Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic materials force dynamic cataloguing: accessible materials in a new digital age

Purpose – A major advantage of storing accessible materials in a digital archive is that it facilitates large‐scale updates. Unfortunately, cataloguing these materials involves manual work and updates of the cataloguing records will not be made easily and quickly enough in the future. This paper aims to tackle these cataloguing problems and to inform about the latest developments and accomplishments in the field of accessible materials. The paper also stresses the importance of profiling cataloguing issues for libraries for the visually‐impaired within the cataloguing community.Design/methodology/approach – The paper begins with an overview of the field of accessible materials and gives an account of certain cataloguing problems. Then follows a detailed description of these cataloguing issues before the paper concludes with a suggested method and a description of a practical workflow to counter the listed problems. The paper is, in a sense, a case study of a trend in the field of librarianship regarding t.
Robust estimation of (partial) autocorrelation

The autocorrelation function (acf) and the partial autocorrelation function (pacf) are elementary tools of linear time series analysis. The sensitivity of the conventional sample acf and pacf to outliers is well known. We review robust estimators and evaluate their performances in different data situations considering Gaussian scenarios with and without outliers as well as times series with heavy tails in a simulation study. WIREs Comput Stat 2015, 7:205–222. doi: 10.1002/wics.1351
Novel reversible watermarking scheme for authentication of military images

This paper presents a novel reversible watermarking scheme based on histogram modification for the authentication of military images. The Hash Message Authentication Code (HMAC) of the image is computed and embedded into the image for authentication. Based on a criterion, the distinct pixel values with Non-Zero Frequencies of the histogram are selected for embedding watermark bits and the preferred pixel values are stored as a key. This key is used for both the watermark extraction and the original image restoration. The bits of extracted HMAC from the marked image and HMAC of the restored image are compared for authentication.
Segmenting dark channel prior in single image dehazing

In image dehazing, the existing transmission estimators bring out the halo artefact at boundaries unless they adopt a refinement procedure with a high computational complexity. How the existing transmission estimation methods suffer from the halo artefact at the boundaries are analysed and it is found that the elaborate, high computational refinement procedure to remove the halo effect are excessive for dehazing. On the basis of the above-mentioned analysis and finding, a simple segmentation logic was embedded in an existing transmission estimator, which is sufficiently accurate for dehazing. The experiment verifies that the proposed method significantly reduces the halo artefact without requiring any refinement procedure.
Bob’s Project Guidelines: Writing a Dissertation for a BSc. in Computer Science

Abstract These detailed guidelines attempt to explain the mechanics of writing a third year project dissertation in Computer Science. They serve as a useful starting point for students (and new advisers) in their third or fourth year of study in the Computer Science Department of a UK-based university. Afterall, no one is born knowing how to write a dissertation. Yet, there are certain elements, a commonality, that can be found in virtually all good dissertations. We give our recommendations as to each section a good dissertation consists of as well as what each section contains. These guidelines are generic and can be customized to fit most projects. The guidelines are open and free to use at any university or institution without permission. We believe that the writing can start with the abstract, however, in practice writing usually starts with the background section. The abstract can be approximately 6–12 sentences. It is a difficult starting point, but it forces the author to write down a concise description of what it is they’re researching and what the benefits are. Chances are, if the author can’t write an abstract, then it is not clear in the author’s mind what the project is about. Of course the abstract will be refined and updated over the lifetime of the project. The abstract can concisely (1) identify the project topic, (2) identify the benefits and advantages that result (3) and if there is novelty, describe the novelty of the presented work. If there’s a description of the project in a previously existing project selection brochure then that can be used as a basis for an abstract. These guidelines are a starting point only and are not a substitute for an advisor.
Neural trees-using neural nets in a tree classifier structure

The concept of tree classifiers is combined with the popular neural net structure. Instead of having one large neural net to capture all the regions in the feature space, the authors suggest the compromise of using small single-output nets at each tree node. This hybrid classifier is referred to as a neural tree. The performance of this classifier is evaluated on real data from a problem in speech recognition. When verified on this particular problem, it turns out that the classifier concept drastically reduces the computational complexity compared with conventional multilevel neural nets. It is also noted that these data make it possible to grow trees online from a continuous data stream. <<ETX>>
A Genetic Threading Method with Combined Structure- and Sequence-Based Information

A threading program based on genetic algorithm, is proposed for the protein fold recognition problem. Proper algorithm is designed in which genetic operators can be effectively implemented, and a more realistic energy model is adopted in this work. The performance of the genetic threading method is tested on a standard fold recognition benchmark, and the results show that the genetic threading method has good alignment accuracy and fold recognition ability. The analysis of the results demonstrates the rationality of our energy model.
DDDAS Computational Model and Environments

The Data Driven Applications Systems (DDDAS) concept entails “the ability to dynamically incorporate data into an executing application simulation, and in reverse, the ability of applications to dynamically steer measurement processes”, creating “application simulations that can dynamically accept and respond to ‘online’ field data and measurements and/or control such measurements”. Through the DDDAS concept, the application modeling capability, its accuracy and its efficiency, are enhanced over the traditional computational modeling methods, by complementing and augmenting the computational modeling with dynamic data inputs, which can be dynamically incorporated into the computation at runtime. In reverse, the ability of the executing application to steer the measurement processes, and conduct targeted measurements guided by the executing application, can result into more efficient and more effective measurement processes. By integrating the computational and measurement aspects of an application in a dynamic feed-back loop, DDDAS changes the paradigm of the traditionally distinct computational and measurement processes, and leads to a unification of the computing and instrumentation platforms of an application. The discussions in the present paper focus on a DDDAS computational model representing the unification of measurement and computational platforms, and on the new measurement capabilities enabled through DDDAS and in particular in architecting and dynamically managing sensor networks and the systems software needs for supporting the unified measurement and computational platforms in DDDAS environments.
Effects of sensory information and processing time in spoken-word recognition

Abstract Current models of spoken-word recognition describe access to lexical representations in terms of activation and decay. This research investigated an important aspect of activation, that is the impact of processing time. In the natural situation of continuous speech, the more acoustic information a listener receives about a word, the more time is available to process earlier parts of this input. These two naturally confounded aspects of spoken-word processing—sensory input and time—were investigated in a cross-modal priming study. The results showed a separable impact of time and signal on the activational state of lexical elements.
Fast and accurate ADC testing via an enhanced sine wave fitting algorithm

A new sine-wave fitting algorithm for A/D converters testing is presented and discussed. The key feature of the algorithm is the possibility of using test signals with amplitude greater than the full-scale range of the device under test. The new technique, combined with an improved evaluation of the conversion noise via weighted mean square error, gives very accurate and repeatable estimates of the number of effective bits, which cannot be obtained by traditional methodologies. The performance of the new method is proved by means of computer simulations and experimental results as well.
Multi-camera System Calibration with Built-in Relative Orientation Constraints (Part 2) Automation, Implementation, and Experimental Results

Multi-camera systems have been widely used as cost-effective tools for the collection of geospatial data for various applications. In order to fully achieve the potential accuracy of these systems for object space reconstruction, careful system calibration should be carried out prior to data collection. Since the structural integrity of the involved cameras’ components and system mounting parameters cannot be guaranteed over time, multi-camera system should be frequently calibrated to confirm the stability of the estimated parameters. Therefore, automated techniques are needed to facilitate and speed up the system calibration procedure. The automation of the multi-camera system calibration approach, which was proposed in the first part of this paper, is contingent on the automated detection, localization, and identification of the object space signalized targets in the images. In this paper, the automation of the proposed camera calibration procedure through automatic target extraction and labelling approaches will be presented. The introduced automated system calibration procedure is then implemented for a newly-developed multi-camera system while considering the optimum configuration for the data collection. Experimental results from the implemented system calibration procedure are finally presented to verify the feasibility the proposed automated procedure. Qualitative and quantitative evaluation of the estimated system calibration parameters from two-calibration sessions is also presented to confirm the stability of the cameras’ interior orientation and system mounting parameters.
Content-based numerical report searching for image enabled case retrieval

One way to improve accuracy of diagnosis and provide better medical treatment to patients is to recall or find records of previous patients with similar disease features from healthcare information systems which already have confirmed diagnostic results. In most situations, features of disease may be described by other kinds of information or data types such as numerical reports or a simple or complicated SR (Structure Reports) generated from Ultrasound Information System (USIS) or from computer assisted detection (CAD) components, or laboratory information system (LIS). In this presentation, we described a new approach to search and retrieve numerical reports based on the contents of parameters from large database of numerical reports. We have tested this approach by using numerical data from an ultrasound information system (USIS) and got desired results both in accuracy and performance. The system can be wrapped as a web service and is being integrated into a USIS and EMR for clinical evaluation without interrupting the normal operations of USIS/RIS/PACS. We give the design architecture and implementation strategy of this novel framework to provide feature based case retrieval capability in an integrated healthcare information system.
Selftuning cascade temperature control

Uncertain and time-variable dynamics calls for self-tuning capacity in superheaters temperature control systems. However, the application of self-tuning control strategies to cascade control configuration is subject to severe drawbacks because usual cascade controllers are not robust against final control variable saturation. The paper develops and analyses a new self-tuning cascade control concept which is integrally robust against saturation. The performance of the control is shown with reference to a large power-unit. <<ETX>>
Examining Barrel Distortion, Super-resolution on Single-view-based Ear Biometrics Rotated in Depth

Numerical simulations were performed using super-resolution images and barrel-distorted images to improve the viability of the authors’ previous work regarding single-view-based ear biometrics. Empirical demonstrations show that, using barrel-distorted images and super-resolution images, accuracy decreases. However, our present proposed method for model normal vector creation applied to these images compensated for losses in accuracy. Furthermore, an experimental investigation of the influence of earrings on results is included. The difference between the averaged rank between images of subjects with and without earrings at each input angle was found to be statistically insignificant.
Study on Key Technology of Topic Tracking Based on SVM

Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. On the basis of VSM and support vector machines (SVM), we have studied how feature space dimension in VSM as well as linearly separable and non-separable SVM affect topic tracking. Then we get the variation law that they affect topic tracking, and add up their optimal values in topic tracking. Finally, TDT evaluation method proves that optimal topic tracking performance based on linearly separable SVM increases by 4.522% more than linearly non-separable SVM.
Term-Centric Active Learning for Naïve Bayes Document Classification

In real world document classification, a subset of documents often needs to be chosen for labeling as a training set for a machine learner. Random sampling is generally not the most effective approach for choosing documents to be la- beled. Active learning selects useful examples for labeling to improve the efficiency of learning. We consider two factors in order to measure the usefulness of a document for labeling. Such a document should be 1) largely unknown to the cur- rent learner 2) influential by being close to many other documents. These factors are stated from a document-centric viewpoint. A similar analysis can be made from a term-centric viewpoint. It is the purpose of this paper to present this term-centric approach to active learning using a naive Bayes classifier. We study both document-centric and our new term-centric active learning methods. We find good performance of the term-centric methods on numerous data sets with different characteristics. In addition, a genetic algorithm is employed to compare our results with estimated optimal per- formance at fixed training set size and our results are between 84% and 99% of the estimated optimum.
Adaptive mean-shift for automated multi object tracking

Mean-shift tracking plays an important role in computer vision applications because of its robustness, ease of implementation and computational efficiency. In this study, a fully automatic multiple-object tracker based on mean-shift algorithm is presented. Foreground is extracted using a mixture of Gaussian followed by shadow and noise removal to initialise the object trackers and also used as a kernel mask to make the system more efficient by decreasing the search area and the number of iterations to converge for the new location of the object. By using foreground detection, new objects entering to the field of view and objects that are leaving the scene could be detected. Trackers are automatically refreshed to solve the potential problems that may occur because of the changes in objects' size, shape, to handle occlusion-split between the tracked objects and to detect newly emerging objects as well as objects that leave the scene. Using a shadow removal method increases the tracking accuracy. As a result, a method that remedies problems of mean-shift tracking and presents an easy to implement, robust and efficient tracking method that can be used for automated static camera video surveillance applications is proposed. Additionally, it is shown that the proposed method is superior to the standard mean-shift.
Practical Control for Two-Mass Positioning Systems in Presence of Saturation

The precision positioning systems generally need a good controller to achieve a fast response, high accuracy and robustness. In addition, ease, simplicity of controller design structure and high motion control performance are very important for practical applications. For satisfying these requirements, nominal characteristic trajectory (NCT) with proportional integral (PI) and notch filter (NF) as a compensator has been proposed as a practical control method for two-mass rotary PTP positioning systems. However, the effect of the actuator saturation cannot be completely compensated due to integrator windup when the object parameter varies. This paper presents a method to further improve nominal characteristic trajectory following (NCTF) controller to overcome the problem of integrator windup by adopting PI anti-windup schemes. The improved NCTF controller is evaluated experimentally using two-mass rotary positioning systems. The effect of the design parameters on the robustness of the improved NCTF with anti-windup integrator controller is evaluated and compared with NCTF without antiwindup integrator and the equivalent PID controller. The results show that the improved NCTF controller is effective to compensate the effect of integrator windup.
A Dynamic Cost Sensitive Support Vector Machine

A lot of cost-sensitive support machine vector methods are used to handle the imbalanced datasets, but the obtained results are not as perfect as expectation. A promising method is proposed in this paper, named ADC-SVM, which uses genetic algorithm to dynamically search the optimal misclassification cost to build a cost sensitive support machine. We empirically evaluate ADC-SVM with SVM and Cost-sensitive SVM over 8 realistic imbalanced bi-class datasets from UCI. The experimental results show that ADC-SVM outperforms the other two methods over all the imbalanced datasets.
E-Mail Filtration and Classification Based on Variable Weights of the Bayesian Algorithm

The co-occurrence word emphasize the word and word internal relations, so its use can improve shortage from the hypothetical of Bayesian algorithm. To build Token Dictionary, Information Gain algorithm is used to choose Tokens, and Synonyms Dictionary is used to acquire more Tokens. By large amounts of training, the matching scores of Token are counted, according to the matching rate the Tokens that is valuable are selected, and the Token Dictionary is established. The proposed method is used to E-mail classification experiment, the results show that the accuracy of spam filter has a well improvement.
The Nature of and Factors Related to Reading Difficulties Among Adolescents in a Transparent Orthography

We examined the nature of and factors related to adolescents’ reading difficulties in a highly transparent orthography. We compared word, pseudoword, and text reading speed and accuracy, rapid naming (RAN) and phonological processing between poor readers (n = 80) and normally developing readers (n = 189). Reading problems were manifested in reading speed and in timed pseudoword reading accuracy. RAN predicted speed, and phonological processing predicted accuracy of reading in both groups. Among poor readers, RAN also explained reading accuracy. For the normally developing sample, phonological processing also predicted reading speed.
Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization

Abstract Much research works address model-free or signal processing and spectral analysis-based fault detection schemes for rotor eccentricity fault in induction motors. Nevertheless, despite existing reliable fault-embedded eccentricity mathematical models such as the winding function method an integrated model-based fault detection algorithm for detecting this fault yet has not been fully explored. This article presents model-based mixed-eccentricity fault detection and diagnosis for induction motors. The proposed algorithm can successfully detect faults and their severity using stator currents. To determine the values of the fault-related parameters, an adaptive synchronization-based parameter estimation algorithm is introduced using particle swarm optimization. Simulation and experiments demonstrate the ability of the algorithm to detect and diagnose these faults. The proposed algorithm can be employed to estimate the parameters, in addition to slowly time varying and abruptly changing parameters.
Priority Parameter-based Analysis and Optimization for EDCA

This article aims at coming up the optimization of the throughput network performance in EDCA. At first, we observe in different network load conditions that the effect of each priority parameter has on the network transmission performance. Then we make a deep research about the EDCA mechanism and come up a new EDCA analysis model and there is a throughput analysis of this model and a optimization scheme of the priority business flows under different network loads. Finally, based on the mathematical analysis and NS2 simulation results, it can be observed from the new optimization scheme that it to some extent improves different priority services under different network loads.
Detection of Abnormal Lung Sounds Through Investigation of Breathing Cycle

The purpose of our research is to develop a method for recognizing abnormal lung sounds without the need of a medical specialist.Listening to the sounds of the human body is one of the most important methods of checking someone's health. However,identification of abnormal lung sounds is difficult for an untrained person.We differentiated true abnormal sounds from interfering noise by investigating the fact that lung sounds are generated periodically in relation to the breathing cycle.
Specification and characterization of CGHs for interferometrical optical testing

Computer-generated holograms (CGHs) are increasingly used in optical shop testing, especially for testing aspheric surfaces. For precise interferometrical measurements the error influence of the CGHs must be known and needs to be characterized. Different error types of two-level binary CGHs are identified and their contribution to the reconstructed wavefront are discussed: pattern errors of the CGH structure, duty cycle errors, surface figure errors of the CGH-substrate, etching depth variations in phase holograms and thickness variations of the chromium layer of amplitude chrome-type holograms. Methods to determine all these different errors are explained and an error budget of the total CGH error is given. Since the pattern error of a CGH is the most critical error it will be discussed in detail. This error depends on the writing technology (e-beam, laser beam) and on the type of the CGH (inline, off-axis). As examples of the two CGH-types a test method for measuring the pattern errors of Fresnel zone plates and linear gratings is presented. Experimental results of the tests are discussed.