Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable-length Functional Output Prediction and Boundary Detection for an Adaptive Flight Control Simulator

Author(s): He, Yuning | Advisor(s): lee, Herbert | Abstract: The general problem addressed in this work concerns the analysis of a function of multiple real variables in which the output is itself a function of a real variable as well as categorical information. We are interested in the prediction of the output and the analysis of the boundaries separating regions during classification. Efficient and accurate prediction of the output curves is important for safety analysis and validation of a complex system, like an adaptive flight control system. An understanding of the boundaries between regions, where the aircraft can maintain stable flight or will break up is essential for safety certification. As a motivating application we are using the NASA IFCS (Intelligent Flight Control System) flight simulator.For output prediction, we developed a new statistical method for emulation of computer models with multiple outputs. An emulator is a computationally efficient statistical model that is used to approximate a computationally expensive simulation by treating the simulator as a black box and learning a mapping from inputs to outputs. Our approach for emulating the curves is to represent them in an orthogonal basis (e.g., PCA, Fourier, or Wavelet), which captures curve characteristics, and then to predict the coefficients. We allow for the possibility of output curves whose length varies with input, which may occur when a simulator fails to run to completion for some inputs and the failures occur at different output time steps. To the best of our knowledge, the variable-length output problem has not yet been addressed. We have developed a hierarchical model, which first uses classification into a few groups and then fitting distinct models for different classes of output curves. For the analysis of boundaries, we developed a new sequential approach based upon design of computer experiments. A dictionary of suitable linear or non-linear parameterized boundary shapes, which capture underlying physical and design knowledge can be provided by the domain expert. We incorporate this knowledge into our modeling and determine the most likely shapes and its parameters. Since each iteration requires a costly run of the system simulator, we developed a candidate selection mechanism, which is specifically tailored toward boundary detection and which can take priors into account in order to reduce the number of required simulation runs. We present results of experiments with artificial and IFCS simulation data sets.
Simulating Biological Neural Network Structure in Computers with help of MATLAB for Handwriting Recognition Tasks

This report presented here describes the basic human neural structure and its artificial competent. The human neurological structure is analyzed in detail to get the techniques of its learning and determination of results. The processing of this structure is tried to simulate using a feed-forward artificial neural network in which the error term is propagated to back -layer for corrections. This artificial neural network is designed and trained for recognition of 500 handwritten letters. The co-relation between the artificial neural network and biological natural neural network is also investigated and explained.
A Model for Predicting Intelligibility of Binaurally Perceived Speech

Abstract : Predicting and modeling intelligibility of monaurally or binaurally presented speech is difficult because it depends primarily on the accuracy and interdependency of frequency, time, and spatial information arriving at the listener. Despite these complex relationships, a new pragmatic model is suggested for speech mixed with broadband noise. A form of the logistic regression function is used to characterize human performance data. The regression of these signal properties onto empirical speech recognition performance data estimates the relationship of these properties to speech recognition. This concept is illustrated by the modeling of human performance on Central Institute for the Deaf W-22 speech items presented monaurally and binaurally in both reverberant and non-reverberant conditions at different signal-to-noise ratios. Although the implementation of the present model is limited to the data considered, it is expected that other data can be modeled after the procedure outlined in this report. The model described is the first step in developing an objective binaural measure for predicting speech perception in noisy environments.
Manifold Constrained Transfer of Facial Geometric Knowledge for 3D Caricature Reconstruction

3D 漫画是在象联机游戏那样的虚拟环境的接口的重要吸引人的元素。 然而，很有限的 3D 漫画在真实世界上存在。 同时，手工地创造 3D 漫画是昂贵、甚至职业的技巧被需要。 这份报纸建议一篇小说并且有效歧管重建的转移算法 3D 漫画根据他们的原来的 2D 漫画。 我们手工地首先与 100 个仅仅 3D 漫画模型一起创造小数据集并且使用他们初始化整个 3D 数据集。 在那以后，歧管转移算法被执行精制数据集。 算法二包括步骤。 第一是表现歧管的在 2D 和 3D 漫画到之间的排列得到一个标准歧管地图；第二是重建所有 3D 漫画基于歧管地图。 建议途径利用并且转 2D 漫画的知识到目标 3D 漫画很好。 途径重建的比较实验表演 3D 漫画更有效地并且结果比基于的主要部件分析(PCA ) 遵守更多到原来的 2D 漫画的式样方法。
Categorisation of Mental Computation Strategies to Support Teaching and to Encourage Classroom Dialogue

Mental strategies are a desired focus for computational instruction in schools and have been the focus of many syllabus documents and research papers. Teachers though, have been slow to adopt such changes in their classroom planning. A possible block to adoption of this approach is their lack of knowledge about possible computation strategies and a lack of a clear organisation of a school program for this end. This paper discusses a framework for the categorisation of mental computation strategies that can support teachers to make the pedagogical shift to use of mental strategies by providing a framework for the development of school and classroom programs and provide a common language for teachers and students to discuss strategies in use.
Does incremental training improve motor learning

In previous years, there has been a multitude of research on motor learning. However, little research has been done to examine the possibility of altering the rate of learning and performance by modification of the task. In this study, we were interested in investigating the effects of learning a complex task with a gradual increase in difficulty. Subjects performed a mirror drawing task with a robotic manipulandum. Subjects were randomly placed into one of four groups. [Complete] group subjects received the most difficult form of the task from the beginning and trained and tested on the same shape (a 5-pointed star). [Gradual] group subjects gradually progressed through ‘levels’ that increased in difficulty before reaching the [Complete] group star. [GradualRotated] group subjects received a similar paradigm to [Gradual] group subjects, only their training levels were rotated 30 degrees. [Complete-Rotated] group subjects trained on a rotated version of the [Complete] group star. [Complete] group subjects demonstrated significantly better initial performance on the star shape when compared to the [Gradual] and [Gradual-Rotated] groups. [Gradual], [Gradual-Rotated], and [Complete-Rotated] groups initially performed at lower errors on their respective tasks when compared to the [Complete] group. We demonstrate that the novel paradigm that we introduce to subjects successfully lowered initial performance error. However, in this specific task, immediate introduction of the final shape proved to result in better early performance. All subjects performed at equal levels at the end of training. We are interested in the application of this training paradigm to various other tasks.
The Optimization of Task Assignments on Hadoop Platform for Large-Number Image Processing

This paper proposes a large-number images processing model based on Hadoop platform. According to this model, the task allocation strategies are investigated for largenumber image processing on the isomorphic Hadoop clusters and heterogeneous Hadoop clusters. Firstly, a series of different experiments is performed on the isomorphic Hadoop clusters. The obtained results show that the equal allocation of all images to each node is proved to be an efficient approach. For the heterogeneous clusters, the genetic algorithm is used as a task allocation strategy to optimize the processing task allocation for a large-number of images. Experimental results show that the GAbased optimization can significantly speed up the image processing so that the proposed approach is promising to apply to process big data of images. Keywords-Hadoop cluster; Image processing; Hadoop streaming; genetic algorithm
Corpus-based and computational approaches to discourse anaphora

1. Discourse anaphora: The need for synthesis (by Botley, Simon Philip) 2. Demonstrative expressions in argumentative discourse: A computer corpus-based comparison of non-native and native English (by Petch-Tyson, Stephanie) 3. Is it possible to predetermine a referent included in a French N De N structure? (by Baltazart, Didier) 4. A corpus-based study of anaphora in English and Portuguese (by Rocha, Marco A.E.) 5. Conversational strategies using full NP anaphors (by Shokouhi, Hussein) 6. Some uses of demonstratives in spoken Swedish (by Lindstrom, Eva) 7. Pronoun resolution: The practical alternative (by Mitkov, Ruslan) 8. Quantitative evaluation of coreference algorithms in an information extraction system (by Gaizauskas, Robert) 9. Anaphoric reference and ellipsis resolution in a telephone-based spoken language system for accessing email (by Williams, Sandra) 10. Processing definite descriptions in corpora (by Vieira, Renata) 11. Indirect reference in Japanese sentences (by Murata, Masaki) 12. Generating coreferential anaphoric definite NPs (by Tutin, Agnes) 13. Name Index 14. Subject Index
First Grade Teachers’ Perspectives on Using Nonfiction Texts in Guided Reading and Read-Aloud Lessons

This research assessed first-grade teachers’ perspectives on using nonfiction text during guided reading and read-aloud lessons. Three teachers were all surveyed, observed, interviewed, and their classroom libraries were inventoried. Later the study revealed teachers’ perspectives on using nonfiction text. The findings showed there was a positive correlation between teachers’ increase in confidence and their use of the texts, and that teachers who had a high number of nonfiction texts in their classrooms incorporated the texts more often. The research gave implications for student learning which were students benefit from being taught about nonfiction text structure and nonfiction text engages students. It is recommended that teachers require education on nonfiction text and students need to be engaged with nonfiction text.
An Internet Education: A Guide to Doing Research on the Internet

From the Publisher: True expertise in information management brings with it enormous personal advancement and satisfaction. To gain this important knowledge, you need An Internet Education: A Guide to Doing Research on the Internet. It will introduce you to the powerful techniques for finding and analyzing information through the Internet. An Internet Education demystifies the logic of the Internet resource structure and provides advice on how to match research questions with the most appropriate sources. It shows new users how to use the network to answer real questions in the most effective manner possible. In addition, An Internet Education suggests ways to use the unique immediacy and global reach of the network to solve research problems in new and creative ways. Other highlights: valuable tips are offered on using electronic mail as a tool for "power research"; helpful information on locating and retrieving files, using databases effectively, and taking advantage of shareware and freeware; complete guides for using online libraries, freenets, research and commercial databases, and discussion groups for research; and easy-to-follow instructions on learning the Internet's "netiquette" and ethics.
Efficient and flexible reading

"Efficient and Flexible Reading" teaches readers how to vary their approach to written texts based on the material and their purpose for reading. In addition to instruction in literal and critical comprehension skills, vocabulary, and study skills, "Efficient and Flexible Reading" teaches readers how to identify text structures and thought patterns for more efficient learning. Emphasizing reading as an active thinking process, the text presents systems for monitoring concentration, comprehension, and recall, encouraging students to assess the reading assignment and to select the appropriate strategy to suit the situation. The seventh edition expands coverage of the most crucial skills in both basic reading comprehension and higher-level critical reading: main ideas, patterns of organization, transitions, and critical analysis. For those who want to improve basic reading skills.
Online Inference for Adaptive Diagnosis via Arithmetic Circuit Compilation of Bayesian Networks

Considering technology and complexity evolution the design of fully reliable embedded systems will be prohibitively complex and costly. Onboard diagnosis is a first solution that can be achieved by means of Bayesian networks. An efficient compilation of Bayesian inference is proposed using Arithmetic Circuits (AC). ACs can be efficiently implemented in hardware to get very fast response time. This approach has been recently experimented in Software Health Management of aircrafts or UAVs. However, there are two kinds of obstacles that must be addressed. First, the tree complexity can lead to intractable solutions and second, an offline static analysis cannot capture the dynamic behaviour of a system that can have multiple configurations and applications. In this paper, we present our direction to solve these issues. Our approach relies on an adaptive version of the diagnosis computation for different kinds of applications/missions of UAVs. In particular, we consider an incremental generation of the AC structure. This adaptive diagnosis can be implemented using dynamic reconfiguration of FPGA circuits.
Software Architecture in Practice (Second Edition)

The architect is faced with a swarm of competing, if not conflicting, influences and demands, surprisingly few of which are concerned with getting the system to work correctly. The organizational and technical environment brings to bear a weighty set of sometimes implicit demands, and in practice these are as important as any of the explicit requirements for the software even though they are almost never written down.
Improving Performance of Biomedical Information Retrieval using Document-Level Field Boosting and BM25F Weighting

Corpora of biomedical information typically contains large amounts of ambiguous data, as proteins and genes can be referred to by a number of different terms, making information retrieval difficult. This thesis investigates a number of methods attempting to increase precision and recall of searches within the biomedical domain, including using the BM25F model for scoring documents and using Named Entity Recognition (NER) to identify biomedical entities in the text. We have implemented a prototype for testing the approaches, and have found that by using a combination of several methods, including using three different NER models at once, a significant increase (up to 11.5%) in mean average precision (MAP) is observed over our baseline result.
Geovisualisation of uncertainty in simulated flood maps

The paper presents a three-dimensional (3D) geovisualisation model of uncertainties in simulated flood maps that can help communicate uncertain information in the data being used. An entropy-based measure was employed for uncertainty quantification. In developing the model, Visualisation Toolkit (VTK) was utilised. Different data derived from earlier simulation study and other maps were represented in the model. Cartographic principles were considered in the map design. A Graphical User Interface (GUI), which was developed in Tkinter, was also created to further support exploratory data analysis. The resulting model allowed visual identification of uncertain areas, as well as displaying spatial relationship between the entropy and the slope values. This geovisualisation has still to be tested to assess its effectiveness as a communication tool. However, this type of uncertainty visualisation in flood mapping is an initial step that can lead to its adoption in decision-making when presented comprehensively to its users. Thus, further improvement and development is still suggested for this kind of information presentation.
Modeling the performance of magnetic nanoparticles in multimodal cancer therapy

Composite magnetic nanoparticles (MNPs) consisting of an MNP core and drug loaded polymer shell can increase the efficacy of cancer therapy by overcoming several limitations of conventional hyperthermia and chemotherapy. Multimodal therapy consisting of simultaneous hyperthermia and chemotherapy can increase therapeutic efficiency compared to individual applications of these modalities. Factors influencing power output in an alternating magnetic field (AMF) for superparamagnetic γ-Fe2O3 and Fe3O4 iron oxide MNP were studied. The optimum MNP properties for in vivo magnetic hyperthermia were identified. For a 375 kHz AMF, 23 nm γ-Fe2O3 MNP and 12 nm Fe3O4 MNP produce maximum heating, heat generation is dependent primarily on Neel relaxation and is insensitive to polymer shell thickness. The heating of tumors by uniformly distributed magnetic clusters of optimized iron oxide MNP was modeled. The MNP mass required to heat tumors to hyperthermia temperatures was calculated, the Fe3O4 MNP concentration in the t.
Les Bains du Jabal Khubthah (Pétra, Jordanie)

Resume – Un releve general au 1/50 des structures visibles au sommet du Jabal Khubthah a permis de decouvrir un complexe thermal inedit dont les vestiges, decouverts au debut du xxe s. par G. Dalman, avaient ete interpretes alors comme cultuels. Situe dans le secteur occidental du massif, ce complexe de 225 m² environ est associe a deux citernes alimentees par deux aqueducs aeriens et offre une vue spectaculaire sur le theâtre et le centre-ville. Topographiquement, il fait echo aux bains decouverts sur le revers oriental du massif d’Umm al-Biyarah. Sont presentees ici une premiere description des vestiges et quelques hypotheses d’interpretation, posees avant l’ouverture des fouilles, programmees pour le printemps 2015.
Transport Properties, Microstructure, and Conduction Model of Cosputtered Au–SiO2 Cermet Films

High‐resistivity Au–SiO2 cermet films have been cosputtered. Their transport and structural properties have been examined and a model for electrical conduction is proposed. The electrical resistivity was found to be thermally activated, and the activation energy increased linearly as the metal content decreased. Activation energies varied from 0.01 to 0.07 eV. At a fixed temperature, the resistivity increased exponentially as the weight percent gold decreased. The cermets obeyed Ohm's law. No Hall effect was observed. Transmission electron micrographs showed very small (30–100 A) discontinuous gold particles separated by very small SiO2 regions. Films were deposited at 20°, 500°, 520°, and 600°C. Gold particle size increased with increasing gold content or with increasing substrate temperature. Only polycrystalline gold was observed by electron diffraction. The typical infrared spectrum of SiO2 was recorded. 20°C cermets annealed at 600°C showed large particle growth and twinning. Electrical conduction in.
Combinación de técnicas de reconocimiento de rostros con imágenes infrarrojas empleando algoritmos genéticos

Resumen—En este articulo se presentan los resultados obtenidos, al emplear algoritmos geneticos para la combinacion de algunas tecnicas y clasificadores utilizados en el reconocimiento de rostros con imagenes infrarrojas. Se muestra que bajo el esquema de combinacion propuesto, el sistema presenta un mayor desempeno que las tecnicas individuales y abre un amplio panorama de investigacion y desarrollo en el campo del reconocimiento de rostros.
Automatic target cueing of hyperspectral image data

Modern imaging sensors produce vast amounts of data, overwhelming human analysts. One such sensor is the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor. The AVIRIS sensor simultaneously collects data in 224 spectral bands that range from 0.4$\mu$m to 2.5$\mu$m in approximately 10nm increments, producing 224 images, each representing a single spectral band. Autonomous systems are required that can fuse "important" spectral bands and then classify regions of interest if all of this data is to be exploited. This dissertation presents a comprehensive solution that consists of a new physiologically motivated fusion algorithm and a novel Bayes optimal self-architecting classifier that processes the outputs of the fusion algorithm. The fusion algorithm which uses a contrast sensitivity weighted wavelet-based multiresolution analysis is shown to outperform other fusion algorithms in both visual aesthetics and signal-to-noise ratios. The self-architecting classifier is a Radial Basis Function (RBF) Iterative Construction Algorithm (RICA) that is designed to autonomously determine the size of its network architecture for optimal classification performance. RICA is shown to outperform several neural network algorithms, including a fixed architecture multi-layer Perceptron (MLP), a fixed architecture RBF, and an adaptive architecture MLP. A proof is also presented demonstrating that RICA produces a network which is a minimum mean squared-error approximate to Bayes optimal discriminant functions. Finally, it is shown that this combination of image fusion and self-architecting classifier provide an excellent means to detect targets in hyperspectral sensor data.
Longman language activator : the world's first production dictionary

Using definitions from the 2000 word Longman Defining Vocabulary, this activator offers many example sentences showing how words are used, taking many of them from authentic texts and conversations. It aims not to be confusing by offering key words that students should already know, and presents them with a choice of words and phrases on menus, similar to those on a computer screen. The dictionary also explains the use of various grammatical patterns and how to use them in context. For example run for the bus/train, run away/off, under the key word "run", and various examples show the many ways that words can be used.
Learning Dynamics as a Many-Body Problem

: An important issue in neural computing concerns the description of learning dynamics with macroscopic dynamical variables. Recent progress on on line learning only addresses the often unrealistic case of an infinite training set. For restricted training sets, previous studies have so far been limited to asymptotic dynamics or simple learning rules. We introduce a many-body theory to model batch learning of restricted sets of examples, widely applicable to any learning cost function, and fully taking into account the temporal correlations introduced by the recycling of the examples.
Studying the high x frontier with A Fixed-Target ExpeRiment at the LHC

The opportunities which are offered by a next generation and multi-purpose fixed-target experiment exploiting the proton and lead LHC beams extracted by a bent crystal are outlined. In particular, such an experiment can greatly complement facilities with lepton beams by unraveling the partonic structure of polarised and unpolarised nucleons and of nuclei, especially at large momentum fractions.