Reviewerly/exHarmony huggingface dataset:
English Listening Teaching Device and Method Based on Virtual Reality Technology under Wireless Sensor Network Environment

This paper presents an in-depth study and analysis of the design and implementation of English listening instruction using wireless sensing microprocessors combined with the technology of virtual environments. Inaccurate speech hinders smooth communication and restricts the development of various skills in foreign language learning. To learn English well, mastering phonetics is a crucial first step. Especially for learners in junior and senior high schools, phonics teaching is considered an essential part. In the face of the new standards which put higher demands on phonetic intonation, the traditional methods of teaching phonetics have little effect on improving phonetics and are even somewhat overwhelming. Therefore, increased researchers are trying to find effective phonics teaching methods to improve the current problems in high school phonics teaching. Important definitions of virtual reality, augmented reality, and teaching resources are collected and organized, typical applications of augmented reality in education at the present stage are summarized, important events in the development of augmented reality are reviewed, key technologies of augmented reality are briefly described, hardware and software resources required to realize the effect of augmented reality are introduced, and pedagogical theories underlying the study of augmented reality in English teaching resources are discussed. The results of the student walk-through quizzes are analyzed using SPSS with independent sample <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>t</mi> </math> -tests, and the distribution of the number of scores is statistically analyzed in the form of graphs, and interviews with students and teachers are used to gain a deeper understanding of the effectiveness of the resource in teaching practice. The value of the application of the augmented reality primary school English teaching resource is explored.
Perkembangan Bahasa Anak Usia Dini 5-6 Tahun Ditinjau dari Aspek Sintaksis dan Pragmatik

This study aims to describe language acquisition in the field of syntax for children aged 5-6 years and describe language acquisition in pragmatics for children aged 5-6 years. This research uses descriptive qualitative research method. The sample of this study were two children aged 5-6 years Masyithoh Kindergarten Karangasem Batang. From the two samples, it is known that in the syntactic aspect, children experience different developments from the ability to tell stories, answer the phone, talk without stopping and speak in slang. In the pragmatic aspect, children also have developmental differences in terms of understanding sign language and interacting with sign language. Researchers concluded that early childhood in the age range of 5-6 years experienced differences in the development of syntactic and pragmatic language. So, the researcher recommends that teachers understand the differences in these developments and stimulate students according to their development so that they continue to improve.
Ensemble Based-Cross Project Defect Prediction

In Software Testing, there are typically two ways to predict defects in the software—within-project defect prediction (WPDP) and cross project defect prediction (CPDP). In this research, we are using a hybrid model for cross project defect prediction. It is a two-phase model consisting of ensemble learning (EL) and genetic algorithm (GA) phase. For our research, we used datasets from the PROMISE repository and created clusters after normalization using k-means clustering algorithm. This further helped us improve the accuracy of the model. Our dataset consists of 22 attributes and were labeled defective or not. Our results show that our hybrid model after implementing k-means clustering achieved an F1 score of 0.666. CPDP is a newer and faster approach for software defect prediction but is often error prone. This method can change the software industry as it will lead to improved software development and faster software delivery.
FUZZY RELATIONAL DATABASE MODEL AND MANAGEMANT SYSTEM FOR IMPRECISE INFORMATION

This paper introduces a fuzzy relational database model (FRDB) and the management system for it. FRDB is built by extending the classical relational database model with the fuzzy membership degree of tuples in relations. The management system for FRDB with the querying language like SQL is built by using a classical open-source management system.
Generating Politeknik Aceh Selatan Students’ Homeland GIS Map

Nowadays, information is an essential element in life because every human activity and business needs it. One of the essential forms of information is geographic information that is used in the field of education. Geographic information is defined as a collection of data and facts about the phenomena of the earth, land, and its inhabitants in space and time. The geographical location of the South Aceh Polytechnic student's homeland is an essential data. If this information is managed properly, the leadership can find out the distribution of data from the students' homeland. The author designs and builds a Geographic Information System to manage student data from the South Aceh Polytechnic to find out the distribution of the homeland of the South Aceh Polytechnic students.This information system is designed using the PHP programming language with the CodeIgniter 3 Framework, MySQL, and the LeafletJS JavaScript Library. Testing the system using the Blackbox method. The results indicate that the system can run well by displaying markers of students' homeland on the map so that this information system can be utilized to determine the distribution of students' homeland effectively and efficiently.
Characterization of specular freeform surfaces from reflected ray directions using experimental ray tracing

Abstract. The applications of freeform surfaces in optical components and systems are increasing more and more. Therefore, appropriate measurement techniques are needed to measure these freeform surfaces for verification. This task is still a challenge for most measurement techniques. In this paper, we propose a measurement technique for optical and other specular freeform surfaces based on experimental ray tracing. This technique is able to measure form and mid-spatial-frequency deviations simultaneously. The focus will be set on the sensing technique and the measurement uncertainties in the setup. As the measurement technique is described, an estimation of the influence of different uncertainties based on simulations is given. The result from an experimental measurement is evaluated in relation to the influence of the uncertainties. A comparison measurement for evaluation is given.
IoT-Based Services in Banking Industry Using a Business Continuity Management Approach

Background: The Internet of things (IoT) is a new technology that expanding rapidly and has led to the emergence of new services in the banking industry. Although IoT is an emerging technology in Iran, it is expected to become a paradigm in the future. IoT applications in banking have helped to provide high quality and fast responses to customers anytime, anywhere.Purpose: This research aims to identify and prioritize IoT applications in the Iranian banking industry based on business continuity indicators.Methods: First, based on the comprehensive literature review and practical experiences, IoT applications in the banking industry are identified. Then, each indicator's weight was calculated using the Grey Decision-making trial and evaluation laboratory (DEMATEL). Finally, IoT-based applications in the Iranian banking industry have prioritized using the multi-attribute utility theory (MAUT). Due to the unstable economic conditions for the financial sector, business continuity indicators are employed in this study. Also, the reason for using MAUT is to consider the utility of banking experts for performing IoT banking services.Findings: According to results, IoT-based services in the banking industry, including Transmit instant reports, smart ATMs, non-contact electronic payments, and electronic checks, are identified for Iranian banks. Then, These appropriate services are prioritized with the capabilities of Iranian banks.
Design Process for a Birthing Bed, Based on User Hierarchy: Promoting Improvement in User Satisfaction

The medical device design process has a responsibility to define the characteristics of the object to ensure its correct interaction with users. This study presents a proposal to improve medical device design processes in order to increase user acceptance by considering two key factors: the user hierarchy and the relationship with the patient’s health status. The goal of this study is to address this research gap and to increase design factors with practical suggestions for the design of new medical devices. The results obtained here will help medical device designers make more informed decisions about the functions and features required in the final product during the development stage. In addition, we aim to help researchers with design process didactics that demonstrate the importance of the correct execution of the process and how the factors considered can have an impact on the final product. An experiment was conducted with 40 design engineering students who designed birthing beds via two design processes: the traditional product design process and the new design process based on hierarchies (proposed in this study). The results showed a significant increase in the user acceptance of the new birthing bed developed with the hierarchical-based design process.
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages

We show that unsupervised sequencesegmentation performance can be transferred to extremely low-resource languages by pretraining a Masked Segmental Language Model (Downey et al., 2021) multilingually.Further, we show that this transfer can be achieved by training over a collection of low-resource languages that are typologically similar (but phylogenetically unrelated) to the target language.In our experiments, we transfer from a collection of 10 Indigenous American languages (AmericasNLP, Mager et al., 2021) to K'iche', a Mayan language.We compare our multilingual model to a monolingual (from-scratch) baseline, as well as a model pre-trained on Quechua only.We show that the multilingual pre-trained approach yields consistent segmentation quality across target dataset sizes, exceeding the monolingual baseline in 6/10 experimental settings.Our model yields especially strong results at small target sizes, including a zero-shot performance of 20.6 F1.These results have promising implications for low-resource NLP pipelines involving human-like linguistic units, such as the sparse transcription framework proposed by Bird (2020).
Evaluating a Taxonomy of Textual Uncertainty for Collaborative Visualisation in the Digital Humanities

The capture, modelling and visualisation of uncertainty has become a hot topic in many areas of science, such as the digital humanities (DH). Fuelled by critical voices among the DH community, DH scholars are becoming more aware of the intrinsic advantages that incorporating the notion of uncertainty into their workflows may bring. Additionally, the increasing availability of ubiquitous, web-based technologies has given rise to many collaborative tools that aim to support DH scholars in performing remote work alongside distant peers from other parts of the world. In this context, this paper describes two user studies seeking to evaluate a taxonomy of textual uncertainty aimed at enabling remote collaborations on digital humanities (DH) research objects in a digital medium. Our study focuses on the task of free annotation of uncertainty in texts in two different scenarios, seeking to establish the requirements of the underlying data and uncertainty models that would be needed to implement a hypothetical collaborative annotation system (CAS) that uses information visualisation and visual analytics techniques to leverage the cognitive effort implied by these tasks. To identify user needs and other requirements, we held two user-driven design experiences with DH experts and lay users, focusing on the annotation of uncertainty in historical recipes and literary texts. The lessons learned from these experiments are gathered in a series of insights and observations on how these different user groups collaborated to adapt an uncertainty taxonomy to solve the proposed exercises. Furthermore, we extract a series of recommendations and future lines of work that we share with the community in an attempt to establish a common agenda of DH research that focuses on collaboration around the idea of uncertainty.
A Fast and Simple Face Detection Algorithm Using Neural Network and Its Implementation on FPGA

Face recognition is one of the interesting types of biometric which determines the presence or absence of human faces in the picture. In this paper, a face recognition system is presented that benefits from an optimized architecture based on the MLP neural network. The proposed method considerably improves the speed and the accuracy of detection compared to traditional architectures of neural network. To reduce the overall computation, neural network is organized so that to be able to rule out the majority of the non-image areas located in the image’s background before applying the main algorithm. An important advantage of this new architecture is its homogeneous structure that makes it suitable for optimized implementation on a hardware platform. In this work, FPGA is used as the platform for implementation of the proposed algorithms. The implementation was done considering Taylor expansion of the activation functions. The performance of the proposed method and the implemented system was evaluated on the BioID dataset. Accomplishment of the proposed method is high precision while reducing training time and total calculations, together with appropriate robustness. Finally, a comparison with other face recognition methods has been done to show the performance of the presented system. The comparison result shows that the proposed system outperforms the other mentioned methods.
Development of Artificial Neural Network-Based Model for Prediction of Temperature Field in Host Rock of a Geological Disposal Facility for Radioactive Waste

Calculation of temperature field in a deep geological repository (DGR) after emplacement of a large number of heat emitting vitrified radioactive canisters is important and requires large computational time and hence in this study an effort has been made towards development of artificial neural network (ANN) based model that can predict the temperature quickly.The datasets required to train the ANN model were generated using an in-house developed GUI tool for simulating heat diffusion process.Various numerical studies were conducted with different configurations of the ANN model and different datasets of size 50, 100, 150, 200, to optimize the number of input data required to train the model.The results in the form of temperature values predicted by the trained ANN model have been compared with those for the same problem calculated using analytical and finite difference based methods.The trained ANN model can predict temperature values with less than 0.001% error.
Learning to Describe Solutions for Bug Reports Based on Developer Discussions

When a software bug is reported, developers engage in a discussion to collaboratively resolve it.While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend and delaying its implementation.To expedite bug resolution, we propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code.We build a corpus for this task using a novel technique for obtaining noisy supervision from repository changes linked to bug reports, with which we establish benchmarks.We also design two systems for generating a description during an ongoing discussion by classifying when sufficient context for performing the task emerges in real-time.With automated and human evaluation, we find this task to form an ideal testbed for complex reasoning in long, bimodal dialogue context.
Six-in-a-Row Artificial Intelligence Design and Its Implementation Based on Java

We can use java language to design the game of chess on eclipse platform and design a new rule for it : &amp;ldquo;Six in a Row&amp;rdquo;, this game is more complex than Five-in-a-Row,and we can add a function of Man-Machine Battle in the program.This paper which based on the practical programming experience introduce the algorithm of the above game. The basic mentality of the algorithm is creating a scoring table and all points of the chessboard are scored by using ergodic method.Finally, the machine will placing the chess in the point which have the highest score.This algorithm is clearly operable, and the machine has a high rate of winning when play with human.
A Multimodal Framework for Video Ads Understanding

There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements effectively. Taking the 2021 TAAC competition as an opportunity, we developed a multimodal system to improve the ability of structured analysis of advertising video content. In our framework, we break down the video structuring analysis problem into two tasks, i.e., scene segmentation and multi-modal tagging. In scene segmentation, we build upon a temporal convolution module for temporal modeling to predict whether adjacent frames belong to the same scene. In multi-modal tagging, we first compute clip-level visual features by aggregating frame-level features with NeXt-SoftDBoF. The visual features are further complemented with textual features that are derived using a global-local attention mechanism to extract useful information from OCR (Optical Character Recognition) and ASR (Audio Speech Recognition) outputs. Our solution achieved a score of 0.2470 measured in consideration of localization and prediction accuracy, ranking fourth in the 2021 TAAC final leaderboard.
Composition of wavelet transforms and wave packet transform involving Kontorovich-Lebedev transform

The main objective of this paper is to study the composition of continuous Kontorovich-Lebedev wavelet transform (KL-wavelet transform) and wave packet transform (WPT) based on the Kontorovich-Lebedev transform (KL-transform). Estimates for KL-wavelet and KL-wavelet transform are obtained, and Plancherel?s relation for composition of KL-wavelet transform and WPT-transform are derived. Reconstruction formula for WPT associated to KL-transform is also deduced and at the end Calderon?s formula related to KL-transform using its convolution property is obtained.
Performance Analysis of DCT and WDCT Algorithms in Image Steganography

Frequency domain techniques such as Discrete Cosine Transform (DCT) and Warped Discrete Cosine Transform (WDCT) ensures high accuracy when compared with the spatial domain techniques. Therefore, these image steganographic methods were evaluated using public datasets to compare the performance of DCT and WDCT. After performing different tests using the datasets in each of the algorithms, a comparative analysis is made in terms of the Peak Signal-to-Noise Ratio (PSNR) metrics. The results indicate that the stego image generated after embedding the secret acquires high imperceptibility and robustness. The performance of the WDCT algorithm is higher as compared to the DCT algorithm and the resultant images produced are very less prone to noise attacks. In DCT and WDCT algorithms, the cover image will be split based on 8×8 pixel blocks and 2D DCT is applied on each pixel. The secret will be embedded inside DCT coefficient and inverse 2D DCT is applied to recover the secret. Therefore, these image steganographic techniques can be adopted to transfer the confidential messages in different sectors. In the future, other data hiding methods using deep learning could be implemented to increase the robustness and imperceptibility of covert messages.
Investigation of Heavy and Trace Metals in Some Kurdistan Region Crude Oils by Spectroscan MAX-G

Four types of Kurdistan crude oils have been studied to determine the heavy and trace metals. The significance of determining trace elements that exist in crude oils is helpful for further information about exploration, production and the refining process. In this work crude oil can be separated into products such as (gasoline, kerosene, diesel, and atmospheric distillation residue +350°C) by using atmospheric distillation unit and vacuum distillation unit. The trace metals can be determined with acceptable accuracy and precision by spectroscan MAX-G. However, the values of Ni concentration were the best in accuracy among the group of metals of interest. The average values of the results will be employed as reference values of the trace metal content in the coming discussion and using Ni and V as test elements. The crude oil samples are characterized by with a dominance of V over Ni with a V/Ni ratio of 3.7 to 2.5. Trace element analysis of the five metals in crude oils and atmospheric distillation residues +350°C, for each crude oils and atmospheric distillation residues +350°C is investigated, samples of these areas has not been previously examined for trace element contents. In this study to find the relation between metals in crude oils and atmospheric distillation residue of samples. To detect the concentration of trace elements by using this instrument.
THE INFLUENCE OF MOVIE TOWARD TO STUDENTS’ WRITING SKILL NARRATIVE TEXT

The background of the study in this research is based on the phonemena that student have difficulty in writing narrative text. Students could not describing thing, place, and person detail because the student don’t have ideas when they are asked to describe thing, place and person. So they need some ways, method or aids which could help them in writing narrative text. Movie media is one of ways that can be used in teaching writing descriptive text. The problem of this research can be stated that how is the implementation influence of movie toward to students’ writing skill narrative text. Its purpose are firstly to know the improvement of students ability in writing skill narrative text. The second is to find out how the use the movie contributes to student improvement in writing skill narrative text. So student are more active and interested in learning writing activities in class, especially the are happy with the application of learning methods to improve their writing skill by using Movie.
Effects of Different Optimization Formulations in Evolutionary Reinforcement Learning on Diverse Behavior Generation

Generating various strategies for a given task is challenging. However, it has already proven to bring many assets to the main learning process, such as improved behavior exploration. With the growth in the interest of heterogeneity in solution in evolutionary computation and reinforcement learning, many promising approaches have emerged. To better understand how one guides multiple policies toward distinct strategies and benefit from diversity, we need to analyze further the influence of the reward signal modulation and other evolutionary mechanisms on the obtained behaviors. To that effect, this paper considers an existing evolutionary reinforcement learning framework which exploits multi-objective optimization as a way to obtain policies that succeed at behavior-related tasks as well as completing the main goal. Experiments on the Atari games stress that optimization formulations which do not consider objectives equally fail at generating diversity and even output agents that are worse at solving the problem at hand, regardless of the obtained behaviors.