Miracle-dz/UniFAR-FTUs huggingface dataset:
Majority vote classifiers: theory and applications

The topic of this thesis is a special family of classi ers known as Majority Vote Classi ers These work by producing a large number of classi cations often using a standard method such as a tree classi er and classifying to the class receiving the largest number of votes or predictions Some recent examples include Boosting Freund and Schapire Bagging Breiman a and Error Correcting Output Coding ECOC Dietterich and Bakiri These classi ers have shown a great deal of promise but it is not fully understood how or why they work The thesis is split into two parts In the rst part we examine in detail the ECOC clas si er We show that it is in fact producing approximately unbiased probability estimates for each class and classifying to the maximum It is therefore a method to approximate the Bayes Classi er We also develop three extensions of ECOC and examine these new classi ers Finally a detailed empirical study is made of nine di erent classi ers including the ones mentioned above In the second part we examine in more generality why majority vote classi ers seem to work so well Many theories have been suggested for the success of these classi ers but none provide a complete explanation The theories tend to fall into one of two categories which we label classical and modern The classical theories rely on generalizations of bias and variance ideas from regression theory This is a very appealing concept However while this area still needs to be further explored it is clear that the nice decompositions that arise from squared error loss do not hold for the loss functions that are used for classi cation problems As a result bias and variance ideas do not seem to be very useful
Rule extraction from technology IPOs in the US stock market

Machine learning techniques for prediction and rule extraction from artificial neural network methods are used. The hypothesis that market sentiment and IPO specific attributes are equally responsible for first-day IPO returns in the US stock market is tested. Machine learning methods used are Bayesian classifications, support vector machines, decision tree techniques, rule learners and artificial neural networks. The outcomes of the research are predictions and rules associated With first-day returns of technology IPOs. The hypothesis that first-day returns of technology IPOs are equally determined by IPO specific and market sentiment is rejected. Instead lower yielding IPOs are determined by IPO specific and market sentiment attributes, while higher yielding IPOs are largely dependent on IPO specific attributes.
Statistical model for occluded object recognition

In this paper we present a model-based statistical algorithm for recognition of partially occluded objects from noisy features. The likelihood ratio of the image features to template features is used for recognition. Two different statistical occlusion models are introduced: an independent prior model and a Markov random field (MRF) prior model. Our experiments show that the MRF model performs more robustly than the independent model in the presence of partial occlusion.
The SISAL 2. 0 reference manual

In this report we describe the SISAL 2.0 programming language. The project began in 1982 as a cooperative venture by Digital Equipment Corporation, the University of Manchester (England), Lawrence Livermore National Laboratory, and Colorado State University. The project`s goals evolved to become: Define a general-purpose functional language that can run efficiently on conventional and novel parallel architectures; Define a dataflow graph intermediate form independent of language and target architecture; Develop optimization techniques for high-performance applicative computing; Develop a thread management environment to support dataflow-style parallel computing on conventional shared-memory multiprocessors; Achieve sequential and parallel execution performance competitive with program written in conventional languages; and Validate the functional style of programming for large-scale scientific applications. The last three goals set SISAL apart from other efforts. We have shown that the dataflow approach can profitably be applied outside the classic dataflow world, without the use of specialized hardware. SISAL runs on uniprocessors, shared-memory multiprocessors, the Cray-X/MP, the Warp systolic processor as well as Manchester`s dataflow machine. Development continues for the Connection Machine and some experimental systems.
On the performance of different detection techniques for OFDM-CDMA in fading channels

The combination of orthogonal frequency division multiplexing (OFDM) with code division multiple access (CDMA) systems has attained increasing significance in mobile radio communications. In this paper basic OFDM-CDMA detection techniques based on simple equalization algorithms, and improved detection techniques, such as the newly proposed iterative detection applying the minimum mean square error (MMSE) criterion, are analyzed in the mobile radio environment. This analysis enables one a comparison between known and new OFDM-CDMA detection techniques in terms of spectral efficiency and receiver complexity.
Combined routing and scheduling for spatial TDMA in multihop ad hoc networks

Spatial time division multiple access (STDMA) is a conflict free MAC protocol for multihop ad hoc networks where links or nodes are scheduled to transmit in periodic slots. The paper analyzes the performance of a novel routing and scheduling strategy to create the schedule in STDMA. It is named reuse adaptive minimum hop algorithm (RA-MHA). RA-MHA uses the set of minimum hop paths between sources and destinations as well as previously suggested schedule strategies in order to assign link transmissions. The results show that when directional antenna patterns are used, RA-MHA produces a substantial improvement in throughput and packet delay.
Efficient Debugging with Slicing and Backtracking

Programmers spend considerable time debugging code. Several tools are available to help them in this task, varying from hexadecimal dumps of program state to windowand mouse-based interactive debuggers, but the task still remains complex and difficult. Most of these conventional debuggers provide breakpoints and traces as their main debugging aids. These features have changed little in the past 15–20 years despite the increased complexity of software systems on which debuggers are used. In this paper we present a prototype tool that enables users to follow their “natural” thought process while debugging. It combines dynamic program slicing and execution backtracking techniques in a novel way. With the help of this powerful combination, program bugs may be localized and removed very quickly. Examples are given to show how our debugger may be used, and how it relates to other research in the area.
MML and Bayesianism: similarities and differences: introduction to minimum encoding inference Part

This paper continues the introduction to minimum encoding inference given by Oliver and Hand. This series of papers were written with the objective of providing an introduction to this area for statisticians. We examine the relationship between Bayesianism and Minimum Message Length (MML) inference. We argue that MML augments Bayesian methods by providing a sound Bayesian method for point estimation which is invariant under non-linear transformations. We explore the issues of invariance of estimators under non-linear transformations, the role of the Fisher Information matrix in MML inference, and the apparent similarity between MML and the adoption of a Jeereys' Prior. We then compare MML to an approximate method of Bayesian Model Class Selection. Despite apparent similarities in their expressions, the properties of the two approaches can be diierent.
A Study on Characteristics of Topic-Specific Information Cascade in Twitter

In this paper, we study patterns of information diffusion and behaviors of participating users in Twitter. We investigate characteristics of hashtag cascade in various topics by exploiting distributions of user influence, which are cascade ratio, tweet ratio, and time interval. We show that topics of major hashtags can be characterized by these distributions. For example, people using political hashtags often influence many of their friends and continuously discuss on the topics. Our experiments also show that the hashtags can be roughly clustered into topics using only those measures, and miss-clustered hashtags have some special roles in their topics.
L'interprétation en sémantique de corpus : le cas de la construction de terminologies

The aim of this paper is to focus on the necessity of a double marking-out when doing the semantic analysis of the data of a corpus. The first mark lies in the situation in which texts are produced and the second one lies in the interpretation of the texts. In both cases, the author suggests to use the notion of genre (textual genre and interpretative genre) in order to classify and categorize situations. The issue is exemplified by the problem of the building of terminologies according to a particular interpretative genre. The paper shows how textual genre influences the functioning of conceptual relations patterns (e.g., the preposition avec is used to spot a meronymic relation). It demonstrates that this kind of analysis may help to refine the descriptions initially made by introspection.
Training a generalized discrete Hopfield network with fuzzy learning rule

A Hopfield network, a type of recurrent neural network, may be used as a tool for classification by storing exemplars as memories. This paper describes a method of growing a hybrid network for use in classification of patterns which incorporates fuzzy membership in the training algorithm. The hybrid network consists of 3 networks in sequence with the middle network being a fully recurrent Hopfield style network which changes in size: starting out as a single neuron. The first network is a one layer feedforward network while the last network is simply a selector network which selects components from the terminal state of the recurrent network. Connection matrices are determined, using a modified Widrow-Hoff learning rule, such that the class exemplars are attracted to exemplars within the same class. An arbitrary element is then classified by the class of its attractor. Before training a membership value is calculated for each training pattern which is made use of during training.
Successful Qualitative Research: A practical guide for beginners

PLEASE NOTE: THIS IS NOT THE FULL BOOK TEXT - PUBLISHER POLICY ONLY ALLOWS ACCESS TO AN EXTRACT OF THIS BOOK IN THE UWE BRISTOL RESEARCH REPOSITORY For Chapter 1: This chapter introduces qualitative research to a reader new to the area, and sets the scene for the rest of the book. It clearly specifies what defines qualitative research, and differentiates the use of a whole qualitative paradigm, or Big Q approach, with the more limited use of qualitative data within a more positivist paradigm. It contextualises qualitative research – within psychology – by providing a brief history of the approach, and by locating it within the learning-context of research degrees at many universities, where quantitative approaches tend to predominate. The chapter explores the question of what makes a good qualitative researcher by introducing the concept of a qualitative sensibility, and by outlining skills important in qualitative research, such as reflexivity, interactional ability, and a critical and inquiring mind. The chapter also introduces the authors and their perspectives on qualitative research. Finally, the chapter introduces the overall approach, organisation and structure of the book, and its specific and unique pedagogical features, including the companion website. The chapter is supported with a range of pedagogical features including annotated further reading, and chapter overviews and summaries. The companion website includes an interactive flashcard glossary of key terms for this chapter and self-test multiple choice questions for section 1: Successfully getting started in qualitative research.
Building Web applications with UML

Building Web Applications with UML is a guide to building robust, scalable, and feature-rich web applications using proven object-oriented techniques. Written for the project manager, architect, analyst, designer, and programmer of web applications, this book examines the unique aspects of modeling web applications with the Web Application Extension (WAE) for the Unified Modeling Language (UML). The UML has been widely accepted as the standard modeling language for software systems, and as a result is often the best option for modeling web application designs.The WAE extends the UML notation with semantics and constraints enabling developers to model web-specific architectural elements using the Rational Unified Process or an alternative methodology. Using UML allows developers to model their web applications as a part of the complete system and the business logic that must be reflected in the application. Readers will gain not only an understanding of the modeling process, but also the ability to map models directly into code.Key topics include: A basic introduction to web servers, browsers, HTTP, and HTML Gathering requirements and defining the system's use cases Transforming requirements into a model and then a design that maps directly into components of the system Defining the architecture of a web application with an examination of three architectural patterns describing architectures for thin web client, thick web client, and web delivery designs Modeling, at the appropriate level of abstraction and detail, the appropriate artifacts, including web application pages, page relationships, navigate routes, client-side scripts, and server-side generation Creating code from UML models using ASP and VBScript Client-side scripting using DHTML, Java Script, VBScript, Applets, ActiveX controls, and DOM Using client/server protocols including DCOM, CORBA/IIOP, and Java's RMI Securing a web application with SET, SSL, PGP, Certificates, and Certificate Authorities 0201615770B04062001
GAUSS and Matlab codes for Multivariate Rational Expectations Models and Macroeconometric Modelling: A Review and Some New Results

Code for the article published in Handbook of Applied Econometrics: Macroeconomics, Oxford: Basil Blackwell, 1995, pp. 139â€“1. Currently, you may download one GAUSS- and one MATLAB program from this page. These programs, RBCQDE.PRG (GAUSS) and RBCQDE.M (MATLAB), solve the real business cycle model of Christiano and Eichenbaum (1992). (See the paper for further details.) To run the GAUSS program, you will also need to download the procedure MATPOW.G.
ENHANCING SENTIMENT LEXICA WITH NEGATION AND MODALITY FOR SENTIMENT ANALYSIS OF TWEETS

Sentiment analysis became one of the core tasks in the field of Natural Language Processing especially with the rise of social media. Public opinion is important for many domains such as commerce, politics, sociology, psychology, or finance. As an important player in social media, Twitter is the most frequently used microblogging platform for public opinion on any topic. In recent years, sentiment analysis in Twitter turned into a recognized shared task challenge. In this thesis, we propose to enhance sentiment lexica with the linguistic notions negation and modality for this challenge. We test the interoperability between various sentiment lexica with each other and with negation and modality and add some Twitter-specific ad-hoc features. The performance of different combinations of these features is analyzed in comprehensive ablation experiments. We participated in two challenges of the International Workshop on Semantic Evaluations (SemEval 2015). Our system performed robustly and reliably in the sentiment classification of tweets task, where it ranked 9th among 40 participants. However, it proved to be the state-of-the-art for measuring degree of sentiment of tweets with figurative language, where it ranked 1st among 35 systems.
An Atlas of Cat Anatomy

"An Atlas of Cat Anatomy" can help a student learn twice as much as he could in the same amount of time using only a written description. The book is spiral bound and stands like an easel, taking a minimum amount of space in the work area. Altogether there are fifty-seven plates featuring the various parts and organ systems in their actual size, making identification remarkably easy. A brief verbal description accompanies each plate. In addition, the extensive glossary includes synonymous terms, derivations, definitions, and keys to pronunciation.
3D Face Modeling, Analysis and Recognition

3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further.The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied.Key features: Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images Contains numerous exercises and algorithms throughout the book
Maximum likelihood estimation with stata

Maximum Likelihood Estimation with Stata, Fourth Edition is written for researchers in all disciplines who need to compute maximum likelihood estimators that are not available as prepackaged routines. Readers are presumed to be familiar with Stata, but no special programming skills are assumed except in the last few chapters, which detail how to add a new estimation command to Stata. The book begins with an introduction to the theory of maximum likelihood estimation with particular attention on the practical implications for applied work. Individual chapters then describe in detail each of the four types of likelihood evaluator programs and provide numerous examples, such as logit and probit regression, Weibull regression, random-effects linear regression, and the Cox proportional hazards model. Later chapters and appendixes provide additional details about the ml command, provide checklists to follow when writing evaluators, and show how to write your own estimation commands.
Application of autoregressive distributed lag model to thermal error compensation of machine tools

Since Thermal error in precision CNC machine tools cannot be ignored, it is essential to construct a simple and effective thermal error compensation mathematical model. In this paper, three modeling methods are introduced in detail. The first is multiple linear regression model; the second is congruence model, which combines multiple linear regression model with AR model of its residual error; and the third is autoregressive distributed lag model(ADL), which is compared and analyzed. Multiple linear regression analysis is used most commonly in thermal error compensation, since it is a simple and quick modeling method. But thermal error is nonlinear and interactive, so it is difficult to model a precise least squares model of thermal error. The congruence model and autoregressive distributed lag model belong to time series analysis method which has the advantage of establishing a precise mathematical model. The distinctions between the two models are that: the congruence model divides the parameter into two parts to estimate them respectively, but autoregressive distributed lag model estimates parameter uniformly, so congruence model is less accurate than autoregressive distributed lag model in modeling. This paper, based upon an actual example, concludes that autoregressive distributed lag model for thermal error of precision CNC machine tools is a good way to improve modeling accuracy.
Radar classification using a neural network

Commonly used signal recognition techniques have many drawbacks. Many signal recognition and analysis techniques rely on complex algorithms which are computationally intensive and require a man in the loop to verify and validate the analysis. Classical signal recognition techniques often are unable to function in near real time. Classical techniques include nearest neighbor classifiers parameter range-matching statistical estimation techniques and heuristic algorithms. Hard-limited decision boundaries can produce ambiguities because signals which are outside these boundaries may not be classified or may be matched to more than one class. Lastly the addition of more signals to the signal recognition database of these algorithms typically necessitates additional software or hardware. We describe the use of an artificial neural network for classifying radar signals collected by a passive receiver. We selected neural classifiers because of their ability to adapt to the environment through training which allows them to avoid many of the problems associated with traditional classifiers. We used an artificial neural network employing a multilayer perceptron with back propagation to solve two common pattern recognition problems encountered when classifying radar signals. The first problem that of pulse sorting or deinterleaving is to sort individual pulses into " bins" associated with the radar emitter each pulse is from. The second problem that of radar classification or identifying radar type is to determine the type (and function) of the radar emitter represented by each bin
Comparison of Methods for Increasing the Performance of a DUA Computation

Computing realistic routes for a given road network and a known demand of vehicles is one of the most important steps when preparing a road traffic simulation. The approach developed by Christian Gawron in 1998 which we use for this purpose computes a dynamic user equilibrium by iteratively performing the simulation and computing new vehicle routes. The results are valid, but the computation is very time consuming due to the need to perform both the complete simulation and rerouting of all vehicles within each iteration step. Herein, we want to discuss some approaches to reduce the needed time and memory consumption. The results show that this can be achieved without reducing the algorithm’s quality.
Ethical Perceptions among Hispanic Students: Differences by Major and Gender.

In this study, the authors examined ethical perceptions of Hispanic students by analyzing differences between (a) accounting and nonaccounting business majors and (b) women and men. The authors used the following five constructs: justice, relativism, egoism, utilitarianism, and deontology. Their study incorporated 12 moral characteristics into eight ethical cases. Each respondent made 72 ethical judgments. The authors found 21 significant differences between majors and 18 significant differences between genders.
English for Medical Purposes

English for medical purposes (EMP), as applied linguists would call it, and communication skills in medicine, as medical educationalists would call it, have developed alongside each other over the last 40 years, though not always being fully aware of each other. Keywords: discourse analysis; language for academic purposes; language for specific purposes