Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic multimedia analysis using knowledge and context

The difficulty of semantic multimedia analysis can be attributed to the extended diversity in form and appearance exhibited by the majority of semantic concepts and the difficulty to express them using a finite number of patterns. In meeting this challenge there has been a scientific debate on whether the problem should be addressed from the perspective of using overwhelming amounts of training data to capture all possible instantiations of a concept, or from the perspective of using explicit knowledge about the concepts’ relations to infer their presence. In this thesis we address three problems of pattern recognition and propose solutions that combine the knowledge extracted implicitly from training data with the knowledge provided explicitly in structured form. First, we propose a BNs modeling approach that defines a conceptual space where both domain related evidence and evidence derived from content analysis can be jointly considered to support or disprove a hypothesis. The use of this space leads to significant gains in performance compared to analysis methods that can not handle combined knowledge. Then, we present an unsupervised method that exploits the collective nature of social media to automatically obtain large amounts of annotated image regions. By proving that the quality of the obtained samples can be almost as good as manually annotated images when working with large datasets, we significantly contribute towards scalable object detection. Finally, we introduce a method that treats images, visual features and tags as the three observable variables of an aspect model and extracts a set of latent topics that incorporates the semantics of both visual and tag information space. By showing that the cross-modal dependencies of tagged images can be exploited to increase the semantic capacity of the resulting space, we advocate the use of all existing information facets in the semantic analysis of social media.
A Fuzzy Logic Decision Support Tool

Abstract : This report describes a fuzzy logic decision tool developed at NOSC under Independent Exploratory Development funding. The tool implements alternate versions of an algorithm based on results published by Zadeh, Bellman, Saaty, and Yager. The tool has broad application to command and control systems since decision making in a military context usually involves imprecise knowledge and subjective goals. Decision making is defined as an act of rational choice. In particular, given a set of possible choices for a given situation, the optimal choice is the one that meets a particular set of objectives best. Best implies that a utility function over objectives can be optimized. An important enhancement of this model uses fuzzy sets to represent uncertain objectives and incomplete information about a situation (Zadeh & Bellman, 1970). The ability to represent multiple-objective decision problems involving vague or fuzzy objectives is, in fact, one of the more useful applications of fuzzy set theory. This report presents an algorithm for solving multiple-objective decision problems when the objectives have differing degrees of importance. Keywords: Command and control systems, Computer logic, Programming languages, Artificial intelligence.
Approximate Kalman filtering

Extended Kalman filters - standard, modified and ideal, M.J. Moorman and T.E. Bullock bias in extended Kalman filters - a mathematical analysis, T.E. Bullock and M.J. Moorman robust adaptive Kalman filtering, A.R. Moghaddamjoo and R.L. Kirlin on-line estimation of signal and noise parameters and adaptive Kalman filtering, P.J. Wojcik adaptive Kalman filtering under irregular environment, G. Chen fisher initialization in the presence of ill-conditioned measurements, D. Catlin initializing the Kalman filter with incompletely specified initial conditions, A. Maravall and V. Gomez set-valued Kalman filtering, D. Morrell and W.C. Stirling distributed filtering using set models for systems with non-Gaussian noise, L. Hong suboptimal Kalman filtering for linear systems with non-Gaussian noises, H.Y. Wu and G. Chen robust stability analysis of Kalman filter under parametric and noise uncertainties, B.S. Chen numerical approximations and other structural issues in practical implementations of Kalman filtering, T.H. Kerr.
Computational Molecular Evolution

Book description:* Authored by a world-renowned specialist in the field * Adopts a statistical approach to phylogenetics * Web-based example data sets used to clarify the theory * Emphasises the models and methods designed for understanding the evolutionary process of genes and genomes * Ideal graduate seminar course material The field of molecular evolution has experienced explosive growth in recent years due to the rapid accumulation of genetic sequence data, continuous improvements to computer hardware and software, and the development of sophisticated analytical methods. The increasing availability of large genomic data sets requires powerful statistical methods to analyse and interpret them, generating both computational and conceptual challenges for the field. Computational Molecular Evolution provides an up-to-date and comprehensive coverage of modern statistical and computational methods used in molecular evolutionary analysis, such as maximum likelihood and Bayesian statistics. Yang describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. The book emphasizes essential concepts rather than mathematical proofs. It includes detailed derivations and implementation details, as well as numerous illustrations, worked examples, and exercises. It will be of relevance and use to students and professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, population genetics, mathematics, statistics and computer science. Biologists who have used phylogenetic software programs to analyze their own data will find the book particularly rewarding, although it should appeal to anyone seeking an authoritative overview of this exciting area of computational biology. Readership: An advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, mathematics and statistics.
KONSEP UPCYCLE SEBAGAI ALTERNATIF SOLUSI PEMANFAATAN LIMBAH KEBAYA LAMA

ABSTRACT Kebaya is one of the characteristical women's clothing from Indonesia that develop continuously. As time goes by, there are new elements that influences the development of kebaya, which makes the look of kebaya more modern and luxurious. By the development of design innovations in kebaya fashion, it causes the kebaya consumer demand on kebaya providers are continue to change. In the end, fashion kebaya that is no longer used becomes waste due to its old-fashioned design. This research was conducted to find out the stages of utilizing kebaya waste from kebaya provider or house of kebaya using upcycle concept. The purpose of it is to reprocess the kebaya waste materials which still have good quality back into the production cycle for the sustainability of it’s use. The conclusion of this research is utilization of old kebaya waste by using upcycle method can be in the form of combining some old kebaya waste materials by using textile surface design and fabric manipulation technique. Keywords: upcycle, kebaya waste, surface design.
Workflow graph editing and visualization in HTML5 and Javascript

Being able to run applications written in a single language on multiple platforms is a strong incentive for migrating applications to the web. This along with the possibility to avoid the sometimes problematic procedure of installing software, makes the case even stronger. This thesis investigates how to migrate a workflow graph editing system into a web technology in order to publish it on the web. We will evaluate a number of different technologies such as WebGL, HTML5 canvas and SVG. SVG is deemed as the preferred technology due to its advantages when it comes to interaction. As it can leverage JavaScripts event system we get a potent way of handling events without writing a single line of code. When combining this with the framework D3JS we achieve a great tool for writing workflow management systems.
Uniformisation de l'éclairage en imagerie rétinienne: Application pour les images d'autofluorescence du fond d'oeil

Dans cet article nous proposons une methode originale pour corriger l'eclairage inhomogene des images de la retine. Notre methode utilise la convolution de l'image par un filtre gaussien de taille appropriee definie automatiquement par l'analyse de la courbe d'entropie de l'image corrigee. La luminosite moyenne de l'image originale est conservee ainsi que les structures et les lesions. Title: Uniformization of lighting in retinal imaging : Application to autofluorescence images of fundus oculi Abstract: In this paper, we propose an original method for the correction of inhomogeneous illumination in the retinal images. Our method uses the convolution of an image by Gaussian filter of an appropriate size. The size of the Gaussian filter is automatically determined by the analysis of entropy curve of the corrected image. The original brightness of the image is preserved as well as the retinal structural and lesions.
Interactive Citation Workbook for The Bluebook: A Uniform System of Citation

The annually revised ICW uses a building-block approach to learning citation. Individual chapters include explanatory text with examples, a checklist summary of the text, and a citation exercise for practicing and reinforcing the rules covered in the chapter. Students can complete a citation exercise either in their ICW Workbook or in the online Workstation, accessible via Lexis Advance. Doing the exercises online provides immediate feedback for both correct and incorrect answers.
Cognitive Learning Approaches to the Design of Accessible E-Learning Systems

The creation of exciting, new, powerful and accessible e-learning systems depends upon innovations in cognitive science, human learning, e-learning implementation principles and derivative, technological e-learning solutions. The issues of user sensitive design and user diversity are central to such developments and so must be one of the focuses of any effective e-learning system. This chapter shows how a unique characterization of the interaction between e-learning requirements, accessibility and cognitive user modeling generates an inimitable set of solutions to current e-learning problems, through a simple and supportive conceptual framework. In so doing, we show how evidence and insights from diverse subjects such as cognitive science, computing science and social sciences can be integrated to provide a robust platform for the next generations of pedagogically enriched e-learning systems.
Using temporal evidence in blog search

In this paper we present a study on the relevance of web documents over time and the use of temporal evidence in blog search tasks. Time is an intrinsic property of social media, most notably in blogs where each post is typically attached with a timestamp representing its publish date. However, due to the challenges in obtaining document collections containing temporal information, research on this field has been scarce. We base our study on the Blog06 collection and the relevance assessments produced in the context of the TREC Blog Track, to investigate the relevance of time-based features in standard retrieval tasks. We observe small, but statistically significant improvements over a BM25 baseline when temporal information is used. Also, we find a direct connection between recency and relevance of documents for ad-hoc retrieval.
A Need Analysis Study Regarding to Develop a School-Based Curriculum for Teaching Principles and Methods Course

Arastirmada, ogretim ilke ve yontemleri dersine yonelik okul temelli bir ogretim programinin ilk asamasi olan ihtiyac analizinin yapilmasi amaclanmistir. Durum calismasina gore desenlenen arastirmanin katilimcilarini olcut ornekleme yontemine gore belirlenmis, 15 sinif ogretmeni adayi, sekiz ogretim elemani, bes sinif ogretmeni ve bes maarif mufettisi olusturmustur. Verilerin toplanmasinda ise gorusme ve dokuman incelemesi yontemleri kullanilmistir. Analiz sonuclarina gore ogretim ilke ve yontemleri dersinin amaclarinin bilissel ve duyussal anlama ile bilissel beceri duzeylerinde olmasi gerektigi belirlenmistir. Bu amaclara ulasmada ise icerik yogunlugunun azaltilarak uygulamada islevsel ve yaygin kullanimi olan konularin derinlemesine ele alinmasi gerektigi ortaya cikmistir. Bununla birlikte bilgiyi sunma ve anlamlandirmayi, ogretim hizmetinin niteligini arttiran degiskenlerin kullanimini iceren, uygulama boyutunun arttigi, otantik ogrenme yasantilari sunulan ve ogrenci merkezli yaklasimi temel alan bir ogrenme ogrenme-ogretme surecine gereksinim duyuldugu belirlenmistir. Ogretimin degerlendirilmesinde ise surece dayali degerlendirmeyi temel alan alternatif olcme araclarinin kullanilmasi, anlama, yorumlama ve beceri duzeyini olcen yazili yoklama yapilmasi ve tum olcme sonuclarina geri bildirim verilmesi gerektigi sonucuna ulasilmistir.
Communication Constructs That Influence Information Technology Project Failure

Communication Constructs That Influence Information Technology Project Failure by Vanessa Mackey MISM, Walden University, 2010 BS, University of Arkansas at Pine Bluff, 1977 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management Walden University May 2015 Abstract Ineffective communication behavioral constructs in the workplace that lead to information technology (IT) project failure and in some cases organization failure are increasingly becoming a management concern. Despite this trend, there is little research on the communication behavioral constructs that contribute to IT project failure rates. The purpose of this phenomenological study was to explore the lived experiences ofIneffective communication behavioral constructs in the workplace that lead to information technology (IT) project failure and in some cases organization failure are increasingly becoming a management concern. Despite this trend, there is little research on the communication behavioral constructs that contribute to IT project failure rates. The purpose of this phenomenological study was to explore the lived experiences of business analysts, programmers, and programmer analysts pertaining to the behavioral constructs associated with effective and ineffective communication. The research questions addressed these behaviors from a conceptual framework based on communication theory, organizational information processing theory, and critical social theory. This framework guided data collection using electronic interviews of a snowball sample of social media participants. Data were coded using open and axial techniques, analyzed for themes and patterns, and member checked to bolster trustworthiness. Findings included 10 communication behavioral constructs that influence communication in IT software development teams. Included in the findings were potential options for improving communication among end users, management, programmers, and other employees. Recommendations to improve communication among stakeholders included involvement of the correct stakeholders, clear project requirements, frequent communication, active listening, and feedback. Other recommendations were stakeholder education and training, and knowledge of goals and processes. Implications for positive social change could be realized by using the findings to improve the way communication is addressed, shared, and implemented to reduce IT project failure for stakeholders. Communication Constructs That Influence Information Technology Project Failure by Vanessa Mackey MISM, Walden University, 2010 BS, University of Arkansas at Pine Bluff, 1977 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Management
Grid-enabled adaptive surrogate modeling for computer aided engineering

A very large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This dissertation presents a mature, flexible, and adaptive framework for regression modeling and adaptive sampling to tackle these issues. The framework brings together algorithms for data fitting, model selection, sample selection, hyperparameter optimalization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for his/her problem.
Hidden process regression for curve modeling, classification and tracking

This research addresses the problem of diagnosis and monitoring for predictive maintenance of the railway infrastructure. In particular, the switch mechanism is a vital organ because its operating state directly impacts the overall safety of the railway system and its proper functioning is required for the full availability of the transportation system; monitoring it is a key task within maintenance team actions. To monitor and diagnose the switch mechanism, the main available data are curves of electric power acquired during several switch operations. This study therefore focuses on modeling curve-valued or functional data presenting regime changes. In this thesis we propose new probabilistic generative machine learning methodologies for curve modeling, classification, clustering and tracking. First, the models we propose for a single curve or independent sets of curves are based on specific regression models incorporating a flexible hidden process. They are able to capture non-stationary (dynamic) behavior within the curves and address the problem of missing information regarding the underlying regimes, and the problem of complex shaped classes. We then propose dynamic models for learning from curve sequences to make decision and prediction over time. The developed approaches rely on autoregressive dynamic models governed by hidden processes. The learning of the models is performed in both a batch mode (in which the curves are stored in advance) and an online mode as the learning proceeds (in which the curves are analyzed one at a time). The obtained results on both simulated curves and the real world switch operation curves demonstrate the practical use of the ideas introduced in this thesis.
Music and Speech Analysis Using the 'Bach' Scale Filter-Bank

The aim of this thesis is to define a perceptual scale for the ‘Time-Frequency’ analysis of music signals. The equal tempered ‘Bach’ scale is a suitable scale, since it covers most of the genres of music and the error is equally distributed for each semi-tone. However, it may be necessary to allow a tolerance of around 50 cents or half the interval of the Bach scale, so that the interval can accommodate other common intonation schemes. The thesis covers the formulation of the Bach scale filter-bank as a time-varying model. It makes a comparative study with other commonly used perceptual scales. Two applications for the Bach scale filter-bank are also proposed, namely automated segmentation of speech signals and transcription of singing voice for query-by-humming applications. Even though this filter-bank is suggested with a motivation from music, it could also be applied to speech. A method for automatically segmenting continuous speech into phonetic units is proposed. The results, obtained from the proposed method, show around 82% accuracy for the English and 85% accuracy for the Hindi databases. This is an improvement of around 2 3% when the performance is compared with other popular methods in the literature. Interestingly, the Bach scale filters perform better than the filters designed for other common perceptual scales, such as Mel and Bark scales. ‘Musical transcription’ refers to the process of converting a musical rendering or performance into a set of symbols or notations. A query in a ‘query-by-humming system’ can be made in several ways, some of which are singing with words, or with arbitrary syllables, or whistling. Two algorithms are suggested to annotate a query. The algorithms are designed to be fairly robust for these various forms of queries. The first algorithm is a frequency selection based method. It works on the basis of selecting the most likely frequency components at
Common Core English and Language Arts K-1 Exemplar Text Set: A Critical Content Analysis of Cultural Representations

10 CHAPTER ONE: INTRODUCTION 11 Purpose of Study 13 Rationale for Content Analysis 13 Research Questions 14 Political Context for This Study: CCSS and Young Children 15 Issues of Representation with Literature 19 Representations that Engages Readers 20 Significance of this Research for Classrooms 21 Reader Response Theory 23 Connecting CCSS, Read Alouds, and Reading Development 26 Support for Multicultural Literature 28 Overview Structure of This Dissertation and Remaining Chapters 32 CHAPTER TWO: CRITICAL FRAMES AND RELATED LITERATURE 36 Postcolonialism as a Lens 36 Postcolonialism and Literature 38 Postcolonial Othering and Stereotyping 40 Feminism as a Critical Lens 41 Feminist Literary Criticism 44 Review of Related Literature 45 The Significance of Research on Children’s Literature 46 Scholars Who Routinely Use Critical Content Analysis 48 Critical Content Analysis in Children’s Literature 49