Miracle-dz/UniFAR-FTUs huggingface dataset:
CHIRON - A new high resolution spectrometer for CTIO

Small telescopes can play an important role in the search for exoplanets because they offer an opportunity for high cadence observations that are not possible with large aperture telescopes. However, there is a shortage of high resolution spectrometers for precision Doppler planet searches. We report on an innovative design for CHIRON, an inexpensive spectrometer that we are building for the 1.5-m telescope at CTIO in Chile.The resolution will be R >80.000, the spectral format spanning 410 to 880 nm. The total throughput of the telescope and spectrometer will be better than 12%, comparable with the efficiency of state-of-the-art spectrometers. The design is driven by the requirements for precision Doppler searches for exoplanets using an iodine cell.The optical layout is a classical echelle with 140 mm beam size. The bench-mounted spectrometer will be fibre-fed followed by an image slicer. An apochromatic refractor is used as the camera. Image quality and throughput of the design are excellent over the full spectral range. Extensive use of commercially available components and avoidance of complicated custom optics are key for quick and resource-efficient implementation.
BRUJA: Question Classification for Spanish. Using Machine Translationand an English Classifier

Question Classification is an important task in Question Answering Systems. This paper presents a Spanish Question Classifier based on machine learning, automatic online translators and different language features. Our system works with English collections and bilingual questions (English/Spanish). We have tested two Spanish-English online translators to identify the lost of precision. We have made experiments using lexical, syntactic and semantic features to test which ones made a better performance. The obtained results show that our system makes good classifications, over a 80% in terms of accuracy using the original English questions and over a 65% using Spanish questions and machine translation systems. Our conclusion about the features is that a lexical, syntactic and semantic features combination obtains the best result.
Exploiting context in kernel-mapping recommender system algorithms

Making e ective recommendations from a domain consisting of millions of ratings is a major research challenge in the application of machine learning. Kernel Mapping Recommender (KMR) algorithms have been proposed providing state-of-the-art performance. In this paper, we show how context information can be added to KMR algorithms. We consider the trusted friends of a user as their social context and show how this information can be used to provide more personalised, refined, and trustworthy recommendations. The limited set of friends; however, restricts the amount of data available to create useful recommendations. This paper sheds light on this issue and specifically on the amount of friends necessary to get satisfactory recommendations.
A MAHALANOBIS DISTANCE BASED CLASSIFIER FOR DIAGNOSIS OF DISEASES

ABSTRACT Multi-dimensional information, including many different biochemical testing items, is conventionally used to characterize medical examination data. However, ambiguous and inconsistent multi-dimensional data are difficult to explore the relationship among them. The conventional means of developing an expert system requires formulating rules to analyze the input data. Formulating such rules is quite difficult with large sets of input data. This study presents a classification approach for diagnosing medical examination information. This is a multivariate analysis approach based on Mahalanobis distance (MD) classifier, creates the MD space in advance using the homogenous examination data. Then, an automatic thresholding technique adopted as a decision criterion in a MD space, is based on the maximum variance between-classes. Comparing the MD classification approach with the neural network approach reveals that the MD classifier is highly effective in diagnosing liver diseases because it has a classification accuracy exceeding 95%. In addition, the MD classifier outperforms neural network classifier by increasing the correct classification up to 5.97% for the testing sets. Finally, some characteristics are discussed.
Signal flow graphs for modelling of switching converters with reduced redundant power processing

This study presents a procedure for modelling of switching converters with reduced redundant power processing ( R 2 P 2 ). This class of converters consists of two basic DC-DC switching converters connected in non-cascading structure. The proposed procedure uses signal flow graphs on converters with R 2 P 2 , according to currents and voltages relationship of each non-cascading structure. In the beginning, the procedure does not require specific knowledge of the basic converters being used, but only the structure of the converter with R 2 P 2 . The corresponding basic converters are incorporated later. Although the procedure is applied to converters with R 2 P 2 , it can also be applied to other configurations that are based on the interconnection of two converters. The resulting models are validated using simulations and experimental results.
Defining a knowledge strategy framework for process aligned organizations: an IBM case

Many organizations struggling to capitalize on their knowledge assets tend to let their knowledge management systems emerge from existing IT systems and infrastructure. Within a complex business environment this can cause a mismatch between how knowledge assets are, and should be managed. In order to help organizations develop dynamic and effective KM systems, the authors' suggest that organizations need to re-think how knowledge is created and shared around their core business processes. To be more specific the author's contend that for organizations where inter/intra organizational collaboration is vital to overall end-to-end performance, such as in a supply chain, organizations need to consider first the relationship between what the authors see as four key components. These are knowledge strategy, core process optimization, core process performance and knowledge barriers. This paper will explain why these components are important, and the relationship between them. The findings put forward in this paper are based on research concerned with improving process performance through knowledge transfer. The research follows a critical theory approach to identify best knowledge transfer practice across complex organizations. The research is exploratory in nature and a case study methodology is used to support this line of inductive theory building. The findings presented are based on data collated within, and across IBM's integrated supply chain. Copyright © 2008 John Wiley & Sons, Ltd.
A Systemic-Constructivist Approach to the Facilitation and Debriefing of Simulations and Games

This article introduces some basic concepts of a systemic-constructivist perspective. These show that gaming simulation corresponds closely to a systemic-constructivist approach to learning and instruction. Some quality aspects of facilitating and debriefing simulation games are described from a systemic-constructivist point of view. Finally, a structure for debriefing, and some concrete examples of systemic-constructivist debriefing-methods are presented.
Learner-Centered Teaching: Five Key Changes to Practice

Preface to the Second Edition vii Acknowledgments xiii The Author xiv PART ONE: FOUNDATIONS OF THE LEARNER-CENTERED APPROACH 1 1. Learner-Centered Teaching: Roots and Origins 3 2. Research: Evidence That Learner-Centered Approaches Work 28 PART TWO: THE FIVE KEY CHANGES TO PRACTICE 57 3. The Role of the Teacher 59 4. The Balance of Power 88 5. The Function of Content 114 6. The Responsibility for Learning 143 7. The Purpose and Processes of Evaluation 168 PART THREE: IMPLEMENTING THE LEARNER-CENTERED APPROACH 197 8. Responding to Resistance 199 9. Taking a Developmental Approach 218 APPENDIXES 239 1. Syllabus and Learning Log Entries for Speech Communications 100A 239 2. Resources That Develop Learning Skills 249 References 259 Name Index 275 Subject Index 279
Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs

The honey-bee mating process is considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is applied to extract the linear monthly operation rules of reservoirs for both irrigation and hydropower purposes. The release rules for each month are considered as a linear function of the reservoir past-month-end storage as well as current monthly inflow to the reservoir. In such a case, the decision variables are 36 for each problem and are set so that water supply deficits are minimized. In both irrigation and hydropower purposes, 60–480 months are considered and results are compared to those from the nonlinear programming solver of the LINGO 8.0 software. The approach and the rules tend to be very promising and denote the capability of the proposed HBMO algorithm in solving reservoir operation problems. Furthermore, the results indicated that, by using the near-optimal solution from the HBMO as a starting point for the NLP solver, the obtained objective function value was enhanced significantly and a better local optimum was found.
The architecture of data collection planning system for environment observing satellite network

With the development of the earth observing satellites science and technologies, environment observing satellites can be considered as a network which can satisfy various observing requests simultaneously. The data collection planning system for the satellites network investigates how to utilize the satellites network cooperatively and optimally, which provides decision support tools to satisfy the various and multilevel observing requests. The data collection planning system, as a bridge linking users and satellite managers, is designed to try to make use of the whole satellites network sufficiently. Currently, most application systems for observing satellites deal with the simple task which only involves a single satellite or a single series satellite. The compound observing task which involves various kinds of satellites is not considered, which results that the limited observing satellites can not be employed optimally.
Using smart cards to authenticate passwords

A password authentication scheme which can verify remote passwords without the use of a verification table is presented. Each user possesses a smart card which is used for login and authentication, as introduced by A. Shamir (1985). During the login, the user types in his identity and the password associated with his smart card, along with the identity of the other user with whom he wishes to communicate. The smartcard uses a signature scheme to check whether or not the login can be accepted. As well as modifying Shamir's algorithm for the signature scheme checking by using a theorem about relatively prime numbers, one also adds communication timestamps to prevent replaying of a previously intercepted login request. <<ETX>>
Subband signal processing adaptive array with a data transmission scheme inserting a cyclic prefix

The advantages of subband signal processing adaptive array (SBAA) are the ability to perform parallel signal processing in each subband division and the substantial reduction in the total computational load. However, the price is not preventing a drop in performance, for example, the incorporation of delayed waves, and a countermeasure must be found. Therefore, in this paper, we propose introducing a scheme that inserts a guard interval (cyclic prefix), which is commonly adopted in Orthogonal Frequency Division Multiplexing (OFDM), into ordinary data transmission, and extracts the best possible performance of SBAA. We also evaluate the performance by a computer simulation. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 86(3): 1–8, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10055
Browser-Based Accessibility Evaluation Tools for Beginners

There are hundreds of Web accessibility software options out in the world that serve many different functions. Not surprisingly, many of them are designed for users with a wide range of abilities, with the intent of making the use of computers and the Internet easier for both work and entertainment. There are, however, numerous products available for developers and librarians that assist in ensuring the computer and Web programs they develop are accessible for users with disabilities
Local studies collections, librarians and the Norwegian local history wiki

Purpose – The purpose of this paper is to discuss the relevance of local studies collections and contributions from local studies librarians to a Norwegian local history wiki.Design/methodology/approach – The paper focuses on access to metadata in the form of bibliographies, databases and catalogues, in addition to general articles on local history. The methodological approach is qualitative and comparative based on semi‐structured interviews with librarians in charge of local studies collections and with the administrators of the wiki. The study includes an analysis of a selection of local studies collections and criteria for inclusion into the local history wiki. A short comparison with history wikis in other countries is added.Findings – Local studies collections contain valuable and unique material for the wiki, especially metadata resources. The expertise of librarians could increase the value of the wiki. Generally, librarians are not active contributors. Strategies for involvement are needed.Origin.
Comparison of Face Recognition Algorithms for Human-Robot Interactions

Face recognition is a cornerstone of many robotic systems in which a robot has to identify and interact with a human being. Choosing a face recognition algorithm arbitrarily may not yield the best results for a researcher and may produce undermined results. In this paper we compare three widely used algorithms in terms of speed and accuracy. Such data can be very useful in choosing an algorithm for a particular task. The algorithms were applied to 36 different situations, and the results indicate the strengths, advantage and limitations of each of the three recognition methods in a certain setting.
Memory management for high-performance applications

Memory managers are a source of performance and robustness problems for application software. Current general-purpose memory managers do not scale on multiprocessors, cause false sharing of heap objects, and systematically leak memory. Even on uniprocessors, the memory manager is often a performance bottleneck. General-purpose memory managers also do not provide the bulk deletion semantics required by many applications, including web servers and compilers. The approaches taken to date to address these and other memory management problems have been largely ad hoc. Programmers often attempt to work around these problems by writing custom memory managers. This approach leads to further difficulties, including data corruption caused when programmers inadvertently free custom-allocated objects to the general-purpose memory manager. In this thesis, we develop a framework for analyzing and designing high-quality memory managers. We develop a memory management infrastructure called heap layers that allows programmers to compose efficient memory managers from reusable and independently testable components. We conduct the first comprehensive examination of custom memory managers and show that most of these achieve slight or no performance improvements over a state-of-the-art general-purpose memory manager. Building on the knowledge gained in this study, that combines the best of both approaches, allowing server applications to manage memory quickly and flexibly while avoiding memory leaks. We identify a number of previously unnoticed problems with concurrent memory management and analyze previous work in the light of these discoveries. We then present a concurrent memory manager called Hoard and prove that it avoids these problems.
Robust face detection based on components and their topology

This paper presents a novel approach for automatic and robust object detection. It utilizes a component-based approach that combines techniques from both statistical and structural pattern recognition domain. While the component detection relies on Haar-like features and an AdaBoost trained classifier cascade, the topology verification is based on graph matching techniques. The system was applied to face detection and the experiments show its outstanding performance in comparison to other face detection approaches. Especially in the presence of partial occlusions, uneven illumination and out-of-plane rotations it yields higher robustness.
A Survey on Computational Linguistics in Design Documents

Design is one of the main activities in industrial manufacture. Researchers in the design field have an increasing number of opportunities to analyse design documents. Some researchers have sought to explore the natural language in these documents, or the design documents. This paper briefly reviews previous research in design document. By describing and analyzing the existing methods, it identifies the gap for the computational linguistics in design documents.
Color segmentation using perceptual attributes

A general approach for achieving color image segmentation using uniform-chromaticity-scale perceptual color attributes is proposed. At first chromatic and achromatic areas in a perceptual IHS color space are defined. Then the image is separated into chromatic and achromatic regions according to the region locations in the color space. 1-D histogram thresholding for each color attribute is performed to split the chromatic and achromatic regions, respectively. Finally the region growing is used to solve the oversegmentation problem. In an experiment the power of the proposed approach is demonstrated. <<ETX>>
Road extraction in SAR images based on fuzzy clustering

In this paper, a road extraction method in SAR images based on fuzzy clustering is proposed. First the fuzzy C means (FCM) algorithm is used to classify the image. And Hough transform is applied in windows to get segment candidates, which increases the robust of the method. Then a fitness function is used to group the segments. The membership values of the connection lines are used to connect the grouped segments. Finally the false road is removed and the main axes of roads are extracted. The results of experiments indicate the availability and the robust of the method.
Prediction of Closing Prices on the Stock Exchange with the Use of Artificial Neural Networks

Abstract Article describes, the use of Artificial Neural Networks (ANN) for predicting values of Stock Exchange shares. Rules of Stock Exchange functioning, principles of technical analysis and the most important stock market indices are described, which support investors, who plan to make transactions. ANN of Multi-Layer Perceptron (MLP) type, and a moving window method are applied. A hybrid method is also proposed, in which time series of CLOSE values as a function of the following trading days are used to stock market indices calculation, such as moving averages and oscillators, which are applied to ANN inputs. Research was conducted for 80 companies, selected from the 1218 companies functioning on Stock Exchange. The achieved maximum error in one day ahead CLOSE value prediction is 1,31%.
Motivational and Achievement Effects of Learner Control over Content Review within CAI

This study investigated the effects of learner and program control over content review within science computer-assisted instruction (CAI). Ninth grade subjects completed learner and program controlled CAI and a unit posttest. Continuing motivation for learner and program control was measured by giving half the subjects a choice of control type for a subsequent session. Results indicated that program control resulted in better posttest performance for males. For females, learner control provided a nonsignificant advantage. This sex difference is discussed relative to a possible differential effort on the part of males and females. Results also reveal significant differences in continuing motivation for learner control, with subjects returning more frequently to learner control than program control in the second session. Strong student preferences for instruction via computers were also noted.
Research on pipeline R2 2 SDF FFT

The resource utilization of butterflies, multipliers, memory size, and control logic was analyzed according to several pipeline FFT processors. Radix-2 2 Single-path Delay Feedback (R2 2 SDF) was proposed due to the limit of hardware resource and real-time in ASIC design; gave the scheme, pipeline architecture, flow of BFI and BFII; did Signal to Quantization Noise Ratio (SQNR) simulation for various bit-widths, round or cut off deal per stage, different input/output word lengths; implemented in Xilinx series FPGA V4SX55 with VHDL, did pulse compression in one radar project to verify R2 2 SDF algorithm; R2 2 SDF FFT need the least resource, has high real-time performance, is suitable for VLSI implementation.
Supporting Reusability in Fixed And Mobile Groupware Applications

Abstract In this paper we present COCHI, a pattern system that supports reusability in the development of groupware applications for fixed and mobile computers at different levels of abstraction: architecture, design and implementation. COCHI has two properties: ease of use and flexibility. In contrast with other groupware tools, COCHI maintains equilibrium between both goals. COCHI provides a class framework that allows a developer to implement groupware applications that can be executed on fixed and mobile computers, reducing the implementation effort. COCHI also provides a set of architectural and design patterns that add flexibility to the platform and permits its extension with new features not originally present. COCHI is particularly useful for research in the CSCW (Computer Supported Cooperative Works) field. We show how the flexibility property of COCHI was used to extend the architectural and design patterns to support features like Quality of Service (QoS), Emotional Awareness, and mobility.
PERFORMANCE ANALYSIS OF IMAGE SECURITY BASED ON ENCRYPTED HYBRID COMPRESSION

In this research, we propose an image security scheme using hybrid compression techniques. In this scheme, the data is being provided two-fold security by both encryption stage and hiding stage. The data/message which has to be secured undergoes encryption technique at the initial stage. In this stage, the permutation algorithm is employed which requires a pair of numbers as a key to permute the original message. Following the encryption stage, the deformed message is then embedded onto a JPEG image by considering the low and high quantization tables. The main motivation behind this research work is to provide image security through compression. The final result is an encrypted and compressed JPEG image with a different image quality. The receiver has to perform the reverse process to extract the original data/information. The performance analysis is performed in terms of PSNR for different quantization tables.
Towards classifying full-text using recurrent neural networks

This paper describes an automatic document classification system called NeuroClass, developed for the air transportation field of Transport Canada. The properties of the system show that for the specific domain for which NeuroClass was developed, recurrent neural networks as developed by Elman (1990) can be used to build systems that classify natural language full-text automatically and reliably with a degree of accuracy proportional to the level of class adherence of the text involved.