Reviewerly/exHarmony huggingface dataset:
USING THREE STEP-INTERVIEW TO IMPROVE STUDENT’S SPEAKING ABILITY

Speaking can be defined as an activity to share or discuss information that helped people to communicate with each other. To improve the students’ speaking learning interest, the teacher needs to recognize how to teach speaking effectively, and what instructional materials are the most appropriate for their students. The aims of the research are to find out whether or not teaching speaking using three – Step interview technique in improving students’speaking ability and to find out the teaching and learning situation when three – Step interview technique implemented in the speaking class.This research is Classroom Action Research. The data was collected through observation, interview and test. The respondents of this research were the seventh grade students of SMP Putra Juang Cianjur.This research revealed that the pre-test data the average was 45,86. After implementing the three-step interview technique in every cycle, the students’ speaking score was getting better. It can be seen from the result of students' average score Cycle 1 was 51,60 and Cycle 2 was 60,26 It can be concluded that the three-step interview technique can improve students’ speaking interest and three step interview technique can improve speaking classroom into a better situation. Keywords: three step interview, speaking interest, CAR.
The status of Chinese handwriting identification and the improvement of methodologies

The testimony of handwriting identification is more uncertain than other kinds of physical evidence identification.If the expert did not employ the principles and methodologies based on neurophysiology, the examination would not be sufficient, and the testimony might be suspect.This research describes how neurophysiology influences the feature of handwriting and the methodology of identification: most handwriting characteristics have high-level individualization determined by the nervous system.The methods of defining the characteristics of handwriting were explored and it was found that writing motion rules and writing norms are effective measures to compare and match writing characteristics.Additionally, the number of characteristics and their degree of individualization are the bases to assess the handwriting characteristics system, quantitatively and qualitatively.Practical cases have been presented to support the theories proposed.
Resolving Logical Contradictions in Description Logic Ontologies Based on Integer Linear Programming

When resolving logical contradictions in ontologies, Reiter's hitting set tree algorithm is often applied to satisfy the minimal change principle. To improve the efficiency, the researchers have proposed various algorithms by using a scoring function, defining new semantics or applying some heuristic strategies. However, these algorithms either sacrifice minimal change or are designed for less expressive ontologies like DL-Lite. In this paper, we propose a mathematic approach based on integer linear programming, which is an optimization problem of maximizing or minimizing a linear objective function, to deal with DL ontologies. Specifically, we define the integer linear programming-based model to resolve logical contradictions. To realize the model, we propose one algorithm to find a cardinality-minimal solution and two algorithms dealing with weighted ontologies. Our experiments are conducted over 70 real-life and artificial ontologies to compare our algorithms with those hitting set tree-based ones. Through the experiments, the prominent efficiency and effectiveness have been exhibited by our algorithms. They usually take about 0.4 s to find a solution while others spend more than 100 s in many cases. The experimental results also show that the first two algorithms could find the cardinality-minimal solutions and those with a minimal sum of weights, respectively.
Chirality from quantum walks without a quantum coin

Quantum walks (QWs) describe the evolution of quantum systems on graphs. An intrinsic degree of freedom---called the coin and represented by a finite-dimensional Hilbert space---is associated to each node. Scalar quantum walks are QWs with a one-dimensional coin. We propose a general strategy allowing one to construct scalar QWs on a broad variety of graphs, which admit embedding in Eulidean spaces, thus having a direct geometric interpretation. After reviewing the technique that allows one to regroup cells of nodes into new nodes, transforming finite spatial blocks into internal degrees of freedom, we prove that no QW with a two-dimensional coin can be derived from an isotropic scalar QW in this way. Finally we show that the Weyl and Dirac QWs can be derived from scalar QWs in spaces of dimension up to three, via our construction.
Evolving Recurrent Neural Network Controllers by Incremental Fitness Shaping

Time varying artificial neural networks are commonly used for dynamic problems such as games controllers and robotics as they give the controller a memory of what occurred in previous states which is important as actions in previous states can influence the final success of the agent. Because of this temporal dependence, methods such as back-propagation can be difficult to use to optimise network parameters and so genetic algorithms (GAs) are often used instead. While recurrent neural networks (RNNs) are a common network used with GAs, long short term memory (LSTM) networks have had less attention. Since, LSTM networks have a wide range of temporal dynamics, in this paper, we evolve an LSTM network as a controller for a lunar lander task with two evolutionary algorithms: a steady state GA (SSGA) and an evolutionary strategy (ES). Due to the presence of a large local optima in the fitness space, we implemented an incremental fitness scheme to both evolutionary algorithms. We also compare the behaviour and evolutionary progress of the LSTM with the behaviour of an RNN evolved via NEAT and ES with the same fitness function. LSTMs proved themselves to be evolvable on such tasks, though the SSGA solution was outperformed by the RNN. However, despite using an incremental scheme, the ES developed solutions far better than both showing that ES can be used both for incremental fitness and for LSTMs and RNNs on dynamic tasks.
MZPAQ: a FASTQ data compression tool

Due to the technological progress in Next Generation Sequencing (NGS), the amount of genomic data that is produced daily has seen a tremendous increase. This increase has shifted the bottleneck of genomic projects from sequencing to computation and specifically storing, managing and analyzing the large amount of NGS data. Compression tools can reduce the physical storage used to save large amount of genomic data as well as the bandwidth used to transfer this data. Recently, DNA sequence compression has gained much attention among researchers. In this paper, we study different techniques and algorithms used to compress genomic data. Most of these techniques take advantage of some properties that are unique to DNA sequences in order to improve the compression rate, and usually perform better than general-purpose compressors. By exploring the performance of available algorithms, we produce a powerful compression tool for NGS data called MZPAQ. Results show that MZPAQ outperforms state-of-the-art tools on all benchmark datasets obtained from a recent survey in terms of compression ratio. MZPAQ offers the best compression ratios regardless of the sequencing platform or the size of the data. Currently, MZPAQ’s strength is its higher compression ratio as well as its compatibility with all major sequencing platforms. MZPAQ is more suitable when the size of compressed data is crucial, such as long-term storage and data transfer. More efforts will be made in the future to target other aspects such as compression speed and memory utilization.
A Universal Framework for Learning the Elliptical Mixture Model

Mixture modelling using elliptical distributions promises enhanced robustness, flexibility and stability over the widely employed Gaussian mixture model (GMM). However, existing studies based on the elliptical mixture model (EMM) are restricted to several specific types of elliptical probability density functions, which are not supported by general solutions or systematic analysis frameworks; this significantly limits the rigour and the power of EMMs in applications. To this end, we propose a novel general framework for estimating and analysing the EMMs, achieved through Riemannian manifold optimisation. First, we investigate the relationships between Riemannian manifolds and elliptical distributions, and the so established connection between the original manifold and a reformulated one indicates a mismatch between those manifolds, the major cause of failure of the existing optimisation for solving general EMMs. We next propose a universal solver which is based on the optimisation of a re-designed cost and prove the existence of the same optimum as in the original problem; this is achieved in a simple, fast and stable way. We further calculate the influence functions of the EMM as theoretical bounds to quantify robustness to outliers. Comprehensive numerical results demonstrate the ability of the proposed framework to accommodate EMMs with different properties of individual functions in a stable way and with fast convergence speed. Finally, the enhanced robustness and flexibility of the proposed framework over the standard GMM are demonstrated both analytically and through comprehensive simulations.
Metalinguistic evaluators and pragmatic strategies in selected hate-inducing speeches in Nigeria

Hate-inducing language, which has become a recurrent decimal in Nigerian socio-political discourse, is not unconnected to the deep-seated boundaries existing amongst different ethnic groups in Nigeria. Linguistic studies on hate language in Nigeria have largely utilised pragmatic and critical discourse analytical tools in identifying the illocutions and ideologies involved but hardly paid attention to the metalinguistic forms deployed in hate speeches. Therefore, the present study, aside adding to the research line of the Natural Semantic Metalanguage (NSM)—which has unduly focused on language typology, explores the metalinguistic evaluators that index hate speech in Nigeria, and relate them to specific pragmatic strategies through which hate speech producers’ intentions are communicated. To achieve this, three full manuscripts of hate speech made by three groups (i.e. Arewa Youth Consultative Forum, Youths of Oduduwa Republic, and Biafra Nation Youth League) from three (northern, western, and eastern, respectively) regions of Nigeria are purposively sampled from Google directories and Radio Biafra archives, subjected to descriptive and quantitative analysis, with insights from the NSM theory and aspects of pragmatic acts. Two categories of metalinguistic evaluators were identified, positive (GOOD) and negative (BAD) evaluators; and these are associated with three pragmatic strategies; namely, blunt condemnation, unshielded exposition, and appeal to emotion. While the condemning and exposing strategies largely utilise negative evaluators in initiating hate on target groups, the emotion-drawing strategy largely employs positive evaluators in boosting the image of the hate-speech producing group in the eyes of the audience. With these findings, the study takes existing scholarship on violence-inducing language a step forward, especially in providing a pragmatic explanation to the proliferation of hate crimes in Nigeria. It also offers a holistic linguistic database and critical meta-language for the teaching of hate-related language and crime, especially in second-language situations.
A Simulation Tool for a Large-Scale Nosql Database

The amount of data on the Web and the number of users accessing those data drastically grows every year. Web-based services have to be able to receive and process those requirements within the shortest possible time. Additionally, they have to manage sudden and unpredictable peaks of user requirements for accessing the data. To tackle these problems, NoSQL databases have been designed to provide scalability and good performance. However, it is important to know in advance the number of resources (i.e. computers, network capacity) to efficiently deploy a database application. In this work, we propose a simulator for a NoSQL database named MongoDB. Our simulator aims to detect saturation levels of the resources to ensure an efficient execution of MongoDB upon different users demands and upon failures of resources.
Exploring the Use of Psycholinguistic Information in Author Profiling

Identifying profile characteristics of the author of a given text is the aim of the Author Profiling (AP) task. In this paper, we explore the use of two well known psycholinguistic dictionaries, the Linguistic Inquirer and Word Count and the General Inquirer, with the objective to capture relevant information for recognizing the age and gender of the author of a given text. The contribution of this paper is two-fold. Firstly, we introduce the use of General Inquirer in the AP task. Secondly, we propose different text representations based on these dictionaries, which help to analyze their relevance and complementariness to accomplish author profiling. We experiment with benchmark corpora on AP. The obtained results are competitive with state-of-the-art, validating the usefulness of psycholinguistic information for recognizing profile attributes of authors.
HOW CARREFOUR REVOLUTIONIZING SUPPLY CHAIN MANAGEMENT: CASE FROM THE UNITED ARAB EMIRATES

Carrefour stands out as one of the largest and most successful hypermarket chains. It provides the customers with the great variety of the household goods, high-quality products, and food brands. Eventually, the hypermarket attracts the customers with the help of special offers and product promotions which customers are interested in. Nowadays, Carrefour in the United Arab Emirates serves over 200,000 customers a day operating 28 hypermarkets and 43 supermarkets (Carrefour Annual Report, 2016). Considering the high dynamics of UAE’s economy development, it is possible to emphasize the role of Carrefour’s power. In fact, it provides all necessary aspects for the cultivation of the effective supply chain, logistics, sourcing, supply management, customer service, demand management, inventory and transportation management, distribution, supply chain outsourcing and other aspects. The current paper is aimed to analyze the advantages and fallbacks of Carrefour within the mentioned area in order to explain the factors contributing to its success and competitiveness to better understand the uniqueness of supply chain in the UAE.
Occlusion and noise tests on the encrypted image produced by a security system based on a joint transform correlator and the Fresnel transform

Abstract In this work, the occlusion and noise test on the encrypted image produced by a joint transform correlator-based encryption system in the Fresnel domain (FrD) are computed and presented, in order to check the performance of this security system with respect to the image quality resulting in the decryption process for the retrieved image. The encryption system based on a joint transform correlator (JTC) in the FrD was proposed by us, with the purpose of using a lensless optical setup. We test the performance of this security system when the encrypted image is affected by common sources of degradation such as noise (additive and multiplicative) or partial occlusion. Finally, we evaluate the performance and robustness of the security system in the FrD by using the metric of the root mean square error (RMSE) between the image to encrypt and the decrypted image when the encrypted image is degraded by noise or modified by occlusion.
Mandarin Electrolaryngeal Speech Recognition Based on WaveNet-CTC

Purpose The application of Chinese Mandarin electrolaryngeal (EL) speech for laryngectomees has been limited by its drawbacks such as single fundamental frequency, mechanical sound, and large radiation noise. To improve the intelligibility of Chinese Mandarin EL speech, a new perspective using the automatic speech recognition (ASR) system was proposed, which can convert EL speech into healthy speech, if combined with text-to-speech. Method An ASR system was designed to recognize EL speech based on a deep learning model WaveNet and the connectionist temporal classification (WaveNet-CTC). This system mainly consists of 3 parts: the acoustic model, the language model, and the decoding model. The acoustic features are extracted during speech preprocessing, and 3,230 utterances of EL speech mixed with 10,000 utterances of healthy speech are used to train the ASR system. Comparative experiment was designed to evaluate the performance of the proposed method. Results The results show that the proposed ASR system has higher stability and generalizability compared with the traditional methods, manifesting superiority in terms of Chinese characters, Chinese words, short sentences, and long sentences. Phoneme confusion occurs more easily in the stop and affricate of EL speech than the healthy speech. However, the highest accuracy of the ASR could reach 83.24% when 3,230 utterances of EL speech were used to train the ASR system. Conclusions This study indicates that EL speech could be recognized effectively by the ASR based on WaveNet-CTC. This proposed method has a higher generalization performance and better stability than the traditional methods. A higher accuracy of the ASR system based on WaveNet-CTC can be obtained, which means that EL speech can be converted into healthy speech. Supplemental Material https://doi.org/10.23641/asha.8250830
Stochastic subspace correction methods and fault tolerance

We present convergence results in expectation for stochastic subspace correction schemes and their accelerated versions to solve symmetric positive-definite variational problems, and discuss their potential for achieving fault tolerance in an unreliable compute network. We employ an overlapping domain decomposition algorithm for PDE discretizations to discuss the latter aspect.
Myanmar Named Entity Recognition with Hidden Markov Model

Named Entity Recognition is the process to detect Named Entities (NEs) in a file , document or from a corpus and to categorize them into certain Named entity classes like name of city, State, Country, organization, person, location, sport, river, quantity etc.This paper introduces the Named Entities Recognition (NER) for Myanmar language using Hidden Markov Model (HMM).The main idea behind the use of HMM language independent and we can apply this system for any language domain.The corpus used by our NER system is also not domain specific.
Analyzing Linguistic Differences between Owner and Staff Attributed Tweets

Research on social media has to date assumed that all posts from an account are authored by the same person.In this study, we challenge this assumption and study the linguistic differences between posts signed by the account owner or attributed to their staff.We introduce a novel data set of tweets posted by U.S. politicians who self-reported their tweets using a signature.We analyze the linguistic topics and style features that distinguish the two types of tweets.Predictive results show that we are able to distinguish between owner and staff attributed tweets with good accuracy, even when not using any training data from that account.
The Photo‐Elastic Constant of Silicon Reviewed in Experiment and Simulation

Results of experiments on single‐crystalline silicon wafers that allow for the determination of the degree of photo‐elastic anisotropy A pe are presented. A pe is introduced as an absolute value of ( π 11 − π 12 − π 44 )/( π 11 − π 12 + π 44 ), where π 11 , π 12 , and π 44 are the piezo‐optical coefficients. These experiments are performed using the measurement system scanning infrared depolarization (SIRD) imager equipped with a special calibration set‐up that produces a defined diametrical loading of the wafer. The generated depolarization maps are compared with simulation results based on a classic 2D stress model, taking the crystallographic anisotropy of silicon into account, by applying the full tensor calculus. The best‐matching material constants used for simulation are compared with data being available from external publications. A pe is determined to be 0.21 ± 0.01 at a wavelength λ = 1.3 μm. Conclusions in view of current studies on other semiconductor material systems are discussed.
Simulating the DNA Overlap Graph in Succinct Space.

Converting a set of sequencing reads into a lossless compact data structure that encodes all the relevant biological information is a major challenge. The classical approaches are to build the string graph or the de Bruijn graph (dBG) of some order k. Each has advantages over the other depending on the application. Still, the ideal setting would be to have an index of the reads that is easy to build and can be adapted to any type of biological analysis. In this paper we propose rBOSS, a new data structure based on the Burrows-Wheeler Transform (BWT), which gets close to that ideal. Our rBOSS simultaneously encodes all the dBGs of a set of sequencing reads up to some order k, and for any dBG node v, it can compute in O(k) time all the other nodes whose labels have an overlap of at least m characters with the label of v, with m being a parameter. If we choose the parameter k equal to the size of the reads (assuming that all have equal length), then we can simulate the overlap graph of the read set. Instead of storing the edges of this graph explicitly, rBOSS computes them on the fly as we traverse the graph. As most BWT-based structures, rBOSS is unidirectional, meaning that we can retrieve only the suffix overlaps of the nodes. However, we exploit the property of the DNA reverse complements to simulate bi-directionality. We implemented a genome assembler on top of rBOSS to demonstrate its usefulness. The experimental results show that, using k=100, our rBOSS-based assembler can process ~500K reads of 150 characters long each (a FASTQ file of 185 MB) in less than 15 minutes and using 110 MB in total. It produces contigs of mean sizes over 10,000, which is twice the size obtained by using a pure de Bruijn graph of fixed length k.
Multisensor RFS Filters for Unknown and Changing Detection Probability

The detection probability is an important parameter in multisensor multitarget tracking. The existing multisensor multi-Bernoulli (MS-MeMBer) filter and multisensor cardinalized probability hypothesis density (MS-CPHD) filter require that detection probability is a priori. However, in reality, the value of the detection probability is constantly changing due to the influence of sensors, targets, and other environmental characteristics. Therefore, to alleviate the performance deterioration caused by the mismatch of the detection probability, this paper applies the inverse gamma Gaussian mixture (IGGM) distribution to both the MS-MeMBer filter and the MS-CPHD filter. Specifically, the feature used for detection is assumed to obey the inverse gamma distribution and is statistically independent of the target’s spatial position. The feature is then integrated into the target state to iteratively estimate the target detection probability as well as the motion state. The experimental results demonstrate that the proposed methods can achieve a better filtering performance in scenarios with unknown and changing detection probability. It is also shown that the distribution of the sensors has a vital influence on the filtering accuracy, and the filters perform better when sensors are dispersed in the monitoring area.