Miracle-dz/UniFAR-FTUs huggingface dataset:
Large Deviation Techniques in Decision, Simulation, and Estimation

Cramir's Theorem and Extensions Sanov's Theorem and the Contraction Principle Gaussian Processes and Wentzell-Freidlin Theory Large Deviations for Markov Processes Applications to Detection Theory Asymptotic Expansions Quick Simulation Applications to Parameter Estimation Applications to Information Theory Appendices Solutions to Exercises References Index.
Big Data Computing Framework: A Compact Review

________ Abstract -Advances in information technology and its widespread growth in several areas of business, engineering,medical, and scientific studies are resulting in information/data explosion. Knowledge discovery anddecision-making from such rapidly growing voluminous data are a challenging task in terms of data organizationand processing, which is an emerging trend known as big data computing, a new paradigm thatcombines large-scale compute, new data-intensive techniques
Predicting cotton production using Infocrop-cotton simulation model, remote sensing and spatial agro-climatic data

A methodology is described to predict cotton production on a regional basis using the integrated approach of remote sensing (RS), geographic information system (GIS) and a crop simulation model, i.e. Infocrop-cotton model. This model is based on an indigenous crop growth simulator called Infocrop. The Infocrop-cotton model was calibrated and validated to simulate the effect of diverse weather, soil, and agronomic management practices on growth, development and yield of cotton varieties and hybrids using results of several diverse field experiments (60 datasets). These experiments were conducted during 2000-01 to 2004-05 in major cotton-producing states of India across locations spreading from Hisar (29°10'N, 75°46'E) to Coimbatore (11°00'N, 77°00'E) with varying management practices, weather and soil. The model satisfactorily simulated the trends in leaf area, dry matter growth, days to flowering and seed cotton yield. The simulated time to flowering and maturity varied between 54 and 80 days as well as 136 to 193 days, with an RMSE value of 3 and 8.5 days respectively. Total biomass and seed cotton yield showed an accuracy of 86 and 89% respectively. The model also precisely simulated water deficit and N stress, the two important abiotic constraints for dryland cotton production. The Infocrop-cotton model was used in conjunction with RS and GIS techniques for developing an integrated approach for deriving cotton production estimates. Resourcesat-1 LISS III data of October/November months corresponding to peak vegetative stage of cotton crop were used to derive spatial distribution of cotton crop. The study area was classified as polythesian polygons based on pedo-climatic variables, namely soil type, soil depth and rainfall pattern using GIS. Cotton yields for each of these polygons were simulated using the crop model and were aggregated to determine the total production of the district. The prediction of cotton production was more accurate to the partially irrigated or irrigated districts and not for the rainfed districts. The utility of the integrated approach in prediction of cotton production at the regional level has been discussed.
DEVELOPMENT OF VISUAL LITERACY LEVELS SCALE IN HIGHER EDUCATION

The aim of this study is to develop a scale determining the visual literacy levels of university students. After reviewing the relevant literature a 75 item draft scale was prepared. The scale was applied to 3 rd and 4 th year students of Education Faculty of Amasya University. Non-functional items have been excluded from the scale as a result of the factor analysis and 41 items have been included. It has been determined that the statements in the scale are gathered in 7 dimensions. These dimensions consisted of: “Identification of the need for visual”, “Finding and accessing visual sources”, “Analyzing and interpreting the visuals”, “Evaluation of visuals and visual sources”, “Effective usage of visuals and visual media”, “Designing and creating visuals” and “Taking ethical and legal issues into consideration”. The General Cronbach Alpha internal consistency of the scale has been determined as α =. 947.
OPTIMASI PARAMETER ARTIFICIAL NEURAL NETWORK DENGAN MENGGUNAKAN ALGORITMA GENETIKA UNTUK MEMPREDIKSI NILAI TUKAR RUPIAH

To predict foreign exchange rate is not easy, accurate prediction is necessary for investor to reduce higrisk about exchange rate volatility. In predicting foreign exchange rate is used Artificial Neural NetworkBackpropagation as a model that applied. There are several parameters to implement Artificial NeuralNetwork that must be determined as training cyclel, learning rate, and momentum, the problem is the lackof standard guidelines in determining the parameters that will be used, therefore in this method used theexperimental method. So that we need a method that can resolve the problem, then that the parametersobtained become more optimal. Solutions that can be applied is to apply the genetic algorithm (GA) onArtificial Neural Networks, in order to optimize the value of training cycle, learning rate and momentumparameters. The results are the application of optimization techniques with Genetic Algorithm canfacilitate the search for optimal parameter values and reduce error (RMSE) or increase the value of theaccuracy of the Artificial Neural Network algorithm, thus the model obtained can be used by investor topredict foreign exchange rate.
Utilizing microblogs for improving automatic news high-lights extraction

Story highlights form a succinct single-document summary consisting of 3-4 highlight sentences that reflect the gist of a news article. Automatically producing news highlights is very challenging. We propose a novel method to improve news highlights extraction by using microblogs. The hypothesis is that microblog posts, although noisy, are not only indicative of important pieces of information in the news story, but also inherently “short and sweet” resulting from the artificial compression effect due to the length limit. Given a news article, we formulate the problem as two rank-then-extract tasks: (1) we find a set of indicative tweets and use them to assist the ranking of news sentences for extraction; (2) we extract top ranked tweets as a substitute of sentence extraction. Results based on our news-tweets pairing corpus indicate that the method significantly outperform some strong baselines for single-document summarization.
Research on Augmented Reality Method Based on Improved ORB Algorithm

Aiming at the problems of time-consuming three-dimensional registration algorithm in augmented reality system, low accuracy of feature point matching and poor registration effect, an improved ORB (S-ORB) algorithm is proposed and applied to the Majiayao painted pottery augmented reality system. Firstly, the scale space is established by Gaussian filtering and downsampling, and the feature points are extracted by FAST corner detection. Secondly, the descriptors of feature points are calculated by rBFEF algorithm. Finally, the feature matching set is obtained by hamming distance and the mismatching point is eliminated by RANSAC algorithm to complete the three-dimensional registration. Make the virtual information stack correctly added to the real scene. The experimental results show that the S-ORB algorithm is better than the ORB, SURF and BRISK algorithms in time and accuracy when the target position is rotated and scaled. It has better real-time and robustness in the AR system of the painted pottery.
Normalized Independent Component Analysis for Face Recognition

This work was carried out in collaboration between all authors. Authors IOA and OOE designed the study. All the authors worked on development of the NICA algorithm and the flow to ensure the system works. Authors IOA and OO wrote the first draft of the manuscript and managed the comparison analysis. Author OOE supervised the project. All authors read and approved the final
Graphical User Interface Extension in R Commander for Evidence Based Medicine Indicators

Background: Evidence Based Medicine (EBM) represents the use of the best latest evidence obtained by systematic search together with personal clinical experience and patient values. Clinical evidences are quantified by a number of indicators. R is a free and powerful environment for statistical computing and graphics, used mainly in command line interface. The aim of this work was to create an extension for R Commander to offer a graphical user interface to calculate the most used EBM indicators. Materials and Methods: The extension was created in R and then tested in R 3.1. 1 and R Commander 2.1-1. Results: The extension was successfully created and was named RcmdrPlugin.EBM, being available for download on Comprehensive R Archive Network servers. The extension creates a new menu called EBM, which offers an interface to calculate the following indicators with their confidence intervals: diagnosis (sensitivity, specificity, diagnostic accuracy, Youden’s index, positive/negative likelihood ratio, positive/negative predictive value, number needed to diagnose), therapeutic (absolute risk reduction, relative risk reduction, odds ratio, relative risk, number needed to treat/harm), prognostic (attributable risk, relative risk, odds ratio). Conclusion: It is now easy to compute EBM indicators in R, with the graphical user interface extension of R Commander.
A Web Service Composition Model k2-MDD-WS

Web Service Dependency Graphs are the abstract model for the Web service composition that are facing great challenges in both increasing size and finite state search space. Compact representation and efficient operation of graph data are urgent topics, have been attracting more and more attentions. A Web service composition model based on multi-valued decision diagram, namely k 2 Multi-valued Decision Diagram of Web Service Dependency Graph ( k 2 -MDD-WS), is proposed to reduce the storage space. The vertices of the Web Service Dependency Graph are encoded, and the edge codes are then encoded according to the codes of the vertices; so, the k 2 -MDD-WS is constructed based on the code sets of the edges. The k 2 - MDD-WS can combine redundant web service nodes caused by a large number of isomorphic sub-trees existing in the k 2 -tree to greatly reduce the number of nodes generated. Additionally, the operations of the Web Service Dependency Graph are transformed into Boolean operations under the k 2 -MDD-WS representation to further improve the efficiency of finding a Web service composition solution that meets user needs. The experimental results indicate that compression ratio in k 2 -MDD-WS is only 3.5%–7.5% of the in k 2 -tree.
Sparse recovery algorithms for streaming and multidimensional signals

As the world is moving toward the era of big data, when a system will accumulate and process an immense amount of information, the cost and complexity of the acquisition and processing of high dimensional data is a critical issue to be addressed. In this respect, compressive sensing (CS), with its capability of utilizing sub-Nyquist sampling to recover a signal of interest, may play a vital role in addressing this problem. In this thesis, we develop sparse recovery algorithms to reconstruct streaming signals and multi-dimensional signals from compressive measurements. First, we address the problem of reconstructing a streaming signal from compressive, streaming measurements. We utilize a lapped transform (LT) for the sparse representation of the streaming signal to reduce the blocking artifacts. To reconstruct the signal, we develop a progressive reconstruction algorithm based on sliding window processing. The recovery algorithm, which is based on sparse Bayesian learning (SBL), utilizes the preliminary information from the preceding processing window to improve the performance of signal reconstruction. Furthermore, we derive fast update formulae and propose a warm-start procedure to reduce the computational cost of the novel SBL algorithm. Next, we extend the solution method for a 2dimensional scenario, in which we reconstruct an image from block compressive sensing (BCS) measurements. The results show that the novel SBL based sliding window method provides a more visually pleasing image without using any post-processing techniques. We then address the problem of recovering an image from compressive measurements. Unlike the traditional image recovery algorithms, we utilize the total variation (TV) minimization and the sparse representation of the image in a given transform to improve the reconstruction performance of the image recovery algorithm. Later, we extend this work to a multiple bases scenario, as many natural signals may have compressible representations in different transforms rather than having a single sparse representation. For these scenarios, we modify the TV minimization problem by introducing an l1-norm penalty. To solve these l1-regularized TV minimization problems we propose customized interior-point methods by efficiently solving the Newton systems.
Institution-independent logic programming

The logic-programming paradigm is a prime example of how the integration of the declarative and the operational aspects of logical systems can be used to provide a uni ed framework for both speci cation and programming languages. In essence, programming in logic amounts to giving appropriate axiomatic formalizations of computable functions, which can then be executed by means of carefully designed goal-directed deduction rules. In this thesis, we examine common features of various conventional logicprogramming languages, ranging from the most traditional variant of the paradigm – de ned over Horn-clause logic – to rst-order and higher-order equational logic programming. Based on these, we propose an abstract model-theoretic framework that allows us to develop and conduct research into logic programming over an arbitrary logical system, without concrete models, sentences, satisfaction, or deduction, and thus to explore the logicprogramming paradigm for other, less conventional formalisms, like the logic of orchestration schemes used in the context of service-oriented computing. Our study is based on abstractions of notions such as logic program, clause, query, solution, and computed answer, which we develop over Goguen and Burstall’s theory of institutions. These give rise to a series of concepts that formalize the interplay between the denotational and the operational semantics of logic programming. We investigate properties concerning the satisfaction of quanti ed sentences, discuss a variant of Herbrand’s theorem that is not limited in scope to any logical formalism or construction of logic programs, and de ne a sound and conditionally complete procedure for computing solutions to queries. Within this setting, we further examine two of the most fundamental aspects of the modularization of logic programs – the preservation and the re ection of solutions along morphisms of programs – leading to results that can be applied not only to unstructured logic programs (plain sets of clauses), but also to elaborate module systems.
Understanding the Mechanism and Improving the Design of a Myocardial Matrix Hydrogel for Post-Infarct Repair

Author(s): Wassenaar, Jean Wang | Advisor(s): Christman, Karen L | Abstract: With improved management of patients with acute myocardial infarctions (MI), the prevalence in heart failure (HF) post-MI is expected to rise. Currently, the only successful treatments for HF are total heart transplantation and left ventricular (LV) assist devices, but their uses are limited by the availability of donor hearts and invasiveness of the procedure. In the last decade, advancements have been made towards developing injectable hydrogels for the purpose of cardiac repair. Injections of hydrogels alone have been shown to attenuate the decline in cardiac function and LV remodeling typically seen after MI in both large and small animals models. One of these hydrogels was previously developed by our lab and derived from decellularized porcine ventricular myocardium. The goal of this thesis was study to the mechanisms by which injections of the myocardial matrix hydrogel improve cardiac repair post-MI and improve upon its cardioreparative effects. To better understand how this myocardial matrix is able to induce the beneficial effects observed post-MI, a whole transcriptome microarray was performed on infarct tissue collected from matrix or saline injected infarcts. We showed through pathway analysis that the effects of the injection were dividable into several tissue level phenotypes. To better understand these in vivo phenomena, we wanted to recapitulate the observations by cell culture in vitro with the myocardial matrix. Several cell behaviors relevant to the infarct milieu were studied, including cardiac progenitor cell migration, cardiomyocyte apoptosis, cardiac fibroblast metallomatrix proteinase (MMP) production, and macrophage polarization. We demonstrated that the form of the matrix that is presented to the cells have a dramatic effect on the cellular response, whether through the 3D hydrogel or as soluble peptides released during degradation. In addition, different fractions of the degradation products also have different bioactivity. Results from these in vitro experiments suggested that the bioactivity of the myocardial matrix and its degradation products seemed to be essential to its cardioreparative effects post-MI; thus, we investigated whether this could be enhanced by prolonging the degradation rate of the hydrogel. Through these studies, we provided the first steps towards elucidating the mechanism of actions of the myocardial matrix, by defining the tissue level changes that it induces in infarcted myocardium and identifying the bioactivity in both the hydrogel form and degradation products.
Tumor Classification using Block wise fine tuning and Transfer learning of Deep Neural Network and KNN classifier on MR Brain Images

Brain tumors identification and classification is a most crucial role in medical diagnosis and treatment plan for the patients. The information acquired by medical imaging machines contains low level information which cannot be perceived my human visual system. An automatic Computer aided diagnosis system with deep convolutional networks can perform well in Medical Image classification, but requires large datasets. Generally it is difficult to obtain a large number of labelled samples in medical image classification tasks. Knowledge transfer with fine tuning of pre-trained networks can apply on small medical datasets for diagnosis. In this work transfer learning and block wise fine tuning with separate KNN classifier on pre trained deep neural network VGG net is used to classify BRATS and CE-MRI datasets. The proposed method is denoted as BT-VGGNet. The labelled BRATS Flair Images and CE-MRI T1-weighted contrast-enhanced datasets are exploited to block wise fine-tune all hidden layers in the VGG deep neural network in feature extraction and KNN for classification. Experimental results show that the proposed model exceeds the state-of - the-art classification with 97.28 percent and 98.69 percent accuracy on the BTDS-2 and CE-MRI datasets respectively.
QoS-Aware Fog Service Orchestration for Industrial Internet of Things

Fog computing is promising for Industrial Internet of Things (IIoT) which are typically large-scale, time-critical, and geo-distributed. However, it needs to address challenges such as scalability and heterogeneity in application provisioning with diverse and strict Quality-of-Service (QoS) requirements from IIoT devices. In this paper, a fog service orchestrator, Q-FSO, is proposed for IIoT application provisioning in which request satisfaction is maximized. With a two-level QoS model that considers key performance metrics for IIoT, including availability, reliability, response time, and cost, Q-FSO can realize large-scale service orchestration by exploiting the advantages of fog computing. Further with a harmonious local QoS assignment model, Q-FSO solves the workflow construction problem by multiple independent local optimization tasks, one for each stage, for the concurrent requests. Each local optimization task can be reduced from the multiple multidimensional knapsack (MMKP) problem. We then propose two practical MMKP heuristic algorithms, namely, Incremental Similarity Matching (ISM) and Greedy Multiple Matching (GMM), to tackle the local QoS assignment problem with polynomial time complexity. The scalability issue in orchestration is then handled by a decentralized workflow construction protocol. The simulation results validate their applicability, demonstrating that Q-FSO is indeed an efficient and effective IIoT application provision mechanism with significant performance benefits in terms of service processing throughput, resource utilization, and speed of service orchestration.
Explanations as Discourse: Towards Ethical Big Data Analytics Services

Big data analytics uses algorithms for decision-making and targeting of customers. These algorithms process large-scale data sets and create efficiencies in the decision-making process for organizations but are often incomprehensible to customers and inherently opaque in nature. Recent European Union regulations require that organizations communicate meaningful information to customers on the use of algorithms and the reasons behind decisions made about them. In this paper, we explore the use of explanations in big data analytics services. We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement and reduced reputation risks for organizations. We conclude the paper by proposing future empirical research directions.
Energy and Information Management of Electric Vehicular Network: A Survey

The connected vehicle paradigm empowers vehicles with the capability to communicate with neighboring vehicles and infrastructure, shifting the role of vehicles from a transportation tool to an intelligent service platform. Meanwhile, the transportation electrification pushes forward the electric vehicle (EV) commercialization to reduce the greenhouse gas emission by petroleum combustion. The unstoppable trends of connected vehicle and EVs transform the traditional vehicular system to an electric vehicular network (EVN), a clean, mobile, and safe system. However, due to the mobility and heterogeneity of the EVN, improper management of the network could result in charging overload and data congestion. Thus, energy and information management of the EVN should be carefully studied. In this paper, we provide a comprehensive survey on the deployment and management of EVN considering all three aspects of energy flow, data communication, and computation. We first introduce the management framework of EVN. Then, research works on the EV aggregator (AG) deployment are reviewed to provide energy and information infrastructure for the EVN. Based on the deployed AGs, we present the research work review on EV scheduling that includes both charging and vehicle-to-grid (V2G) scheduling. Moreover, related works on information communication and computing are surveyed under each scenario. Finally, we discuss open research issues in the EVN.
Enhancing EFL students’ intracultural learning through virtual reality

ABSTRACT Intracultural learning has been neglected in English curricula in Taiwan since textbooks as an important learning resource have been mainly focused on English linguistic content and developing students’ language skills and and target culture to develop students’ language skill. This study investigated how making VR content helped students enhance their intracultural learning. This study engaged 60 advanced Taiwanese EFL college students in VR content making. The results demonstrated that students developed better intracultural awareness through the features of VR technology including panorama, audio, interaction, and structuring. Further discussions were provided to explain how each of these four features enhanced students’ local cultural learning.