Reviewerly/exHarmony huggingface dataset:
Evaluation of the Effect of Voice Technology in a Game Teaching E-Book on English Learning

The progress of modern technology has led to diversified teaching methods. Currently, many teaching tools are gradually being combined with technology to create interactive experiences that differ from those of the past. PlayTalk English Teaching E-book was developed in this study through the use of the Voiceflow platform. Teaching e-book for the fifth and sixth grades on an online education platform was used as a reference for the teaching contents. This study used Learning Motivation Scale and General Self-Efficacy Scale to evaluate students’ learning effects under two of the above-mentioned different teaching methods. During the experiment process, the subjects were divided into two groups. In the experimental group, the subjects learned English by means of the PlayTalk English Teaching E-book, while in the control group, the subjects were taught by means of a web-based education platform e-book. All the subjects were tested by the Learning Motivation Scale and General Self-Efficacy Scale before and after teaching. The experimental results indicated that the average score of PlayTalk English Teaching E-book teaching on the Learning Motivation Scale was 4.396 (out of 5.0), which was higher than the average score (3.770) of the web-based education platform e-book. The conclusions of this study can be used as a reference for future educators who design e-books to improve school children’s learning interest and to improve the existing learning platforms, as well as extend the application of new technologies in teaching so as to provide students with a higher quality teaching system.
Service Quality Factors of Healthcare System from COVID-19 Patients Perspectives

This paper presents a study to investigate the service quality of healthcare system in UAE. The respondents are the users of the healthcare system. They were asked to grade the healthcare systemthrough 25 items in five domains which are Reliability; Responsiveness; Assurance; Empathy and Tangibles. A total of 384 respondents participated in the questionnaire survey are COVID-19 patients discharged from UAE healthcare system. The analysis on the collected data found that three domains which are reliability, responsiveness and tangibles are having high level of satisfaction according to the respondent’s perspective in experiencing the healthcare during COVID19 treatment. In term of ranking analysis, the responsiveness domain is ranked first with the highest score of 3.890, followed by reliability domain with mean score of 3.572, then tangibles domain with 3.533, empathy domain with 3.370 and finally assurance domain with 3.170. The findings from this study helps the healthcare practitioners in improving theirservice quality to the patients.
Rich Querying with Always Encrypted Enclaves

In this chapter, we’re going to look at the extra querying capabilities available when you’re using Always Encrypted with secure enclaves. Alongside that we’ll also look at indexes on columns encrypted with randomized encryption, functionality that wasn’t available before for very logical reasons we’ve already discussed.
A Case Study on the Chinese Writing Course Based on Team-based Learning in the Untact Era

This paper mainly introduces the application of TBL method in Chinese writing course in untact era. After briefly summarizing the characteristics of the Chinese writing course, especially the latest Process-Oriented teaching method was introduced intensively. Chapter3 introduces the concept and process of TBL teaching method. Chapter 4 introduces the case of the application of Chinese writing courses in the second semester of 2021 of S university, including TBL design, making team, determination of discussion topics and activities, and evaluation. The final section summarizes the problems and improvements in the application process. In conclusion, considering the improvements of learning content understanding, Chinese communication skills, competence as a team member, interest in lectures, and completeness of writing results, it seems that the expected effect was achieved by designing successful team activities in Chinese writing class. Through this study, we hope that more effective Chinese writing education can be achieved by actively interacting between learners and instructors or learners through TBL class activities.
Adopting Augmented Reality (AR) Technology in Retail Sector: Systematic Review of Prisma Methods

One of the most popular technologies nowadays is augmented reality (AR). popular technologies in various industries. Many industries have adopted this AR technology, one of which is with the aim of marketing the products offered to their customers. With more and more adopting this technology, retailers are also using it. The goal of this research is to determine out what important variables are often encountered in the application of AR technology. A qualitative technique is used in this study, as well as a Systematic Literature Review Prism approach. The result are 56 papers, 35 candidate papers and 26 papers were selected. We examine where and how in retail, augmented reality (AR) is used, the aspects of Augmented reality technology that are frequently examined, and the psychological and behavioral effects that AR can have.
Pengaruh Pemberian Tugas Rumah Secara Daring di Masa Pandemi Wabah Covid-19 terhadap Hasil Belajar IPS Siswa Kelas VII di SMP IT Darul Ikhsan NW Balik Batang Tahun Ajaran 2019/2020

Online homework assignments are given to continue learning activities that are only implemented at home during the Covid-19 pandemic. By giving assignments online at least students can learn independently or individually. Giving homework is also a means of communication between teachers, students and parents. This research was conducted to determine the effect of giving homework online in the midst of the Covid-19 pandemic on student social studies learning outcomes. This research is a quantitative research, with an associative descriptive quantitative type of research that aims to obtain information about a symptom in the study. The subjects of this study were students of SMP IT Darul Ihsan NW Balik Batang with a total sample of 23 people. Sampling was carried out with a total sample. Hypothesis testing in this study can be concluded: From the average value, online learning provides lower results than direct or face-to-face learning. The difference in these results is not significant as shown by the results of the average difference test, namely the t count is smaller than the t table and the significant value is greater than 0.05. So it can be concluded that giving homework online does not have a significant effect on the social studies learning outcomes of seventh grade students of SMP IT Darul Ihsan NW Balik Batang.
Farallon Flat-Slab Subduction and New Mexico Geology

Flat-slab subduction of the Farallon plate is inferred from western U.S. magmatic and deformation histories which are similar to those observed at modern flat-slab subduction zones.At its maximum extent, the Farallon flat-slab hinge was under present-day New Mexico, affecting lithosphere deformation, magmatism, and the upper mantle.Here we present results of geodynamic models of flat-slab subduction, that form an interpretive framework for the lithosphere and upper mantle structure below New Mexico, as well as patterns of magmatism.Our models show that arc magmatism ended when the Farallon flat slab advanced.This happened because the asthenospheric wedge filled with lowermost lithosphere of the North American plate, which was scraped off by the advancing slab.As the slab flattened, it compressed the North American plate through end loading.This resulted in compressional deformation far east of the slab hinge, into present-day central US.Slab removal opened the asthenospheric wedge, resulting in magmatism of the San Juan, Mogollon-Datil and Trans-Pecos volcanic fields.It left a step in the lithosphere-asthenosphere boundary which focused Rio Grande rift opening.A keel, consisting of bulldozed material that had been scraped off by the flat slab, is present below southeastern New Mexico, leaving a fast seismic velocity anomaly in the upper mantle that inhibits magmatism.
The Implementation of Data Mining to Get The Pattern for Selecting Students' Thesis Title

Management Study Program is one of the study programs at the Faculty of Economics, The University of Dehasen Bengkulu. Every academic year, students who have fulfilled the credits or are in semester 6 or 7, submit a file for submission of titles to the staff of the study program, then the Head of Study Program will check related to the title submitted, if possible and in accordance with the disside of science, the title is issued will approved and the study program will appoint supervisor 1 and supervisor 2 from the student concerned. The application of the pattern for selecting students’ thesis titles at the Management Study Program, Faculty of Economics, The University of Dehasen Bengkulu was made using the Visual Basic .Net programming language and SQL Server 2008r2 database. In the application, the Levenstein Distance Algorithm has been applied to the new title submission form, where a comparison will be made against previously accepted titles that have been stored in the database. The title is accepted if the similarity level is below 50%, while the title is Rejected if the similarity level is above 50%. Based on the trial for submitting a new title, the application is able to provide information on whether the title is accepted or Rejected based on the Levenstein Distance Algorithm approach. Based on the results of the tests that have been carried out, the functionalities of the application run as expected and the application is able to display the results of the analysis through the stages of the Levenstein Distance Algorithm for the submission of new titles.
Modelling and forecasting cash outflow-inflow using ARIMA-Feedforward Neural Network

Time series data modelling is often done in the world of the economy. In this study, modelling and forecasting of cash outflows and inflows were carried out, especially at the Bank Indonesia Representative Office in Malang City. The amount of money going out and going in needs to be modelled and forecasted to estimate people's money needs in the next period. Forecasting and modelling time-series data that is often used is the Autoregressive Integrated Moving Average. However, the modelling can be said to be a simple time series modelling and cannot model non-linear models. One of the developments of machine learning modelling to predict time series data is a Feedforward Neural Network model which is used for forecasting cash outflows. ARIMA (1,1,1) model is used to forecast cash inflow. The FFNN model cannot be used for these data because the cash inflow does not meet the non-linearity assumption. Meanwhile, cash outflow uses the FFNN model with ARIMA input (2,1,0) and eight hidden layers. The cash outflow modelling input used is based on the best ARIMA model and the determination of the number of hidden layers is done by selecting the smallest MAPE and RMSE values.
Construction of Higher Vocational Teaching Resource Database and Application of Network Teaching

In the context of China's educational system reform, teachers in higher vocational colleges must change their teaching ideas and actively implement online teaching, so as to make use of the resources of the teaching resource library to realize a shared, open and flexible teaching resource library, and give full play to the synergy to improve the effect of online teaching.This paper focuses on the problems existing in the construction of online teaching resources in higher vocational colleges, and puts forward the corresponding countermeasures.
The Influence of Gamification Implementation on Customer Loyalty in Mobile Commerce Applications

Up until today, various studies have discussed gamification but none of them have explored how different types of gamification will affect customer satisfaction and loyalty. Therefore, this study aims to analyze the impact of several types of gamification in loyalty programs on customer loyalty in mobile commerce applications (m-commerce). This study covers the types of gamification found in m-commerce applications in Indonesia, including points, tasks/quests, and levels-related gamification. Data collection in this study was carried out through an online questionnaire distributed to users who have used gamified loyalty programs on m-commerce applications in Indonesia. To test the hypothesis, this study uses a covariance-based structural equation modeling method with the help of AMOS 26. The results found that points, tasks/quests, and levels-related gamification significantly affected utilitarian and hedonic values. It was also found that utilitarian value affects satisfaction and loyalty, but hedonic value only affects satisfaction. This study explains the importance of utilitarian values in implementing gamification in loyalty programs and provides insight into the type of gamification that has the most significant influence on customer loyalty, which is levels-related gamification.
Software Rotation Sensor Based on High-Speed Video Analysis

In this study, we propose a novel concept of real-time software rotation sensor that can simultaneously detect the angles of multiple rotational objects in a high-speed video sequence. Our software sensor can be executed in real time at 500 fps by parallel-implementing digital image correlation processes to inspect the similarities among the input image and 360 reference images at different angles on a GPU high-speed vision system. Its performance in measurement accuracy was verified by showing several experiments results for multiple rotating gears that are captured with partial occlusion in 500-fps videos,including multiple gears fast-rotating at 2400 rpm.
The Information Technology and Employee Performance in the Indian Telecom Industry

Abstract-The study aims to analyze the impact of information technology on the performance of the employees working in the telecom sector. This descriptive study used regression for identifying the relationships of the variables. Data was collected from 305 respondents through the online questionnaire. After reviewing the existing literature, the data is analyzed using the t-test, Fisher's test, and the coefficient of determination. It was found through the results of regression analysis, that information technology has a significant positive impact on the performance of the employees working in the telecom sector. Therefore, the hypothesis is been accepted for the study. It is also proven by administering simulation testing. In addition to this, this study also throws light on the benefits and the contributions of technology to improving employee performance.
«Signor generale!». Due inediti di Nievo garibaldino

This article is part of an inventory dedicated to Ippolito Nievo’s military service letters, and it begins with a general outline of the project. The most extensive part of the study is an annotated critical edition of two letters signed by Nievo, which were written in the summer of 1860 when he was vice intendant general in Garibaldi’s army. The first letter is presumably an early version of his Resoconto amministrativo della prima spedizione in Sicilia, an account of the first expedition of the Thousand in Sicily, while the second letter refers to a mishap involving a shipment of weapons for the Battle of Milazzo. The final pages of the article present a possible classification for the service letters based on pragmatic and stylistic criteria.
In-Situ Animal Behavior Classification Using Knowledge Distillation and Fixed-Point Quantization

We explore the use of knowledge distillation (KD) for learning compact but accurate models that enable classification of animal behavior from accelerometry data on wearable devices. To this end, we take a deep and complex convolutional neural network, known as residual neural network (ResNet), as the teacher model. ResNet is specifically designed for multivariate time-series classification. We use ResNet to distill the knowledge of animal behavior classification datasets into soft labels, which consist of the predicted pseduolikelihoods of every class for each datapoint. We then use the soft labels to train our significantly less complex student models, which are based on the gated recurrent unit (GRU) and multilayer perceptron (MLP). The evaluation results using two real-world animal behavior classification datasets show that the classification accuracy of the student GRU-MLP models improves appreciably through KD, approaching that of the teacher ResNet model. To further reduce the computational and memory requirements of performing inference using the student models trained via KD, we utilize dynamic fixed-point quantization through an appropriate modification of the computational graphs of the models. We implement both unquantized and quantized versions of the developed KD-based models on the embedded systems of our purpose-built collar and ear tag devices to classify animal behavior in situ and in real time. The results corroborate the effectiveness of KD and quantization in improving the inference performance in terms of both classification accuracy and computational and memory efficiency.
A random forest-neural network coupled model for predicting the recurrence location of uterine cancer

With increasing developments and progress, the status and influence of women in society have significantly improved, with more attention paid to women's health.According to relevant statistics, uterine cancer is a highly-incident malignant tumor in females and urges more research to improve the survival rate of uterine cancer patients.In this study, we established a prediction model to determine the location of uterine cancer recurrence by combining random forest and neural network algorithms.Data of uterine cancer patients were collected from major hospitals, and professional doctors evaluated and graded the patients' physical fitness indicators based on their experience, which were then used to construct the model and obtain the prediction results.Compared to traditional method, the proposed method of this paper showed that the model was more effective and accurate in predicting the location of uterine cancer recurrence, with a prediction accuracy rate of up to 88.63%.
WebIoT: Classifying Internet of Things Devices at Internet Scale through Web Characteristics

The number of Internet of Things (IoT) devices connected to the Internet has been growing rapidly. Such a large number of IoT devices bring significant challenges to device man-agement and cyberspace security. The discovery and classification of IoT devices are the prerequisites for monitoring and protecting them. However, existing Internet-scale IoT device classification methods mainly rely on textual analysis of the device response data, whose performance can be affected by the complexity or the multilingualism of the response texts. In this paper, we propose WebIoT, which mainly utilizes the image characteristics of the IoT devices' web interfaces to classify them for the first time. We leverage the observation that many IoT devices have web interfaces for device configuration and device status display, whose visual presentations contain abundant characteristics for device classification. Experiment results show that our method achieves 95.4% precision and 91.5% recall, which significantly outperforms other text analysis-based methods.
An Improved Technique for Identification of Forgery Image Detection Using Clustering Method

In forensics, the detection of forgery images is very important. In this method of forgery, the same image of the region can be able to copied and pasted. The keypoint-based method was advertised to reveal ineffective forgery detection against different attacks like geometric transformations because the number of keypoints is less in small regions. To overcome these problems, the following methods are involved. In the first stage, we extract many keypoints by reducing the low enhancement by the CLAHE algorithm. After extraction, there will be some keypoint matching problems to solve this problem the FANN algorithm is used. At the final stage, the tampered region is localized, and in order to overcome FPR, an iterative localization technique called the Density-based clustering algorithm and GORE method is used. By using the software MATLAB R2017a, the performance of experimental results is evaluated and accuracy of 93% by image and pixel level.
Research on Semantic Role Labeling based on Dependency Parsing

Semantic role labeling is an implementation of shallow semantic analysis of sentences. Semantic role labeling has been widely used in natural language processing fields such as information extraction, information retrieval and machine translation, which greatly improves the performance of the system. Therefore, the research of Chinese semantic role labeling has attracted more and more attention. This paper introduces the process of semantic role Labeling and the construction and development of Chinese semantic role labeling corpus, studies the semantic role labeling method based on syntactic analysis, and looks forward to the semantic role labeling method.
Pedestrian Detection and Target Tracking Based on Person Re-identification in Crowded Crowd

As a high-level task of computer vision, pedestrian recognition and target tracking involves many application scenarios has great research significance and practical value. In this paper, we use the Repulsion Loss to solve the problem of the non-maximum suppression operation in the post-processing phase of target detection is sensitive to the threshold value in the crowded and obstructed pedestrian scenarios, which results in mis-judgement and missed detection of the target. In order to solve the undetected pedestrian problem in the improved YOLOV3, we added a deformable convolution to the Mask R-CNN algorithm, which can better meet and adapt to the scale changes of natural pedestrians. The experimental results show that the mAP of the improved YOLOV3 increases from 68% to 73.4%. The mAP of the improved Mask R-CNN increased from 85% to 87.5%. We also present a pedestrian recognition GCPRN, to overcome the problem of insufficient representation of pedestrian appearance features in deep sort target tracking algorithm, we proposed a target tracking FRTN network. The experimental results indicated that the FRTN network is more effective than Deep Sort algorithm on MOTP and MOTA.
James Clerk Maxwell. T-Shirt Cliché

And God said ∇ • E = ρ/ε0 ∇ • B = 0 ∇ × E = −∂B/∂t ∇ × B = μ0ε0 ∂E/∂t And there was light. It is not possible to attend a physics convention without seeing at least one of these inscribed on a wearer's T-shirt. However, Maxwell earned it. That electricity in a wire produced a magnet was observed by Hans Christian Ørsted. That moving a magnet and a wire with respect to each other could produce current in the wire was observed by Michael Faraday. These discoveries led to the invention of the electric motor and generator. Maxwell's stroke of genius was to generalize the ideas: oscillating magnetic fields produce oscillating electric fields, which in turn produce oscillating magnetic fields and so on—in empty space. The combination of these waves produced light. Thus, Maxwell showed that light was an electromagnetic wave. Among other things, this led to Einstein's theory of relativity.