Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding constraints for semantic relations via clustering

Automatic recognition of semantic relations constitutes an important part of information extraction. Many existing information extraction systems rely on syntactic information found in a sentence to accomplish this task. In this paper, we look into relation arguments and claim that some semantic relations can be described by constraints imposed on them. This information would provide more insight on the nature of semantic relations and could be further combined with the evidence found in a sentence to arrive at actual extractions.
Identifying physiologically significant pumping states in implantable rotary blood pumps using non-invasive system observers

The VentraCor implantable rotary blood pump (IRBP) is a centrifugal pump that uses a hydrodynamic bearing to support its impeller. The speed of the pump may be controlled externally. However, under- or over-pumping can result in severe complications to the patient, so a means to control pump flow within a predefined range is clinically important. In vitro (mock loop) and ex vivo experiments in the sheep (n=3) were conducted. By varying pump speed in the mock loop, five physiologically significant states were identified: regurgitant pump flow, ventricular ejection, full aortic valve closure over the cardiac cycle, intermittent collapse of the ventricle wall, and total collapse of the ventricle. The hypothesis is that by using only the non-invasive measure of instantaneous pump impeller speed to assess flow dynamics and an average flow estimate developed from speed and input power, it is possible to detect the afore-mentioned states. Using a classification and regression tree derived in the mock loop, it was possible to detect pump states with 100% accuracy (0% false-positives and 0% false-negatives) in both the mock loop (under the same and different loading conditions) and in the ex vivo experiments (under pharmacological and other interventions).
Tracking influential haze source areas in North China using an adjoint model, GRAPES-CUACE

10 Based upon the adjoint theory, the adjoint of the aerosol module in the atmospheric chemical 11 modelling system GRAPES-CUACE (Global/Regional Assimilation and PrEdiction System 12 coupled with the CMA Unified Atmospheric Chemistry Environment) was developed and 13 tested for its correctness. Through statistic comparison, BC (black carbon aerosol) 14 concentrations simulated by GRAPES-CUACE were generally consistent with observations 15 from Nanjiao (one urban observation station) and Shangdianzi (one rural observation station) 16 stations. To track the most influential emission-sources regions and the most influential time 17 intervals for the high BC concentration during the simulation period, the adjoint model was 18 adopted to simulate the sensitivity of average BC concentration over Beijing at the highest 19 concentration time point (referred to as the Objective Function) with respect to BC emission 20 amount over Beijing-Tianjin-Hebei region. Four types of regions were selected based on 21 administrative division and sensitivity coefficient distribution. The adjoint model was used to 22 quantify the effects of emission-sources reduction in different time intervals over different 23 regions by one independent simulation. Effects of different emission reduction strategies 24 based on adjoint sensitivity information show that the more influential regions (regions with 25
Development of islanding early warning mechanism for power systems

This paper discusses a computer algorithm which can be used to detect the conditions that will separate the power system into two or more islands. In order to provide an early warning mechanism for the dispatchers in the control center such that some actions can be taken before an islanding condition occurs, the proposed algorithm converts the power system topology into an undirected graph first. Based on the graph theory, the algorithm will indicate that outages occur on one or more of the transmission lines will lead to an islanding situation. The Tai-Power Company 345 kV and 161 kV systems will be used to validate the algorithm and evaluate the performance.
Bayesian and information-theoretic tools for neuroscience

The overarching purpose of the studies presented in this report is the exploration of the uses of information theory and Bayesian inference applied to neural codes. Two approaches were taken: Starting from first principles, a coding mechanism is proposed, the results are compared to a biological neural code. Secondly, tools from information theory are used to measure the information contained in a biological neural code. Chapter 3: The REC model proposed by Harpur and Prager [33] codes inputs into a sparse, factorial representation, maintaining reconstruction accuracy. Here I propose a modification of the REC model to determine the optimal network dimensionality. The resulting code for unfiltered natural images is accurate, highly sparse and a large fraction of the code elements show localized features. Furthermore, I propose an activation algorithm for the network that is faster and more accurate than a gradient descent based activation method. Moreover, it is demonstrated that asymmetric noise promotes sparseness. Chapter 4: A fast, exact alternative to Bayesian classification is introduced. Computational time is quadratic in both the number of observed data points and the number of degrees of freedom of the underlying model. As an example application, responses of single neurons from high-level visual cortex (area STSa) to rapid sequences of complex visual stimuli are analyzed. Chapter 5: I present an exact Bayesian treatment of a simple, yet sufficiently general probability distribution model. The model complexity, exact values of the expectations of entropies and their variances can be computed with polynomial effort given the data. The expectation of the mutual information becomes thus available, too, and a strict upper bound on its variance. The resulting algorithm is first tested on artificial data. To that end, an information theoretic similarity measure is derived. Second, the algorithm is demonstrated to be useful in neuroscience by studying the information content of the neural responses analyzed in the previous chapter. It is shown that the information throughput of STS neurons is maximized for stimulus durations ≈ 60ms.
A Study of Organizational Identification of Faculty Members in Hong Kong Business Schools

The authors examine how four organizational antecedents affect the organizational identification (OI) and in-role and extra-role performance of Hong Kong business school faculty. OI was tested to be a mediator. The survey results indicated a high level of OI, consistent with the collectivist cultural value of Chinese employees. However, OI was positively associated with two antecedents only. And contrary to the existing literature that OI only affects extra-role behavior, OI was positively associated with both consequences. The data did not support OI as a mediator. Further research in the Chinese context and extension of studies with additional variables are recommended.
Stability: A Criterion for Software Evolution Enhancement

Diierent surveys points to user request evolution as the most important cause for software maintenance. Such process is seen as a main challenge in software practice, and represents a substantial cost for many organization. This paper proposes the introduction of the concept of stability as a main analysis and design criterion. This criterion aims to identify those aspects of the real world likely to evolve, and to explicitly separate between stable and volatile user requirements. In so doing, volatile requirements can be changed without impacting unnecessarily on the underlying domain, thus easing requirement modi-cations and nally, enhancing software evolution.
Post-maintenance testing based on path change analysis

A post-maintenance testing strategy based on path-change analysis is presented. This analysis is based on an underlying algebraic representation of the design and/or code of the program to be tested, and is characterized by the ease with which the modified, cancelled, and new paths produced by maintenance operations are identified by elementary algebraic operations. The methodologies and techniques illustrated, and the tools supporting them, are part of the post-maintenance testing subsystem of an integrated maintenance environment. <<ETX>>
Information Visualization Of An Agent-Based Financial System Model

This paper considers the application of information visualization techniques to an agent-based model of a financial system. The minority game is a simple agent-based model which can be used to simulate the events in a real-world financial market. To aid understanding of this model, we can apply information visualization techniques. Treemap and sunburst are two such information visualization techniques, which previous research tells us can effectively represent information similar to that generated by the minority game. Another information visualization technique, called logical fisheye-lens, can be used to augment treemap and sunburst, allowing users to magnify areas of interest in these visualizations. In this paper, treemap and sunburst, both with and without fisheye-lens, are applied to the minority game, and their effectiveness is evaluated. This evaluation is carried out through an analysis of users performing various tasks on (simulated) financial market data using the visualization techniques. A subjective questionnaire is also used to measure the usersâ€™ impressions of the visualization techniques.
Efficient XML Interchange (EXI) compression and performance benefits : development, implementation and evaluation

Abstract : The Department of Defense (DoD) Network-Centric data sharing strategy for the Global Information Grid (GIG) is to XMLize all data. The goal of this strategy is to ensure all data is visible, usable and interoperable, when and where needed, to accelerate decision cycles. However, this XML-based data approach comes at the cost of limiting real-time network edge device connectivity to the GIG because they are seldom able to meet the necessary bandwidth and processing requirements due to XML's intrinsic nature of being verbose and often complex to process. This research explores a powerful and robust solution to XML's network depth limits by means of the World Wide Web Consortium's (W3C) proposed alternative XML format, Efficient XML Interchange (EXI). The EXI format removes redundant tags and values from XML documents and encodes numeric content in a binary format. This format delivers significant file size savings and processing efficiencies compared to existing practices. The evolution of XML's path to EXI is summarized based on the results of the XML Binary Characterization (XBC) working group and the W3C's design points of XML. Followed are recommended steps for EXI development and enterprise integration, focusing on a public open source licensing philosophy. EXI algorithms are described with detailed explanations, Java code samples, and part-task test XML documents. Experiments are conducted evaluating the effectiveness of EXI for DoD tactical use and is followed with a recommended optimal EXI configuration. Several predictive models of EXI's performance are presented to enable potential EXI adopters a measurement tool of expected EXI benefit for various XML domains. This research concludes that for XML-based data, a doubling of bandwidth potential is achievable and CPU burdens minimized when EXI is applied.
Vision-based lane recognition under adverse weather conditions using optical flow

Lane recognition is the basis for many driver assistance systems, including lane departure warning (LDW), the assignment of vehicles to specific lanes and fully autonomous driving. A major problem of common vision-based lane recognition systems is their susceptibility to weather. Especially when driving in adverse weather conditions such as rain or snow it is difficult to estimate the road course. The contrast between the white lane markings and the pavement is poor, sometimes the colors of the markings are negated. Furthermore the range of sight is reduced enormously causing a bad prediction of the lane parameters, particularly the curvature. We present a solution which relies not only on finding white markings. Correlating structures parallel to the road over time the horizontal optical flow is calculated. It is then integrated in the lane recognition system estimating the position of the vehicle within the lane and the curvature parameters of the road ahead. The system allows to assign obstacles detected by radar or vision reliably to specific lanes and to drive laterally controlled autonomously even under adverse weather conditions.
Moving object velocity estimation by appropriately choosing spatial frequencies in the MixeD domain

To estimate moving objects velocity in an image sequence is useful for a variety of applications, such as computer vision and monitoring systems. An effective way is to approach it in the transform/spatiotemporal mixed domain (MixeD), but it remains a problem how to select several 2-D spatial frequency points in MixeD domain which may influence the accuracy of velocity estimation and object detection. We propose a method to approach the velocity estimation problem by automatically selecting only two appropriate points in 2-D spatial frequency domain, the selection is achieved by using the established evaluation function together with a threshold. The simulation results show that the proposed method is effective on the selection of spatial frequency points.
Challenges in Human/Computer Interfaces: Making the Technology Serve the User

In this position statement, challenges in three areas of information and data interface devel­opment are considered. These are: interfaces to the technology that are used to deliver commu­nication primitives; interfaces to the presentation of a single information projection on a particu­lar system; and interfaces to the abstract informa­tion containing the ultimate message that the technological messenger is trying to present. Since the nature of this statement makes the presentation cursory, I will focus on the needs of the user rather than the potential of the infrastructure.
Runtime translation of X interfaces to support visually-impaired users

This paper describes work to provide mappings between X-based graphical user interfaces and auditory interfaces transparently to applications. The primary motivation for this work is to provide accessibility to graphical applications for users who are blind. We describe our architecture for capturing, modeling, and translating X-based interfaces. We conclude with some ideas for future work, a description of some aspects of the X Window System which caused difficulties in the design and implementation of our system, and some indications of possible solutions to the problems we encountered.
Word recognition with recurrent network automata

The authors report a method to directly encode temporal information into a neural network by explicitly modeling that information with a left-to-right automaton, and teaching a recurrent network to identify the automaton states. The state length and position are adjusted with the usual train and re-segment iterative procedure. The global model is a hybrid of a recurrent neural network which implements the state transition models, and dynamic programming, which finds the best state sequence. The advantages achieved by using recurrent networks are outlined by applying the method to a speaker-independent digit recognition task. <<ETX>>
The Envisat-1 Advanced Synthetic Aperture Radar instrument

The Advanced Synthetic Aperture Radar (ASAR) selected for the ENVISAT-1 mission of the European Space Agency, is a sensor of paramount importance for the remote sensing community due to its enhanced flexibility with respect to the ERS AMI. The combination of different spatial resolutions, incidence angles, swath width sizes and polarisation diversity will reflect in an increased number of operational modes tailored for a large set of scientific and operational applications. The use of an active antenna concept, a digital generation waveform technique and a block adaptive quantization compression scheme are among the innovative design features of this radar. In this paper, the status of the ASAR instrument development is described, the accomplished project milestones will he highlighted together with preliminary instrument performance.
Channel estimation algorithm based on chaotic signals and artificial neural networks

Two new chaotic based coding and decoding methods are introduced and discussed. Channel estimation is evaluated when chaotic coded sequences are employed. A speech transmission scenario using chaotic coded speech is considered and channel estimation is carried out. The results show that a modelling misadjustment improvement of 24 dB/100 iterations is achievable. This represents a 6 fold improvement compared to LMS adaptation for a 128 tap digital adaptive filter using a white noise process.
Implementation Of Mmse Equalizer Using Variants Of Lms

Channel equalization is one of the applications of adaptive filtering. This paper includes an efficient approach to compare the MMSE equalizers with variants of LMS algorithm for the removal of noise in the corrupted signals. The main aim of designing the transmitter and receiver having a channel in communication systems is to minimize the corruption of the input signals. Here equalized output of the model is compared for LMS algorithm, NLMS, Complex NLMS and NLMSSIGN algorithm. It has been observed the better BER vs. SNR plot considerably.
Users guide for ROMIO: A high-performance, portable MPI-IO implementation

ROMIO is a high-performance, portable implementation of MPI-IO (the I/O chapter in MPI-2). This document describes how to install and use ROMIO version 1.0. 0 on the following machines: IBM SP; Intel Paragon; HP/Convex Exemplar; SGI Origin 2000, Challenge, and Power Challenge; and networks of workstations (Sun4, Solaris, IBM, DEC, SGI, HP, FreeBSD, and Linux).
Native language identification

The main objective of this chapter is to examine the automatic techniques that can be applied to learner corpora to identify learners’ mother tongue backgrounds from their patterns of production in the target language. The chapter focuses particularly on aspects of learner corpus design that have a direct bearing on the results (size, topic homogeneity, type and level of annotation). Special attention is also paid to the contribution of NLI research to SLA, more particularly in the form of the detection-based approach to transfer. NLI is a relatively new line of enquiry: research so far has focused on identifying the native language of writers using English as a foreign language and the chapter will thus deal with English as a target language.
3D from compressed 2D video

In this paper, we present an efficient method to synthesize 3D video from compressed 2D video. The 2D video is analyzed by computing frame-by-frame motion maps. For this computation, MPEG motion vectors extraction was performed. Using the extracted motion vector maps, the video undergoes analysis and the frames are segmented to provide object-wise depth ordering. The frames are then used to synthesize stereo pairs. This is performed by resampling the video frames on a grid that is governed by a corresponding depth-map. In order to improve the quality of the synthetic video, as well as to enable 2D viewing where 3D visualization is not possible, several techniques for image enhancement are used. In our test case, anaglyph projection was selected as the 3D visualization method, as the method is mostly suited to standard displays. The drawback of this method is ghosting artifacts. In our implementation we minimize these unwanted artifacts by modifying the computed depth-maps using non-linear transformations. Defocusing of one anaglyph color component was also used to counter such artifacts. Our results show that the suggested methods enable synthesis of high quality 3D videos in real-time.
Exploring ESL Learners' Use of Hypermedia Reading Glosses

This study explores the types of annotations intermediate and advanced ESL learners preferred to use while they were engaged in reading a hypermedia text. The study also investigated learners' attitudes towards reading in a hypermedia environment. The participants were 84 ESL adult learners enrolled at the Center for English as a Second Language at the University of Arizona. Data were collected through a tracking tool, a reading comprehension test, a questionnaire, and interviews. Quantitative data indicated that the intermediate ESL learners accessed annotations more frequently than those in the advanced group. However, they did not differ in the amount of time they spent on annotations. On the other hand, the advanced learners performed better on the reading comprehension test. Both groups of learners preferred word definitions to pronunciations of words and graphics in order to get information about the text at the word level. However, they preferred videos and graphics to get extra information about the topic. Analysis of the qualitative data revealed that hypermedia reading had a positive impact on the participants' attitudes towards reading on the computer. The participants indicated that the hypermedia environment made reading more enjoyable and comprehensible.
VISUALIZATION AND ANALYSIS OF LARGE DATASETS BY BEANPLOT PCA

Advances in computer technology have made large data ubiquitous and have determined the need to handles these data accordingly. In particular these data need to be aggregated using some function, but this process can lead to a loss of information. Beanplot series in this context can represent a solution in terms of special symbolic data: in fact the parameters of the density models based on a mixture of distribution can represent the original data accordingly and contribute to solve the problem of data storage. In this work we propose an approach to beanplot data analysis by PCA on the parameters of the models. The aim is building synthesis of multiple beanplot time series as indicators which can have relevant applications in Finance, in Risk Management and in other disciplines.
Concept Extraction on Quranic Translation Text

The Semantic knowledge that based on ontology learning technology introduced is to reduce the overall time of ontology construction. Ontology construction process includes several aspects and layers, and extraction domain concept is one of the most important aspects. This step becomes a prerequisite process in developing ontology and also becomes a seed to the next step. In this paper, we carried out several experiments based on linguistics, statistical and hybrid approaches in order to identify which are the best techniques and approaches to extract terms from Quranic translation text. For linguistic approach, we used POS pattern, for statistical approaches, we choose seven frequency-based as the techniques to choose frequency terms and for hybrid, we combined both linguistic and statistical approaches. The results obtained show that the hybrid approach is the best in identifying and filtering relevant concepts in Quranic domain corpus.
Web Scraping with Python: Collecting Data from the Modern Web

Learn web scraping and crawling techniques to access unlimited data from any web source in any format. With this practical guide, you'll learn how to use Python scripts and web APIs to gather and process data from thousands-or even millions-of web pages at once. Ideal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. Code samples are available to help you understand the concepts in practice.