Reviewerly/exHarmony huggingface dataset:
A System for Trusted Recovery of Data Based on Blockchain and Coding Techniques

With the continuous development of information technology, the Internet of Things has also been widely used. At the same time, in the power Internet of Things environment, reliable data is essential for data use and accurate analysis. Data security has become a key factor in ensuring the stable operation of the power grid. However, the power Internet of Things devices is extremely vulnerable to network attacks, leading to data tampering and deletion. Resisting tampering, preventing data loss, and reliably restoring data have become difficult to ensure data security. In order to solve this problem, this paper proposes a trusted data recovery system based on blockchain and coding technology. Data nodes of the power Internet of Things encode key data and back them up to the blockchain network through a data processing server located on the edge. The data processing server performs real-time detection of the data integrity of the data nodes. When the data is tampered with or deleted, the data processing server promptly obtains the corresponding data encoding blocks from the blockchain network, decodes them, and sends them to the data node to complete the data recovery task. According to the test result, the data backup speed of this system is increased by 15.3%, and the data recovery speed is increased by 19.8% compared with the traditional scheme. It has good security and real-time performance. Meanwhile, it reduces the network and storage resource overhead in the data backup and recovery process.
Exploring the Use of WhatsApp for Teaching Speaking to English Language Learners: A Case Study

Being able to speak or communicate orally is an ultimate goal for most English language learners. Thus, teaching speaking is a main component of the curriculum in most communicative-based English language classrooms. However, teachers struggle to teach speaking in English language classrooms. Two main reasons contribute to this struggle: insufficient time for speaking practice in language classrooms and students’ unwillingness to communicate due to anxiety. Asynchronous Computer-Mediated Communication (ACMC) may have the potential to provide a solution to this difficulty. A widely accessible mobile application that can provide an easily implemented ACMC tool is WhatsApp. The current case study explored the potential use of WhatsApp for teaching English speaking in an English as a Foreign Language (EFL) context. 15 Saudi EFL learners of English were recruited to participate in the present study. A mixed-method approach comprising pre and post-tests, closed-ended questionnaire, and semi-structured interviews were used to collect the data for the current study. The findings showed that using WhatsApp can have the potential for improving EFL learners’ speaking performance, increasing their motivation and attitude towards speaking, and decreasing their anxiety and fear of making mistakes when speaking. WhatsApp can provide an effective platform for teaching speaking in an EFL setting where there is a scarcity of time available for oral communication.
DIY Cruelty: The Global Political Micro-Practices of Hateful Memes

Abstract Cruel memes spread messages of hate via social media. The Internet itself extends the memes’ geographical reach, and many such cruel memes circulate across borders. This article examines the activities of cruel memeing—practices of creating, commenting on, reinforcing (“liking”), sharing, remixing, and otherwise endorsing cruel memes—as microscale hostile engagements in global politics. This is a politics of the everyday that is accessible to people in their ordinary lives and that is designed to be entertaining as well as cruel. The research draws on a large dataset of memes, comment threads, and related information from two opposed Reddit communities, r/TheLeftCantMeme and r/TheRightCantMeme. The power-flow theoretical framework structures an interpretive analysis of how cruel memes circulate within the social media space. We examine content (including narrative, degree of cruelty, and other components), and velocity of information flow, as well as access to the flow. Focusing on racism, antisemitism, and disdain for political opponents, we draw on interpretive methods to analyze the flow of information that spreads hate. We find: (1) normalization of divisiveness, derisiveness, and bigotry; (2) justifications of violence; and (3) emergence of agents despite pseudonymity. Cruel memeing activities combine with the structure of the online communities to spread hatred far beyond social media platforms.
Pelatihan Mendesain Pembelajaran Daring Menarik Selama Pandemi Covid-19 dengan Teknologi Pembelajaran Sederhana

This community service was carried out to provide assistance to the situation experienced by English high school teachers in the sudden change of the learning modes due to the Covid-19 pandemic. It was conducted in the form of an online seminar on how to design interesting online learning by using simple instructional technology and how to combine asynchronous and synchronous learning modes. The 150-minute-online seminar was held on the Zoom platform and YouTube livestreaming. The session was divided into three main parts, namely 1) introduction of the 5E framework for online learning, 2) examples of material design using simple instructional technology, and 3) examples of simple instructional technology. In general, it can be concluded that this community service not only provided an introduction and understanding of online learning design with simple instructional technology, but also enhanced the participants' pedagogical knowledge of online learning design.
Students’ Perceptions of Using Machine Translation Tools In the EFL Classroom

Machine Translation (MT) refers to automatically trying to translate words, phrases, text, or speech from one language to another (Arnold et al., 1994). This study aims to analyze English as Foreign Language (EFL) students’ perceptions on utilizing machine translation (MT) in translating words, phrases, text, or speech. This research used descriptive qualitative method, 13 EFL students as respondents using this type of technology were described and analyzed. Data were gained from the analysis of the translation quality supported by machine translation procedures and questionnaires to 13 English students in translation subject. The results showed that, EFL students in the sixth and seventh semester of English education department of Megarezky University in translating words, phrases, texts, paragraphs had to recheck and rearrange to get a good translation by their own understandings. Machine Translation (MT) becomes another option to recognize the meaning of foreign language. Machine Translation can be used as dictionary as well. MT is incredibly useful and helpful, it can provide a general description to the users, it gives an inspiration or consideration to the users to understand the meaning.
Mobile Learning Based on Procedural and Conceptual Knowledge on Fractional for Elementary School

As the impact of the Covid-19 pandemic, distance learning requires teachers to make learning more interesting yet achieve learning outcomes. This study aims to produce a valid, practical, and effective product in mobile learning based on procedural and conceptual knowledge for elementary school students. This study uses Research and Development with the ADDIE model. The subjects of this study were 11 elementary school fifth-grade students. The material and media expert is a lecturer in mathematics education and information technology education, respectively. The validity test was carried out using the expert judgment method. Eleven fifth-grade elementary school students assessed the practicality and effectiveness tests to determine the responses and results in fractional mathematics after using the developed mobile learning. This study uses both quantitative and qualitative data, which were collected using questionnaires and learning outcomes tests. The validity of mobile learning produces an average of 3.59 or is in the interval 2.50 ≤ Rv ≤ 4.00 with valid criteria. The effectiveness of mobile learning is obtained from the pretest and posttest data. The practicality of mobile learning obtained an average of 3.82 or is in the interval 2.50 ≤ Rk ≤ 4.00 with practical criteria. The effectiveness of mobile learning shows a significance value of 0.0010 or &lt; 0.0050, which means that there are differences in student learning outcomes before and after using mobile learning. Thus, the development of procedural and conceptual-based mobile learning on fractional material in fifth-grade elementary school can be categorized as valid, practical, and effective.
PrismAR: A Mobile Augmented Reality Mathematics Card Game for Learning Prism

Mathematics education has benefited from the increase in popularity of augmented reality (AR) technology.However, no game-based AR application has been developed specifically for prism topic.This paper aims to report PrismAR app that we have developed.It has interactive exercises and quiz and shows the prism's edges and vertices, in 2D and 3D views.This project aims to assist primary 3 students (9 years old) in developing their mathematical abilities and making prism mathematical concepts more interactive.An exploratory study design was used to evaluate the app with 20 students from a Malaysian school.We wanted to ascertain their degree of approval for the app's design, learning content, user interface, and interactivity with the augmented reality card game teachers.Overall, the assessment returned an average mean of 4.98, which was interpreted as 'highly acceptable.'The pre-test mean result is 12.6 (S.D. = 2.012), while the post-test mean result is 14.6 (S.D. = 1.789).The results indicate that using an augmented reality-based card game could be a successful method for increasing students' mathematics competence.This study serves as a call to action for potential developers and educators to improve AR integration in math education in the future.
A feasibility study of multisite networked digital pathology reporting in England

The objective of the project was to evaluate the feasibility of introducing a single-networked digital histopathology reporting platform in the Southwest Peninsula region of England by allowing pathologists to experience the technology and recording their perceptions. This information was then used in planning future service development. The project was funded by the National Health Service (NHS) Peninsula Cancer Alliance and took place in 2020 during the COVID-19 pandemic.Digital slides of 500 cases from Taunton were reported remotely in Truro, Plymouth, Exeter, Bristol, or Bath by using a single remote reporting platform located on the secure Health and Social Care Network (HSCN) that links NHS sites. These were mainly small gastrointestinal, skin, and gynecological specimens. The digital diagnoses were compared with the diagnoses issued on reporting the glass slides. At the end of the project, the pathologists completed a Google Forms questionnaire of their perceptions of digital pathology. The results were presented at a meeting with the funder and discussed.From the 500 cases there were nine cases of significant diagnostic discrepancy, seven of which involved the misrecognition of Helicobacter pylori in gastric biopsies. The questionnaire at the end of the project showed that there was a general agreement that the platform was easy to use, and the image quality was acceptable. It was agreed that extra work, such as deeper levels, was easy to request on the software platform. Most pathologists did not agree that digital reporting was quicker than glass slide reporting. Some were less confident in their digital diagnoses than glass diagnoses. They agreed that some types of specimens cannot easily be reported digitally. All users indicated that they would like to report at least half of their work digitally in the future if they could, and all strongly agreed that digital pathology would improve access to expert opinions, teaching, and multidisciplinary meetings. It was difficult to find pathologists with time to undertake remote digital reporting, in addition to their existing commitments.Overall, the pathologists developed a positive perception of digital pathology and wished to continue using it.
Learning Vocabulary in the App Store by EFL College Students

This article gives examples of mobile vocabulary apps (MVAs) that can be downloaded from the Google Play or Apple App Stores and used as extension activities or a supplement to in-class vocabulary instruction in EFL such as English word study, advanced English vocabulary, test your English vocabulary, English vocabulary level I, II &amp; III, 1000 TOEFL words and others. There are also mobile dictionary apps, graded vocabulary tests for assessing vocabulary knowledge and skills and vocabulary trainers for the TOEFL and IELTS test vocabulary. In addition, the article gives guidelines for searching for, selecting, and using MVAs, the phases of teaching with MVAs and the instructor's role. The instructor can help the students find and download relevant MVAs, give pre-questions, and follow the students up to make sure they are making the best use of them. Recommendations for using MVAs with EFL freshman students are given.
Tokenizing Behavior Change: A Pathway for the Sustainable Development Goals

To be successful and sustainable, social impact programs require individuals and groups to change aspects of their behavior. As blockchain-based tokens are increasingly adopted to target social outcomes, it is important to properly define these activities as “behavior change interventions” and assess their design and management as such—otherwise, there is significant risk of possible unintended consequences. Designing tokens as behavior change interventions requires new constructs beyond those currently in use to model the interdependence of digital and social ecosystems, and integration of token engineering, cryptoeconomics, and behavioral skill sets to test token designs within various ecosystems. New token design and testing protocols that integrate behavior measures around the targeted social outcomes are needed, to fill a critical gap in current practice. Hence, new standards, operational frameworks, and ethics are needed to guide the use of tokens at scale, as tools to achieve social impacts such as attaining the United Nations’ Sustainable Development Goals. Meeting these needs requires a collaborative approach between token design actors (computer scientists, cryptoeconomists, token engineers, etc.) and social impact practitioners who will be increasingly called upon to use tokens as behavior change tools. This paper begins to identify common ground and address areas to further develop research and practice of tokens being used for social impact.
BloSM: Blockchain-Based Service Migration for Connected Cars in Embedded Edge Environment

Edge computing represents the future of computing paradigms that perform tasks near the user plane. The integration of blockchain with edge computing provides the added advantage of secured and trusted communication. In this paper, we propose a blockchain-based service migration by developing edge clusters using NVIDIA Jetson boards in an embedded edge environment, using containers and Kubernetes as a container orchestration capable of handling real-time computation-intensive deep learning tasks. Resource constraints in the edge and client movement are the proposed scenarios for service migration. Container migration due to mobile clients is integrated with blockchain to find a suitable destination, meta-based node evaluation, and secured data transfer in the connected car environment. Each service request migration takes, on average, 361 ms. The employed container migration method takes 75.11 s and 70.46 s to migrate application containers that use NVIDIA CUDA Toolkit. Finally, we evaluate the efficiency of blockchain to find the destination node through performance parameters such as latency, throughput, storage, and bandwidth.
SIMULATION RESEARCH ON CARBON EMISSIONS TRADING BASED ON BLOCKCHAIN

Global warming caused by greenhouse gases is one of the problems that need to be solved urgently. Blockchain technology can achieve automatic quota certification and settlement, providing a new direction for carbon emissions trading. This paper provides a quantitative analysis of blockchain-based carbon emissions trading through the Repast simulation platform. Firstly, it designs the blockchain-based carbon emissions trading simulation framework from a macro perspective, including identity and quota certification, quota trading, risk prevention and smart contracts management. Then, it establishes a blockchain-based carbon emissions trading simulation model and formulates the behavior rules of the government, investors and company agents and market transaction processes. Finally, it simulates the carbon emissions trading based on public chain and private chain on the Repast platform, and analyzes the simulation results.
Neural Machine Translation For English to Hindi using GRU

Language translation helps people to communicate, share information and establish a worldwide relationship. Neural Machine Translation helps to build the performances since it translates text from one language into another. This paper generates a summary with a headline and also compares three Neural Machine Translation models based on different Techniques for English-Hindi language pairwise: Sequence Architecture with both encoder and decoder (1) Long Short Term Memory (2) Bidirectional Long Short Term Memory (BI-LSTM) Conditional Random Field (CRF) and (3) Gated recurrent units (GRUs) with attention mechanism applied in three models. The comparison showed that GRU is better in performances than LSTM and Bi-LSTM CRF.
“I feel lonely, I don't understand you when you talk, and I find it hard to breathe”. Analysis of the emotional tensions of physical education students in the Spanish setting of COVID-19

The aim of this research is to analyse the emotional tensions experienced by PE students in the setting of the COVID-19 pandemic. Twenty-four students participated in the study. A qualitative methodology was used in which, taking into account the safety and prevention conditions established for the COVID-19 situation, three categories of analysis were obtained: a) experiences regarding the use of the face mask; b) experiences regarding physical distancing; and c) social interaction between students. The researchers' logbooks and three discussion groups were used as data collection instruments. The results show how the COVID-19 safety measures generated emotional changes in students.
Anomaly Detection in Real-Time Videos Using Match Subspace System and Deep Belief Networks

Anomaly occurrence detection in crowd scenes is one of the most essential concepts nowadays. Therefore, many existing studies are presented in this area. However, all of the existing studies use handcrafted features to identify and detect the anomalies. This chapter proposes a novel supervised learning context to detect anomalies in various crowded scenes. Crowded scenes have different features like visual features, motion features and energy features. Those features are extracted using spatiotemporal measurements. For mid-level feature representation, three convolutional machines are trained after the multimodal fusion model is exploited to deep learn such crowd patterns. It depends upon the result of multimodal fusion one session support vector machine used to trace and detect the anomalies present in a crowded scene. The proposed algorithm is tested and assessed using obtainable data sets and associated with various available existing algorithms. The results for the proposed algorithm are better than various existing studies for detecting anomalies in 3D images and even in videos.
Single-exposure multi-wavelength diffraction imaging with blazed grating

Multi-wavelength diffraction imaging is a lensless, high-resolution imaging technology. To avoid multiple exposures and enable high-speed data collection, here an innovative setup for the single-exposure multi-wavelength diffraction imaging based on a blazed grating is proposed. Since the blazed angle varies with the wavelength, the diffraction patterns for the individual wavelengths can be separated from each other and recorded in a single measurement at one time. A method of high-precision position alignment between different wavelength patterns is proposed in our system to achieve good image quality and high resolution. Experiments on a phase-only USAF resolution target and biological samples were carried out to verify the effectiveness of our proposed method. This proposed setup has such advantages as a simpler structure, fast recording, and algorithm robustness.
Comparison of Anomaly Detectors: Context Matters

Deep generative models are challenging the classical methods in the field of anomaly detection nowadays. Every newly published method provides evidence of outperforming its predecessors, sometimes with contradictory results. The objective of this article is twofold: to compare anomaly detection methods of various paradigms with a focus on deep generative models and identification of sources of variability that can yield different results. The methods were compared on popular tabular and image datasets. We identified that the main sources of variability are the experimental conditions: 1) the type of dataset (tabular or image) and the nature of anomalies (statistical or semantic) and 2) strategy of selection of hyperparameters, especially the number of available anomalies in the validation set. Methods perform differently in different contexts, i.e., under a different combination of experimental conditions together with computational time. This explains the variability of the previous results and highlights the importance of careful specification of the context in the publication of a new method. All our code and results are available for download.
Person Re-Identification With Hierarchical Discriminative Spatial Aggregation

Practically, person re-identification (re-ID) may suffer from the critical spatial misalignment problem due to inaccurate human detection, variation on human pose and camera viewpoint, etc. To address this, a hierarchical discriminative spatial aggregation method is proposed. The key idea is to conduct spatial aggregation on local human parts via global average-pooling to acquire the strong spatial misalignment tolerance, with VALD encoding on the local parts for facilitating discriminative power jointly. This proposition is built on NetVLAD to ensure end-to-end deep learning capacity. Due to the fine-grained property of person re-ID task that has not been well concerned by the original NetVLAD model for scene recognition, a feature refinement layer that consists of 1 fully-connected (FC) layer and 2 batch normalization (BN) layers is added on top of the raw NetVLAD layer to enhance the discriminative power and training convergence. And, a human body occlusion and background component dropout manner is also proposed to resist the effect of serious occlusion. Technically, a refined codeword initialization manner is proposed to alleviate the potential codeword imbalance problem caused by naive random initialization. The proposed discriminative spatial aggregation approach is then conducted on multi-resolution convolutional feature map layers hierarchically via early feature fusion, to involve richer semantic and fine-grained visual clues jointly. Wide-range experiments on 6 datasets (i.e., CUHK03, DukeMTMC-reID, Occluded-DukeMTMC, Market-1501, MSMT17 and Occluded-REID) verifies the effectiveness of our proposition. The source code and supporting material is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zmyme/HDSA-reID</uri> .