Reviewerly/exHarmony huggingface dataset:
Code-Switching between Arabic and English in Selected Egyptian Talk Shows: A Sociolinguistic Study

The present study examines Arabic-English Code-Switching as a persuasive strategy in selected Egyptian talk shows. The sample of the current study contains 17 recordings taken from 15 talk shows broadcast on different Egyptian TV channels between August 2014 and December 2017. This study investigates the patterns of Code-Switching that occur within the speech of the participants in the Egyptian talk shows. In addition, it sheds light on the persuasive techniques adopted by the interlocutors. Finally, it examines the sociolinguistic factors that might affect the participants' code-switched utterances. The findings of the study reveal that three main types of Code-Switching are employed in the Egyptian talk shows, namely; intra-sentential, inter-sentential and tag Code-Switching. Intra-sentential Code-Switching is utilized for 363 times with the percentage of 91.9%. Followed by inter-sentential Code-Switching with 20 examples with the percentage of 5.1%. Tag Code-Switching is adopted for 12 times with the percentage of 3%. In addition, the analysis concludes that a variety of persuasive techniques are employed by the participants in the Egyptian talk shows while switching codes. Such strategies include repetition, jargon, narration, analogy, cliche, humor, emotion, intertextuality, evidence, fear, cognitive dissonance and Monroe’s motivated sequence. Finally, the results show that a number of sociolinguistic factors might affect the code-switched utterances of the participants in the Egyptian talk shows. Such variables are; education, occupation, social class, gender, age, religion and ethnicity.Key words: Code-Switching, talk shows, persuasive techniques.
The intention to use QR code payment in an emerging market – the role of “Attitude” as mediator

As one of the applications of mobile banking, payment by scanning a QR code not only reduces the risks when paying via cards but also brings about more convenience than cash payment and more safety than other forms of payment, especially in the context of the current strong and complicated Covid-19 epidemic. However, the adoption of this payment technology seems to be left open in emerging markets, especially Vietnam. Within this context, this study conducted a survey on 441 customers about their intention to use QR code payment when shopping in Vietnam market. Research results show that personal innovation, perceived security and usefulness, perceived ease of use and facilitating conditions affect the attitudes towards intention to use the QR code payment service. In particular, perceived ease of use, perceived security and usefulness are the two factors that most strongly influence the intention to accept the use of QR code payment service though attitude mediator. The results of this research can assist managers with considerations in constructing development plan for new technological products and services in such an emerging market as Vietnam
An Intelligent Agent Model and a Simulation for a Given Task in a Specific Environment

This paper introduces An Intelligent Agent Model for a Given Task in a Specified Environment.The methodology adopted in this work is based on mixing computational methods and functions to build an intelligent agent model.This paper focuses on building an intelligent agent model as a knowledge-based system that interacts with a dynamic environment for performing tasks.The class structure used to represent the environment in the knowledge base relies on three types of knowledge representation forms: production rule, semantic net, and frames.Each object in the environment is an instance of the class environment.Algorithms and functions are used to get knowledge from the state space of an environment to construct a task.The intelligent agent model can understand the environment from any position and can detect many subtasks, arrange them in a queue for execution, and can make decisions at a high scale of thinking.This model is proposed to maintain that an agent which is characterized by sufficiently low computational costs can interact with the environment in real-time but is powerful enough to reach the assigned goals in complex environments and within an acceptable time period.The intelligent agent model can calculate persistent changes in an external dynamic environment and any unexpected change, for example detecting the being of any problem in the environment and avoiding it.The intelligent agent can also learn and take reasonable decisions in the dynamic environment and automatically select an action based on task features.Thus, the intelligent agent can resolve several different kinds of difficulties.
Entrepreneurship Education Through Mobile Augmented Reality for Introducing SMEs in Higher Education

Education is a place to introduce culture and micro, small and medium enterprises in areas far from the capital. In Technology Education Study Program, Universitas Negeri Jakarta has the vision to produce educators and education personnel in the field of learning to engineer based on academic principles and ethics. Engineering new media learning through augmented reality becomes a tool for lectures introducing SMEs. For this reason, researchers collaborated with material expert lecturers, design experts, media experts, and 30 students in developing augmented reality as a new learning application. The results of the review obtained from the instructional design experts were 3.20, material expert 3,30, and media experts 3.14. Based on the test results obtained from students in the one-to-one stage of 3.55; and at the small group stage of 3.23. Based on the results of the evaluation, it can be concluded that Augmented Reality can be implemented and used to introduce SMEs to college students. Students can use this product as material strengthening in engineering learning using new media applications
Key exchange based on Diffie-Hellman protocol and image registration

&lt;span&gt;Nowadays, with the advences in ICT and rapid development of mobile internet; media information shared on the various communication networks requires the existence of adequate security measures. Cryptography becoming an effective way to meet these requirements and for maintain the confidentiality. However, communicating with encrypted messages requires secret key exchange, which is a part of a complex protocol. In this paper, we propose a new method for exchanging key based on Diffie-Hellman protocol and image registration with fast fourier transform, the principle of this method consists to concealing the key in a set of transformed images. Therefore, image registration allows finding transformations between images, which become a tool for recovering the key by the receiver.&lt;/span&gt;
Detecting Pronunciation Errors in Spoken English Tests Based on Multifeature Fusion Algorithm

In this study, multidimensional feature extraction is performed on the U-language recordings of the test takers, and these features are evaluated separately, with five categories of features: pronunciation, fluency, vocabulary, grammar, and semantics. A deep neural network model is constructed to model the feature values to obtain the final score. Based on the previous research, this study uses a deep neural network training model instead of linear regression to improve the correlation between model score and expert score. The method of using word frequency for semantic scoring is replaced by the LDA topic model for semantic analysis, which eliminates the need for experts to manually label keywords before scoring and truly automates the critique. Also, this paper introduces text cleaning after speech recognition and deep learning-based speech noise reduction technology in the scoring model, which improves the accuracy of speech recognition and the overall accuracy of the scoring model. Also, innovative applications and improvements are made to key technologies, and the latest technical solutions are integrated and improved. A new open oral grading model is proposed and implemented, and innovations are made in the method of speech feature extraction to improve the dimensionality of open oral grading.
Robust Digital Data Hiding in Low Coefficient Region of Image

Digital images have many uses in the field of health, research, military, art, etc.Digital Images need annotation for retrieval and protection from piracy, attacks, and modification.To perform this retrieval and protection, some of the information was hidden in the image matrix.This paper proposes a data embedding and extraction algorithm.Low-frequency regions of the image are identified to embed secret information in LSV (Least Significant Value) and MSV (Most Significant Value) of a selected coefficient once the data hiding in the embedding section of the proposed model image gets retransformed in the original image structure as per the number of bits used in LSV and MSV for data hiding image gets secured from different spatial attacks.The experiment is performed on real images.A comparison of the proposed model is done on PSNR, MSE, evaluation parameters.It is shown that the proposed model is better as compared to the existing model.
Arboreal Categories: An Axiomatic Theory of Resources

Game comonads provide a categorical syntax-free approach to finite model theory, and their Eilenberg-Moore coalgebras typically encode important combinatorial parameters of structures. In this paper, we develop a framework whereby the essential properties of these categories of coalgebras are captured in a purely axiomatic fashion. To this end, we introduce arboreal categories, which have an intrinsic process structure, allowing dynamic notions such as bisimulation and back-and-forth games, and resource notions such as number of rounds of a game, to be defined. These are related to extensional or "static" structures via arboreal covers, which are resource-indexed comonadic adjunctions. These ideas are developed in a general, axiomatic setting, and applied to relational structures, where the comonadic constructions for pebbling, Ehrenfeucht-Fra\"iss\'e and modal bisimulation games recently introduced by Abramsky et al. are recovered, showing that many of the fundamental notions of finite model theory and descriptive complexity arise from instances of arboreal covers.
Data mining techniques for classifying and predicting Teachers’ performance based on their evaluation reports

Background/Objectives: Teachers' performance is a key bridge to ensure successful pedagogical and educational objectives.However, the evaluation of teachers' performance has been used to be a manual and temperamental task for school principals.This traditional context limits the teachers' engagement to develop his/her performance as well as the principle to predict the strengths and weaknesses attached.Hence, schools' principals need to use initiative methods to evaluate the teachers' performance.In this study, a comparative approach was developed to evaluate the teachers' performance aiming at avoiding the potential biased and temperamental human behaves in the teacher's evaluation process.Methods: It involves different Data Mining (DM) techniques to identify the key patterns that are driving the teachers' performance evaluation process.Therefore, the proposed approach extracts several potential and influential indicators mined from a paper-based on teachers' performance reports at the Directorate of Education/ Southern Ghawrs, along with some demographics variables.Several DM algorithms are used to analyze teachers' performance reports and predict their performance, such as NB Tree, Naïve Bayes, and Conjunctive Rule methods.Findings: The experimental results show a significant prediction accuracy improvement by (33%) when applying NB Tree compared to Conjunctive rule, and (12%) when compared to Naïve Bayes techniques respectively.
Assessing the level of heavy metals concentration in soil around transformer at Akoko community of OndoState, Nigeria

Some heavy metals have bio-importance as trace elements but the bio-toxic effects of many of them in human biochemistry are of great concern. The level of heavy metals concentrations of transformer oil polluted soil was assessed in Akungba Akoko community of Ondo State, Nigeria using Atomic Absorption Spectrophotometer. The concentration of heavy metals investigated ranges from: Cu (1400-18)mg/kg, Fe (96700- 15500)mg/kg, Cd (2- 0.5)mg/kg, Mn (62-29)kg/mg, Ni(5-1)mg/kg, Al(27-11)mg/kg, Zn(1400- 44)mg/kg, Pd (68-20)mg/kg and Ca (570- 135)mg/kg. From the study, it was observed that some transformer sites had more concentration of heavy metals than others, as a result of the heavy metals content of the transformer soil released to the surrounding soil, while in some samples of these heavy metals were absent or are below detection limit. Comparing the results with the World Health Organisation (W.H.O) maximum allowable limit in soil with results of the results of different samples collected, majority of the results were found to fall below W.H.O limit which implies that those sites are still contamination free with respect to the specific heavy metal and caution needs to be taken urgently to avoid potential contamination. Meanwhile, heavy metal concentration at some sites have gone beyond the W.H.O maximum allowable limit, calling for immediate remediation of the site.
The effectiveness of a training program based on active learning strategies among students of the College of Basic Education and their logical thinking

The aim of the current research is to identify (the effectiveness of the training program based on active learning strategies among students of the College of Basic Education and their logical thinking, and to achieve the goal of the research, the experimental design with partial control was used for two equivalent groups, one experimental and the other a control. The research sample was chosen randomly from the research community and the number of the sample was selected) 80) male and female students, where the training program was applied to the members of the experimental group after the researcher built and prepared it by reviewing the studies and special resources in building programs as well as the literature on active learning and its strategies, as well as building and preparing the test for logical thinking, Where it was applied to all the research sample after the completion of the training program on the experimental group, which was for a period of (12) weeks in the first semester of the year (2019-2020), and the researcher reached a set of results through which conclusions, recommendations and proposals that feed the field were drawn Educational information that helps the development of the educational progress process.
Enhancing model accuracy and calibration efficiency with image based pattern selection using machine learning techniques

Calibration pattern coverage is critical for achieving a high quality, computational lithographic model. An optimized calibration pattern set carries sufficient physics for tuning model parameters and controlling pattern redundancy as well as saving metrology costs. In addition, as advanced technology nodes require tighter full chip specifications and full contour prediction accuracy, pattern selection needs accommodate these and consider contour fidelity EP (Edge Placement) gauges beyond conventional test pattern sets and cutline gauge scopes. Here we demonstrate an innovative pattern selection workflow to support this industry trend. 1) It is capable of processing a massive candidate pattern set at the full chip level. 2) It considers physical signals from all of the candidate pattern contours. 3) It implements our unsupervised machine learning technology to process the massive amount of physical signals. 4) It offers our users flexibility for customization and tuning for different selection and layer needs. This new pattern selection solution, connected with ASML Brion’s MXP (Metrology of eXtreme Performance) contour fidelity gauges and superior, accurate Newron (deep learning) resist model, fulfills the advanced technology node demands for OPC modeling, thus offering full chip prediction power.
Development of Knowledge Sharing Model Using Decision Support System In Determine Feasible on the Job Training In Vocational School Using Vikor Method

Decision support system (SPK) as a system that can process input data into one information that can be used as a more accurate decision support for the school in recommending students who meet the categories or requirements in following the Job Training. The system that was built carried out the process of determining decision support based on the Vise Kriterijumska Optimizajica I Kompromisno Resenje (VIKOR) method which is able to simplify complex problems in determining feasible to follow the On The Job Trainings based on criteria using formulas whose results are right on target.
Label-Based Convolutional Neural Network for Text Classification

The neural network models based on word embedding have achieved remarkable results in text classification. Even so, these models hardly consider that the importance of each word and labels for text classification is beneficial to obtain informative text representation. The attention mechanisms usually are used to measure the weights of words to improve predictive performance, but we attempt to achieve the same goal in a simple way. Since that word embedding can capture semantic regularities between words. we introduce a text representation based on label by embedding each label and the word vectors in the same space in this paper. In this label-based text representation, each word has weight information of the number of classes, which play an important role in the final performance. So we proposed a labelbased convolutional neural network (LBCNN) to obtain the importance of different word in the label-based text sequence and the most influential semantic features in the word vector respectively. The experimental results show that our proposed method outperforms the state-of-art methods on the several large text classification datasets.
CONTENT BASED MEDICAL IMAGE RETRIEVAL SYSTEM FOR ACCURATE DISEASE DIAGNOSES USING MODIFIED MULTI FEATURE FUSED XCEPTION MODEL

The Content Based Medical Image Retrieval (CBMIR) system helps to fulfill the need to diagnose the disease and provide proper treatment.The numerous complex deep neural networks are being applied to solve the same problem.This paper aims to design a simpler and accurate deep neural network-based CBMIR system.To achieve the same, the original Xception model is compressed by 58.33% by reducing the number of layers.The features of intermediate layers are fused to increase the accuracy of the proposed modified Xception model.This modified Multi-Feature Fused Xception (MFF-Xception) model is tested for two different X-ray image datasets: Image Retrieval in Medical Application (IRMA) dataset and the human's chest dataset.It gives an Average Precision Rate (APR) of 90.14% for the IRMA dataset while providing 100% APR for the human chest dataset.It is also tested for another dataset containing skin images of seven different diseases and gives an APR of 100% for the skin dataset.The proposed modified MFF-Xception model also shows significant performance improvement over the original Xception model for all three datasets.
KONTRIBUSI PENGUASAAN UNSUR INTRINSIK TEKS CERPEN TERHADAP KETERAMPILAN MENULIS TEKS CERPEN SISWA KELAS XI SMA NEGERI 2 BUKITTINGGI

ABSTRACT This article reveals the contribution of mastery of the intrinsic elements to writing skills of the short story text grade students XI SMAN 2 Bukittinggi. The purpose of this research there are three. First, it describes the mastery of the intrinsic elements. Second, it describes the writing skills of the short story text. Third, describing the contribution of mastery of the intrinsic elements to writing skills of the short story text. This research type is quantitative, descriptive method, and design contribution. This research population is grade XI that are registered on the school year 2018/2019, i.e. as many as 311 students. The sample in this research are determined by proportional random sampling of 10% of the population, i.e. 30 students. The research data are scores as a result of mastering the intrinsic elements and the scores of writing skills from the short story text. The data obtained through two types of tests i.e. objective tests to measure the mastery of the instrinsic elements and performance tests to measure the skills of writing a short story text. The results of the research there are three. First, the mastery of the intrinsic elements are on good qualifications (B) by the value of the average 77,67. Second, the skills of writing text short story are on more than enough qualifications (LdC) with an average of 70,00. Third, there is a contribution of the intrinsic elements to writing skills of the short story text grade students XI SMAN 2 Bukittinggi at 66,26%. Kata kunci: Kontribusi, Penguasaan, Unsur Intrinsik, Keterampilan, Menulis, Teks Cerpen
CONSULTATION ON THE WHITE PAPER ON ARTIFICIAL INTELLIGENCE - A EUROPEAN APPROACH

SUMMARY From 19 February to 14 June 2020, the European Commission held a Public Consultation on several policy and regulatory proposals that are currently being considered in the area of Artificial Intelligence (AI). This consultation was centered on two main documents presented by the Commission: the White Paper on Artificial Intelligence[1] and the “Report on the safety and liability implications of Artificial Intelligence, the Internet of Things and robotics”[2]. The consultation also included an online survey[3], where the central themes of those two documents were covered in a summarized way. In November 2020, the results of the consultation were presented, as well as the texts accepted for publication[4]. In order to participate in this pre-legislative process, a working group was created within the Faculty of Law and Political Science of the Lusófona University of Porto, which presented a contribution that was accepted and published by the European Commission[5]. The White Paper is centred in one powerful objective which is “to enable a trustworthy and secure development of AI in Europe, in full respect of the values and rights of EU citizens”, and for that presents two central ideas considered essential to attain it that are to create an ecosystem of excellence along the entire value chain and an ecosystem of trust that ensure compliance with EU rules, including rules protecting fundamental rights and consumers’ rights. The text that follows is divided in two main parts: Part I is focused on presenting an overview on the three main topics pointed out at the consultation: Excellence, Trust and Liability; Part II corresponds to text of the contribution submitted in the Public Consultation held by the European Commission. Keywords: Artificial Intelligence; Liability; Cybercrime; Ethics; Competition
The Revitalization of “Aek Sipitu Dai” Batak Toba Folklore into Teaching Materials in English Subject for Junior High School

This study aimed to inform the capability the folklore of Aek Sipitu Dai into learning materials at Junior High School. The objective of the studies were to develope Aek Sipitu Dai Batak Toba Folklore into teaching materials in English subject for Junior High School, to transcribe Aek Sipitu Dai Batak Toba Folklore into teaching materials in English subject for Junior High School, to refresh Aek Sipitu Dai Batak Toba Folklore into teaching materials in English subject for Junior High School. This study used descriptive qualitative approach in analyzing the data. The data of this research are interview in observation in Samosir Island. The result of this study, there are a complete story about Aek Sipitu Dai that can be used as teaching materials in English Subject, the complete story are able to be transcribe as a complete story, there was found a Batak Toba Folklore that can be used as teaching material in English Subject