Reviewerly/exHarmony huggingface dataset:
AN ANALYSIS OF DIFFICULTIES IN ONLINE ENGLISH LEARNING EXPERIENCED BY THE EFL TEACHER

The purpose of this research was to describe the analysis of difficulties in Online English Learning experienced by the EFL teacher at SMP 5 Parepare. This research used descriptive qualitative method with the population in this research was the English teacher at SMPN 5 Parepare. The sample technique used was totally sampling so that the sample of 4 English teachers obtained. Data collection techniques used in this research were interviews and voice recordings. The research of data analysis from Interview showed that the way the English Teacher delivered the class by online system in this pandemic covid-19. It was dominated by the using of learning applications as media of this new learning method. All of the English teacher at SMP 5 Parepare used WhatssApp application to delivered their class by using another application to prepare the materials. The students used the specific learning application for examination or task such as G-Form and Quizizz. The writer found some troubles/difficulties experienced by the English teachers at SMP 5 Parepare. The troubles/difficulties experienced by the English teachers at SMP 5 Parepare such as, network constraints, limited internet quota, there are some students who do not have their own learning tools in the form of mobile phone or Laptop, and the lack of enthusiasm of the students themselves. The students who lack of response in this case, could be the students those do not have mobile phone or laptop. So, it was started from the cause that is the student do not have private online learning tools.
Pemanfaatan Teknologi Pendidikan Kawasan Pengembangan dalam Membuat Media Pembelajaran

The purpose of this research is to find out how the utilization of education technology development area in the manufacture of learning media by certified teachers in Garut Regency, West Java. This research is qualitative research using descriptive method. The research subjects involved were ten certified teachers. As a result of the research, all teachers have utilized educational technology in the manufacture of learning media, especially during the COVID-19 outbreak which has made the government implement distance learning, so that teachers are required to understand and master a variety of interesting learning media creation. Most teachers take advantage of computer-based media both online and offline, because it is more accessible to students. In addition, audio visual media is also the most used media by teachers, because it is considered to be a medium that can interest students in learning. In conclusion, the use of learning media by certified teachers mostly leads to the utilization of multimedia computer-based technology with more choice on audio visual materials both online and offline in providing learning materials, taking into account aspects of the availability of supporting facilities and infrastructure in schools.&#x0D; &#x0D; Keywords: Learning Media, Teaching Materials Development, Educational Technology.
A Study of English Vocabulary Translation Based on Quantitative and Qualitative Analysis of Internet Database

In the process of English vocabulary translation, translators make full use of the advantages of electronic means such as CNKI translation assistant, online corpus of COCA, etc., a systematic, automatic and manual quantitative and qualitative analysis of the target English vocabulary is carried out to obtain accurate and accurate translation results. Therefore, the method of quantitative and qualitative analysis based on internet database and corpus can help translators solve the problem of English vocabulary translation and improve the quality of English vocabulary translation. Based on the characteristics of English vocabulary translation and the TECHNIQOF quantitative and qualitative analysis in internet database, the strategies of English vocabulary translation are put forward.
Aspects of the Intrinsic Motivation as Accessibility Factors in the Inclusive "STEAM" Education

The paper presents the current development of the proposed by the authors approach to defining digital and e-learning accessibility for effective pedagogical rehabilitation of learners with special learning needs. A novel aspect – intrinsic accessibility – is proposed as complementary to the previously identified extrinsic aspects of accessibility – institutional, informational and cultural. The intrinsic aspects of accessibility – lack of skills, knowledge, or motivation – influence the process of pedagogical rehabilitation of children with special learning needs. Two different pedagogical scenarios implementing educational robots are discussed in a STEAM context.
Adaptive Feature Interpolation for Low-Shot Image Generation

Training of generative models especially Generative Adversarial Networks can easily diverge in low-data setting. To mitigate this issue, we propose a novel implicit data augmentation approach which facilitates stable training and synthesize high-quality samples without need of label information. Specifically, we view the discriminator as a metric embedding of the real data manifold, which offers proper distances between real data points. We then utilize information in the feature space to develop a fully unsupervised and data-driven augmentation method. Experiments on few-shot generation tasks show the proposed method significantly improve results from strong baselines with hundreds of training samples.
The Good, the Bad, and the Neutral: Twitter Users’ Opinion on the ASUU Strike

Aim/Purpose: Nigeria’s university education goes through incessant strikes by the Academic Staff Union of Universities (ASUU). This strike has led to shared emotion on micro-blogging sites like Twitter. This study analyzed selected historical tweets from the “ASUU” to understand citizens’ opinions. Background: The researchers conducted sentiment analysis and topic modelling to understand Twitter users’ opinions on the strike. Methodology: The researchers used the Valence Aware Dictionary for Sentiment Reasoning (VADER) technique for sentiment analysis, and the Latent Dirichlet allocation (LDA) was used for topic modelling. A total of 10,000 tweets were first extracted for the study. After data cleaning, 1323 tweets were left. Contribution: To the researcher’s best knowledge, no published study has presented a sentiment analysis on the topic of the ASUU strike using the Twitter dataset. This research will fill this gap by providing a sentiment analysis and drawing out subjects by exploring the tweets on the phrase “ASUU.” Findings: The sentiment analysis result using VADER returned 567 tweets as ‘Negative,’ with the remaining 544 and 212 categorized as Positive and Neutral. The result of the LDA returned six topics, all comprising seven keywords. The topics were the solution to the strike, ASUU strike effect, strike Call-off, appeal to ASUU, student protest and student appeal. Recommendation for Researchers: Researchers can use this study’s findings to compare with other contexts of opinion mining. Practitioners may also use the research to understand better the attitudes of their staff and students about the strikes to create actionable solutions before the suspension of the strike. Future Research: Future studies can collect information from other social networking and blogging sites.
Exploring the Use of Padlet in Synchronous Learning: Students’ Perceptions of its Advantages and Disadvantages

&lt;em&gt;As a result of the Covid-19 epidemic, educators in Indonesia have had experience dealing with synchronous learning, which has challenged them to select an interesting and engaging method, strategy, or tool to aid the teaching and learning process. Padlet was eventually chosen as the solution to this study. Padlet is a virtual wall that is used to build students' interest in learning English, specifically writing. In this study, the researchers used a descriptive quantitative research design with a sample of 96 students to assess their perceptions of using Padlet as a virtual wall. Numerous factors such as utility, knowledge, engagement, enjoyment, motivation, and ease of use were determined through a questionnaire-based survey which was then distributed as benefits and drawbacks of using Padlet. According to the findings, the students agreed that Padlet provided several benefits to their learning process. On the other hand, students disagreed with Padlet's disadvantages. Therefore, the result implied that the expectation to enhance knowledge is the major factor influencing students’ perceptions of the Padlet utilization in synchronous learning&lt;/em&gt;
The Role of Digital Twins in Personalized Sleep Medicine

The digital twin concept has been widely known for asset monitoring in the industry for a long time. A clear example is the automotive industry. Recently, there has also been significant interest in the application of digital twins in healthcare, especially in genomics in what is known as precision medicine. This work focuses on another medical speciality where digital twins can be applied, sleep medicine. However, there is still great controversy about the fundamentals that constitute digital twins, such as what this concept is based on and how it can be included in healthcare effectively and sustainably. This article reviews digital twins and their role so far in what is known as personalized medicine. In addition, a series of steps will be exposed for a possible implementation of a digital twin for a patient suffering from sleep disorders. For this, artificial intelligence techniques, clinical data management, and possible solutions for explaining the results derived from artificial intelligence models will be addressed.
Extractive text summarization model based on advantage actor-critic and graph matrix methodology

&lt;abstract&gt; &lt;p&gt;The automatic text summarization task faces great challenges. The main issue in the area is to identify the most informative segments in the input text. Establishing an effective evaluation mechanism has also been identified as a major challenge in the area. Currently, the mainstream solution is to use deep learning for training. However, a serious exposure bias in training prevents them from achieving better results. Therefore, this paper introduces an extractive text summarization model based on a graph matrix and advantage actor-critic (GA2C) method. The articles were pre-processed to generate a graph matrix. Based on the states provided by the graph matrix, the decision-making network made decisions and sent the results to the evaluation network for scoring. The evaluation network got the decision results of the decision-making network and then scored them. The decision-making network modified the probability of the action based on the scores of the evaluation network. Specifically, compared with the baseline reinforcement learning-based extractive summarization (Refresh) model, experimental results on the CNN/Daily Mail dataset showed that the GA2C model led on Rouge-1, Rouge-2 and Rouge-A by 0.70, 9.01 and 2.73, respectively. Moreover, we conducted multiple ablation experiments to verify the GA2C model from different perspectives. Different activation functions and evaluation networks were used in the GA2C model to obtain the best activation function and evaluation network. Two different reward functions (Set fixed reward value for accumulation (ADD), Rouge) and two different similarity matrices (cosine, Jaccard) were combined for the experiments.&lt;/p&gt; &lt;/abstract&gt;
On Formal Choreographic Modelling: A Case Study in EU Business Processes

Formal choreographic modelling advocates a correctness-by-construction principle for the development of sound communication protocols. This principle usually hinges on syntactic or semantic restrictions to rule out models that could lead to communication glitches like message losses or deadlocks. This paper explores how these restrictions impact on the usability of formal modelling. More precisely, we benchmark the use of a formal choreographic modelling language designed to support the correctness-by-construction principle of message-passing systems. To this purpose, we consider the formal choreographic modelling of real business processes taken from the official documentation of European customs business process models. In fact, following a steadily increasing trend, the European Union started to use BPMN to support the legal provisions of the customs business process models.
Silq2Qiskit - Developing a quantum language source-to-source translator

Quantum Computers are quickly becoming capable of solving certain tasks substantially faster than classical computers and the promise of quantum-driven advancements in research and economy continues to accelerate the development of quantum technology. However, most software development for quantum computers relies on the tedious manual implementation of quantum circuits on a very low level of abstraction, with tools such as the prominent IBM Qiskit SDK. In 2020, Silq, a quantum language to enable more intuitive and robust quantum development, was presented. While it substantially simplifies the write- and readability of quantum programs, Silq Code can only be run through its simulator on classical hardware. In comparison, Qiskit and its close integration with IBM’s Quantum Experience even enable users to run and evaluate quantum programs on physical quantum hardware. This paper proposes an automatic source-to-source translator for basic Silq Code and the extension of Qiskit by core concepts of Silq’s abstraction layers, such as Quantum Indexing and Quantum Control Flow.
On the effectiveness of machine learning experiment management tools

Machine learning experiment management tools support developers and data scientists on planning, tracking, and retrieving machine-learning experiments and assets when building intelligent software systems. Among others, they allow tracing back system behavior to experiment runs, for instance, when model performance drifts. Unfortunately, despite a surge of these tools, they are not well integrated with traditional software engineering tooling, and no hard empirical data exists on their effectiveness and value for users. We present a short research agenda and early results towards unified and effective software engineering and experiment management software.
SECURING DATA NETWORK FOR GROWING BUSINESS VPN ARCHITECTURES CELLULAR NETWORK CONNECTIVITY

Private networks allow organizations to leverage, customize and dedicate LTE network capabilities to their own service needs. These networks are controlled and managed locally and can be optimized for specific network services and applications. Often they are used because there may be gaps in coverage where there is no cellular connectivity. Enterprises and businesses running on light application systems, using small bandwidth and requiring fast deployment such as ATMs, vending machines, digital signage kiosks and small store branches We propose a method and concept and implementation, in the form of a Data Network Security Configuration For Business Growth VPN architecture Mobile Network Connectivity for various businesses. By implementing products and services by developing the configuration and model of VPN Tunneling Protocol rules, using the EOIP Protocol and SSTP Protocol methods and virtualization schemes using the VLAN Bridging method on Wide Area Network (WAN) network connectivity. This method takes advantage of the LTE features found on the RB751U-2hnd and then integrates Huawei Mobile Broadband LTE. We also present a general Service Level Agreement (SLA) and an open source tools-based SLA network system Zabbix. Then configure the VPN Tunneling Protocol and its features on the RB751U-2hnd using Paramiko Network Automation. The focus of the results of this research is that by utilizing the available tools we will build a VPN with system monitoring facilities with the results achieved are network performance and availability and show that the design we build can be used for private connections for growing businesses. the results obtained from testing for 1 month for the 3 providers used, for the average throughput value with VPN tunneling applied TSEL 14.2 Mbps, ISAT 13.9 Mbps and XL 13.5 Mbps. The SLA value obtained is based on acceptable criteria, TSEL 91.5%, ISAT 91.8% and XL 90.6%.
Mammogram image classification system using deep learning for breast cancer diagnosis

Mammography is the common screening method of breast cancer, a deadly disease among women in the world with a high mortality rate. Breast cancer is the uncontrollable cell growth as a tumor that may be cancerous or non-cancerous. This study employs a Deep Convolution Neural Network (DCNN) with Transfer Learning (TL) that utilizes mammogram image samples for breast cancer diagnosis. The contrast enhancement of the suspicious image tumor is done by using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique on Region of Interest (ROI). The VGG-16 network model is utilized with reduced convolution layers and max-pooling layers providing more feature datasets for efficient mammogram classification into either benign or malignant cancer for early diagnosis for its appropriate treatment. The performance of the VGG16 model is compared with the VGG-19 net. Results show that VGG-16 architecture provides promising results than VGG-19 on Mammographic Image Analysis Society (MIAS) database images with 82.5% accuracy.
Diagnosis Using Brain Tumors Two-Dimensional Principal Component Analysis (2D-PCA) with K-nearest Neighbor (KNN) Classification Algorithm

The rapid development of computer technology has brought more and more benefits to human life. Currently, computers can make decisions by imitating the human brain to be used in the health sector to play a role in solving existing problems. One of the technologies used is digital image processing technology on MRI images of brain tumors. Brain tumor images have various variations and large dimensions; therefore, an appropriate method is needed to recognize images maximally. Dimensional reduction uses the Two-Dimensional Principal Component Analysis (2DPCA) method. The classification process uses the K-Nearest Neighbor (KNN) method by calculating the euclidean distance (Euclidean Distance). From 3 tests with the number of data 200 images, the results of the accuracy of the 1st test were 90.0% with 60 test data and 140 training data, the second test was 85.0% with 80 test data and 120 training data, and the 3rd test is worth 83.0% with 100 test data and 100 training data. Based on the research above, it can be concluded that the highest accuracy is obtained in the 1st test, while the lowest accuracy is on the 3rd test. The more amount of training data compared to the test data, the greater the accuracy value obtained. This research is expected to be a reference for further research so that the results obtained are more optimal.
Codesign for Extreme Heterogeneity: Integrating Custom Hardware With Commodity Computing Technology to Support Next-Generation HPC Converged Workloads

The future of high-performance computing (HPC) will be driven by the convergence of physical simulation, artificial intelligence, machine learning, and data science computing capabilities. While computational performance gains afforded by technology scaling, as predicted by Moore's Law, have enabled large-scale HPC system design and deployment using commodity CPU and GPU processing components, emerging technologies will be required to effectively support such converged workloads. These emerging technologies will integrate commodity computing components with custom processing and networking accelerators into ever-more heterogeneous architectures resulting in a diverse ecosystem of industry technology developers, university, and U.S. Government researchers. In this article, we describe efforts at the U.S. Department of Energy's Pacific Northwest National Laboratory to construct an end-to-end codesign framework that lays a groundwork for such an ecosystem, including notable outcomes, remaining challenges, and future opportunities.
Implications of Ad Hoc Artificial Intelligence in Music

This paper is an examination of several well-known applications of artificial intelligence in music generation. The algorithms in EMI, GenJam, WolframTones, and Swarm Music are examined in pursuit of ad hoc modifications. Based on these programs, it is clear that ad hoc modifications occur in most algorithmic music programs. We must keep this in mind when generalizing about computational creativity based on these programs. Ad hoc algorithms model a specific task, rather than a general creative algorithm. The musical metacreation discourse could benefit from the skepticism of the procedural content practitioners at AIIDE.
Applying reconfiguration cost and control pattern modeling to self-adaptive systems

Self-adaptive systems have become a popular research topic to overcome challenges of developing highly complex, interconnected, and heterogeneous systems and networks. These systems aim to autonomously adapt to a changing environment by adapting system behavior or composition to improve performance. Many self-adaptive systems are designed in a purely reactive way and without considering costs that may be incurred by performing adaptation. This thesis therefore aims to develop an approach for proactive self-adaptive systems and evaluate the impact of reconfiguration cost and proactive adaptation in an edge computing system. Additionally the autonomous adaptation of different control patterns for centralized or decentralized control will be explored and evaluated. This thesis proposes to extend feature models, as used in dynamic software product lines, with modeling for reconfiguration cost and of uncertainty in the system's environment.
Analysis of the Intuitive Teleoperated System of the TxRob Multimodal Robot

Natural disasters such as earthquakes, avalanches, landslides, among others, leave in their path people who may be trapped in the rubble, which are hardly found by rescue agents, so a reliable system in the operation of an exploration and rescue robot is essential.This paper aims to evaluate the systems proposed for the operation of the TxRob exploration robot.The teleoperated control systems that were developed for the manipulation of the robot are: a multimodal system feedback with information through different sensors, and a GUI control system using joystick buttons.These systems were analyzed using subjective metrics such as NASA-TLX, Scale Utility System (SUS) and Microsoft Reaction Cards, which provide interesting data when evaluating the performance of an interface, as well as the workload, user satisfaction and usability; these aspects are used to conclude which system is the most intuitive when performing rescue operations in case of a disaster, among others.15 operators were evaluated to validate this system; the age range of the operators was between 20 and 43 years old and 20% of them had previously used VR headsets.Priority is given to the most immersive, easy to use and the most efficient system to perform the task of handling the robot.