Miracle-dz/UniFAR-FTUs huggingface dataset:
Propositional knowledge for conceptual understanding of statistics.

The applications of statistics in a daily and professional context are numerous, but there are even more examples of faulty applications of statistics in such a context. Given the important role statistics play in research, politics, policy etc. it is of great importance that students and professional develop a good understanding of statistics. This requires a structured and phased approach. Characteristic for this approach is a fading degree of guidance by the teacher in consecutive phases, while the possibility of getting feedback on learning results continues to exist.
Text as Scene: Discourse Deixis and Bridging Relations

This paper presents a new framework, "text as scene", which lays the foundations for the annotation of two coreferential links: discourse deixis and bridging relations. The incorporation of what we call textual and contextual scenes provides more flexible annotation guidelines, broad type categories being clearly differentiated. Such a framework that is capable of dealing with discourse deixis and bridging relations from a common perspective aims at improving the poor reliability scores obtained by previous annotation schemes, which fail to capture the vague references inherent in both these links. The guidelines presented here complete the annotation scheme designed to enrich the Spanish CESS-ECE corpus with coreference information, thus building the CESS-Ancora corpus.
Enhancing MPEG DASH performance via server and network assistance

MPEG DASH provides formats that are suitable to stream media content over HTTP. Typically, the DASH client adaptively requests small chunks of media based on the available bandwidth and other resources. This client-pull technology has proven to be more flexible, firewall-friendly and CDN-scalable than server-push technologies. However, service providers have less control given the decentralized and client-driven nature of DASH, which introduces new challenges for them to offer a consistent and possibly higher quality of service for premium users. This issue is addressed by MPEG in a new work referred to as SAND: Server and Networkassisted DASH. The key features of SAND are asynchronous network-to-client and network-to-network communication, and the exchange of quality-related assisting information in such a way that it does not delay or interfere with the delivery of the streaming media content. MPEG is expected to publish and complete the work on SAND as a new part of the MPEG DASH standard by early 2016.
A Dimensionality Reduction Method for Efficient Search of High-Dimensional Databases

SUMMARY In this paper, we present a novel approach for efficient search of high-dimensional databases, such as video shots. The idea is to map feature vectors from the highdimensional feature space into a point in a low-dimensional distance space. Then, a spatial access method, such as an R-tree, is used to cluster these points based on their distances in the low-dimensional space. Our mapping method, called topological mapping, guarantees no false dismissals in the result of a query. However, the result of a query might contain some false alarms. Hence, two refinement steps are performed to remove these false alarms. Comparative experiments on a database of video shots show the superior efficiency of the topological mapping method over other known methods.
Recurrent nets and signal classification

The feature space map used for analysis of feedforward backpropagation neural networks is extended to recurrent neural networks. The existence of difficulties in signal classification is shown. It is finally investigated whether this difficulty means that recurrent nets are not useful in signal-recognition tasks; the authors conclude that this is not so. <<ETX>>
Comparative Research of Clustering Algorithms for Prediction of Academic Performance of Students

—-Clustering is a task of assigning a set of objects into groups called clusters. In general the clustering algorithms can be classified into two categories. One is hard clustering; another one is soft (fuzzy) clustering. Hard clustering, the data’s are divided into distinct clusters, where each data element belongs to exactly one cluster. In soft clustering, data elements belong to more than one cluster, and associated with each element is a set of membership levels In order to monitor the progress of students efficiently, different clustering algorithms are applied to the academic results of students so as to categorize in appropriate class as per their performance. We proposed the use of FCM and KFCM clustering algorithms for prediction of students’ academic performance. Euclidean distance as a measure of similarity measurement is taken into consideration. These algorithms are applied and performance is evaluated on the basis of clustering output. FCM allows data points to belong to more than one cluster where each data point has a degree of membership of belonging to each cluster. The KFCM whereas uses a mapping function and gives better performance as compared to FCM. The summarized result shows that KFCM gives better performance than FCM
Opening a Pandora's Box: Proper Names in English Phraseology

This article explores the linguistic-cultural aspects and usage of phraseological units involving personal and place names in English. The introductory sections outline the linguistic features of proper names and phraseological units. The qualitative part of this study provides a list of units belonging to four phraseological types (idioms, stereotyped similes, binomials, formulae), drawn from idiom dictionaries. An investigation of the sources of names shows that the personal and place names involved are historically, socially or culturally prominent in British culture. Here is noted a predominance of personal over place names, and within the former, a predominance of male over female names, and first names over family names, with a number of hypocorisms. The quantitative part of the study consists of a corpus search of the selected units in the British National Corpus in order to find their frequency and distribution across registers. The search reveals that they have very low levels of occurrence, and are more commonly used in written registers, in particular, in fiction, journalism and miscellaneous texts.
Ontology generation, information harvesting and semantic annotation for machine-generated web pages

The current World Wide Web is a web of pages. Users have to guess possible keywords that might lead through search engines to the pages that contain information of interest and browse hundreds or even thousands of the returned pages in order to obtain what they want. This frustrating problem motivates an approach to turn the web of pages into a web of knowledge, so that web users can query the information of interest directly. This dissertation provides a step in this direction and a way to partially overcome the challenges. Specifically, this dissertation shows how to turn machine-generated web pages like those on the hidden web into semantic web pages for the web of knowledge. We design and develop three systems to address the challenge of turning the web pages into web-of-knowledge pages: TISP (Table Interpretation for Sibling Pages), TISP++, and FOCIH (Form-based Ontology Creation and Information Harvesting). TISP can automatically interpret hidden-web tables. Given interpreted tables, TISP++ can generate ontologies and semantically annotate the information present in the interpreted tables automatically. This way, we can offer a way to make the hidden information publicly accessible. We also provide users with a way where they can generate personalized ontologies. FOCIH provides users with an interface with which they can provide their own view by creating a form that specifies the information they want. Based on the form, FOCIH can generate user-specific ontologies, and based on patterns in hidden-web pages, FOCIH can harvest information and annotate these pages with respect to the generated ontology. Users can directly query on the annotated information. With these contributions, this dissertation serves as a foundational pillar for turning the current web of pages into a web of knowledge.
A Novel Scheduling Algorithm based on Clustering Analysis and Data Partitioning For Big Data

With the development of the computer technology and network technology, the size of data collected is increasing rapidly. It is difficult to process and analyze so big data in real-time. Cloud computing is an effective tool for real-time processing for big data. How to make full use of cloud computing to analyze big data is an hot issue in recent year. Because of the limit of current network transmission speed, there is much communication between the selected cloud computing nodes during big data processing, which will cause a heavy transmission delay and lead to the reduce of real- time. The current methods select cloud nodes for data processing depend on the quality of services of nodes, which cannot reflect the relations between different cloud nodes. To address this problem, the paper proposes a novel scheduling algorithm based on clustering analysis for big data analysis in cloud environment. The presented method divides the cloud nodes into clusters according to communication cost between different nodes, and then selects a cluster for the big data analysis services. Experimental results show the effectiveness of our scheduling algorithm. Index Terms - big data, clustering analysis, cloud computing.
The Collaboratory Notebook: A Networked Knowledge-Building Environment for Project Learning.

The Collaboratory Notebook is a networked, multimedia knowledge- building environment which has been designed to help students, teachers and scientists share inquiry over the boundaries of time and space. Developed as part of the Learning Through Collaborative Visualization Project (CoVis), the Collaboratory Notebook extends the metaphor of a scientists' laboratory notebook with facilities for sharing inquiry among multiple project partners who may be distributed across institutions, or across the country. Within these facilities, the software provides a supporting structure for scientific dialogue which is tailored to students' attempts to learn about science through project-enhanced science learning. 1.0 The CoVis Project
Writing theories and writing pedagogies

This paper explores the main approaches to understanding and teaching writing. Making a broad distinction between theories concerned with texts, with writers and with readers, I will show what each approach offers and neglects and what each means for teachers. The categorisation implies no rigid divisions, and, in fact the three approaches respond to, critique, and draw on each other in a variety of ways. I believe, however, this offers a useful way of comparing and evaluating the research each approach has produced and the pedagogic practices they have generated.
Neural Networks and Learning Machines

For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Neural Networks and Learning Machines, Third Edition is renowned for its thoroughness and readability. This well-organized and completely upto-date text remains the most comprehensive treatment of neural networks from an engineering perspective. This is ideal for professional engineers and research scientists. Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered. com/haykin/ Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
Contemporary Statistics: A Computer Approach

From the Publisher: This text is written for the introductory algebra-based statistics course found in the departments of mathematics and/or statistics. It is unique in that it provides students with a visual framework for understanding statistical concepts through the integral use of software. The software found in the book consists of 40 computer graphics programs that offer simulations and demonstrations of virtually every concept encountered in a statistics course.
Monte Carlo Simulation for Advanced Option Pricing: A Simplifying Tool

Finance students at the undergraduate and MBA levels are increasingly in possession of significant mathematical skills, corresponding with the rise in cross-listings of courses between mathematics and finance departments. This increase in mathematical skill has opened the door for the Black Scholes model to be presented to advanced undergraduate and MBA students. However, although these students can grasp the weaknesses of the Black Scholes model, they are often not mathematically advanced enough to handle more realistic option pricing models. We demonstrate how Monte Carlo simulation may be employed to open the field of advanced option pricing to students without requiring any more mathematical knowledge than basic calculus and intermediate statistics. As an example, we demonstrate how to simulate option values when the underlying process follows Heston's stochastic volatility process, and motivate the example by demonstrating the significant improvement of a properly specified stochastic volatility model over the Black Scholes model.
A perspective on active machinery isolation

A summary of general considerations in active vibration isolation obtained from experience in developing a testbed system is given. Application of this approach is illustrated by a description of the testing facility and the results achieved to date. These include broadband active vibration isolation of 20 dB over a decade-wide frequency band on the nearly-full-scale testbed. Significant features of the testing facility are a small diesel engine mounted on a representatively complex structural foundation, the correspondingly complex open-loop transfer function of the system, and the use of a hybrid analog/digital controller to partition the compensation filter frequency coverage. <<ETX>>
Risk and reward in classical music concert attendance : investigating the engagement of 'art' and 'entertainment' audiences with a regional symphony orchestra in the UK

Classical music organisations across the UK are under increasing pressure to grow and diversify their audiences. ‘Populist’ concerts are designed to attract new audience members by being more accessible and informal than core classical concerts, with programmes structured around well-known short pieces within a broadly-defined classical repertoire. Populist programming has been criticised in mainstream press for ‘dumbing down’ classical music in favour of attracting larger audiences. This thesis investigates how the distinction between populist and core programming is perceived and negotiated by audiences for a regional symphony orchestra, in order to explore cultural hierarchies operating in classical music today. This thesis is the product of a three-year Collaborative Doctoral Award with the City of Birmingham Symphony Orchestra (CBSO). It was intended to supplement the orchestra’s existing knowledge of their audiences, whilst reflecting on the value and challenges of conducting academic research within the arts industry. Semi-structured interviews were carried out with 42 CBSO attenders from core and populist classical concerts, using Interpretative Phenomenological Analysis to investigate how participants evaluate and assimilate their experiences of live classical music. The interviews explored participants’ musical engagement over a lifetime, considering: routes into concert-going, the decision to attend, the value of concert-going to the individual, the live concert experience, and their views on the classical music industry. This qualitative study was complemented by quantitative analysis of the orchestra’s customer records and ticket sales data. This thesis questions the relevance of the inherited model of ‘barriers’ to concert-going in understanding non-attendance. Instead, the analysis reveals that the decision to attend can be understood through an effort-risk-reward framework; audience members assess the amount of effort needed to attend a concert against their confidence that it will be enjoyable. For all participants, enjoyment of a concert was comprised of a mixture of ‘aesthetic’ and ‘extrinsic’ forms of value, thus complicating traditional models of ‘art’ and ‘entertainment’ audiences. In highlighting the idiosyncratic nature of attendance, this analysis challenges the extent to which conclusions can be drawn about attenders’ motivations for concert-going from their ticket booking history alone. This study reveals that audience members believe there to be ‘right’ and ‘wrong’ ways of listening, and suggests that making concerts more informal and less elitist, and providing attenders with support to engage with the music, may be beneficial to attracting new audiences.
Genetic Algorithms And Fuzzy Logic Systems Soft Computing Perspectives

Helicopter flight control with fuzzy logic and genetic algorithms, C. Philips et al skill acquisition and skill-based motion planning for hierarchical intelligent control of a redundant manipulator, T. Shibata a creative design of fuzzy logic controller using a genetic algorithm, T. Hashiyama et al automatic fuzzy tuning and its applications, H. Ishigami et al an evolutionary algorithm for fuzzy controller synthesis and optimization based on SGS-Thomson's W.A. R.P. fuzzy processor, R. Poluzzi et al on-line self-structuring fuzzy inference systems for function approximation, H. Bersini fuzzy classification based on adaptive networks and genetic algorithms, C. -T. Sun and J.-S. Jang intelligent systems for fraud detection, J. Kingdon genetic algorithms for query optimization in information retrieval - relevance feedback, D.H. Kraft et al fuzzy fitness assignment in an interactive genetic algorithm for a cartoon face search, K. Nishio et al an evolutionary approach to simulate cognitive feedback learning in medical domain, H.S. Lopes et al a classified review on the combination fuzzy logic-genetic algorithms bibliography - 1989-1995, O. Cordon et al.
Design of Moving Target Detection and Tracking System Based on the Improved Optical Flow Method

Moving target detection is an important content of the visual field of research,This paper designs a system based on the Linux embedded platform to realize the moving target detection and tracking. This system choose the optical flow method as the algorithm, which is one of the important algorithms in the computer vision field, it can be achieved on moving targets detection and tracking. The experimental result shows that this system can be achieved on moving target detection and tracking. Keywords-optical flow; moving target detection; lnux;
Software based fingerprint liveness detection= : Detecção de vivacidade de impressões digitais baseada em software

Com o uso crescente de sistemas de autenticacao por biometria nos ultimos anos, a deteccao de impressoes digitais falsas tem se tornado cada vez mais importante. Neste trabalho, nos implementamos e comparamos varias tecnicas baseadas em software para deteccao de vivacidade de impressoes digitais. Utilizamos como extratores de caracteristicas as redes convolucionais, que foram usadas pela primeira vez nesta area, e Local Binary Patterns (LBP). As tecnicas foram usadas em conjunto com reducao de dimensionalidade atraves da Analise de Componentes Principais (PCA) e um classificador Support Vector Machine (SVM). O aumento artificial de dados foi usado de forma bem sucedida para melhorar o desempenho do classificador. Testamos uma variedade de operacoes de pre-processamento, tais como filtragem em frequencia, equalizacao de contraste e filtragem da regiao de interesse. Gracas aos computadores de alto desempenho disponiveis como servicos em nuvem, foi possivel realizar uma busca extensa e automatica para encontrar a melhor combinacao de operacoes de pre-processamento, arquiteturas e hiper-parâmetros. Os experimentos foram realizados nos conjuntos de dados usados nas competicoes Liveness Detection nos anos de 2009, 2011 e 2013, que juntos somam quase 50.000 imagens de impressoes digitais falsas e verdadeiras. Nosso melhor metodo atinge uma taxa media de amostras classificadas corretamente de 95,2%, o que representa uma melhora de 59% na taxa de erro quando comparado com os melhores resultados publicados anteriormente. Abstract
Reasoning with Rules and Precedents: A Computational Model Of Legal Analysis

List of Figures. List of Tables. Acknowledgments. 1. Introduction. 2. A Framework for Integrating Rules and Exemplars. 2. A Formal Model of Ratio Decidendi. 4. Grebe: Integrating Rules and Precedents for Legal Analysis. 5. Examples of Grebe's Legal Analysis. 6. Evaluation. 7. Related Work. 8. Research Contributions. References. Appendices. Index.
The analysis of methods of interaction between elementary filters in multiple model tracking filter in marine radars

Radar target tracking on a sea-going ship is a basic source of information about movement of other vessels, influencing directly safety of navigation. The research on improving of tracking methods has led to a new concept of multiple model neural filtration, which is a combination of multiple model approach with the use of artificial neural networks for the needs of estimation of movement vector. One of the key issue during designing of filter id to establish rules of interaction between elementary filters. The paper presents the most popular methods of manoeuvre detection and interaction of elementary filters in the numerical filtration. The modifications of them for the needs of neural tracking are proposed. Additionally, a concept of use of probabilistic neural network for this purpose is described. The idea was checked in the experimental research with the use of simulation. The result of the research confirmed usefulness of using PNN in multiple model filtration, showing however simultaneously the directions of future research in this. The research was financed by Polish National Centre of Science under the research project “Development of radar target tracking methods of floating targets with the use of multiple model neural filtering”.