Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Toponym Resolution in Chinese Text

This paper explores approaches for Toponym resolution in Chinese text,and proposes a geo-parsing approach based on conditional random fields and discourse toponym relations,and a geo-coding approach based on partial fuzzy matching and cognitive salience calculation.The proposed geo-parsing approach deals with the recognition of toponym in three major steps.The experiment shows that the key factors that may influence the performance of toponym resolution in Chinese text are the coverage of gazetteer,the performance of geo-parsing and the performance of semantic disambiguation of toponyms.In our experiment,there are about 17% toponyms can not locate their semantics in the gazetteer.Ambiguity in geo-parsing and geo-coding are the next prominent factors that affect the overall performance of toponym resolution.
Image Compression Of JPEG2000 In Low Image Bitrate Case

Briefly introduce the feature and the structure of JPEG2000, as well as DWT and Embedded Block Coding which are the most important parts of JPEG2000 codec. Analyze the difference between DWT and DCT, and the advantage of DWT. Introduce the core algorithm of JPEG2000——EBCOT in detail. Introduce the application of JPEG2000 in low bitrate case. Give a comparison of JPEG2000 and JPEG in low image bitrate case.
Automatic detection of lameness in dairy cattle - Image features related to dairy cow lameness

Claudia Bahr, Toon Leroy, Xiangyu Song, Arno Pluk, Willem Maertens, Juergen Vangeyte, Annelies Van Nuffel, Bart Sonck, and Daniel Berckmans Division of Measure, Model & Manage Bioresponses (M3-BIORES), Katholieke Universiteit Leuven, Leuven, Belgium, e-mail, claudia.bahr@biw.kuleuven. be Institute for Agricultural and Fisheries Research Technology & Food Unit Agricultural Engineering, Merelbeke, Belgium
Homocysteine acts via nitric oxide synthase to sensitize the crayfish neuromuscular junction to hydrogen-peroxide-induced oxidative damage

Oxidative stress may be a significant source of damage in many neurodegenerative disorders, including amyotrophic lateral sclerosis. Reactive oxygen species such as hydrogen peroxide cause oxidative stress. We hypothesized that: (1) hydrogen-peroxide-induced oxidative damage would decrease excitatory junction potential amplitude at the crayfish neuromuscular junction, (2) incubation in homocysteine would enhance the amplitude decrease caused by hydrogen peroxide, and (3) inhibiting nitric oxide synthase would negate the effect of homocysteine. We used intracellular recording to measure excitatory junction potential amplitudes in each of these conditions before and after the neuromuscular junction was exposed to hydrogen peroxide for twenty minutes. Our findings support these hypotheses, suggesting that homocysteine sensitizes neurons to oxidative damage via stimulation of nitric oxide synthase.
Unfolding Methods in Particle Physics

Measured distributions in particle physics are distorted by the finite resolution and limited acceptance of the detectors. The transformation to the underlying true distribution is called unfolding in particle physics and belongs to the class of linear inverse problems. 1 Inverse problems 1.1 Direct and inverse processes The distributions f(t) of a physics variable t to be measured in particle physics experiments are often not directly accessible. Because of limited acceptance and finite resolution the distribution g(s) of the measured variable s is related to the distribution f(t) by migration, distortions and transformations. Using Monte Carlo (MC) methods the direct process from an assumption f(t)model on the true distribution f(t) to the expected measured distribution g(s) can be simulated. The inverse process from the actually measured distribution g(s) to the related true distribution f(t) is difficult and ill-posed: small changes in the measured distribution can cause large changes in the reconstructed true distribution, if naive methods are used. In particle physics the inverse process is usually called unfolding. The direct and the inverse process direct process (MC) true/MC dist. f(t) =⇒ g(s) measured dist. inverse process (unfolding) measured dist. g(s) =⇒ f(t) true dist. are described by the Fredholm integral equation of the first kind ∫ Ω K(s, t) f(t) dt = g(s) (1) with a Kernel function K(s, t) describing the physical measurement process (Refs. [1]– [4] and references therein). In particle physics the Kernel function K(s, t) is usually implicitly known from a Monte Carlo sample based on an assumption f(t)model. 1.2 Discretization and linear solution The inverse problem given by the Fredholm integral equation has to be discretized in order to allow a numerical solution, with the result of the linear equation
Speech recognition tested at fixed, positive signal-to-noise ratios using time compression: Methods and applications

In daily communication the signal-to-noise ratio (SNR) of speech and background noise is usually positive. To evaluate the performance of hearing aids in such situations, a speech-in-noise test was introduced that presents time-compressed speech at fixed positive SNRs. Based on the Oldenburg sentence test, speech recognition thresholds were determined as a function of SNR and of the speech rate. A uniform and a non-uniform time compression algorithm were tested. Subjective and objective analysis indicated advantages of the uniform algorithm. Learning effects of the time-compressed and the original speech test were compared. A test procedure using adaptive speech rate was optimized for effective recognition measurements. Evaluating two single-microphone noise reduction algorithms showed the potential of the method for discriminating across these hearing aid algorithms at positive SNRs. The method can extend tests of speech in noise and of hearing aid benefit to a range of positive SNRs.
Method for Detecting a Malicious Domain by Using WHOIS and DNS Features

Damages caused by targeted attacks are a serious problem. It is not enough to prevent only the initial infections, because techniques for targeted attacks have become more sophisticated every year, especially those seeking to illegally acquire confidential information. In a targeted attack, various communications are performed between the command and control server (C&C server) and the local area network (LAN), including the terminal infected with malware. Therefore, it is possible to find the infected terminal in the LAN by monitoring the communications with the C&C server. In this study, we propose a method for identifying the C&C server by using supervised machine learning and the feature points obtained from WHOIS and the DNS of domains of C&C servers and normal domains. Moreover, we conduct an experiment that applies real data, and we verify the usefulness of our method by a cross-validation method. As a result of the experiment, we could obtain a high detection rate of about 98.5%.
A Survey on Pilot Symbol Assisted Channel Estimation in OFDM Systems

:- On account of frequency and time selective nature of fading channels, channel estimation is needed in Orthogonal Frequency Division Multiplexing (OFDM) systems and is investigated using pilot arrangement. Channel estimation based on comb type pilot arrangement is taken into account using different algorithms for estimating channel at pilot frequencies and interpolating the channel. Least Square(LS) and Minimum mean square(MMSE) error based estimators are used for estimating the channel at pilot frequencies and channel interpolation is done using linear interpolation, second order interpolation, spline cubic interpolation. Furthermore, comparison of various schemes is performed by measuring bit error rate against signal to noise ratio (SNR) with 16 QAM, QPSK as modulation techniques and multipath Rayleigh fading channel as channel model.
Analysis of AR Model and Neural Network for Forecasting Stock Prices

Three types of price forecasting models based on AR model、RBF and GRNN neural network are developed by using MATLAB software,to predict the opening price,the highest price,low and closing price of the finaly 5 days by rolling prediction and compare them with the actual price to analyze error.As a result,the three kinds of models execute close prediction,error is small.AR model is instable,the more potential for individual prediction; both RBF and GRNN training speed are fast,but the prediction of GRNN network is better than RBF network.
Speeding up a memetic algorithm for the max-bisection problem

Max-bisection of a weighted graph $G = (V, E)$ is to partition the vertex set $V$ into two subsets that have equal cardinality, such that the sum of the weights of the edges connecting vertices in different subsets is maximized. It is an NP-complete problem. Various heuristics have been proposed for solving the max-bisection problem and some of them get very good quality solutions, but are time consuming. In this paper, we propose several techniques to speeding up Lin and Zhu's memetic algorithm for this problem. It consists of a crossover operator with a greedy strategy, a fast modified Kernighan-Lin local search, and a new population updating function. Experiments are performed on 71 well-known G-set benchmark instances. Results show that the memetic algorithm is much faster than Wu and Hao's memetic algorithm, which can get best solutions up to now, on middle-scale and large-scale instances. Specifically, some current best known solutions of the test instances are improved.
An Evaluation of Feature Selection Methods for Text Categorization

This paper evaluates five feature selection methods for text categorization. We study the following feature selection methods: Document Frequency (DF), Mutual Information (MI); Information Gains (IG); statistics; Term Strength (TS). We use naive Bayes as text classifier and conduct the experiments on a Chinese text corpus. The experimental results show that DF and x2 are top performers in this evaluation. In contrast, IG and MI provide a lower performance. In specially, MI and IG perform worse when the feature size is small. When the feature size is 1000, MI yields 64.60% F,, IG is 69.36% and DF provides 87.01%.
Computational Tools to Aid the Design and Development of a Genetic Reference Population

CATHERINE E. WELSH. COMPUTATIONAL TOOLS TO AID THE DESIGN AND DEVELOPMENT OF A GENETIC REFERENCE POPULATION. (Under the direction of Leonard McMillan.) Model organisms are important tools used in biological and medical research. A key component of a genetics model organism is a known and reproducible genome. In the early 1900s, geneticists developed methods for fixing genomes by inbreeding. First generation genetic models used inbreeding to create disease models from animals with spontaneous or stimulated mutations. Recently, geneticists have begun to develop a second generation of models which better represent the human population in terms of diversity. One such model is the Collaborative Cross (CC), which is a mouse model derived from 8 founders. I have been involved in developing the CC since its early stages. In particular, I am interested in speeding up the inbreeding process, since it currently takes an average of thirty-six generations to achieve complete fixation. To speed up the inbreeding process, I developed a simulator that replicates the breeding process and tested various breeding strategies before applying them to a CC. To apply the simulation techniques to live mice, a fast, low-cost way to monitor their genomes at each generation was needed. As a result, two genotyping arrays were designed, a first generation array with 7,851 markers called MUGA and a second generation array called MegaMUGA with 77,800 markers. Both arrays were designed specifically to be maximally informative for the CC population. Using these genotyping arrays, one can determine from which of the eight CC founders each part of a developing mouse lines genome is inherited. I refer to these as haplotype reconstructions, and they are used as the input into my simulations as well as various other monitoring tools. To determine the accuracy of these haplotype reconstructions, I used DNA sequencing data for three samples which were also genotyped, and compared the haplotype reconstructions from the DNA
OPINION MINING IN PERSIAN LANGUAGE USING SUPERVISED ALGORITHMS

Rapid growth of Internet results in large amount of user-generated contents in social media, forums, blogs, and etc. Automatic analysis of this content is needed to extract valuable information from these contents. Opinion mining is a process of analyzing opinions, sentiments and emotions to recognize people’s preferences about different subjects. One of the main tasks of opinion mining is classifying a text document into positive or negative classes. Most of the researches in this field applied opinion mining for English language. Although Persian language is spoken in different countries, but there are few studies for opinion mining in Persian language. In this article, a comprehensive study of opinion mining for Persian language is conducted to examine performance of opinion mining in different conditions. First we create a Persian SentiWordNet using Persian WordNet. Then this lexicon is used to weight features. Results of applying three machine learning algorithms Support vector machine (SVM), naive Bayes (NB) and logistic regression are compared before and after weighting by lexicon. Experiments show support vector machine and logistic regression achieve better results in most cases and applying SO (semantic orientation) improves the accuracy of logistic regression. Increasing number of instances and using unbalanced dataset has a positive effect on the performance of opinion mining. Generally this research provides better results comparing to other researches in opinion mining of Persian language.
Summary of Text Automatic Proofreading Technology

The emerging background for text automatic proofreading technology is generally stated in this article.Each characteristic of the text of Chinese and Western as well as the similarities and differences between them have been analyzed,the review and comment for domestic and international research present status as well as the technical difficulty and solving methods of the text proofreading of Chinese and Western are given,and the future developing directions and the problems need to be solved of text automatic proofreading technology are discussed.
Synthesis and Catalytic Activity of Cryptomelane-Type Manganese Dioxide Nanomaterials Produced by a Novel Solvent-Free Method

Cryptomelane-type K-OMS-2 nanomaterials with high surface area (156 m2/g) have been synthesized via a low-temperature solvent-free method in a very short time (1 h). Field emission scanning electron microscopy and high-resolution transmission electron microscopy images reveal that these materials have nanorod morphologies with average diameters of about 10 nm and lengths of about 50 nm. These are different from the long fiberous morphologies of OMS-2 materials made by conventional reflux or hydrothermal methods. X-ray diffraction and Brunauer−Emmett−Teller studies indicate that these materials have small crystallite sizes (∼9.8 nm) and that they are mesoporous with a uniform pore size distribution centered at 12 nm. These K-OMS-2 materials show improved catalytic activity for the oxidation of alcohols compared with the conventional K-OMS-2 materials, which may be due to their higher surface areas and novel surface properties. This fast, inexpensive, and environmentally friendly solvent-free method has the.
Using Artificial Neural Networks for Predicting Mental Workload in Nuclear Power Plants Based on Eye Tracking

Abstract The development of a model for mental workload (MWL) prediction of an operator in nuclear power plants (NPPs) is necessary but challenging. In this study, the validity, sensitivity, and relationship between the four indices of eye tracking (i.e., pupil dilation, blink rate, fixation rate, and saccadic rate) and subjective rating method (i.e., the National Aeronautics and Space Administration-Task Load Index) of both experts and nonexperts when they are operating the state-oriented procedure system in NPPs are analyzed. An artificial neural network (ANN) is used to develop the MWL prediction model using the data of nonexperts. The correlation analysis results indicate that four eye tracking indices are sensitive to the subjective MWL, but there is no significant difference in the pupil diameter and saccadic rate between the experts and nonexperts. The validity of the proposed ANN-based prediction model is proven by the high correlation coefficient (higher than 0.95) between the original and predicted data. However, when the proposed ANN model was applied to the experts’ data, there was a significant difference between the original and predicted data. Therefore, the proposed prediction model can be applied to the experts’ data but with a certain adjustment to obtain the most possibly reasonable results.
Augmented Reality Game-Based Learning: Enriching Students’ Experience During Reading Comprehension Activities

Program for International Student Assessment results indicate that while reading comprehension needs to be promoted, teachers are struggling to find ways to motivate students to do reading comprehension activities and although technology-enhanced learning approaches are entering the classroom, researchers are still experimenting with them to determine their benefits and implications. Among such technology-enhanced learning approaches, we find augmented reality and game-based learning, both of which have proven to be useful in educational settings; nonetheless, few studies have observed them being used jointly. Some open questions to be asked are as follows: Does the use of augmented reality games in the classroom benefit students in terms of performance and motivation? Is the reading activity experience enriched when we use them to promote reading comprehension? In this study, and with the help of teachers, we devised an augmented reality game using a design-based research approach. We then tested it in a real classroom and carried out both quantitative and qualitative observations. Our results show that while results in reading comprehension using the game show no difference to results from the more traditional approaches, children do display greater motivation and interest in the activity and the activity is enriched as it promotes problem solving, exploration, and socialization behavior.
Saturation-based quality assessment for colorful multi-exposure image fusion

Multi-exposure image fusion is becoming increasingly influential in enhancing the quality of experience of consumer electronics. However, until now few works have been conducted on the performance evaluation of multi-exposure image fusion, especially colorful multi-exposure image fusion. Conventional quality assessment methods for multi-exposure image fusion mainly focus on grayscale information, while ignoring the color components, which also convey vital visual information. We propose an objective method for the quality assessment of colored multi-exposure image fusion based on image saturation, together with texture and structure similarities, which are able to measure the perceived color, texture, and structure information of fused images. The final image quality is predicted using an extreme learning machine with texture, structure, and saturation similarities as image features. Experimental results for a public multi-exposure image fusion database show that the proposed model can accurately predict colored multi-exposure image fusion image quality and correlates well with human perception. Compared with state-of-the-art image quality assessment models for image fusion, the proposed metric has better evaluation performance.
Developing a Hybrid Intelligent Classifier by using Evolutionary Learning (Genetic Algorithm and Decision Tree)

Objective: The objective of this paper is to give a hybrid classifier by combining the genetic algorithm and decision tree based on evolutionary learning. Methods: The proposed algorithm on the 8 data samples was tested. In order to implement the proposed algorithm, MATLAB software was used. In all the obtained results, standardized data sets are used, making assembly by using genetic algorithm which is very suitable. Results: The learning technique of sub-spaces is proposed. In this study, we tried to compare a series of different methods and updated of integrated distribution. It showed that, in cases that the number of information or the number of properties are high, the proposed hybrid classification approach that implements genetic algorithm can be used as the best approach. Conclusion: In this study, we tried a usual approach for clustering in error prone environments. A main excess in the precision on the tested information or on the validation is clear. It should be noted that this increasing is in comparison with the assembly classifiers which has stable accuracy.
Comparing Conventional and Artificial Neural Network Models for the Pricing of Options

INTRODUCTION Whilst conventional option pricing models are derived using sophisticated stochastic partial differential calculus and are reasonably accurate, they are known to provide systematically biased prices (Lajbcygier et al., 1995). It is shown that a feedforward artificial neural network (ANN)  a non-parametric, inductive technique  can learn these systematic biases and can therefore provide more accurate pricing than conventional models. In this study, we have used Australian data for options on futures, namely American-style call options on Australian All Ordinaries Share Price Index (SPI) futures. These are the Australian equivalent of the data used in previous studies (Hutchinson et al., 1994). The data used in this study is described in more
Overcoming resource limitations in the processing of unlimited speech: applications to speaker and language recognition

We live an era with almost unlimited access to data. Yet without their proper tagging and annotation, we often struggle to make effective use of most of it. And sometimes, the labels we have access to are not even the ones we really need for the task at hand. Asking human experts for input can be time-consuming and expensive, thus bringing to bear a need for better ways to handle and process unlabeled data. In particular, successful methods in unsupervised domain adaptation can automatically recognize and adapt existing algorithms to systematic changes in the input. Furthermore, methods that can organize incoming streams of information can allow us to derive insights with minimal manual labeling effort – this is the notion of weakly supervised learning. In this thesis, we explore these two themes in the context of speaker and language recognition. First, we consider the problem of adapting an existing algorithm for speaker recognition to a systematic change in our input domain. Then we undertake the scenario in which we start with only unlabeled data and are allowed to select a subset of examples to be labeled, with the goal of minimizing the number of actively labeled examples needed to achieve acceptable speaker recognition performance. Turning to language recognition, we aim to decrease our reliance on transcribed speech via the use of a large-scale model for discovering sub-word units from multilingual data in an unsupervised manner. In doing so, we observe the impact of even small bits of linguistic knowledge and use this as inspiration to improve our sub-word unit discovery methods via the use of weak, pronunciation-equivalent constraints. Thesis Supervisor: James R. Glass Title: Senior Research Scientist Thesis Supervisor: Najim Dehak Title: Assistant Professor, Johns Hopkins University
New functionalized pyrazoles from isoxazolopyridazinones

L'hydrolyse basique de dihydro-6,7 methyl-3 isoxazolo [3,4-d] pyridazinones-7 conduit, selon les substituants aux acetyl-4 amino-5 pyrazoles ou aux cyano- ou carbamoyl-4 methyl-5 pyrazoles