Miracle-dz/UniFAR-FTUs huggingface dataset:
Responsive Web Site Development at the Library, Institute of Technology Tallaght: A Case Study

ABSTRACT This case study reviews the responsive Web design project undertaken by the Library, Institute of Technology Tallaght, with Granite Digital, including the preparatory literature review, the design process, task allocation, and the technologies leveraged to deliver the final design. The library is a small academic library with limited resources, and the project took place during a particularly challenging period in Ireland. The different types of testing the site underwent before launch are discussed, including testing of the design itself across browsers, accessibility testing using free online resources, testing the responsive design using free online resources, and physical device testing. The article outlines how feedback was gathered and addressed, and discusses plans for future upkeep and development. Finally, the article concludes that it is possible for a small academic library to successfully deliver a high quality library Web site using responsive design.
Adaptive Fuzzy Output-Feedback Control for Switched Nonlinear Systems With Stable and Unstable Unmodeled Dynamics

Dynamic uncertainty is a potential factor destabilizing the closed-loop system. This paper aims at constructing an adaptive fuzzy output-feedback control scheme for a class of switched nonlinear systems interconnected with unmodeled dynamics. In the investigated model, the <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula>-system is interconnected with the unmodeled <inline-formula><tex-math notation="LaTeX">$z$</tex-math></inline-formula>-dynamics. Two types of unmodeled dynamics (i.e., all modes are stable and some modes are unstable) are considered in this paper. Separate switched state observer and adaptive fuzzy controller are designed for each mode of the <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula>-system. In our control scheme, only two adaptive parameters are required to update online. With the help of multiple Lyapunov function, two lemmas are proposed to guide us how to determine the input-to-output gains of the <inline-formula><tex-math notation="LaTeX">$x$</tex-math></inline-formula>-system and the unmodeled dynamics when the influence of switching is considered. By using the small-gain approach, the closed-loop switched nonlinear system is guaranteed to be of input-to-state practically stablility (ISpS). With the concept of ISpS, we prove that the closed-loop system's output is convergent to a small neighborhood of zero, and all the signals in the closed-loop system are bounded. Finally, three simulations are also given to illustrate the effectiveness of our main result.
Node selection for target tracking in UWSNs under measurement origin uncertainty

Underwater wireless sensor networks (UWSNs) can provide a promising solution for underwater target tracking. Due to the limitation of energy consumption, only a part of nodes are selected to track the target. As we know, most research about node selection for underwater target tracking in UWSNs is carried out under the ideal underwater condition. Actually, the activated sensor nodes may acquire more than one measurement due to clutter. This paper presents a new node selection scheme under measurement origin uncertainty. Firstly, the problem of measurement origin uncertainty is dealt via probabilistic data association filter (PDAF) and the multi-sensor particle filter (PF) under measurement origin uncertainty is designed. Secondly, the relationship between node topology and posterior Cramer-Rao lower bound (PCRLB) is derived to select sensor nodes. Then the node selection scheme is proposed for underwater target tracking under measurement origin uncertainty. Finally, simulation results are presented to verify the effectiveness of our method.
Operation of Parallel Inverters in Microgrid Using New Adaptive PI Controllers Based on Least Mean Fourth Technique

This paper shows the operation of the microgrid using a new adaptive PI controller based operational (control) scheme. The core of the proposed control scheme is the suggested adaptive PI controller. The parameters of the PI controller are adaptively tuned using a variable step-size least mean fourth algorithm with no need for any system model to operate this adaptive controller. The main merit of the proposed scheme is that it stabilizes the magnitude and frequency of the voltage at any loading condition such as variable balanced loads, variable unbalanced loads, and nonlinear loads. The proposed scheme has a simple structure and accurate performance. In addition, the structure of proposed scheme provides a seamless transition toward any loss or reconnection of any inverter in the microgrid. Furthermore, the suggested operational scheme is flexible enough to enable the microgrid to be operative in a grid-connected mode and to transfer from the voltage control mode to power control mode with a smooth transitional procedure. To validate the meritorious performance of the suggested scheme, its performance is compared to similar schemes based on a linear controller (regular PI controller), single-neuron PI controller (adaptive PI controller), recursive least square-support vector machine based PI controller (another adaptive PI controller), and nonlinear controller (sliding mode controller) for different operations of the microgrid.
A MODIFIED KOHONEN SELF-ORGANIZING MAP (KSOM) CLUSTERING FOR FOUR CATEGORICAL DATA

The Kohonen Self-Organizing Map (KSOM) is one of the Neural Network unsupervised learning algorithms. This algorithm is used in solving problems in various areas, especially in clustering complex data sets. Despite its advantages, the KSOM algorithm has a few drawbacks; such as overlapped cluster and non-linear separable problems. Therefore, this paper proposes a modified KSOM that inspired from pheromone approach in Ant Colony Optimization. The modification is focusing on the distance calculation amongst objects. The proposed algorithm has been tested on four real categorical data that are obtained from UCI machine learning repository; Iris, Seeds, Glass and Wisconsin Breast Cancer Database. From the results, it shows that the modified KSOM has produced accurate clustering result and all clusters can clearly be identified.
A New Bidirectional Wideband Circularly Polarized Cylindrical Dielectric Resonator Antenna using Modified J-shaped Ground Plane for WiMAX/LTE Applications

A new singly-fed bidirectional wideband circularly polarized cylindrical dielectric resonator antenna (CDRA) using modified J-shaped ground plane is investigated and presented in this paper. The designed antenna excited by a single microstrip feed line with the modified Jshaped ground plane, which supports the orthogonal mode generation i.e. HEM11δ mode for realizing circular polarization (CP). Measured results show that the proposed antenna achieved S11 ≤ –10 dB, input impedance bandwidth of 41.44% (centered at 3.74 GHz) and 3-dB axial ratio bandwidth in broadside direction of 29.91% (centered at 3.61 GHz). The average measured gain and simulated radiation efficiency in broadside are 2.84 dBic and 94.69% respectively, throughout the working band. The proposed CPDRA is a bidirectional radiator, and the radiation patterns on both sides are nearly the same. The antenna radiates left-handed CP wave in +z-direction and right-handed CP wave in –z-direction for both the resonance frequencies, which has been confirmed by plotting CP radiation pattern. The presented design can be used for Wi-MAX (3.3–3.7 GHz) and LTE3400 (3400–3800 MHz) bands.
Decentralized prognosis of fuzzy discrete-event systems

This paper gives a decentralized approach to the problem of failure prognosis in the framework of fuzzy discrete event systems (FDES). A notion of co-predictability is formalized for decentralized prognosis of FDESs, where several local agents with fuzzy observability rather than crisp observability are used in the prognosis task. An FDES is said to be co-predictable if each faulty event can be predicted prior to its occurrence by at least one local agent using the observability of fuzzy events. The verification of the decentralized predictability is performed by constructing a fuzzy co-verifier from a given FDES. The complexity of the fuzzy co-verifier is polynomial with respect to the FDES being predicted, and is exponential with respect to the number of the local prognosis agents. Then, a necessary and sufficient condition for the co-predictability of FDESs is given. In addition, we show that the proposed method may be used to deal with the decentralized prognosis for both FDESs and crisp DESs. Finally, to illustrate the effectiveness of the approach, some examples are provided.
Parallel Algorithm for Bayesian Networks Parameter Learning

Because the EM algorithm requires significant computational resources for Bayesian Networks parameter learning under large databases,the PL-EM algorithm is proposed to improve the learning speed.The PL-EM algorithm parallel computes the posteriori probabilities of hidden variables and expected sufficient statistics at E step;at M step,the algorithm makes use of conditional independence and the decomposability of the likelihood function to parallel compute each local likelihood function.Experimental results show the PL-EM algorithm is an effective method to solve Bayesian parameter learning for large datasets.
Fractional Wishart Processes and $\varepsilon$-Fractional Wishart Processes with Applications

In this paper, we introduce two new matrix stochastic processes: fractional Wishart processes and $\varepsilon$-fractional Wishart processes with integer indices which are based on the fractional Brownian motions and then extend $\varepsilon$-fractional Wishart processes to the case with non-integer indices. Both of two kinds of processes include classic Wishart processes when the Hurst index $H$ equals $\frac{1}{2}$ and present serial correlation of stochastic processes. Applying $\varepsilon$-fractional Wishart processes to financial volatility theory, the financial models account for the stochastic volatilities of the assets and for the stochastic correlations not only between the underlying assets' returns but also between their volatilities and for stochastic serial correlation of the relevant assets.
Supporting web-based and crowdsourced evaluations of data visualizations

data to amplify human cognition [1]. InfoVis researchers study algorithms, visual designs, and interaction techniques for visualizing and analyzing data. This thesis targets evaluation studies involving real users that allow visualization designers to obtain empirical evidence of the usability of their designs [2]. User studies play an important role in data visualization research because they allow us to measure the strengths and weaknesses of different visualization techniques, provide insight into what makes one technique more effective than another, demonstrate the practical use of new techniques, and inform refinement and redesigns of techniques [2, 5]. User studies are fundamental to validating research contributions in the field of visualization. For example, a new algorithm, visual encoding, or interaction technique is not considered a contribution unless it has been validated to be better than the state of the art and its competing alternatives or has been validated to be useful to intended users. User studies are also instrumental in informing the choices visualization designers make between the wide array of visualization types
ENERGY PACKET NETWORKS WITH MULTIPLE ENERGY PACKET REQUIREMENTS

We analyze Energy Packet Networks (EPNs) in which the service centers consist of multiclass queues and the Data Packets (DPs) initiate the transfer (i.e., the arrival of a DP at the battery triggers the movement) with multiple energy packet requirements. In other words, a class-k DP in cell i is sent successfully to the next cell if there are $c_i^{(k)}$ energy packets and it is dropped otherwise. Besides, we consider that the queues handling DPs operate under one of the following disciplines: First-Input-First-Output (FIFO), Processor Sharing (PS), or Preemptive Last-Input-First-Output (LIFO-PR). This model is an extension of previously studied EPNs [6,16] where the steady-state distribution of the number of jobs in the queues has a product form. In our model, we show the existence of a product form of the steady-state stationary distribution, where the load of the servers is given by a fixed point expression. We study the existence of a solution to the derived fixed point problem and we provide sufficient conditions for the stability of our model. Finally, we show that, for feed forward EPNs, the load of all the queues can be fully characterized.
Facilitating Web Service Discovery in Distributed Web Service Registries

The management of distributed Web Service registries is an important issue in the context of a Web Services based distributed system. The current registry management systems and proposals for distributed service registries are investigated. Through this investigation, it was discovered that none of the existing proposals satisfy all the requirements of scalability, availability, ease of management and flexibility in terms of the management of the registries. Therefore, a Meta-Directory based registry management system is introduced in this thesis. The system uses distributed MetaDirectory nodes for the management of the service registries. The system also provides flexibility as the routing used for inter-connecting the distributed Meta-Directory nodes can be selected during deployment to accommodate system requirements. The distributed Meta-Directory architecture enhances system performance by allowing the user to choose an appropriate network model, during configuration, based on the system state such as bandwidth available and maximum allowable delay per query message.
Machine Learning using Principal Manifolds and Mode Seeking

A wide range of machine learning methods have taken advantage of density estimates and their derivatives, including methodology related to principal manifolds and mode seeking, finding use in a number of real applications. However, research concerned with improving density derivative estimation and its practical use have received relatively limited attention. Also, the fact that the derivatives of a distribution over a point set can provide a statistical framework for manifold learning has not yet been used to its full potential. The aim of this thesis is to help fill these gaps, and to provide novel machine learning algorithms and tools based on principal manifolds using density derivatives. We present three different lines of works aiming towards this goal. The first work presents a fast and exact kernel density derivative estimator. The method takes advantage of the fact that the derivatives of a multivariate product kernel can be decomposed into a product of univariate differentiations. By cutting redundant multiplications we obtain significant speedup while retaining an exact estimator. Next, we present a novel algorithm for manifold unwrapping based on tracing the gradient flow along a manifold estimated using density derivatives. This allows a direct and geometrically intuitive approach consistent with theory from differential geometry. Promising results are shown on both real and synthetic data sets. Finally, we provide a novel framework for robust mode seeking. It is based on ensemble clustering and resampling techniques. This allows a clustering algorithm that is both robust with respect to parameter choices as well as being capable of handling data sets of very high dimension. Concretely, we build the ensemble by running multiple instances of a k nearest neighbor mode seeking algorithm. We show good results on benchmark tests, as well as a case study involving medical health records.
Student-Instructor Perception of Computer Technologies in Developing Countries: The Case of Jordan

The purpose of this study was to analyze students' and instructors' perceptions about computer technologies in developing countries. Using Jordan as a case study, this paper examined computer usage, computer literacy, attitude toward computer technologies, and perceptions about computers and productivity. A questionnaire was distributed to a random sample of students and instructors at three universities in Jordan during the spring of 2002. Results showed that approximately one-third of students and two-thirds of instructors had computers at home, used computers mostly for word processing, had a mixed attitude toward computer technologies, felt confident about their ability to learn about these technologies, and agreed that technology increased their productivity. While students reported using the Internet primarily for communication (such as e-mails), instructors utilized the Internet for research activities. Students were more likely to avoid using computer technologies than instructors. In this study, males were twice more likely than females to feel comfortable while learning about computer technologies. Individuals with high income were more likely to have a positive attitude toward computer technologies than people with low income. Individuals who had computers and access to the Internet from home were more likely to believe that having computers and access to the Internet would make them more productive.
Interstellar Extinction in the Direction of Young Open Star Clusters

The total to selective extinction law in optical and near-IR wavelengths for twenty young open star clusters namely Berkeley 7, Collinder 69, Hogg 10, NGC 2362, Czernik 43, NGC 6530, NGC 6871, Bochum 10, Haffner 18, IC 4996, NGC 2384, NGC 6193, NGC 6618, NGC 7160, Collinder 232, Haffner 19, NGC 2401, NGC 6231, NGC 6823, NGC 7380 have been studied. It is found that fifteen clusters follow normal extinction law and five clusters show an anomalous behavior.
Extensions of nonparametric randomization-based analysis of covariance

MICHAEL A. HUSSEY: Extensions of Nonparametric Randomization-Based Analysis of Covariance (Under the direction of Gary G. Koch) Nonparametric Randomization-Based Analysis of Covariance (Koch et. al. (1998)) provides covariate-adjusted estimates of treatment effects for randomized clinical trials. It has application in the regulatory setting where analyses are specified a priori, and any statistical assumptions of parametric methods are not verifiable until after data collection. Using (1) a vector containing differences in means of outcomes and differences in means of baseline covariables between the two randomized groups and (2) an empirical covariance matrix for the vector, weighted least squares is applied to force the difference in means of baseline covariables to zero (as expected with valid randomization) to obtain covariate-adjusted estimates. The covariate-adjusted estimates have a population-averaged interpretation and only require a valid randomization and adequate sample size (for approximate normal distributions). Saville and Koch (2012) have developed methodology combining cross-products of DFBETA residuals from a treatment-only model with covariate information to obtain a covariance matrix for use in the nonparametric covariate adjustment. For this research, the methodology is extended to analysis of matched sets with a dichotomous outcome. In the 1:1 setting with randomization or M:1 setting, methods are provided for obtaining an adjusted difference in proportions and an adjusted odds ratio, including techniques for obtaining an exact p-value (for the difference). Application of the methods to the 1:1 observational matched case-control study is also described.
Deep & Shallow Linguistically Based Parsing: Parameterizing Ambiguity in a Hybrid Parser

In this paper we will present an approach to natural language processing which we define as "hybrid", in which symbolic and statistical approaches are reconciled. In fact, we claim that the analysis of natural language sentences (and texts) is at the same time a deterministic and a probabilistic process. In particular, the search space for syntactic analysis is inherently deterministic, in that it is severely limited by the grammar of the specific language, which in turn is constituted by a set of peripheral rules to be applied in concomitance with the more general rules of core grammar. Variations on these rules are only determined by genre, which can thus contribute a new set of peripheral rules, or sometimes just a set of partially overlapping rules to the ones already accepted by a given linguistic community. In the paper we will criticize current statistical approaches for being inherently illfounded and derived from a false presupposition. People working in the empirical framework have tried to credit the point of view that what happened to the speech research paradigm was also applicable to the NLP paradigm as a whole. In other words, the independence hypothesis which is at the heart of the use of Markov models imported from the speech community into the empirical statistical approaches to NLP does not seem to be well suited to the task at hand simply because the linguistic unit under consideration – the word, the single tag or both – are insufficient to assure enough contextual information, given language model building techniques based on word-tags with tagsets containing only lexical and part-of-speech information. In line with the effort carried out by innovative approaches like the ones proposed within the LinGO ERG proposal, we purport our view that the implementation of sound parsing algorithm must go hand in hand with sound grammar construction. Extragrammaticalities can be better coped with within a solid linguistic framework rather than without it. A number of parsing strategies and graceful recovery procedures are then proposed which follow a strictly parameterized approach to their definition and implementation. Finally, a shallow or partial parser needs to be implemented in order to produce the default baseline output to be used by further computation, like Information Retrieval, Summarization and other similar tools which however need precise linguistic information with a much higher coverage than what is being recently offered by the currently available parsers.
Comparative Prediction of Groundwater Fluctuation by CWTFT-ANFIS and WT-ANFIS

Objective: To evaluate the ability of prediction performance of ANFIS (Adaptive Neuro Fuzzy Inference System) model on groundwater level fluctuation in Lower Bhavani River Basin (LBRB). Method: To improve the accuracy of prediction of ANFIS model, WT (Wavelet Transform) and CWTFT (Continuous Wavelet Fast Fourier Transform) are performed as preprocessing techniques. Model development is carried out through three different input parameters (duration, groundwater recharge and groundwater discharge) and one output parameter (groundwater fluctuation) for the period of 2009-2015 on monthly stress basis. Findings: Based on the comparative prediction by the models CWTFT-ANFIS and WTANFIS over conventional ANFIS model through calibration and validation process on different reaches of the study area, optimum model is identified. Statistical indices are performed to measure the best fitting and trend identification for the each prediction.