Miracle-dz/UniFAR-FTUs huggingface dataset:
Creating large semantic lexical resources for the Finnish language

Finnish belongs into the Finno-Ugric language family, and it is spoken by the vast majority of the people living in Finland. The motivation for this thesis is to contribute to the development of a semantic tagger for Finnish. This tool is a parallel of the English Semantic Tagger which has been developed at the University Centre for Computer Corpus Research on Language (UCREL) at Lancaster University since the beginning of the 1990s and which has over the years proven to be a very powerful tool in automatic semantic analysis of English spoken and written data. The English Semantic Tagger has various successful applications in the fields of natural language processing and corpus linguistics, and new application areas emerge all the time. The semantic lexical resources that I have created in this thesis provide the knowledge base for the Finnish Semantic Tagger. My main contributions are the lexical resources themselves, along with a set of methods and guidelines for their creation and expansion as a general language resource and as tailored for domain-specific applications. Furthermore, I propose and carry out several methods for evaluating semantic lexical resources. In addition to the English Semantic Tagger, which was developed first, and the Finnish Semantic Tagger second, equivalent semantic taggers have now been developed for Czech, Chinese, Dutch, French, Italian, Malay, Portuguese, Russian, Spanish, Urdu, and Welsh. All these semantic taggers taken together form a program framework called the UCREL Semantic Analysis System (USAS) which enables the development of not only monolingual but also various types of multilingual applications. Large-scale semantic lexical resources designed for Finnish using semantic fields as the organizing principle have not been attempted previously. Thus, the Finnish semantic lexicons created in this thesis are a unique and novel resource. The lexical coverage on the test corpora containing general modern standard Finnish, which has been the focus of the lexicon development, ranges from 94.58% to 97.91%. However, the results are also very promising in the analysis of domain-specific text (95.36%), older Finnish text (92.11–93.05%), and Internet discussions (91.97–94.14%). The results of the evaluation of lexical coverage are comparable to the results obtained with the English equivalents and thus indicate that the Finnish semantic lexical resources indeed cover the majority of core Finnish vocabulary.
OJIT: A Novel Obfuscation Approach Using Standard Just-In-Time Compiler Transformations

With the adoption of cloud computing, securing remote program execution becomes an important issue. Relying on standard data encryption is not enough, since code execution happens on remote servers, possibly allowing for eavesdropping from potential adversaries; thus the full execution process requires protection from such threats. In this paper, we introduce OJIT system as a novel approach for obfuscating programs, making it difficult for adversaries to reverse-engineer. The system exploits the JIT compilation technology to dynamically transform the code, making it constantly changing, thereby complicating the execution state. This paper quantitatively studies the effect of this approach by considering a set of obfuscation metrics borrowed from the software engineering field. The paper constructs a testbed system using the LLVM compilation framework that frequently applies random sequences of standard compiler optimizations on the currently running program. Results on using selected benchmarks from the SPEC CPU 2006 suite show a significant sustainable increase in obfuscation for a large number of standard optimizations over the run-time course of the programs.
Improving a Particle Swarm Optimization-based Clustering Method

This thesis discusses clustering related works with emphasis on Particle Swarm Optimization (PSO) principles. Specifically, we review in detail the PSO clustering algorithm proposed by Van Der Merwe & Engelbrecht, the particle swarm clustering (PSC) algorithm proposed by Cohen & de Castro, Szabo’s modified PSC (mPSC), and Georgieva & Engelbrecht’s Cooperative-Multi-Population PSO (CMPSO). In this thesis, an improvement over Van Der Merwe & Engelbrecht’s PSO clustering has been proposed and tested for standard datasets. The improvements observed in those experiments vary from slight to moderate, both in terms of minimizing the cost function, and in terms of run time.
Interpreting noun compounds using paraphrases Interpretación de los compuestos nominales mediante paráfrasis

Noun compounds are abundant in English and their interpretation is crucial for many natural language processing tasks. We propose a method for automatic two-noun noun compound interpretation that searches for suitable paraphrases in static corpora and then issues Web search engine queries to validate them. Native speakers were recruited to evaluate the returned paraphrases for noun compounds: those ranked first, second and third received an average score of 3.1842, 2.7687 and 2.5583 (on a scale of 1 to 5), respectively, which is considered promising given the difficulty of the task.
Evolutionary tuning of neural networks for gesture recognition

This paper is about a data glove/neural network system as a powerful input device for virtual reality and multi media applications. In contrast to conventional keyboards, space balls, and two-dimensional mice, which allow for only rudimental inputs, the data glove system allows the user to present the system with a rich set of intuitive commands. Previous research has employed different neural networks to recognize various hand gestures. Due to their on-line adaptation capabilities, radial basis function networks are preferably over backpropagation. Unfortunately, the latter have shown better recognition rates. This paper applies evolutionary algorithms to fine tune pre-learned radial basis function networks. After optimization, the networks achieves a recognition rate of up to 100%, and is therefore comparable or even better than that of backpropagation networks.
Input Layer Optimization of Neural Networks by Sensitivity Analysis and its Application to Recognition of Numerals

A major problem in applying a multi-layered neural network to image processing is how to select input components from among a number of features or pixels extracted from a recognized object. This is especially important for achieving both high speed processing and a high recognition rate.This paper proposes a method of optimizing the number of input components, i. e. that of input neurons without lowering a recognition rate. In general, neural networks learn a recognition algorithm from input patterns and their desired output patterns. But if some input components are redundant, namely they are expressed by other input components or they do not contribute to recognition, their effect on the recognition algorithm, or their sensitivity to outputs is considered to be very small. Therefore, by analyzing input-output sensitivity of neural networks, redundant input components can be deleted. Repeatedly applying this method, the useful input components for recognition can be selected. For an experimental neural network for recognition of numerals with 12 feature input components, the input components could be reduced to eight without lowering a recognition rate.
A HYBRID GREY-GAME-MCDM METHOD FOR ERP SELECTING BASED ON BSC

An enterprise resource planning (ERP) software is needed for industries and companies that want to develop in future. Many of the manufactures and companies have a problem with ERP software selection. An inappropriate selection process can affect both the implementation and the performance of the company signiﬁcantly. Although several models are proposed to solve this problem many of them did not consider uncertainty as an effective environmental factor. In the current paper a new model is designed. This model is based on balanced score card (BSC) and in addition, uncertainty is considered. This paper used three-parameter interval grey numbers concept that derived from Grey-theory in order to reduce uncertainty. Beside, hybrid model for weighting based on Shapley and Entropy methods are used. This combination approach is also because of reducing uncertainty. And at the end, a new method named projection attribute function method is used for ranking. There is a case study at the end of this paper that shows how this model works.
Evolvable hardware with development

Recent progress in semiconductor technologies enable us to use programmable hardware devices, such as the Field Programmable Gate Array (FPGA). When such devices are combined with evolutionary computing techniques, we can develop hardware capable of evolving its circuit configurations on-the-fly. This is an attractive technology since it simultaneously attains real-time performance and adaptive capability. In this paper, we propose the incorporation of the developmental stage in order to generate a circuit configuration matrix. Using this approach, called the grammar encoding method, we evolve a set of graph rewriting rules, whereas the direct encoding method, a conventional approach, evolves the circuit configuration matrix itself. Our approach allows for non-trivial mapping of genotype and phenotype, as well as structure operation capability. Experimental results demonstrate that our approach consistently outperforms the direct encoding method.
Large-Scale Merging of Histograms using Distributed In-Memory Computing

Most high-energy physics analysis jobs are embarrassingly parallel except for the final merging of the output objects, which are typically histograms. Currently, the merging of output histograms scales badly. The running time for distributed merging depends not only on the overall number of bins but also on the number partial histogram output files. That means, while the time to analyze data decreases linearly with the number of worker nodes, the time to merge the histograms in fact increases with the number of worker nodes. On the grid, merging jobs that take a few hours are not unusual. In order to improve the situation, we present a distributed and decentral merging algorithm whose running time is independent of the number of worker nodes. We exploit full bisection bandwidth of local networks and we keep all intermediate results in memory. We present benchmarks from an implementation using the parallel ROOT facility (PROOF) and RAMCloud, a distributed key-value store that keeps all data in DRAM.
Synthetic On-line Handwriting Generation by Distortions and Analogy

One of the difficulties to improve on the fly writer-dependent handwriting recognition systems is the lack of data available at the beginning of the adapting phase. In this paper we explore three possible strategies to generate synthetic handwriting characters from few samples of a writer. We explore in this paper both classical image distortions and two original ways to generate on-line handwritten characters: distortions based on specificities of the on-line handwriting and a generation based on analogical proportion. The experimentations show that these three approaches generate different distortions which are complementary. Indeed the combination of them allows to achieve using only 4 original characters for the learning phase a mean of 91.3% of recognition rate for 12 writers.
Tele-proximity:the experienced educators’ perspective of human to human communication in distance education

Chryssoula Themeli, MA, MScTele-Proximity: The Experienced Educator Perspective on Human to Human Connection in Distance Education Doctor of Philosophy, June, 2013 Abstract Distance education is an integral part of many universities worldwide, aiming to offer students opportunities for life-long learning and equitable access. Distant learners face many barriers and as a result, they drop out more frequently than on-campus students. Educatorsseem to be equally affected by the ‘transactional distance’ and the new digital skills neededfor teaching online. The purpose of this study is to explore the educators’ perspective on how synchronous video communication (SVC) could offer alternative educational forms for distance learning. Specifically the study aims to fill gaps in the existing research literature: to describe synchronous teaching approaches that are used worldwide to support e-learners, to investigate the role of the educator, to discover how learning and subject content are affected by SVC, to see if it enhances the sense of togetherness (immediacy and intimacy) to specify the contextual factors influencing teaching and learning synchronously, and to formulate a theory. The Informed Grounded Theory (Thornberg, 2012) and the Community of Inquiry model ( Garrison, Anderson & Archer, 2000) provided a scaffolding framework for designing interview questions and analysing findings.The findings of the study illustrate the empirical evidence of the value of SVC, its potential forms (teaching approaches), possibilities ( linking educational purposes to the medium) and contextual factors (University policy, time zones, technologies, learning objectives, educator and student identities). Specifically, audiovisual cues have an impact on educators, the learning process and immediacy. The theory of Tele-proximity formulated to explain how educators and students are connected in synchronous networked environment via tele-operations. The study aims at helping educators/instructional designers, and administrators to face the difficulties of transactional distance and make informed decisions about synchronous video enhanced communication. Researchers may use Tele-proximity as a heuristic tool to continue the scientific dialogue on the potential of synchronous video-enhanced technologies in distance education.
Multimodal Multitasking: The Combined Effects of Postural and Cognitive Demands on Overall Workload

Workers are challenged by the increasingly complex multitasking environments they experience. To interact effectively with these environments, they must avoid overload. When workers get overloaded (when their mental demands exceed the resource capacity) quality drops, performance degrades, and safety suffers. What is largely unknown, however, is whether these results translate to postural tasks. Postural stability exhibits an entirely different set of challenges: injury, the danger of slips and falls, and risks associated with aging workers or those who have mental or physical challenges. An assembly line worker, for example, must assume different postures, interact with the product in some way, and react to visual and auditory alarms. Mistakes could be dangerous. It is clearly important, then, to understand the interactive effects of mental and postural workload. The goal of this research was to quantify the effects of mental and postural demands on overall workload. To accomplish this, we implemented three studies that were designed to capture the synergistic effects of different task types on overall workload and compare different types of workload measures against each other to help further design research in the area. We designed a dual-task mental/postural protocol to test the differential effects of a series of cognitive demands found in dual-task postural studied. The results of the first study depict a clear picture: the addition of an auditory task to unstable seating decreases postural sway. Based solely on this result, it might be concluded that workload did not increase. Using the same protocol while measuring mental workload however, we found that workload did in fact increase both subjectively and objectively, even when similar postural benefit was found. Even as performance seemed to improve, the participant moved nearer to possible overload and performance decrement (a condition we did not induce in this research). Based on the differences found between the different measures, we believe the importance of measuring overall workload as well as individual task performance in cognitive/postural dual-task research is very high.
SYSTEM Research note on: the association of lack of adequate allocation concealment with effect-size overestimation

OBJECTIVE: To test the null hypothesis that clinical dental trials with adequate random sequence generation together with adequate allocation concealment do not differ in their effect size from trials lacking adequate concealment. STUDY DESIGN AND SETTING: The oral health section of the Cochrane database was searched online until 20 July 2011. All listed entries, except reviews in protocol stage and duplications, were included. Included systematic reviews were accepted for data extraction if they included bias assessment for random sequence generation and allocation concealment, as well as an analysis report of dichotomous data. From the accepted systematic reviews, datasets including number of observed events and total number of evaluated units for test- and control group; assessment results for selection bias concerning random sequence generation and allocation concealment were extracted. The extracted datasets were statistically analysed. RESULTS: A 28% effect size overestimation in association with the lack of adequate allocation concealment in selected dental randomised control trials was established and the null hypothesis was rejected. CONCLUSION: Adequate random sequence allocation without adequate concealment of such allocation appears to be insufficient to prevent exaggeration of results due to selection bias in clinical dental trials.
New Image Compression Algorithm using Haar Wavelet Transform

The compression is a process of Image Processing which interested to change the information representation in order to reduce the stockage capacity and transmission time. In this work we propose a new image compression algorithm based on Haar wavelets by introducing a compression coefficient that controls the compression levels. This method reduces the complexity in obtaining the desired level of compression from the original image only and without using intermediate levels.
Application of theoretical model for microwave remote sensing of forest

In order to interpret measurements made by airborne and spaceborne synthetic aperture radar (SAR) systems, a variety of theoretical models have been developed. Among them, the radiative transfer theory has been applied extensively. A theoretical model based on the radiative transfer theory is used to calculate the polarimetric backscattering from forest. This model is used to interpret the measured data collected over a pine forest (The Landes forest, France) using the NASA/JPL airborne SAR system, and is subsequently applied to investigate the sensitivity of backscattering coefficients to different forest parameters of interest. The sensitivity study at different frequencies and polarizations can be used to determine the feasibility of inverting for the forest parameters from the radar backscattering measurements. <<ETX>>
An Introduction to Monte Carlo Simulations in Criminal Psychology

Purpose – Studies in criminal psychology are inevitably undertaken in a context of uncertainty. One class of methods addressing such uncertainties is Monte Carlo (MC) simulation. The purpose of this paper is to provide an introduction to MC simulation for representing uncertainty and focusses on likely uses in studies of criminology and psychology. In addition to describing the method and providing a step-by-step guide to implementing a MC simulation, this paper provides examples using the Fragile Families and Child Wellbeing Survey data. Results show MC simulations can be a useful technique to test biased estimators and to evaluate the effect of bias on power for statistical tests. Design/methodology/approach – After describing MC simulation methods in detail, this paper provides a step-by-step guide to conducting a simulation. Then, a series of examples are provided. First, the authors present a brief example of how to generate data using MC simulation and the implications of alternative probability dis.
A feedback control scheme of fluid-induced vibration from a high-frequency perturbation approach

This research work presents a feedback control scheme to suppress FlowInduced Vibration (FIV). The design rationale is based on the study of a simple and phenomenological model, which is usually used in the literature to describe the behavior of FIV. It is predicted that high-frequency perturbation can be used to increase the damping of the wake model, which may consequently suppress wake and then structural vibration. Experimental results have shown the validation of the idea. As a result, in order to control FIV effectively, a multi-high-frequency perturbation controller is proposed by using an active resonator and a hard limiter from a closed-loop control view point.
Random walk planning: theory, practice, and application

This thesis introduces random walk (RW) planning as a new search paradigm for satisficing planning by studying its theory, its practical relevance, and applications. We develop a theoretical framework that explains the strengths and weaknesses of random walks as a tool for heuristic search. Based on the theory, we propose a general framework for random walk search (RWS). We identify and experimentally study the key components of RWS and for each component, design and test practical and adaptive algorithms. We study resource-constrained planning as an application of RWS and show that the developed techniques implemented on top of RWS greatly outperform the state of the art in solving resource-constrained tasks. While RWS alone can lead to inefficient long plans, we introduce efficient postprocessing techniques that can significantly improve the results. We push the state of the art in planning by developing several RW planners that have strong performance in terms of both coverage and solution quality.
Image Segmentation Using De-Textured Images

Image segmentation is one of the fundamental problems in computer vision. The outputs of segmentation are used to extract regions of interest and carry out identification or classification tasks. For these tasks to be reliable, segmentation has to be made more reliable. Although there are exceptionally well-built algorithms available today, they perform poorly in many instances by producing over-merged (combining many unrelated objects) or under-merged (one object appeared as many) results. This leads to far fewer or more segments than expected. Such problems primarily arise due to varying textures within a single object and/or common textures near borders of adjacent objects. The main goal of this report is to pre-process the input images to nullify the effects of such textures. We introduce a pre-processing technique that prepares the input images, before the application of segmentation algorithms. This technique has demonstrated an enhancement in quality of the segments produced. This pre-processing method is called the de-texturing method. We experimented with the effect of the proposed de-texturing method on two existing segmentation methods, namely, the Statistical Region Merging (SRM) method [1] and a k-means-based method as suggested in [2].
Modeling the class operations in B: a case study on the pump component

We propose an approach for modeling in B the class operations which is up to now an open issue. Each class operation is mapped to a B operation. The operation and concerned data are mapped into the same B abstract machine. The calling-called dependency between class operations is used to allocate B operations of class operations in B abstract machines. The B implementation construct and imports primitive are used to model the calling-called dependency betwen class operations. The combination of our approach with rules for data mapping from UML notation into B notation, which are proposed elsewhere, give rise to a complete aproach for deriving B specification from an object oriented specification.