2,851 research outputs found
The Development Impact of Information Technology in Trade Facilitation
Full text linkThe main purpose of this chapter is to provide an overview and context of the country studies on Information Technology (IT) for Trade Facilitation (TF) in Small and Medium Enterprises (SMEs).Impact of Information Techonology, Trade Facilitation, SMEs
Evaluation of ITER TF Coil Joint Performance
Full text linkTo evaluate the ITER TF joint performance, the joint test sample, which consists of two short TF conductors and has full size joint, shall be tested using NIFS test facility under the condition of current of 68 kA and external field of 2 T. For high accuracy, the issue of voltage difference between cable and jacket had been anticipated in the evaluation of joint resistance. If a voltage difference exist between them, it is difficult to measure real joint resistance using voltage taps on the jacket. Therefore, the author first calculated the position where voltage of cable and jacket become equipotential and then decided the voltage tap position where the influence of voltage drop could be avoided. Thus, a high accuracy measurement of joint resistance could be achieved and the joint resistance was accurately evaluated as around 1 n Ω , which is well below the ITER requirement of 3 n Ω .journal articl
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
No full textTerm frequency-inverse document frequency, or TF-IDF for short, and its many variants form a class of term weighting functions the members of which are widely used in text analysis applications. While TF-IDF was originally proposed as a heuristic, theoretical justifications grounded in information theory, probability, and the divergence from randomness paradigm have been advanced. In this work, we present an empirical study showing that TF-IDF corresponds very nearly with the hypergeometric test of statistical significance on selected real-data document retrieval, summarization, and classification tasks. These findings suggest that a fundamental mathematical connection between TF-IDF and the negative logarithm of the hypergeometric test P-value (i.e., a hypergeometric distribution tail probability) remains to be elucidated. We advance the empirical analyses herein as a first step toward explaining the long-standing effectiveness of TF-IDF from a statistical significance testing lens. It is our aspiration that these results will open the door to the systematic evaluation of significance testing derived term weighting functions in text analysis applications
Who said that? Comparing performance of TF-IDF and fastText to identify authorship of short sentences
No full textAuthorship identification is often applied to large documents, but less so to short, everyday sentences. The ability of identifying who said a short line could provide help to chatbots or personal assistants. This research compares performance of TF-IDF and fastText when identifying authorship of short sentences, by applying these feature extraction techniques to the television series Friends' transcripts. TF-IDF outperforms fastText in every measurement, but its performance is only marginally better than randomly guessing the original character, reaching an accuracy of 28 percent when making a distinction between 6 characters. Accuracy increases linearly at the same rate for both techniques as the minimum word count per sentence set on the test data increases. TF-IDF's confidence remains constant as this limit is set on either the test or training data, whereas fastText's confidence decreases and increases, respectively. Cross-entropy loss, however, remains constant for fastText and decreases for TF-IDF as the minimum word count set on the test data increases.CSE3000 Research ProjectComputer Science and Engineerin
An ensemble-based model for two-class imbalanced financial problem
No full text[[abstract]]This study proposes an ensemble-based model (EBM) for the two-class imbalanced classification problem by joining together the support vector machine (SVM), multiple feature selection combination, back-propagation neural network (BPNN) ensemble, and rough set theory (RST). To improve the significance of the rare and specific region belonging to the minority class in the decision region, we take the SVM as a pre-processor to balance the training dataset and use multiple feature selection combination grounded on ensemble learning in order to determine the most representative features from the re-sized dataset The representative features are then fed into the BPNN ensemble to construct an effective financial pre-warning mechanism. Lacking comprehensibility and readability is one of the fatal weaknesses of an ensemble classifier and it impedes its real-life application. Thus, the study executes RST to extract knowledge from the BPNN ensemble for decision makers to make suitable judgments. Decision makers can take the decision rules as a roadrnap to modify a firm's capital structure so as to survive in an extremely turbulent financial market. Empirical results reveal that the introduced EBM's prediction accuracy is very promising in financial risk mining, relative to other detection approaches in this study. (C) 2013 Elsevier B.V. All rights reserved.[[note]]SSC
Evaluation of ITER TF Coil Joint Performance
No full textTo evaluate the ITER TF joint performance, the joint test sample, which consists of two short TF conductors and has full size joint, shall be tested using NIFS test facility under the condition of current of 68 kA and external field of 2 T. For high accuracy, the issue of voltage difference between cable and jacket had been anticipated in the evaluation of joint resistance. If a voltage difference exist between them, it is difficult to measure real joint resistance using voltage taps on the jacket. Therefore, the author first calculated the position where voltage of cable and jacket become equipotential and then decided the voltage tap position where the influence of voltage drop could be avoided. Thus, a high accuracy measurement of joint resistance could be achieved and the joint resistance was accurately evaluated as around 1 n Ω , which is well below the ITER requirement of 3 n Ω
Extracting location context from transcripts: a comparison of ELMo and TF-IDF
No full textUsing transcripts of the TV-series FRIENDS, this paper explores the problem of predicting the location in which a sentence was said. The research focuses on using feature extraction on the sentences, and training a logistic regression model on those features. Specifically looking at the differences in performance between using ELMo and TF-IDF for this feature extraction, achieving an accuracy rate of 58\% and 67\% respectively on a binary classification. The paper also explores the effect of several data cleaning techniques on the results. Git repository containing the source code used in the paper - https://github.com/David-Happel/scene-location-NLPCSE3000 Research ProjectComputer Science and Engineerin
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