Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Expression Recognition System

A key requirement for developing any innovative system in a computing environment is to integrate a sufficiently friendly interface with the average end user. Accurate design of such a user-centered interface, however, means more than just the ergonomics of the panels and displays. It also requires that designers precisely define what information to use and how, where, and when to use it. Facial expression as a natural, non-intrusive and efficient way of communication has been considered as one of the potential inputs of such interfaces. The work of this thesis aims at designing a robust Facial Expression Recognition (FER) system by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition where a lot of research has been done and a vast array of algorithms have been introduced. FER can also be considered as a special case of a pattern recognition problem and many techniques are available. In the designing of an FER system, we can take advantage of these resources and use existing algorithms as building blocks of our system. So a major part of this work is to determine the optimal combination of algorithms. To do this, we first divide the system into 3 modules, i.e. Preprocessing, Feature Extraction and Classification, then for each of them some candidate methods are implemented, and eventually the optimal configuration is found by comparing the performance of different combinations. Another issue that is of great interest to facial expression recognition systems designers is the classifier which is the core of the system. Conventional classification algorithms assume the image is a single variable function of a underlying class label. However this is not true in face recognition area where the appearance of the face is influenced by multiple factors: identity, expression, illumination and so on. To solve this problem, in this thesis we propose two new algorithms, namely Higher Order Canonical Correlation Analysis and Simple Multifactor Analysis which model the image as a multivariable function. The addressed issues are challenging problems and are substantial for developing a facial expression recognition system.
Integrating Data Mining Techniques into Telemedicine Systems

The medical system is facing a wide range of challenges nowadays due to changes that are taking place in the global healthcare systems. These challenges are represented mostly by economic constraints (spiraling costs, financial issues), but also, by the increased emphasis on accountability and transparency, changes that were made in the education field, the fact that the biomedical research keeps growing in what concerns the complexities of the specific studies etc. Also the new partnerships that were made in medical care systems and the great advances in IT industry suggest that a predominant paradigm shift is occurring. This needs a focus on interaction, collaboration and increased sharing of information and knowledge, all of these may is in turn be leading healthcare organizations to embrace the techniques of data mining in order to create and sustain optimal healthcare outcomes. Data mining is a domain of great importance nowadays as it provides advanced data analysis techniques for extracting the knowledge from the huge volumes of data collected and stored by every system of a daily basis. In the healthcare organizations data mining can provide valuable information for patient's diagnosis and treatment planning, customer relationship management, organization resources management or fraud detection. In this article we focus on describing the importance of data mining techniques and systems for healthcare organizations with a focus on developing and implementing telemedicine solution in order to improve the healthcare services provided to the patients. We provide architecture for integrating data mining techniques into telemedicine systems and also offer an overview on understanding and improving the implemented solution by using Business Process Management methods.
Analysis of network error correction based on network coding

For network error-correcting codes based on network coding an explicit lower bound on the size of source alphabet is presented. This bound is deduced by a Gilbert-Varshamov greedy algorithm. The difficulty of constructing good error-correcting codes for networks is considered and ways of constructing good codes for small-scale networks are dealt with in detail.
A kernel method for fuzzy systems modeling and approximate reasoning

Fuzzy systems modeling has been an active research topic for almost twenty years. In general, two approaches have been proposed in the literature: 1) translate experts' prior knowledge into fuzzy rules; 2) learn a set of fuzzy rules from given training data. The first approach applies to the case that prior knowledge can be easily obtained and training data are not sufficient. However, in many applications, the amount of training data is usually large. In that case, the second approach may provide more objective rules than the first approach. In this paper, we show that a class of fuzzy systems is in essence kernel machines. Therefore, the support vector machine (SVM) method can be used to construct fuzzy systems. This method has been tested on real weather forecast data. Experimental results demonstrate the effectiveness of the method.
Network traffic workload classification methods for workstation GUI applications

The authors attempt to provide a set of benchmarks for GUI (graphical user interface) applications developed under the X Window System, by proposing a method of classifying the GUI characteristics into classes based on a measure of graphical complexity. They conducted data gathering experiments on sample X applications from each GUI class measuring resource usage. The data were compiled, and the results are compared against the proposed classes to make some projections about how X based GUI applications may consume resources. The method of gathering data about the applications is presented. <<ETX>>
Energy-efficient high-accuracy switching method for SAR ADCs

A new tri-level switching method for successive approximation register (SAR) analogue-to-digital converters (ADCs) is presented. The proposed switching method enhances the efficiency of digital-to-analogue converter switching energy by 93.7% and achieves a 75% reduction in the total capacitor size, compared with the conventional SAR ADC. In addition, the accuracy of the proposed SAR ADC has no dependency on the accuracy of the mid-level reference voltage (V cm) except in the least significant bit, and the common-mode voltage at the input of the comparator will remain approximately unchanged. Analytical calculations and behavioural simulation results are provided to demonstrate the effectiveness of the proposed switching scheme.
Automatic recognition of prosodic phrases

The authors report on the development of two algorithms to automatically detect prosodic phrases. The first algorithm uses simple frame-based likelihood classifiers to detect breaths and silences, which yields a large percentage of major phrase breaks. To label other levels of prosodic structure, a second algorithm is introduced that uses phoneme durations given by a speech recognizer in conjunction with a tree quantizer and hidden Markov model to label a hierarchy of prosodic phrase breaks. This second algorithm yields phrase break predictions that have good correlation with hand labels, and correctly detects more than 90% of the major phrase boundaries. <<ETX>>
Toward Privacy in High-Dimensional Data Publishing

Nowadays data sharing among multiple parties has become inevitable in various application domains for diverse reasons, such as decision support, policy development and data mining. Yet, data in its raw format often contains person-specific sensitive information, and publishing such data without proper protection may jeopardize individual privacy. This fact has spawned extensive research on privacy-preserving data publishing (PPDP), which balances the fundamental trade-off between individual privacy and the utility of published data. Early research of PPDP focuses on protecting private and sensitive information in relational and statistical data. However, the recent prevalence of several emerging types of high-dimensional data has rendered unique challenges that prevent traditional PPDP techniques from being directly used. In this thesis, we address the privacy concerns in publishing four types of high-dimensional data, namely set-valued data, trajectory data, sequential data and network data. We develop effective and efficient non-interactive data publishing solutions for various utility requirements. Most of our solutions satisfy a rigorous privacy guarantee known as differential privacy, which has been the de facto standard for privacy protection. This thesis demonstrates that our solutions have exhibited great promise for releasing useful high-dimensional data without endangering individual privacy.
Implementation of self-tuning generalized predictive controllers for the process industry

This paper presents a set of simple Ziegler-Nichols-type functions for implementing generalized predictive controllers (GPCs). By using this set of functions, the implementation of GPCs is simplified considerably and the heavy computational requirements needed are avoided. The functions have been obtained for a CARIMA model that is valid for most processes in industry, as is the case of plants whose behaviour can be described in terms of a static gain, time constant and effective dead time.
Games Online: Social Icebreakers That Orient Students to Synchronous Protocol and Team Formation

With the advent of the Internet, schools are now able to better fulfill student's needs in terms of how they offer classes. Life is faster paced, with many people juggling work as well as study, so the ability for a university or college to offer classes online is now an option that many schools are deciding to focus on in order to cater to the needs of students. However, with the move away from the traditional face-to-face classroom interaction to a more impersonal computer classroom, many instructors have to deal with the fact that their online classes are not offering the same kind of feel of community that a traditional classroom can offer. Online classes are social classes that rely heavily on one's ability to communicate effectively in a group setting. Community, the sense of belonging to a group of same interest, is something very key to the success of many face-to-face classes. How does one transfer that same community and closeness to an online class for students that may have never met each other in real life and have only ever interacted together across the Internet?
The Efficacy of a Low-Level Program Visualization Tool for Teaching Programming Concepts to Novice C Programmers

It is widely agreed that learning to program is difficult. Program visualization tools make visible aspects of program execution which are often hidden from the user. While several program visualization tools aimed at novice programmers have been developed over the past decade there is little empirical evidence showing that novices actually benefit from their use [1]. In this article we describe a “Glass-box Interpreter” called Bradman. An experiment is presented which tests the efficacy of Bradman in assisting novice programmers learn programming concepts. We show that students that used the glass-box interpreter achieved greater understanding of some programming concepts than those without access. We also give evidence that the student's ability to assimilate new concepts was enhanced by exposure to the glass-box interpreter. This is experimental confirmation that such tools are beneficial in helping novices learn programming.
Face Recognition by Fiducial Points Based Gabor and LBP Features

The accuracy of a real facial recognition system can be varied according to the accuracy of the eye detection algorithm when we design and implement a semi-automatic facial recognition algorithm depending on the eye position of a database. In this paper, a fully automatic facial recognition algorithm is proposed such that Gabor and LBP features are extracted from fiducial points of a face graph which was created by using fiducial points based on the eyes, nose, mouth and border lines of a face, fitted on the face image. In this algorithm, the recognition performance could be increased because a face graph can be fitted on a face image automatically and fiducial points based LPB features are implemented with the basic Gabor features. The simulation results show that the proposed algorithm can be used in real-time recognition for more than 1,000 faces and produce good recognition performance for each data set.
Parallelized multilevel characteristic basis function method for solving electromagnetic scattering problems

Parallel implementation of the multilevel characteristic basis function method is discussed in this article. The use of this method enables us to solve very large electromagnetic problems in a direct manner via a recursive application of the characteristic basis function method. Furthermore, we show that, unlike the iterative schemes, the present method is very well suited for parallelization. Examples are presented to show that upto one million unknown problems can be solved on a workstation using the present scheme, and that the results show very good agreement with those derived by using analytical or fast multipole methods. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 2963–2969, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24767
Scientific and Engineering Problem-Solving with the Computer

A process for preparing microcapsules which comprises; dissolving a film-forming polymer in an organic solvent to form a solution, said solvent having a dielectric constant between 10 and 40 and a poor compatibility with liquid paraffins and silicone oils; dispersing a core substance in the solution, to form a first dispersion; dispersing said first dispersion in an encapsulating medium comprising a liquid paraffin or a silicone oil in the form of fine droplets; and evaporating the solvent and the encapsulating medium from the dispersion.
Separating the Wheat from the Chaff: Applications of Automated Document Classification Using Support Vector Machines

Due in large part to the proliferation of digitized text, much of it available for little or no cost from the Internet, political science research has experienced a substantial increase in the number of data sets and large-n research initiatives. As the ability to collect detailed information on events of interest expands, so does the need to efficiently sort through the volumes of available information. Automated document classification presents a particularly attractive methodology for accomplishing this task. It is efficient, widely applicable to a variety of data collection efforts, and considerably flexible in tailoring its application for specific research needs. This article offers a holistic review of the application of automated document classification for data collection in political science research by discussing the process in its entirety. We argue that the application of a two-stage support vector machine (SVM) classification process offers advantages over other well-known alternatives, due to the nature of SVMs being a discriminative classifier and having the ability to effectively address two primary attributes of textual data: high dimensionality and extreme sparseness. Evidence for this claim is presented through a discussion of the efficiency gains derived from using automated document classification on the Militarized Interstate Dispute 4 (MID4) data collection project.
Multiresolutional feedforward/feedback loops

It is maintained that hierarchies of task decomposition such as planner-navigator-pilot controllers in autonomous vehicles, pyramidal connectionist schemes, and their hybrids are just different reincarnations of the principles of multiresolutional control. An attempt is made to demonstrate how this unity emerges in an intelligent controller for autonomous mobile robots as a system of multiresolutional feedforward-feedback control loops. <<ETX>>
Computer Assisted Characterization for Ultrasonic Liver Tissue by Fusion of Classifiers

Ultrasonography is one of the safest methods used in imaging human organs. The visual interpretation of B-scan images principally depends on the ability of the clinician to observe certain textural characteristics. However, a visual criterion of diagnosing liver diseases primarily depends on the clinical experience of physicians and it is extremely subjective. Therefore, a method that can provides a possibility of abnormalities is valuable for physicians. Since the sets of pattern misclassified by the different classifiers designs potentially offered complementary information about the pattern to be classified this could be harnessed to improve the performance of the selected classifiers. In this study, we intend to integrate the real numerical outputs of different classifiers in order to assign a better quantitative measure of certainty about an object in question. And, a quantitative characterization about suspicious disease can be provided to a physician that can decide whether need further examination.
New highly efficient binary codes for structured light methods

Structured light methods are triangulation-based methods for acquiring range data. Light sections are projected and encoded by sequences of light intensities. The association of sequences of intensities to light sections, called 'coding,' plays a major role in range data acquisition by structured light methods. The coding has an influence on the resolution, the speed and the robustness of the measurement. In this paper we present binary codes, used in structured light methods. Ideally we want codes, which combine high resolution with a short measuring time. Such a code would be called 'efficient. ' We present important properties of binary codes for structured light and rules for the construction of efficient codes. By applying these rules of construction we have found new, highly efficient, binary codes. The efficiency of the new codes is compared to the efficiency of well-known codes and the maximum efficiency that can be achieved.
Evaluation of wormhole routed networks under hybrid traffic loads

The authors investigate the performance of wormhole-routed networks under hybrid traffic loads to examine the impact of long and short messages on each other. The short messages not only experience increased average latency but also experience a large increase in the variance of that latency. The negative performance impact of long messages increases proportionally with both the fraction and size of the long messages. The authors also explore two techniques, virtual lanes and adaptive routing, to improve network performance under the hybrid traffic loads. Performance losses due to long messages can be dramatically reduced with either technique. The two techniques are complementary, so employing them simultaneously yields the best performance, virtually eliminating performance losses experienced by short messages. This means that packetization may not be necessary in such networks. <<ETX>>
Modeling Natural Variation Through Distribution

This design study tracks the development of student thinking about natural variation as late elementary grade students learned about distribution in the context of modeling plant growth at the population level. The data-modeling approach assisted children in coordinating their understanding of particular cases with an evolving notion of data as an aggregate of cases. Students learned to “read” shapes of distributions as signatures of prospective mechanisms of plant growth and conducted sampling investigations to represent repeated growth. These investigations, in turn, supported students’ interpretations of the effects of added light and fertilizer. The authors argue for both the feasibility and the importance of tools such as distribution and inference for supporting education that builds on children’s own investigations of the world.
Formulation of a weakly compressible two-fluid flow solver and the development of a compressive surface capturing scheme using the volume-of-fluid approach

Formulation of a weakly compressible two-fluid flow solver and the development of a compressive surface capturing scheme using the volume-of-fluid approach J.A. Heyns Dissertation: PhD (Mech Eng) December 2012 This study presents the development and extension of free-surface modelling techniques for the purpose of modelling two-fluid systems accurately and efficiently. The volume-of-fluid (VOF) method is extended in two ways: Firstly, it is extended to account for variations in the gas density through a weakly compressible formulation. Secondly, a compressive free-surface interface capturing formulation that preserves the integrity of the interface shape is detailed. These formulations were implemented and evaluated using the Elemental software. Under certain flow conditions liquid-gas systems may be subjected to large variations in pressure, making it necessary to account for changes in gas density. Modelling this effectively has received relatively little attention in the context of free-surface modelling and remains a challenge to date. To account for the variations in gas density a weakly compressible free-surface modelling formulation is developed for low Mach number flows. The latter is formally substantiated via a non-dimensional analysis. It is proposed that the new formulation advances on existing free-surface modelling formulations by effecting an accurate representation of the dominant physics in an efficient and effective manner. The proposed weakly compressible formulation is discretised using a vertexcentred edge-base finite volume approach, which provides a computationally efficient method of data structuring and memory usage. Furthermore, this implementation is applicable to unstructured spatial discretisation and parallel computing. In this light, the discretisation is formulated to ensure a stable, oscillatory free solution. Furthermore, the governing equations are solved in a fully coupled manner using a combination of dual time-stepping and a Generalised Minimum Residual solver with Lower-Upper Symmetric Gauss-Seidel preconditioning, ensuring a fast and efficient solution. ii Stellenbosch University http://scholar.sun.ac.za
Real Time Artifact-Free Image Upscaling

Image upscaling (and more generally image interpolation) is the process of resizing a digital image. Enlarging an image is generally common for making smaller imagery fit a bigger screen in full screen mode, for example. In "zooming" an image, it is not possible to discover any more information in the image than already exists, and image quality inevitably suffers, for that reason several methods have been proposed to obtain better results, involving simple heuristics, edge modeling or statistical learning. The most powerful ones, however, present a high computational complexity and are not suitable for real time applications, while fast methods, even if edge-adaptive, are not able to provide artifacts-free images. So that's why a new method for image upscaling is proposed i.e. Iterative Curvature Based Interpolation (ICBI), it is based on a two-step grid filling and an iterative correction of the interpolated pixels obtained by minimizing an objective function depending on the second order directional derivatives of the image intensity. These are implemented in a variety of computer tools like printers, digital TV, media players, image processing packages, graphics renderers and so on.