Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study on Optimal Feature Identification and Combination for Korean Dialogue Act Classification

In this paper, we have evaluated and compared each feature and feature combinations necessary for statistical Korean dialogue act classification. We have implemented a Korean dialogue act classification system by using the Support Vector Machine method. The experimental results show that the POS bigram does not work well and the morpheme-POS pair and other features can be complementary to each other. In addition, a small number of features, which are selected by a feature selection technique such as chi-square, are enough to show steady performance of dialogue act classification. We also found that the last eojeol plays an important role in classifying an entire sentence, and that Korean characteristics such as free order and frequent subject ellipsis can affect the performance of dialogue act classification.
Estimating kinetic parameters of chemical and biochemical reaction with general genetic algorithm

Genetic algorithms are developed through the use of simulated evolution, ate, the survival of the fittest strategy. They are efficient search methods and high parallel, stochastic and adaptable. Genetic algorithms search the solution space of a function through mutation, crossover, and selection operations applied to individuals in the population, the fittest individuals of any population tend to reproduce and survive to the next generation, thus improving successive generations. So in the paper, the kinetic parameters of pyroly-sis and dehydrogenation of benzene, as well as batch fermentation for lysine, were estimated with MATLAB toolbox GAOT published by C. R. Houck ate and based on general genetic algorithm. The coincidence of calculated and experimental data was similar to or better than results in literature. The method used in the paper can be applied to estimate model parameters or find optimal values in a such class of processes with differential equations as their mathematical model.
Planning for teaching and learning in EFL Effects of standardisation and standardised testing on the learning and teaching of EFL at lower secondary level

This paper describes the washback of competence orientation and competence oriented testing on planning teaching and learning in English as a foreign language (EFL) at Austrian lower secondary schools. It analyses the perceived needs of teachers working in EFL classrooms in general secondary, new middle and academic secondary schools based on a mixed methods study. This study relies on data from on-line surveys, interviews, non-participant observation and document research and this paper puts a focus on planning teaching and learning. Findings point towards the need to encourage long-term planning on the basis of a set of descriptors for each of the four years of lower secondary foreign language education to build a basis for assessing and testing communicative competence at beginner and lower intermediate level in order to provide formative feedback that can inform the planning of teaching and learning in EFL classrooms.
Associations between socio-economic factors and alcohol consumption: A population survey of adults in England

Aim To gain a better understanding of the complex relationships of different measures of social position, educational level and income with alcohol consumption in England. Method Between March 2014 and April 2018 data were collected on n = 57,807 alcohol drinkers in England taking part in the Alcohol Toolkit Study (ATS). Respondents completed the AUDIT-C measure of frequency of alcohol consumption, amount consumed on a typical day and binge drinking frequency. The first two questions were used to derive a secondary measure of quantity: average weekly unit consumption. Socio-economic factors measured were: social-grade (based on occupation), employment status, educational qualifications, home and car ownership and income. Models were constructed using ridge regression to assess the contribution of each predictor taking account of high collinearity. Models were adjusted for age, gender and ethnicity. Results The strongest predictor of frequency of alcohol consumption was social-grade. Those in the two lowest occupational categories of social grade (e.g. semi-skilled and unskilled manual workers, and unemployed, pensioners, casual workers) has fewer drinking occasions than those in professional-managerial occupations (β = -0.29, 95%CI -0.34 to -0.25; β = -0.31, 95%CI -0.33 to -0.29). The strongest predictor of consumed volume and binge drinking frequency was lower educational attainment: those whose highest qualification was an A-level (i.e. college/high school qualification) drank substantially more on a typical day (β = 0.28, 95%CI 0.25 to 0.31) and had a higher weekly unit intake (β = 3.55, 95%CI 3.04 to 4.05) than those with a university qualification. They also reported a higher frequency of binge drinking (β = 0.11, 95%CI 0.09 to 0.14). Housing tenure was a strong predictor of all drinking outcomes, while employment status and car ownership were the weakest predictors of most outcomes. Conclusion Social-grade and educational attainment appear to be the strongest socioeconomic predictors of alcohol consumption indices in England, followed closely by housing tenure. Employment status and car ownership have the lowest predictive power.
Deep Convolution Neural Network Recognition Algorithm Based on Improved Fisher Criterion

To effectively make use of deep learning technology automatic feature extraction ability,and solve the problem when the training sample size reduced or the iteration times reduced the recognition performance fell sharply,this paper proposed a deep learning algorithm based on Fisher criterion.In the feed forward spread,this method used convolution neural network to extract automatically image features such as structural information,and used convolution network of sharing weights and pooling,subsampling methods to reduce the weight number,and the method reduced the model complexity.When the back propagation adjusted the weights,it adopted the constraints based on Fisher criterion.At the same time,it kept the samples in small distance with-class and large distance between-class,so that the weights could be more close the optimal value for classification.It improved the recognition rate effectively when the sample size was insufficient or when it had few training iterations.A large number of experiments show that when the label samples are insufficient and the training iteration fewer,the hybrid deep learning algorithm based on Fisher criterion still achieves good recognition effect.
Exposure to antimuscarinic medications for treatment of overactive bladder and risk of lung cancer and colon cancer

Introduction One out of six adults has symptoms of overactive bladder (OAB). Antimuscarinic medication is the main pharmacological group used in the treatment of OAB. In preclinical studies, antimuscarinic compounds have been found to inhibit cell proliferation in lung cancer and colon cancer. Objective The aim of this study was to investigate the association between exposure to anti-muscarinic medication and the risk of lung cancer and colon cancer. Methods Individuals in Sweden who first filled a prescription for an antimuscarinic medication used to treat OAB (ie, oxybutynin, solifenacin, darifenacin, fesoterodine, or tolterodine) between July 1, 2006, and December 31, 2012, were identified and classified as exposed. Each exposed individual was individually matched with up to ten unexposed individuals from the Swedish general population, based on year of birth, sex, and county of residence. Cox proportional hazard models with follow-up time as the underlying time scale were used to estimate HRs with 95% CIs. Results In total, 164,000 exposed and 1,446,472 unexposed individuals were included in this study. The estimated HRs for lung cancer, in follow-up time intervals of <1 year, 1–4 years, and ≥4 years, were as follows: 0.86 (95% CI: 0.75–0.98), 0.63 (95% CI: 0.56–0.70), and 0.43 (0.34–0.55), respectively. The corresponding estimates for colon cancer were as follows: 0.91 (95% CI: 0.80–1.03), 0.81 (95% CI: 0.74–0.88), and 0.61 (95% CI: 0.51–0.73), respectively. Conclusion There was an inverse association between exposure to antimuscarinic medications, used in the treatment of OAB, and a diagnosis of colon cancer or lung cancer, which is in line with the findings in preclinical studies.
Privacy Preserving Data Mining For Horizontally Distributed Medical Data Analysis

To build reliable prediction models and identify useful patterns, assembling data sets from databases maintained by different sources such as hospitals becomes increasingly common; however, it might divulge sensitive information about individuals and thus leads to increased concerns about privacy, which in turn prevents different parties from sharing information. Privacy Preserving Distributed Data Mining (PPDDM) provides a means to address this issue without accessing actual data values to avoid the disclosure of information beyond the final result. In recent years, a number of state-of-the-art PPDDM approaches have been developed, most of which are based on Secure Multiparty Computation (SMC). SMC requires expensive communication cost and sophisticated secure computation. Besides, the mining progress is inevitable to slow down due to the increasing volume of the aggregated data. In this work, a new framework named Privacy-Aware Non-linear SVM (PAN-SVM) is proposed to build a PPDDM model from multiple data sources. PAN-SVM employs the Secure Sum Protocol to protect privacy at the bottom layer, and reduces the complex communication and computation via Nystrom matrix approximation and Eigen decomposition methods at the medium layer. The top layer of PAN-SVM speeds up the whole algorithm for large scale datasets. Based on the proposed framework of PAN-SVM, a Privacy Preserving Multi-class Classifier is built, and the experimental results on several benchmark datasets and microarray datasets show its abilities to improve classification accuracy compared with a regular SVM. In addition, two Privacy Preserving Feature Selection methods are also proposed based on PAN-SVM, and tested by using benchmark data and real world data. PAN-SVM does not depend on a trusted third party; all participants collaborate equally. Many experimental results show that PAN-SVM can not only effectively solve the problem of collaborative privacy-preserving data mining by building non-linear classification rules, but also significantly improve the performance of built classifiers. INDEX WORDS: Privacy preserving, Distributed data mining, Classification, Feature selection, Support Vector Machine, Kernel matrix approximation and decomposition PRIVACY PRESERVING DATA MINING FOR HORIZONTALLY DISTRIBUTED MEDICAL DATA ANALYSIS
Majorana neutrino mass structure induced by rigid instantons on toroidal orbifold

We study effects of D-brane instantons wrapping rigid cycles on Z2*Z2' toroidal orbifold. We compute Majorana masses induced by rigid D-brane instantons and realize bimaximal mixing matrices in certain models. We can also derive more generic mass matrices in other models. The bimaximal mixing Majorana mass matrix has a possibility to explain observed mixing angles. We also compute the mu-term matrix among more than one pairs of Higgs fields induced by rigid D-brane instantons.
A PROBABILISTIC CELLULAR AUTOMATON TO FORECAST PORT CHOICE DECISIONS

The port choice problem consists in predicting the selection of a port, made by an agent who has alternatives to choose from. Most of the literature has tackled this problem assuming a discrete choice model dependent on the ports’ characteristics and agents’ attributes. However, in practice the port choice decision depends also on the choices made by other agents as well as decisions made by these agents in the past. There are only a few examples that incorporate the complexity generated by spatio-temporal interactions between agents. However, those modelling structures are rather cumbersome, precluding their use in practical cases. This article presents a new modelling framework to predict port choice decisions, based on the theory of Cellular Automaton (CA), which is simple in structure and can be quickly calibrated and applied. This framework is a probabilistic CA intended to imitate the decision processes made from multiple shippers that interact with each other. These shippers face similar alternatives of seaports for exporting their products within a certain time span. The port choice here is a dynamic decision that depends on the ports’ characteristics and attributes of each shipper at a given time, as well as the decisions made by their neighbours. The outcome of the interaction is a discrete decision that evolves in time according to the dynamics of the system as a whole. The specified CA was applied to the case of vehicle exports from Brazil and the calibration was performed through a genetic algorithm. The results show that the probabilistic CA is able to replicate the historic behaviour of the port choice decisions in the Brazilian vehicle industry, with a high degree of success. The spatial component of the CA turned out to be of major relevance in the dynamic decision process along with the attributes and geographical location of ports.
Improvement on Paillier-Pointcheval Probabilistic Public-key Encryption Scheme

Security analysis of P. Paillier(etc)’s public-key encryption scheme(P-P) is proposed. Equivalent relations of one-wayness of P-P encryption scheme with other problems are verified. So without lowering the security, this paper improves the efficiency and reduces the data to be transferred by using proper parameters. The improved encryption scheme is specified, with the process of encryption/decryption being more efficient than that of P-P encryption scheme.
Review: Ad-Hoc Cloud Architecture & Modern Cryptography

This paper is showing the review study on cloud architecture that uses donation based resources in a network & helps multiple organizations to collaborate and yet compete with each other. The resources are utilized non intrusively. Organizations collaborate to create a Data-centre, that doesn’t harm their existence or profitability. At the same time, these organizations can compete by spreading to those locations where they carry certain edge over theirs. An ad-hoc cloud in heterogeneous environment helps to venture into remote areas. To achieve this some, ad-hoc cloud architecture is proposed along with issues and strategies. This is being elaborated in this paper with some effective sense.
PictureSensation – a mobile application to help the blind explore the visual world through touch and sound

We present PictureSensation, a mobile application for the hapto-acoustic exploration of images. It is designed to allow for the visually impaired to gain direct perceptual access to images via an acoustic signal. PictureSensation introduces a swipe-gesture based, speech-guided, barrier free user interface to guarantee autonomous usage by a blind user. It implements a recently proposed exploration and audification principle, which harnesses exploration methods that the visually impaired are used to from everyday life. In brief, a user explores an image actively on a touch screen and receives auditory feedback about its content at his current finger position. PictureSensation provides an extensive tutorial and training mode, to allow for a blind user to become familiar with the use of the application itself as well as the principles of image content to sound transformations, without any assistance from a normal-sighted person. We show our application’s potential to help visually impaired individuals explore, interpret and understand entire scenes, even on small smartphone screens. Providing more than just verbal scene descriptions, PictureSensation presents a valuable mobile tool to grant the blind access to the visual world through exploration, anywhere.
New cross-platform control software for Brewer Spectrophotometer

The current control software for the Brewer Ozone Spectrophotometer was created over 30 years ago for MS-DOS computers and has poor compatibility with modern software and hardware platforms. The new cross-platform software for controlling the Brewer is able to run on actual operating system for computers, tablets and smartphones, and at the same time has a one codebase. The observation data and configuration constants of the spectrophotometer are stored in the embedded relational SQLite database. The core of the application is written in C++ based cross-platform Qt framework, and the observation routines is implemented in Qt script interpreted scripting language and is integrated into the core of the application.
Resource assignment algorithms for vehicular clouds

In this thesis, we study the task scheduling problem in vehicular clouds. It falls in the category of unrelated parallel machine scheduling problems. Resource assignment in vehicular clouds must deal with the transient nature of the cloud resources and a relaxed definition of non-preemptive tasks. Despite a rich literature in machine scheduling and grid computing, the resource assignment problem in vehicular clouds has not been examined yet. We show that even the problem of finding a minimum cost schedule for a single task over unrelated machines is NP-hard. We then provide a fully polynomial time approximation scheme and a greedy approximation for scheduling a single task. We extend these algorithms to the case of scheduling n tasks. We validate our algorithms through extensive simulations that use synthetically generated data as well as real data extracted from vehicle mobility and grid computing workload traces. Our contributions are, to the best of our knowledge, the first quantitative analysis of the computational power of vehicular clouds.
A Novel Non-Reconstruction Algorithm Based on Compressed Sensing for Spectrum Detection

A novel non-reconstruction spectrum detection algorithm based on compressed sensing is proposed.The measurements from the compressed sensing process are directly used for the primary user detection to address the wind-band spectrum detection problem.We quantitatively analyze the performance of detection,and improve it by cooperative spectrum detection.Simulation results show that the new algorithm has good detection effect where SNR is above 0 dB.Cooperative spectrum detection can be an effective acternative to improve the algorithm performance under low SNR.Meanwhile,compared with the algorithm based on reconstruction,the proposed algorithm greatly reduces the amount of data and algorithm complexity,and significantly reduces the detection time.
Application of rough set and support vector machines in hepatitis diagnosis

A method applied in hepatitis diagnosis was proposed, which is based on rough set and Support Vector Machines (SVM). The method uses rough set to reduce the original features and to obtain a number of feature subsets. Then a set selection algorithm was employed to execute the feature reduction again, and a new data set was acquired in the light of the reduced subset. SVM was used for the new data set training and prediction. UCI machine learning public data set was adopted. Experiment results and data analysis show that, comparing with SVM, Neural Network (NN) and decision tree, the proposed method has higher diagnostic accuracy and can diagnose whether the data being negative or positive.
Adaptación preliminar del cuestionario de intensidad afectiva de Randy Larsen: estudio de consistencia interna y composición factorial

Introducción: Dentro la psicología, el estudio de la emoción ha revelado una serie de dimensiones entre las que destaca la intensidad afectiva como medida de auto-reporte. No obstante, no hay a disposición instrumentos válidos y confiables que midan dicho constructo y, si bien existen métodos indirectos para medir su presencia, es imperante la necesidad de validar los instrumentos tanto por sus propiedades teóricas como su uso en población clínica; esta situación constituye una problemática. Objetivo: El presente estudio pretende revisar tanto la consistencia interna como el análisis factorial de la traducción de la escala propuesta por Larsen, para la población de la zona metropolitana de Guadalajara, Jalisco (México). Método: Participaron un total de 108 sujetos con edades entre 19 y 61 años; el 50.9% fueron mujeres y el 49.1% restante hombres. La aplicación del cuestionario fue realizada tanto de forma individual como grupal. Resultados: Se obtuvo un alfa de Cronbach de . 835, un omega de McDonald de . 877 y una capacidad discriminativa estadísticamente significativa por ítem, además 44.02% de la varianza pudo ser explicada con cuatro factores. Discusión: Fueron obtenidos resultados similares a los de la adaptación española del cuestionario y un resultado cercano al obtenido por los autores originales. Se considera que la semejanza encontrada en la estructura factorial de la adaptación española es debida a cuestiones culturales. Conclusión: El instrumento tiene una consistencia interna aceptable, sin embargo, es necesario calcular la validez convergente, así como fortalecer la validez del constructo, generando nuevas investigaciones.
Multiple Classifiers Combination Based on Fuzzy Integral in Intrusion Detection

As pattern recognition method used in intrusion detection system,the limitation in traditional detection methods based on rules is cancelled.In this paper,a new intrusion detection approach using multiple classifiers combination based on fuzzy integral is proposed.At same time,a new method for computing fuzzy density is proposed.Experiment has been done on intrusion detection dataset in KDD'99 and the result shows that it has high detection accuracy.
Research on the Organization-based Method for Modeling Multi-agent System

The environment of Internet is heterogeneous and openness,which brings new challenges to the MAS Modeling,such as high complexity and low efficiency.To resolve these problems,this paper presents a MAS modeling method based on Organization,which is called OrgMAS.So as to reduce the complexity of system modeling,this method divides the complex system into several organizations,and analyzes these organizations separately.This method also performs Ontology modeling during the process of OrgMAS,so that,the heterogeneous agents can cooperate beyond the same semantics with the help of ontology mapping.This paper also introduced a Case tool,OrgMAS Tool,which can support OrgMAS to model complex system with high efficiency.
Robust Model-Based Data Management

Making new connections according to personal preferences is a crucial service in mobile social networking, where the initiating user can find matching users within physical proximity of him/her. In existing systems for such services, usually all the users directly publish their complete profiles for others to search. However, in many applications, the users' personal profiles may contain sensitive information that they do not want to make public. In this paper, we propose Find U, the first privacy-preserving personal profile matching schemes for mobile social networks. In Find U, an initiating user can find from a group of users the one whose profile best matches with his/her; to limit the risk of privacy exposure, only necessary and minimal information about the private attributes of the participating users is exchanged. Matching user profiles using their physical proximity via mobile social networking is a critical thing. We propose Find U, the concept used to limit the privacy levels and also to find the best matching profiles. To realize the user privacy levels here we are using secure multiparty computation (SMC) techniques. We also propose protocols such as PSI, PCSI to prove their security proofs. We evaluate the efficiency of the protocols by adopting the total run time and energy consumption.