation. Even the suggestion of the term customer can arouse many emotions, preconceptions, and misconceptions. The idea that students are partners in developing and delivering quality education threatens the historic, traditional academic role of faculty as purveyor of knowledge. Nevertheless, one fact has been proven over and over again. Customer-driven organizations are effective because they are fully committed to satisfying and anticipating customer needs. The future success of colleges and universities will increasingly be determined by how they identify and satisfy their various customers. This paper accentuates the subject by initially reviewing a number of theoretical viewpoints as to why a customer perspective should be sought when assessing organizational phenomena such as market orientation. The findings showed that all the proposed relationship were significant. The result further demonstrated that service quality acts as a partial mediator where customer satisfaction was not derived completely by service quality. This paper eventually concludes by elaborating the various conclusions derived from the study.
Real-Time Panoramic Video Construction Using Harris Corners Detector

This paper discusses the possibility of stitching two video streams to create a panoramic view in real-time, The challenge with constructing Panorama Video in real-time is the time-consuming in stitching images. Image alignment didn't need to detect all interest point, only few well distributed point is enough to calculate good projection matrix, and the great similarity between the frames in video stream provides temporal information which can be used to reduce the number of selected points In this paper the stitching images method which using Harris Corners Detector has been customized to be appropriate to meet the requirements of viewing a video in real time, through reducing the number of Interest Points in each stitching and employ the Parallel Processing to separate the calculation of transform matrix from the process of blending. Reducing the number of Interest Points has been done by fragmentation of images into numerous regions and getting a feedback information from the stitching of the previous frame to limitate the search space based on the area of overlapping. Results show that (i) panoramas generated from the proposed algorithm feature a smooth transition in image overlapping areas and satisfy human visual requirements; and (ii) the preview speed of the generated panorama satisfies the real-time requirements that are commonly accepted in video panorama stitching.
CREATING PRODUCT MODELS FROM POINT CLOUD OF CIVIL STRUCTURES BASED ON GEOMETRIC SIMILARITY

The existing civil structures must be maintained in order to ensure their expected lifelong serviceability. Careful rehabilitation and maintenance planning plays a significant role in that effort. Recently, construction information modelling (CIM) techniques, such as product models, are increasingly being used to facilitate structure maintenance. Using this methodology, laser scanning systems can provide point cloud data that are used to produce highly accurate and dense representations of civil structures. However, while numerous methods for creating a single surface exist, part decomposition is required in order to create product models consisting of more than one part. This research aims at the development of a surface reconstruction system that utilizes point cloud data efficiently in order to create complete product models. The research proposes using the application of local shape matching to the input point clouds in order to define a set of representative parts. These representative parts are then polygonized and copied to locations where the same types of parts exist. The results of our experiments show that the proposed method can efficiently create product models using input point cloud data.
Insight: An Application of Information Visualisation Techniques to Digital Forensics Investigations

As digital devices are becoming ever more ubiquitous in our day to day lives, more of our personal information and behavioural patterns are recorded on these devices. The volume of data held on these devices is substantial, and people investigating these datasets are facing growing backlog as a result. This is worsened by the fact that many software tools used in this area are text based and do not lend themselves to rapid processing by humans. This body of work looks at several case studies in which these datasets were visualised in attempt to expedite processing by humans. A number of different 2D and 3D visualisation methods were trialled, and the results from these case studies fed into the design of a final tool which was tested with the assistance of a group of individuals studying Digital Forensics. The results of this research show some encouraging results which indicate visualisation may assist analysis in some aspects, and indicates useful paths for future work. Keyword: digital forensics, information visualisation, computer security, usable security
Analysis of Achievement Motivation and Academic Engagement of Students in the Nigerian Classroom

Motivating students so that they can succeed in school is one of the greatest challenges of this century. Getting students to learn and sustaining their interest in what they are learning is one of the major objectives of teachers in the classroom. This study therefore attempts to determine the impact of achievement motivation on students’ academic engagement. Three hypotheses were stated. Using Achievement Motivation and Academic Engagement questionnaire administered on 540 senior secondary students’ data was generated for the study. Analysis of the data revealed that achievement motivation has a significant impact on academic engagement with highly motivated students being more academically engaged than the moderately and lowly motivated students. Also result revealed that male students were more motivated than female student and, age also had a significant influence on achievement motivation. Based on this result, recommendations were made on how to raise the achievement motivation of students in order to get them more engaged in school work. DOI: 10.5901/ajis.2013. v2n3p385
Hybrid Adaptive Multilevel Monte Carlo Algorithm for Non-Smooth Observables of Itô Stochastic Differential Equations

Hybrid Adaptive Multilevel Monte Carlo Algorithm for Non-Smooth Observables of Itô Stochastic Differential Equations. Nadhir Ben Rached The Monte Carlo forward Euler method with uniform time stepping is the standard technique to compute an approximation of the expected payoff of a solution of an Itô Stochastic Differential Equation (SDE). For a given accuracy requirement TOL, the complexity of this technique for well behaved problems, that is the amount of computational work to solve the problem, is O(TOL−3). A new hybrid adaptive Monte Carlo forward Euler algorithm for SDEs with nonsmooth coefficients and low regular observables is developed in this thesis. This adaptive method is based on the derivation of a new error expansion with computable leading-order terms. The basic idea of the new expansion is the use of a mixture of prior information to determine the weight functions and posterior information to compute the local error. In a number of numerical examples the superior efficiency of the hybrid adaptive algorithm over the standard uniform time stepping technique is verified. When a non-smooth binary payoff with either Geometric Brownian Motion (GBM) or drift singularity type of SDEs is considered, the new adaptive method achieves the same complexity as the uniform discretization with smooth prob5 lems. Moreover, the new developed algorithm is extended to the Multilevel Monte Carlo (MLMC) forward Euler setting which reduces the complexity from O(TOL−3) to O(TOL−2(log(TOL))2). For the binary option case with the same type of Itô SDEs, the hybrid adaptive MLMC forward Euler recovers the standard multilevel computational cost O(TOL−2(log(TOL))2). When considering a higher order Milstein scheme, a similar complexity result was obtained by Giles using the uniform time stepping for one dimensional SDEs. The difficulty to extend Giles’ Milstein MLMC method to the multidimensional case is an argument for the flexibility of our new constructed adaptive MLMC forward Euler method which can be easily adapted to this setting. Similarly, the expected complexity O(TOL−2(log(TOL))2) is reached for the multidimensional case and verified numerically.
The Survey-Web Search Results Clustering and Basic of Clustering Process

As the number of documents on the web has proliferated, the low precision of conventional web search engines and the flat ranked list presentation make it difficult for users to locate specific information of interest. Grouping web search results into a hierarchy of topics provides an alternative to the flat ranked list and facilitates searching and browsing. In this paper, we have a brief survey of previous work on web search results clustering and basic steps of web search results clustering.
STUDY OF PASSIVE SELF-TUNING RESONATOR FOR BROADBAND POWER HARVESTING

The concept of passive self-tuning resonator for broadband power harvesting is described. A self- tuning resonant power harvester adapts its internal degrees of freedom to stay in resonance with the drive in a wide range of frequencies. This will enable energy harvesting from a variety of vibration sources that have different and sometimes variable resonant frequency. A mechanical resonator with a mass that is free to move along its length is analyzed, and the potential energy analysis of the system confirms that it adapts its resonant frequency to the drive as the mass moves to find the potential minimum. The parameter requirements for the existence of the self-tuning behavior and the expressions for the tuning range are derived. The experiments designed to demonstrate this effect are described and experimental challenges are discussed.
Extraction of functional total RNA from Cayratia trifolia

RNA extraction is the initial and can be the most critical step in the study of gene expression. Extraction of RNA from Cayratia trifolia plant tissue which contains high levels of polysaccharides, polyphenols and other secondary metabolites is tedious and difficult. We had tested a commercial RNA extraction kit, RNeasy Plant Mini Kit (Qiagen) and made some modification. The use of both RLT and RLC buffers did not produce any useable RNA. Addition of PEG to the buffers improved RNA yield. However, RLC buffer with PEG 8000 gave better yield compared to RLT buffer with PEG 8000. RNA analysis on agarose gel indicated the presence of 2 clear and distinct 18S and 25S rRNA bands proven that the RNA samples are of high quality. Spectrophotometry analysis on RNA samples showed the OD260/230 and OD 260/280 ratios were more than 1.8. cDNA display experiments indicated the presence of differentially displayed bands. These results proved that the RNA obtained were functional and of a good quality.
TACIT KNOWLEDGE AND ITS CORELATION TO INNOVATION AND PERFORMANCE IN OBTAINING COMPETITIVE ADVANTAGE: A STUDY IN A BUSINESS OPERATING IN THE TEXTILE INDUSTRY

Today, the redefinition of competition and its ever diversifying characteristics drive enterprises to search for new remedies. Besides, the ordinary solutions that businesses learn in competition might lead them to dead ends. This could be possible through the correct exploitation of tacit knowledge by businesses and through acquiring new competencies in the performance cycle. The purpose of this study is to describe research on the relationship between tacit knowledge, innovation and performance in assuring competitive advantage, depending on an empirical study made in a business operating in the textile industry lo-cated in Istanbul, Turkey. The data obtained through the use of questionnaires, regarding the study, was analyzed with the SPSS 11.5 program by also using factor analysis, correlation analysis, regression analysis and complementary statistical methods. As a result of the study, it is evident that there is high level of correlation and significance between tacit knowledge, innovation and performance. It was observed that tacit knowledge has considerable impact on innovation and performance. Besides, innovation has substantial effect on business performance as well.
Selection of Variables in Multivariate Regression Models for Large Dimensions

The Akaike information criterion, AIC, and Mallows’ C p statistic have been proposed for selecting a smaller number of regressors in the multivariate regression models with fully unknown covariance matrix. All of these criteria are, however, based on the implicit assumption that the sample size is substantially larger than the dimension of the covariance matrix. To obtain a stable estimator of the covariance matrix, it is required that the dimension of the covariance matrix is much smaller than the sample size. When the dimension is close to the sample size, it is necessary to use ridge-type estimators for the covariance matrix. In this article, we use a ridge-type estimators for the covariance matrix and obtain the modified AIC and modified C p statistic under the asymptotic theory that both the sample size and the dimension go to infinity. It is numerically shown that these modified procedures perform very well in the sense of selecting the true model in large dimensional cases.
Advances in Biomedical Imaging and Image Fusion

Biomedical imaging is a series of procedures which create images of the human body, or parts of the body, to help screen for possible illness or injury, diagnose the likely cause of symptoms and monitor health conditions or the effects of treatment. The objective of the paper is to provide an overview about various bio medical imaging techniques used in detection and diagnosis of Cancer. Each of these imaging techniques provides information about the anatomy, chemical or physiologic phenomena of the human body which are studied independently by doctors to identify Cancer. The biomedical imaging systems, applications, benefits, drawbacks and research challenges are discussed. Image Fusion and its role in Bio medical imaging is also discussed. Image Fusion is the process of fusing two or more bio medical images which contain complementary information into a single composite image. These enrich image quality and avoid redundancy thereby increase the clinical applicability of medical images for cancer detection, prognosis and treatment planning of Cancer.
Spatio-temporal rainfall variation and stock management in arid Namibia

Abstract The high inherent variability in rainfall and forage availability in arid regions makes it very difficult for a rancher to establish a herd size that is suited to the environment and also complicates management decision-making regarding the distribution of animals within and between areas of a single ranch. We recorded the temporal and spatial variability in rainfall and grass production on a local scale on 3 ranches in arid Namibia over 3 years, to determine their effects on potential stocking density, stock movement decisions and timing of grazing. We conducted a 3-year experiment to test the effects of the timing of grazing on grass regrowth. Furthermore, we tested the reliability of a rule-of-thumb used by ranchers to ascertain whether a drought year is likely and whether they should sell some of their cattle in advance of drought. We expanded the scale of the study to include 31 ranches over a 700 km-long rainfall gradient to determine the reliability of extrapolating conclusions obtained at a local scale to a larger geographic scale. We found that rainfall and grass production varied widely, resulting in recommended stocking densities up to 10-fold smaller than those currently recommended by the Namibian Ministry of Agriculture. We found that there is no optimal spatial or temporal scale of ranch management, and that ranchers could do no better than a simple rule of “move the cattle to wherever there is most grass”. This simple rule's validity was reinforced by the results of our experiment, which showed that there was no significant effect of timing of grazing on grass regrowth. Statistical analyses of long-term rainfall records indicated that the ranchers' rule-of-thumb regarding approaching drought was unreliable and that this could lead to substantial loss of profit if adhered to.
Ants (Hymenoptera: Formicidae) and their aphid partners (Homoptera: Aphididae) in Mashhad region, Razavi Khorasan Province, with new records of aphids and ant species for Fauna of Iran

A survey of ant-aphid associations was conducted by collecting and identifying samples of ants and aphids found together on aphid host plants in Mashhad region, Razavi Khorasan province of Iran. As a result, a total of 21 ant species representing 13 genera and 3 subfamilies and 26 aphid species belonging to 13 genera from 37 host plant species were collected and identified. Among the recorded ant species, the genus Crematogaster with four species had the highest species richness. The three most frequent aphid attendant ants were Lepisiota nigra (Dalla Torre, 1893), Tapinoma erraticum (Latreille, 1798) and Crematogaster inermis Mayr, 1862 associated with 11, 10 and 9 aphid species, respectively. Eleven ant species were recorded from the colonies of one aphid species. Among the recorded ants, the species Crematogaster sordidula (Nylander, 1849) is new to Iranian ant fauna. This record increases the recorded ant-fauna of Iran to over 171 species. Among the identified aphid species, Aphis craccivora Koch, 1856 had the highest ant attraction. Also, Aphis salicariae Koch, 1855; Chaitophorus hillerislambersi Pintera 1987; Chaitophorus israeliticus Eastop and Hille Ris Lambers, 1976; Cinara maghrebica Mimeur, 1934 and Schizaphis nigerrima (Hille Ris Lambers, 1931) are first records for aphid fauna of Iran. The aphids, their attendant ants, and host plants collected in this study are given. Findings of this preliminary study indicated that much more detailed study should be conducted to investigate aphid-ant mutualistic associations in Iran.
of the Annual Meeting of the Cognitive Science Society Title Episodic grammar : a computational model of the interaction between episodic and semantic memory in language processing

We present a model of the interaction of semantic and episodic memory in language processing. Our work shows how language processing can be understood in terms of memory retrieval. We point out that the perceived dichotomy between rule-based versus exemplar-based language modelling can be interpreted in a neuro-biological perspective in terms of the interaction between a semantic memory system that encodes linguistic knowledge in the form of abstract rules, and an episodic memory that stores concrete linguistic events. We implement the idea of a semantic-episodic memory integration in a probabilistic grammar, and evaluate its performance as a syntactic parser on corpora of natural language. Our labeled precision and recall results are competitive with state-of-the-art syntactic parsers, with F-scores up to 90.68 on section 22 of the Penn WSJ corpus.
Accuracy and precision analysis of chamber-based nitrous oxide gas flux estimates.

Chamber-based estimates ofsoil-to-atmosphere nitrous oxide (N 2 O) gas flux tend to underestimate actual emission rates due to inherently nonlinear time series data. In theory, this limitation can be minimized by adjusting measurement conditions to reduce nonlinearity and/or by using flux-calculation (FC) schemes that account for the so-called "chamber effect. " The current study utilizes gas transport theory and stochastic analysis to evaluate accuracy and precision of N 2 O flux determinations under specific soil and chamber conditions. The analysis demonstrates that measures taken to increase the absolute accuracy of flux estimates, including shorter deployment times, larger chamber heights, and nonlinear FC schemes, will also increase the variance in flux estimates to an extent that depends on errors associated with sampling techniques and analytical instrument performance. These effects, in the absence of any actual variation in fluxes, can generate coefficients of variation ranging from 3 to 70% depending on measurement conditions. It is also shown that nonlinear FC schemes are prone to generating positively skewed distributions. These effects decrease confidence in N 2 O flux estimates and inhibit the detection of differences arising from experimental factors. In general, a linear FC scheme will be more likely to detect relative differences in fluxes, although less accurate in absolute terms than nonlinear schemes. The techniques described here have been codified into an accessible spreadsheet-based tool for evaluating accuracy and precision trade-offs under specific measurement conditions.