Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification

High spatial resolution (1–5 m) remotely sensed datasets are increasingly being used to map land covers over large geographic areas using supervised machine learning algorithms. Although many studies have compared machine learning classification methods, sample selection methods for acquiring training and validation data for machine learning, and cross-validation techniques for tuning classifier parameters are rarely investigated, particularly on large, high spatial resolution datasets. This work, therefore, examines four sample selection methods—simple random, proportional stratified random, disproportional stratified random, and deliberative sampling—as well as three cross-validation tuning approaches—k-fold, leave-one-out, and Monte Carlo methods. In addition, the effect on the accuracy of localizing sample selections to a small geographic subset of the entire area, an approach that is sometimes used to reduce costs associated with training data collection, is investigated. These methods are investigated in the context of support vector machines (SVM) classification and geographic object-based image analysis (GEOBIA), using high spatial resolution National Agricultural Imagery Program (NAIP) orthoimagery and LIDAR-derived rasters, covering a 2,609 km2 regional-scale area in northeastern West Virginia, USA. Stratified-statistical-based sampling methods were found to generate the highest classification accuracy. Using a small number of training samples collected from only a subset of the study area provided a similar level of overall accuracy to a sample of equivalent size collected in a dispersed manner across the entire regional-scale dataset. There were minimal differences in accuracy for the different cross-validation tuning methods. The processing time for Monte Carlo and leave-one-out cross-validation were high, especially with large training sets. For this reason, k-fold cross-validation appears to be a good choice. Classifications trained with samples collected deliberately (i.e., not randomly) were less accurate than classifiers trained from statistical-based samples. This may be due to the high positive spatial autocorrelation in the deliberative training set. Thus, if possible, samples for training should be selected randomly; deliberative samples should be avoided.
Organisation of a Cliometric Database

Often the empirical validation of theoretical as sumptions starting from retrospective time series imposes the handling of an important volume of data. Once col lected, this data can be structured in order to facilitate their conservation. This paper is particularly interesting in this in termediate phase and proposes to describe the organization of a database intended to be used as support for cliometric or econometric analysis. For that, we detail the stages of the creation of a database whose main objective is to store, or ganize and structure a unit of statistical series resulting from the satellite account of the Spanish education system. Our framework, even if it is based on an example, should be
Language processing and grammars : the role of functionally oriented computational models

1. Introduction (by Nolan, Brian) 2. From the extraction of continuous features in parallel texts to visual analytics of heterogeneous areal-typological datasets (by Mayer, Thomas) 3. Lexical-syntactic analysis model of Spanish multi-word expressions (by Leoni de Leon, Jorge Antonio) 4. Three-place predicates in RRG: A computational approach (by Gottschalk, Judith) 5. A Role and Reference Grammar parser for German (by Diedrichsen, Elke) 6. Extending a lexicalist functional grammar through speech acts, constructions and conversational software agents (by Nolan, Brian) 7. The implementation of the CLS constructor in ARTEMIS (by Perinan-Pascual, Carlos) 8. FrameNet and FunGramKB: A comparison of two computational resources for semantic knowledge representation (by Luzondo Oyon, Alba) 9. Exploring the thematic-frame mapping in FunGramKB (by Guerra Garcia, Fatima) 10. FunGramKB term extractor: A tool for building terminological ontologies from specialised corpora (by Felices-Lago, Angel) 11. Deep semantic representation in a domain-specific ontology: Linking EcoLexicon to FunGramKB (by San Martin, Antonio) 12. A functional and constructional approach for specialized knowledge resources (by Sanchez-Cardenas, Beatriz) 13. Applying the lexical constructional model to ontology building (by Montiel-Ponsoda, Elena) 14. The interaction of non-linguistic and linguistic knowledge in FunGramKB (by Guerra Garcia, Fatima) 15. Low-level situational cognitive models within the Lexical Constructional Model and their computational implementation in FunGramKB (by Ruiz de Mendoza Ibanez, Francisco Jose) 16. Index
The ‘assessment for learning’ pedagogical approach in an Academic Integrity online course development

The online academic integrity course development at the University of Auckland has confirmed recent studies on the practice and impacts of an ‘assessment for learning’ approach in higher education (Gikandi, Morrow & Davis, 2011; McDowell, Sambell & Montgomery, 2013). While assessment is commonly given subsequent to and independent of learning, an ‘assessment for learning’ approach can be defined as “any assessment for which the first priority in its design and practice is to serve the purpose of promoting students’ learning” (Black et al, 2003, p. 10). This article demonstrates how the ‘assessment for learning’ approach was constructively applied in an Academic Integrity online course development. The University of Auckland’s compulsory Academic Integrity course consists of five modules. Each module has formative assessment activities and a set of summative assessment online tests. Usability testing results and students’ feedback informed the course design throughout its development. Five rounds of usability testing were conducted. The findings showed that students responded positively to a combination of multi-media interactive formative assessment activities and online summative tests. This includes real life problem-based scenarios, self-assessment quizzes and fill-in-the-gaps storylines. Each summative assessment question was linked back to a relevant section of the online module. Students were asked to find and read specific information in a course module (e.g. view video, read scenario, complete an activity) and then select the correct answer. A question bank consisting of over 250 multi-choice, scenario-based questions was created, and every student was given a completely new set of six randomly selected questions for each of the five modules in order to prevent repetition and the sharing of answers. This project is significant because it effectively applies the ‘assessment for learning’ approach. By creating combined formative and summative assessments in an online learning environment and applying informed learning design, the University of Auckland’s Academic Integrity online course was successfully developed and well received by students and staff during its pilot in 2012 and faculty-based implementation in 2013.
Creating Designs through Mathematical Functions

This study aims at generating textile patterns and innovative artistic and constructional designs for the artists and architects. It is the development of programming procedure for the analysis and designs of textile patterns and artistic instructional designs. The programming procedure utilizes MATLAB graphics tools for coding program which uses mathematical functions as input(s). The graphs of the functions are animated through several frames of iterations which eventually turn into designs. Trigonometric functions, hyperbolic functions, elliptical functions, parabolic functions and polar functions are inputs of the program. Every step of the iteration generates beautiful design. Most of the designs were found to be patterns for clothing, tiles, wall clothes, dresses, logo and other textile materials. Other designs were found ideal for artistic works. It must be noted that the designs generated are new and valuable patterns for textiles. They are also valuable innovative designs for artistic technology and also for other purposes.
FPGA implementation of PWM pattern generators [for PWM invertors]

Pulse width modulation is used in power converters to modulate a reference signal into gating pulses for IGBT switches. This conversion can be carried out using analog circuits or digital circuits, such as DSPs and microcontrollers. A more efficient and faster solution is the use of field programmable gate arrays (FPGA). This paper demonstrates how to build a variable pulse-width modulation (PWM) waveform based on Xilinx FPGAs. It involves the design, VHDL coding, functional and timing simulation and synthesis using Spartan/sup TM/-II FPGAs. Simulation results have proven the design principles and theoretical analysis.
Forecasting storms over Lake Victoria using a high resolution model

Lake Victoria in East Africa is one of the world's largest freshwater lakes and is used on a daily basis by thousands of fishermen. Each year, severe storms on the lake cause multiple boating accidents which often result in fatalities. Recent initiatives have seen an effort to reduce accidents by issuing storm warnings when severe weather is expected. Here the Met Office global Unified Model is evaluated along with a 4 km limited‐area model which has been set up to assist forecasters in the region to issue these warnings. Findings indicate the 4 km model is capable of producing more realistic strong wind speeds and rain rates than the global model. Case studies relating to fatal boating accidents on 1 March and 4 March 2012, showed improved warning signals of severe storms in the 4 km model compared to the global model. Objective comparisons between model and observations were conducted on 2 months of data. An objective method was used to determine ‘storm’/‘no storm’ in the model forecasts. These were then compared against cloud top temperature from IR satellite and lightning data from the arrival time difference (ATD) radio ground network to determine whether each model was successful at forecasting storm/calm events. The 4 km model was able to capture more storm hits (thus had fewer storm misses), but also gained more false alarm events. Overall, the objective analysis showed that both models had some predictive skill and both were an improvement on a persistence forecast.
An Introduction to Generalized Linear Models, Third Edition

- Introduces GLMs in a way that enables readers to understand the unifying structure that underpins them. - Discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis. - Connects Bayesian analysis and MCMC methods to fit GLMs. - Contains numerous examples from business, medicine, engineering, and the social sciences. - Provides the example code for R, Stata, and WinBUGS to encourage implementation of the methods. - Offers the data sets and solutions to the exercises online. Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.
Active Learning Via Sequential Design and Uncertainty Sampling

Classification is an important task in many fields including biomedical research and machine learning. Traditionally, a classification rule is constructed based a bunch of labeled data. Recently, due to technological innovation and automatic data collection schemes, we easily encounter with data sets containing large amounts of unlabeled samples. Because to label each of them is usually costly and inefficient, how to utilize these unlabeled data in a classifier construction process becomes an important problem. In machine learning literature, active learning or semi-supervised learning are popular concepts discussed under this situation, where classification algorithms recruit new unlabeled subjects sequentially based on the information learned from previous stages of its learning process, and these new subjects are then labeled and included as new training samples. From a statistical aspect, these methods can be recognized as a hybrid of the sequential design and stochastic approximation procedure. In this paper, we study sequential learning procedures for building efficient and effective classifiers, where only the selected subjects are labeled and included in its learning stage. The proposed algorithm combines the ideas of Bayesian sequential optimal design and uncertainty sampling. Computational issues of the algorithm are discussed. Numerical results using both synthesized data and real examples are reported.
An Analysis between Traditional and Motion Detection Game - Using Ict Techniques

Human relaxation and stress management always need different forms of entertainment. Soft games generate concentration and focus on objects. The modern games with latest ICT tools are preferred by youngster, especially college students. By keeping this background as the base for the study, the present study chose the students who had the experience of playing games through traditional and modern sources as sampling unit and required data were collected through systematic opinnaire. The collected output were analysed for the comparison of play consistency in both traditional and modern sources. It concludes for the effectiveness of play consistency in modern games.
Parallel and distributed Controlled Random Search algorithms; case study results

CRS (Controlled Random Search) algorithms for global optimization are considered. The main objective is to present the advantages of developing the parallel and distributed random search algorithms to search for the global solution. A practical example, application of parallel CRS2, CRS4, CRS6, CRSI algorithms and distributed CRS2 algorithm to calculate the optimal prices of products that are sold in the market, are presented. In the final part of the paper the results of numerical experiments performed on the historical data are described and discussed.
Wavelet preprocessing of acoustic signals

The authors describe results on using the wavelet transform to preprocess acoustic broadband signals in a system that discriminates between different classes of acoustic bursts. This is motivated by the similarity between the proportional bandwidth filters provided by the wavelet transform and those found in biological hearing systems. The experiment involves comparing statistical pattern classifier effects of wavelet and FFT (fast Fourier transform) preprocessed acoustic signals. The data used are from the DARPA Phase I database, which consists of artificially generated signals with real ocean background. The result show that the wavelet transform did provide improved performance when classifying in a frame-by-frame basis. The DARPA Phase 1 database is well matched to proportional bandwidth filtering, i.e., signal classes that contain high frequencies compensate for the poor match of the wavelet transform for long-duration (high-frequency) signals. <<ETX>>
Interpreting and Visualizing Regression Models Using Stata

Michael Mitchell’s Interpreting and Visualizing Regression Models Using Stata is a clear treatment of how to carefully present results from model-fitting in a wide variety of settings. It is a boon to anyone who has to present the tangible meaning of a complex model in a clear fashion, regardless of the audience. As an example, many experienced researchers start to squirm when asked to give a simple explanation of the practical meaning of interactions in nonlinear models such as logistic regression. The techniques presented in Mitchell's book make answering those questions easy. The overarching theme of the book is that graphs make interpreting even the most complicated models containing interaction terms, categorical variables, and other intricacies straightforward.
Mutliscale Facial Expression Recognition using Convolutional Neural Networks

Automatic face analysis has to cope with pose and lighting variations. Especially pose variations are difficult to tackle and many face analysis methods require the use of sophisticated normalization procedures. We propose a data-driven face analysis approach that is not only capable of extracting features relevant to a given face analysis task, but is also robust with regard to face location changes and scale variations. This is achieved by deploying convolutional neural networks. We show that the use of multi-scale feature extractors and whole-field feature map summing neurons allow to improve facial expression recognition results, especially with test sets that feature scale, respectively, translation changes.
Self-timed rings and their application to division

Self-timed systems avoid the problems associated with the global clocks of synchronous systems. This thesis introduces a new type of structure called a self-timed ring that can pass data multiple times through the same function blocks without requiring any external control signals or clocking. The latency and throughput of self-timed rings are analyzed by a method that also determines the performance of asynchronous pipelines as a special case. By meeting certain constraints suggested by this analysis, a self-timed ring can completely hide its control logic delays and achieve operation with zero overhead. If, in addition, the ring is composed of a proposed domino stage configuration without latches, then the ring achieves, in much less area, the same minimal-latency operation as an unrolled combinational array implementing the same function. A prime example of a problem for which a self-timed ring implementation achieves high performance is the iterative computation of the arithmetic function of division. This thesis compares two self-timed divider chips: a preliminary design and an improved one that adheres to the constraints determined by this research. Measurements showed that design techniques increased performance by a factor of 2.2 due to architecture alone. The self-timed ring of the new divider occupies 7mm$\sp2$ in a 1.2$\mu$ CMOS process and computes quotient bits in 2.9nS, requiring a total latency of 45nS to 160nS for a full 54-bit result, depending on the data operands.
Robust Speaker Diarization for Single Channel Recorded Meetings

This thesis describes research into speaker diarization for recorded meetings. It explores the algorithms and the implementation of an off-line speaker segmentation and clustering system for meetings that have been recorded using one microphone. Speaker diarization is defined as a process of partitioning a spoken record into speaker-homogeneous regions. The meeting record contains different kinds of noise and the length of the noise varies significantly. The average speech-turn is short and the number of speakers is unknown. To reduce the influence of these aural characteristics on the performance of the speaker diarization system, this thesis proposed four new algorithms. First, a new speech activity detection method, which adjusts the non-speech model complexity according to the noise length ratio. Second, a new speaker change point detection measure was derived based on the Fisher Linear Discriminate Analysis to help detect short speaker turns. Third, the Equal Weight Penalty Criterion was formulated as a new model complexity selection criterion to train both the speakers' models and the Universal Background Model (UBM). It contains two penalty terms, one penalizes the model dimensions and removes mixtures with small mixing probability, the other penalizes the Kullback Leibler divergence between the prior and posterior distribution of the mixing parameters, removing those components that share the same location. This criterion can be adjusted by the prior distribution parameter delta, which controls how many components are used in the model. Fourth, a weight and mean adaptation method was developed to adapt potential speaker models from the UBM. In addition, a potential speaker merging termination scheme, based on the Normalized Cuts, was introduced into the system. Combining all the new techniques derived in this thesis together, the error rate of the baseline system was reduced from 18.61% to 9.24% on the development set, 18.89% to 10.50% on the evaluation set from AMI corpus, and 21.35% to 15.48% on the evaluation set from ISL corpus. When using the Normalized Cuts based potential speaker merging termination scheme, the error rate of the baseline system was reduced 18.61% to 10.33% on the development set, 18.89% to 9.99% on the evaluation set from AMI corpus, and 21.35% to 13.70% percentage points on the evaluation set from ISL corpus.
An approach to calculate minimum support-confidence using MCAR with GA

Classification is a crucial subject in data mining and machine learning, that's been studied extensively and has a wide range of applications. Classification based on association rules, otherwise known as associative classification, is usually a technique that uses association rules to create classifier. CMAR employs a novel data structure, association rule, to compactly store and efficiently retrieve many rules for classification. Association rule is really a prefix rule structure to research the sharing among rules, which achieves substantial compactness. To speed in the mining of complete pair of rules, CMAR adopts a variant of recently developed FP growth method. FP-growth is quite a bit faster than Apriori-like methods utilized in previous association-based classification, for example particularly there exist a huge number of rules, large training data sets, and long pattern rules. We use classification using association rules not just to solve classification problems, but that compares the standard of different association rule mining approaches. Therein context we show which the quality of rule sets in the standard algorithm for association rule mining might be improved using a different association rule mining strategy Above classification minute rates are 80%( MAX) hence the 20% data are unclassified. This is the challenge in the field of data classification. In this paper, we used multiple relational Bayesian classification algorithm depending on genetic algorithm employed for optimization of classification rate, generated by association rule.
Music perception and cochlear implants — A review of major breakthroughs

Music perception aspect in the Cochlear Implants (CIs) is being acknowledged to be one of the most appreciated and evolving innovations these days. Cochlear Implants, also termed as Bionic Ears, have aided the benefit of speech recognition to the profoundly deaf (>60dB) especially in quiet scenarios. Series of research and technological advancements, throughout these years, have not brought out ultimate improvement in the perceptual accuracy and appreciation of music in Cochlear Implant (CI) users. Recent research has thrown light on an aspect of harmonic modulation which could improve music perception. This work revolves around a study of various milestones or developments in the area of music perception improvement in Cochlear Implantees.