Reviewerly/exHarmony huggingface dataset:
THE DEVELOPMENT OF REACHING HAND-PRINTED READING MATERIALS (PREAM) FOR GRADE 2 LEARNERS

The purpose of the study was to determine the acceptability of the Reaching Hand Printed Reading Materials (PREAM) for Grade 2 learners among 75 Elementary teachers of District 7 in the Division of Binan City and 75 Grade 2 parents.The researcher developed reading materials to lessen the number of struggling readers.The researcher identified the teachers' and parents' level of acceptance in terms of the materials objectives, content, reading strategies, comprehension questions, accuracy, visual appeal, depth of knowledge, and ease of use and support.The descriptive method of research was used to determine the acceptability of the created reading materials among the respondents.A 5-point Likert scale was the research instrument used to rate the reading materials based on the indicators.Mean, standard deviation, and t-test were used to determine the average and difference of the ratings.Based on the collected data, objectives, content, reading strategies, and comprehension questions of the materials were highly appropriate for the Grade 2 learners.The reading materials were highly accurate and highly appealing.Depth of knowledge and ease of use and support were observed by the respondents of the study.The null hypothesis which states that there is no significant difference between the ratings made by the teachers and parents was rejected.It showed the acceptance of the reading materials which the teachers and parents could use with the learners.It is recommended that Elementary teachers and parents use the reading materials as an intervention in the school and at home.
Risk Risk Assessment and Development of Access Control Information Security Governance Based on ISO/IEC 27001:2013 At XYZ University

The rapid development of information technology at this time also has an impact on the use of information technology in the university environment. XYZ University as a university that has quite a lot of students also applies information technology to support their distance learning. The role of information technology is quite crucial and important. Unfortunately, the issue of information security which is an important part of information technology often gets less attention. Its undeniable that the emergence of threats or weaknesses in information technology can disrupt the course of service activities using information technology. Therefore, it is necessary to manage information technology and risk-based document standard procedures as outlined in governance to manage emerging threats or weaknesses. ISO/IEC 27001:2013 is an framework of information security management system that can be used as a basis for managing information security. This study identifies assets, threats, weaknesses, risk analysis, BIA, risk assessment, and risk mapping based on clauses to produce recommendations for policy documents, procedures, and work instructions to improve information security control based on ISO 27001:2013 clauses. Considering its high risk value, this study produced several recommendations for security documents, namely 5 policy documents, 6 procedure guidelines, 8 work instructions, and 12 forms.
Language Learning Strategies Employed by Lower Secondary ESL Learners

Language learning in education has evolved over the years as students of various cultures and traditional backgrounds have been enrolled in language learning programmes as part of the education system.The English language is the second language of the Malaysian education system.The learners in the Malaysian education system cope with English language learning differently.The purpose of this study is to identify strategies used by lower secondary ESL learners to improve their English language skills.A survey was administered in a chosen secondary school in a suburban district.The survey used in the study was adopted and adapted from Oxford's (1990) Strategy Inventory for Language Learning (SILL) version 7.0.The modified version consisted of 30 statements on a Likert scale of 1 to 5. The survey was conducted through an online google form.A total number of thirty Form 1 students have been selected through purposive sampling as respondents.The data collected then analysed with descriptive statistics using frequency and percentage counts.The findings portrayed that lower secondary ESL learners preferred metacognitive-related strategies over the other strategies.Thus, the findings could be useful for researchers to study suitable learning materials to cater to students who practice various learning styles in language learning.
Automatic Detection and Decryption of AES Using Dynamic Analysis

Abstract In this paper we propose a set of algorithms that can automatically detect the use of AES and automatically recover both the encryption key and the plaintext, assuming that we can control the code flow of the encrypting program, e.g., when an application is performing encryption without the user’s permission. The first algorithm makes use of the fact that we can monitor accesses to the AES S-Box and deduce the desired data from these accesses; the approach is suitable to software-based AES implementations, both naïve and optimized. To demonstrate the feasibility of this approach we designed a tool which implements the algorithm for Microsoft Windows running on the Intel x86 architecture. The tool has been successfully tested against a set of applications using different cryptographic libraries and common user applications. We also discuss the options of recovering the same data when hardware-assisted AES implementations on Intel-compatible architectures are used.
Self-Supervised Pretraining for Differentially Private Learning

We demonstrate self-supervised pretraining (SSP) is a scalable solution to deep learning with differential privacy (DP) regardless of the size of available public datasets in image classification. When facing the lack of public datasets, we show the features generated by SSP on only one single image enable a private classifier to obtain much better utility than the non-learned handcrafted features under the same privacy budget. When a moderate or large size public dataset is available, the features produced by SSP greatly outperform the features trained with labels on various complex private datasets under the same private budget. We also compared multiple DP-enabled training frameworks to train a private classifier on the features generated by SSP. Finally, we report a non-trivial utility 25.3\% of a private ImageNet-1K dataset when $\epsilon=3$. Our source code can be found at \url{https://github.com/UnchartedRLab/SSP}.
Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022

This technical report describes the SViT approach for the Ego4D Point of No Return (PNR) Temporal Localization Challenge. We propose a learning framework StructureViT (SViT for short), which demonstrates how utilizing the structure of a small number of images only available during training can improve a video model. SViT relies on two key insights. First, as both images and videos contain structured information, we enrich a transformer model with a set of \emph{object tokens} that can be used across images and videos. Second, the scene representations of individual frames in video should "align" with those of still images. This is achieved via a "Frame-Clip Consistency" loss, which ensures the flow of structured information between images and videos. SViT obtains strong performance on the challenge test set with 0.656 absolute temporal localization error.
NLP Assisted Text Annotation

This paper presents the importance and use of text annotation in NLP models and reviews BRAT - an NLP- assisted text annotation tool and other powerful and modern methods. BRAT is an NLP-based tool for text annotation (annotation: making notes, adding comments to a text in order to give an explanation or opinion to the source article). It is used for high-quality structured annotation for various NLP activities and focuses on increasing annotator productivity using NLP techniques. Annotation teaches NLP models how to identify parts of speech, real-world objects (named entities such as a person, location, place), and key phrases within a text. NLP algorithms are trained using large, annotated text datasets and annotation is the backbone of services like automatic speech recognition, chatbots, and sentimental analysis. Keywords: NLP, ML, CNN, TCN, Ensemble, Corpus.
SPENDER: A Platform for Secure and Privacy-Preserving Decentralized P2P E-Commerce

The blockchain technology empowers secure, trustless, and privacy-preserving trading with cryptocurrencies. However, existing blockchain-based trading platforms only support trading cryptocurrencies with digital assets (e.g., NFTs). Although several payment service providers have started to accept cryptocurrency as a payment method for tangible goods (e.g., Visa, PayPal), customers still need to trust and hand over their private information to centralized E-commerce platforms (e.g., Amazon, eBay). To enable trustless and privacy-preserving trading between cryptocurrencies and real goods, we propose SPENDER, a smart-contract-based platform for Secure and Privacy-PresErviNg Decentralized P2P E-commeRce. The design of our platform enables various advantageous features and brings unlimited future potential. Moreover, our platform provides a complete paradigm for designing real-world Web3 infrastructures on the blockchain, which broadens the application scope and exploits the intrinsic values of cryptocurrencies. The platform has been built and tested on the Terra ecosystem, and we plan to open-source the code later.
Neural Network-Based Sentiment Classification of Russian Sentences into Four Classes

The paper is devoted to the classification of Russian sentences into four classes: positive, negative, mixed, and neutral. Unlike the majority of modern study in this area, the mixed sentiment class is introduced. Mixed sentiment sentences contain positive and negative sentiments simultaneously.To solve the problem, the following tools were applied: the attention-based LSTM neural network, the dual attention-based GRU neural network, the BERT neural network with several modifications of the output layer to provide classification into four classes. The experimental comparison of the efficiency of various neural networks were performed on three corpora of Russian sentences. Two of them consist of users’ reviews: one with wear reviews and another with hotel reviews. The third corpus contains news from Russian media. The highest weighted F-measure in experiments (0.90) was achieved when using BERT on the wear reviews corpus, as well as the highest weighted F-measure for positive and negative sentences (0.92 and 0.93, respectively). The best classification results for neutral and mixed sentences were achieved on the news corpus. For them F-measure was 0.72 and 0.58, respectively. As a result of experiments, the significant superiority of the BERT transfer network was demonstrated in comparison with older neural networks LTSM and GRU, especially for classification of sentences with weakly expressed sentiments. The error analysis showed that “adjacent” (positive/negative and mixed) classes are worse classified with BERT than “opposite” classes (positive and negative, neutral and mixed).
A Systematic Analysis on Cryptocurrencies as a Financial Asset

Cryptocurrencies are considered digital asset that is saved on a computerized database in a public ledger format using strong cryptography methods or blockchain to secure the transaction record. In recent times it has attracted a large number of holders. Cryptocurrencies are based on entirely new technology, whose prospects are still undiscovered and unknown, which in turn attracted considerable academic studies directed towards creating theoretical models of cryptocurrencies and their possible prospects. In this study, we have considered the topmost cryptocurrencies namely Bitcoin, Ethereum, Ripple, and Litecoin as they have a major part in the cryptocurrency market capitalization which is around 83.4&#x0025; of the total market. The objective of the study is to understand whether there is a significant association between 4 cryptocurrency prices namely Bitcoin, Ethereum, Litecoin, Ripple, and understand whether there is a significant relationship between the Bitcoin cryptocurrency pricing concerning Ethereum, Litecoin Ripple cryptocurrency prices. Secondary and primary data from different websites and questionnaires have been used for the study. It has emerged from the study that there is a high correlation between these cryptocurrencies&#x0027; prices. It has also been found that Bitcoin prices increases, if Ethereum and Litecoin prices increases. Bitcoin prices decrease, only if Ripple prices increase.
Masked Siamese ConvNets

Self-supervised learning has shown superior performances over supervised methods on various vision benchmarks. The siamese network, which encourages embeddings to be invariant to distortions, is one of the most successful self-supervised visual representation learning approaches. Among all the augmentation methods, masking is the most general and straightforward method that has the potential to be applied to all kinds of input and requires the least amount of domain knowledge. However, masked siamese networks require particular inductive bias and practically only work well with Vision Transformers. This work empirically studies the problems behind masked siamese networks with ConvNets. We propose several empirical designs to overcome these problems gradually. Our method performs competitively on low-shot image classification and outperforms previous methods on object detection benchmarks. We discuss several remaining issues and hope this work can provide useful data points for future general-purpose self-supervised learning.
Analisis Manajemen Resiko Aplikasi Rene Kasir Di Restoran Oemah Djari Salatiga Menggunakan ISO 31000

The Rene Cashier application is an information system that serves to make it easier for staff to overcome customer queues in printing order receipts, inputting data sold by restaurants and storing restaurant sales data and making it easier for staff to arrange orders and customer seats, the information contained in Rene Cashier has been adjusted to the applicable restaurant regulations. In implementing an information system there must be risks that hinder the process of the system, so here a risk analysis is needed that aims to prevent or minimize the risks that will occur. By using ISO 31000 at Oemah Djari Restaurant Salatiga, it is expected to minimize the possible risks that will occur around the Rene Cashier application. The results of the ISO 31000 risk analysis are documents of possible risks that will occur around the Rene Cashier application, classifying possible risks and their impacts. So, the results of this study can be useful for Oemah Djari Restaurant Salatiga to prevent or at least minimize the risks that may occur in the Rene Cashier application.
Introduction to the Handbook of Digital Higher Education

This handbook takes as its starting point the need for a holistic conceptualization of digital higher education which takes into account personal, pedagogic and organizational change. It is tempting not to offer an alternative conceptualization of digital higher education in such a contested space. However, we have been struck, through our reading of the chapters collected together in this volume, of the role of people and practices in shaping digital higher education. A definition grounded in such a way recognizes that educational technologies are tools put to use to meet organizational objectives and that those objectives will inevitably be shaped by the wider political, economic and societal changes. Within this context we have chosen to frame digital higher education as institutional engagement with the social and cultural implications of technology-enabled educational change.
Category-Agnostic 6D Pose Estimation with Conditional Neural Processes

We present a novel meta-learning approach for 6D pose estimation on unknown objects. In contrast to ``instance-level" and ``category-level" pose estimation methods, our algorithm learns object representation in a category-agnostic way, which endows it with strong generalization capabilities across object categories. Specifically, we employ a neural process-based meta-learning approach to train an encoder to capture texture and geometry of an object in a latent representation, based on very few RGB-D images and ground-truth keypoints. The latent representation is then used by a simultaneously meta-trained decoder to predict the 6D pose of the object in new images. Furthermore, we propose a novel geometry-aware decoder for the keypoint prediction using a Graph Neural Network (GNN), which explicitly takes geometric constraints specific to each object into consideration. To evaluate our algorithm, extensive experiments are conducted on the \linemod dataset, and on our new fully-annotated synthetic datasets generated from Multiple Categories in Multiple Scenes (MCMS). Experimental results demonstrate that our model performs well on unseen objects with very different shapes and appearances. Remarkably, our model also shows robust performance on occluded scenes although trained fully on data without occlusion. To our knowledge, this is the first work exploring \textbf{cross-category level} 6D pose estimation.
Coming to terms with design wickedness: Reflections from a forum theatre on design thinking

Contemporary design thinking is often described as a designerly approach to dealing with wicked problems -problems that are too complex and deemed impossible to fix, but that can be tamed and solved with proper design methods.Wickedness is a fundamental justification for designing things as a leap of faith or even as a kind of magic.This practice-based design research questions this justification while also opening up new understandings of wickedness.By creating a Forum Theatre session with characters inspired by the musical Wicked as allegories for different design agents/subjects in an online event, the authors engaged design spectators in critical thinking of their own roles and practices from a broader social and political perspective.We conclude that wickedness is not necessarily a nasty quality of design problems and solutions but a relational quality that can be explored by anti-oppressive approaches to design thinking and design doing.
MEMBANGUN SISTEM INFORMASI WEBSITE SEKOLAH DENGAN MENGGUNAKAN GOOGLE SITES

Website is an internet media facility that serves to display, introduce and even function as a medium for finding information that is needed. School institutions, especially those that still really need interaction and socialization so that the school can be better known to the wider community and the students themselves. This study aims to design a school website for SMP Negeri 3 Tongkuno so that it can be used as a medium of information to students, teachers or the general public. The method used in this research is the waterfall development method. The development stage in this research begins with analyzing needs, designing, creating a school website, then applying and testing its effectiveness. Based on the results of the black box testing that has been carried out, it shows that the functional website of SMP Negeri 3 Tongkuno as a medium of information, is free from syntax errors and functions as it should. The results of these tests indicate that each menu contained on the school's website functions properly or in other words according to what is expected. Therefore, it can be concluded that the school website information system using google sites at SMP Negeri 3 Tongkuno is feasible to use.
Study of trust of government handling of Covid-19 in India and USA and disinformation tactics used by the government

This research aims to find out the status of trust the people of the world’s two prominent democracies – The United States of America, known worldwide as the most powerful democracy, and the Republic of India, known as the World’s Largest Democracy in the handling of the COVID-19 epidemic, that has gripped the entire world by storm. Also, the second objective of this study is to find out if the population of the two nations believe that their governments have actively used disinformation tactics – once thought to be used only by a despot or autocratic governments, on its populace to control the COVID-19 panic and hysteria surrounding it. This study also aims to understand the relationship between trust and disinformation, if any. The study aims to fulfill its objective via individual responses. The survey was conducted via Google Forms and was floated via social media apps like Face book, Linked In, Whatsapp, Instagram, and popular chat sites such as Omegle and Reddit.
Test-Time Adaptation for Visual Document Understanding

For visual document understanding (VDU), self-supervised pretraining has been shown to successfully generate transferable representations, yet, effective adaptation of such representations to distribution shifts at test-time remains to be an unexplored area. We propose DocTTA, a novel test-time adaptation method for documents, that does source-free domain adaptation using unlabeled target document data. DocTTA leverages cross-modality self-supervised learning via masked visual language modeling, as well as pseudo labeling to adapt models learned on a \textit{source} domain to an unlabeled \textit{target} domain at test time. We introduce new benchmarks using existing public datasets for various VDU tasks, including entity recognition, key-value extraction, and document visual question answering. DocTTA shows significant improvements on these compared to the source model performance, up to 1.89\% in (F1 score), 3.43\% (F1 score), and 17.68\% (ANLS score), respectively. Our benchmark datasets are available at \url{https://saynaebrahimi.github.io/DocTTA.html}.
Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction

Normalization layers (e.g., Batch Normalization, Layer Normalization) were introduced to help with optimization difficulties in very deep nets, but they clearly also help generalization, even in not-so-deep nets. Motivated by the long-held belief that flatter minima lead to better generalization, this paper gives mathematical analysis and supporting experiments suggesting that normalization (together with accompanying weight-decay) encourages GD to reduce the sharpness of loss surface. Here "sharpness" is carefully defined given that the loss is scale-invariant, a known consequence of normalization. Specifically, for a fairly broad class of neural nets with normalization, our theory explains how GD with a finite learning rate enters the so-called Edge of Stability (EoS) regime, and characterizes the trajectory of GD in this regime via a continuous sharpness-reduction flow.