Reviewerly/exHarmony huggingface dataset:
Efektivitas Penggunaan Microsoft Teams sebagai Media Pembelajaran pada Mata Pelajaran IPS (Studi Kasus Kelas VIII MTsN 1 Kota Tangerang Selatan

Abstrak Penelitian ini bertujuan untuk mengetahui efektivitas penggunaan Microsoft Teams sebagai media pembelajaran pada mata pelajaran IPS. Penelitian ini dilakukan di MTsN 1 Kota Tangerang Selatan. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan teknik analisis data Miles dan Huberman yang terdiri dari reduksi data, penyajian data dan kesimpulan atau verifikasi. Instrumen yang digunakan dalam penelitian ini yaitu pedoman observasi, wawancara dan dokumentasi. Hasil penelitian ini menunjukkan bahwa penggunaan Microsoft Teams sebagai media pembelajaran pada mata pelajaran IPS dikategorikan tidak efektif, dapat dilihat berdasarkan pada indikator efektivitas pembelajaran menurut Slavin yaitu mutu pengajaran, tingkat pengajaran yang tepat, insentif dan waktu yang harus saling berkesinambungan antara satu dengan lainnya agar dapat dinyatakan efektif. Semua aspek atau indikator dari efektivitas saling berkesinambungan antara satu dengan lainnya dan telah memenuhi kriteria untuk dinyatakan efektifKata Kunci: Efektivitas, Microsoft Teams, Media Pembelajaran AbstractThis study aims to determine the effectiveness of using Microsoft Teams as a learning medium in social studies subjects. This research was conducted at MTsN 1 South Tangerang City. The research method used is descriptive qualitative with data analysis techniques Miles and Huberman consisting of data reduction, data presentation and conclusions or verification. The instruments used in this research are observation, interview and documentation guidelines. The results of this study indicate that the use of Microsoft Teams as a learning medium in social studies subjects is categorized as ineffective, it can be seen based on the indicators of learning effectiveness according to Slavin, namely the quality of teaching, the right level of teaching, incentives and time that must be mutually sustainable with one another in order to achieve success. declared effective. All aspects or indicators of effectiveness are mutually sustainable with each other and have met the criteria to be declared effectiveKeywords: Effectiveness, Microsoft Teams, Learning Media
Unlocking Potential: The Crucial Link between School Infrastructure and Educational Quality

Unlocking the potentials of students and improving educational quality depend significantly on the link between school infrastructure and educational outcomes This paper examines what is meant by educational quality and how crucial school facilities are to its improvement. It names variables that affect educational quality, including infrastructural accessibility, resource distribution, technological integration, and teacher capacity. The effects of poor infrastructure on education are also covered, including constrained learning settings, a lack of resources, safety concerns, and inequity. The paper offers recommendations for approaches and solutions to address these problems, including improving infrastructure, incorporating technology, offering teacher professional development, implementing student-centered methodologies, promoting inclusive education, involving communities, and using data-driven decision making. It also discusses issues with funding and resource distribution and offers suggestions for removing obstacles such financial inequality, linguistic and cultural barriers, limited access to high-quality education, gender inequities, and a lack of parental involvement. These suggestions can help educational systems. Addressing the gap and understanding the crucial link between school infrastructure and educational quality is vital for fostering a conducive learning environment that empowers students to unlock their full potential. By acknowledging the impact of school infrastructure and prioritizing investments in this area, educational systems can take a significant step towards providing equitable, high-quality education for all students.
A Modified-PSO Algorithm to Schedule Scientific Workflows in Cloud

Cloud Computing offers users pay as you go model to provision resources as needed. This model helped customers to utilize the cloud to execute tasks that are either independent or dependent. The dependent tasks termed as workflows are to be executed meeting the QoS requirements. Many approaches were presented in the literature to address this challenge. Heuristic and meta-heuristic Algorithms such as HEFT, SHEFT, ILP, GA, PSO and ACO are implemented to address workflow scheduling different QoS parameters. In this paper, we propose to use the particle swarm optimization technique to assign tasks to virtual machines to minimize makespan, cost and Schedule length Ratio (SLR). The makespan and cost are two primary goals in the current workflow scheduling. The proposed method implemented on cloudsim, the makespan time and cost parameters of the proposed approach outperform the other algorithms.
Concurrency with AsyncIO

The Async IO facilities in Python are relatively recent additions originally introduced in Python 3.4 and evolving up to and including Python 3.7. They are comprised of two new keywords async and await (introduced in Python 3.7) and the Async IO Python package. In this chapter we first discuss Asynchronous IO before introducing the async and await keywords. We then present Async IO Tasks, how they are created used and managed.
Working with pprint

It is common to need to print containers/collections in Python. This is often done to the standard output stream which in most cases is the terminal window from which Python is being run. The problem with this is that lists of lists or dictionaries of key-value pairs can become long and difficult to read if merely printed across the screen. The built-in pprint module is designed to help with this. The pprint module provides functions that can be used to print out collections with options to control how the data contained within the collection should be displayed. It is a very useful module which is often overlooked when learning Python.
Python Turtle Graphics

Python is very well supported in terms of graphics libraries. One of the most widely used graphics libraries is the Turtle graphics library. This is partly because it is straight forward to use and partly because it is provided by default with the Python environment (and thus you do not need to install any additional libraries to use it). This chapter introduces the Python Turtle Graphics library and concludes by briefly considering a number of other graphic libraries including PyOpenGL.
Research on Intelligent Information Screening Algorithm of Japanese Network Corpus Based on Computer Corpus

Corpus is a collection of data that contains a large amount of real language information and can be processed by computer. Using corpus to study the characteristics of language has become one of the most important applications of corpus. At present, corpus based linguistic research is in the ascendant and is becoming one of the new research methods in linguistics. Discourse analysis is also a study of real corpus. The combination of the two can inject new vitality into the study of discourse analysis. With the deepening of the research on information screening of Japanese network corpus, some problems to be solved gradually appear. This paper reviews the background and research paradigm of corpus based discourse analysis model, looks forward to the future research, and points out the existing problems and challenges of this research paradigm. The innovation of information teaching methods and the diversification trend of foreign language teaching methods. With the vigorous development of the Internet, big data and artificial intelligence and other technologies, foreign language teaching methods inevitably move towards the direction of informatization and dataization, and the diversified development of teaching methods, the use of advanced information and multimedia technology to provide students with an effective language learning environment, but also poses challenges to teachers' teaching ability. The teaching methods, teaching content, and teaching methods of the Japanese language major must be further optimized to meet the needs of students and society in the new era. While facing the above challenges, it is necessary to seize the development opportunities, innovate teaching reforms, and respond to the needs of the times and society for the development of Japanese language majors. These urgently require the intervention of new language teaching methods.
Proposal for a Participatory Methodology for the Creation of Serious Games

We are facing a new generation of students, who are not only looking for theoretical knowledge, but also want learning to be practical, interesting, and fun. They request didactic techniques that allow them to have a more prominent role in their learning, models that are not focused on teaching, but on learning. They are students with different characteristics because they were born in the digital age. Technology has changed the way they relate to the world. They are not comfortable with respect to traditional models and try to incorporate new technologies into all aspects of their lives. Within the educational field, new technologies are seen as a tool that facilitates learning and develops skills in students. From this perspective, the development of simulators and serious games are now used as a strategy to facilitate learning. The advantages of using a serious game seem evident, they help develop critical thinking, encourage creativity, increase problem-solving skills, increase retention, among other benefits. However, when games are not well designed, they lose both their appeal and their playful essence. One reason for these problems is the absence of the application of any design methodology, and that many of these problems come from the requirements definition phase. Specifically, most of the problems occur in the game design phase, where there is a deficiency in the writing of the requirements requested by the teachers. When the requirements are vague, or ambiguous, experts in the development area are unable to interpret the interaction design, the game mechanics, or the way in which users will interact with the software. This article aims to discuss the specifications and requirements that instructional designers and developers should address before migrating to project development, implementation, or evaluation. This article is important because it analyzes the need to establish clear requirements and objectives that will facilitate the creation of serious games in education.
A Double Embedding of an Anti-Removable Visible Logo Using Discrete Wavelet Transform and Chaotic Map

The aims of visible watermarking are to prevent the illegal copying of videos and images and to avoid the theft and unauthorized advertisement of the legitimate owners' content. The visible watermark is distinguishable with the naked eye. This makes it easily removed by an attacker. This paper proposes an anti-removable visible logo by double embedding using the discrete wavelet transform (DWT) and a chaotic map. The main purpose of this method is to prevent the removal attack and restore the original video without information loss after the logo is legally removed. DWT is used for both the cover area and the logo to embed sub-bands of information from the cover area into sub-bands of the logo. The main idea of using a chaotic map is to make it difficult to recover the original video when the logo is removed by illegal means. The experiment results show that the proposed method is robust against illegal watermark removal. In addition, video frames were recovered without loss after the legal extraction process.
An RNN-based Hybrid Model for Classification of Electrooculogram Signal for HCI

In recent years, there has been a rise in the amount of research conducted in the field of human-computer interaction (HCI) employing electrooculography (EOG), which is a technology that is effectively and widely used to detect human eye activity. The use of EOG signals as a control signal for HCI is essential for understanding, characterizing, and classifying eye movements, which can be applied to a wide range of applications including virtual mouse and keyboard control, electric power wheelchairs, industrial assistive robots, and patient rehabilitation or communication purposes. In the field of HCI, EOG signals classification has continuously been performed to make the system more effective and reliable than ever. In this paper, a Recurrent neural network model is proposed for classifying eye movement directions utilizing several informative feature extraction methods and noise filtering. Our classification model is comprised of Gated Recurrent Unit (GRU) with a Bidirectional GRU followed by dense layers. The classifier is investigated to find a better classification performance of four directional eye movements: Up and Down for the vertical channel, along with Left and Right for the horizontal channel of EOG signals. The classifier achieved 99.77% and 99.74% accuracy for vertical and horizontal channels, respectively, which outperforms the compared state-of-the-art studies. The proposed classifier allows disabled people to make life-improving decisions using computers, achieving the highest classification performance for rehabilitation and other applications.
Privacy, Intellectual Freedom, and Learning Analytics

Academic libraries are increasingly participating in the collection and analysis of student data and engaging with campus learning analytics programs. The intent is to understand how libraries contribute to student experiences, learning, and success and to design effective and efficient programs toward these ends. Our earlier research established that academic library professionals recognize the ethical issues that learning analytics raise, particularly with respect to privacy, and that they need specific training related to these ethical issues in the learning analytics context. Though addressable through continuing education, those findings also pointed to the need to strengthen LIS education to prepare graduates for that work. This poster reports on efforts to adapt a proven-effective continuing education program, Prioritizing Privacy, to the LIS classroom through the development of an Open Education Resource curriculum packet and related student learning assessment resources. The poster will present the curriculum and identified LIS course contexts in which the curriculum might be enacted. Additional information is available on the project website (https://prioritizingprivacy.org).
Employing Applying Big Data Analytics Lifecycle in Uncovering the Factors that Relate to Causing Road Traffic Accidents to Reach Sustainable Smart Cities

This paper aims to identify the factors that relate to causing road traffic accidents to reach sustainable smart cities—this issue is significant to society and people. To that end, the paper pro-poses a model for uncovering such factors using big data analytics lifecycle: discovery, data preparation, model planning, model building, communication of results, and operationalization. In other words, the paper presents a simplified methodology taking advantage of the big data analysis life cycle and the velocity of analyzing data to reduce traffic accidents—velocity is the most important characteristic of big data. The velocity in analyzing data and identifying the factors that relate to causing car accidents helps decision-makers quickly make appropriate decisions. Worthy of mentioning that Google Data Studio (GDS) was used here to produce the visualizations and reports needed. Hopefully, researchers will build on this methodology in the future to achieve sustainability, to propose a more inclusive and effective methodology to minimize the negative effects of traffic accidents in the smart cities.
A Novel Paradigm for Sentiment Analysis on COVID-19 Tweets with Transfer Learning Based Fine-Tuned BERT

The rapid escalation in global COVID-19 cases has engendered profound emotions of fear, agitation, and despondency within society. It is evident from COVID-19-related tweets that spark panic and elevate stress among individuals. Analyzing the sentiment expressed in online comments aids various stakeholders in monitoring the situation. This research aims to improve the performance of pre-trained bidirectional encoder representations from transformers (BERT) by employing transfer learning (TL) and fine hyper-parameter tuning (FT). The model is applied to three distinct COVID-19-related datasets, and each of the datasets belongs to a different class. The evaluation of the model’s performance involves six different machine learning (ML) classification models. This model is trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. Heat maps are generated for each model to visualize the results. The performance of the model demonstrates accuracies of 83%, 97%, and 98% for Class-5, Class-3, and binary classifications, respectively.
Automating Audio Attack Vectors

Over the years, speech recognition voice-assistant systems have become better in performance and now are commonplace in the daily lives of millions of people. But are they secure? Voice-assistant systems usually have high system privilege and no authentication system in place, giving them access to a huge amount of personal data. Studies conducted in recent years use different technologies and methods such as laser light, machine learning, to exploit the vulnerabilities of these systems, but these attacks have been carried out in laboratory environments with bulky equipment. In this paper, we build a portable and easy-to-use proof-of-concept tool to automate two audio-based attack vectors. We then proceed to conduct tests of our tool under varying metrics to determine its efficacy under real-life like conditions. Finally, we conclude by analysing our results and highlighting design changes to mitigate these types of attacks.
Influence of Extension Connectivity on Youth Engagement into Paddy Farming in Kano State, Nigeria

The clarion call by the international development agencies to the developing countries, upon strengthening the agricultural sector with modern technological connectivity, so that massive unemployed youth can be employed in the sector could not be realized without effective youth policies. However, designing such policies that can encourage more youth into farming activities becomes a major challenge in these countries due to insufficient youth data and studies. Thus, this study examines the effect of extension connectivity technology in influencing youth participation in paddy farming in Kano, Nigeria. The study selected 180 youth respondents from mixed sampling techniques and the questionnaire was administered in five paddies local government areas in Kano state. The binary logit was used in the estimation of different models. The findings proved that extension connectivity used in farming can inspire youth to participate and remain in paddy farming. The study highlights the need for greater attention in utilising the extension connectivity in enlightening and educating youths to access information to aids paddy farming activities.
AdaptNet: Policy Adaptation for Physics-Based Character Control

Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new behaviors to be quickly learned from like tasks in comparison to learning from scratch. Building on top of a given reinforcement learning controller, AdaptNet uses a two-tier hierarchy that augments the original state embedding to support modest changes in a behavior and further modifies the policy network layers to make more substantive changes. The technique is shown to be effective for adapting existing physics-based controllers to a wide range of new styles for locomotion, new task targets, changes in character morphology and extensive changes in environment. Furthermore, it exhibits significant increase in learning efficiency, as indicated by greatly reduced training times when compared to training from scratch or using other approaches that modify existing policies. Code is available at https://motion-lab.github.io/AdaptNet .
Harnessing digital technology for COVID-19 response in Uganda: lessons and implications for future public health emergencies

COVID-19 was one of the greatest disruptors of the 21st century, causing significant morbidity and mortality globally. Countries around the world adopted digital technologies and innovations to support the containment of the pandemic. This study explored the use of digital technology and barriers to its utilisation in responding to COVID-19 and sustaining essential health services in Uganda to inform response to future public health emergencies in low-resource settings. We reviewed published and grey literature on the use of digital technology in Uganda’s response from March 2020 to April 2021 and conducted interviews with key informants. We thematically synthesised and summarised information on digital technology use as well as related challenges. During the COVID-19 response, digital technology was used in testing, contact tracing and surveillance, risk communication, supportive supervision and training, and maintenance of essential health services. The challenges with technology use were the disparate digital tools and health information systems leading to duplication of effort; limited access and coverage of digital tools, poor data quality; inaccessibility of data and an inability to support data manipulation, analysis and visualisation. Moreover, the inherent inadequate technology support systems such as poor internet and electricity infrastructure in some areas posed challenges of inequity. The harnessing of technology was key in supporting the COVID-19 response in Uganda. However, gaps existed in access, adoption, harmonisation, evaluation, sustainability and scale up of technology options. These issues should be addressed in preparedness efforts to foster technology adoption and application in public health emergencies with a focus on equity.
Pearls and Pitfalls of Interpretation of Automated Breast US

Dense breast tissue is an independent risk factor for breast cancer and reduces the sensitivity of mammography. Patients with dense breast tissue are more likely to present with interval cancers and higher-stage disease. Successful breast cancer screening outcomes rely on detection of early-stage breast cancers; therefore, several supplemental screening modalities have been developed to improve cancer detection in dense breast tissue. US is the most widely used supplemental screening modality worldwide and has been proven to demonstrate additional mammographically occult cancers that are predominantly invasive and node negative. According to the American College of Radiology, intermediate-risk women with dense breast tissue may benefit from adjunctive screening US due to the limitations of mammography. Several studies have demonstrated handheld US (HHUS) and automated breast US (AUS) to be comparable in the screening setting. The advantages of AUS over HHUS include lack of operator dependence and a formal training requirement, image reproducibility, and ability for temporal comparison. However, AUS exhibits unique features that can result in high false-positive rates and long interpretation times for new users. Familiarity with the common appearance of benign mammographic findings and artifacts, technical challenges, and unique AUS features is essential for fast, efficient, and accurate interpretation. The goals of this article are to (a) examine the role of AUS as a supplemental screening modality and (b) review the pearls and pitfalls of AUS interpretation. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.