Reviewerly/exHarmony huggingface dataset:
APLIKASI PEMBELAJARAN HURUF KATAKANA MENGGUNAKAN METODE MARKER AUGMENTED REALITY BERBASIS ANDROID

In the process of learning katakana media letters that are often used SMK SWASTA PAB 8 comes from books and references from the internet. Therefore, researchers designed an application that will one day be used as an alternative medium for early introduction learning of katakana letters that will be implemented through smartphones using Augmented Reality software. The application design results in the introduction and learning of katakana letters that inform students about katakana letters with Augmented Reality technology by utilizing tools or software unity 3D and vuforia so that japanese language learning is more interactive. Designed for users using smartphones with the Android operating system. The way the application works is by scanning the letters of the target image of the card to be created, the results that will be displayed on the smartphone in the form of letter writing animations, learning media in the form of quizzes that can help basic learning katakana letters. The purpose of designing katakana letter recognition learning app using Augmented Reality software is to provide katakana letter recognition in an easy and interesting way.
AN ERROR ANALYSIS OF SEMANTIC LEVEL IN RIAU NEWSPAPER ARTICLES: ANALISIS KESALAHAN BERBAHASA TATARAN SEMANTIK DALAM ARTIKEL SURAT KABAR KORAN RIAU

Language errors often occur when communicating, both oral communication and written communication. This happens because of the lack of understanding of the communicator in delivering messages to the communicant or the author's lack of understanding in providing information to his readers. In addition, language errors also often occur due to spoken language factors, so that when applied in written language, there tend to be errors. Language errors at the semantic level still often occur in written language, especially articles in the Riau newspaper. These errors can be in the form of errors due to confused pairs, errors due to incorrect choice of words, and errors due to cognate pairs. This study uses a prescriptive content analysis method with the data source from the newspaper Koran Riau. Data is collected by means of documentation with data collection techniques including, collecting error samples, identifying errors, explaining errors, classifying errors, and evaluating or correcting errors. Based on the results and discussion, there are two errors in the language of the semantic level in the Riau Koran newspaper article. First, the fault of a confused partner. Second, errors due to incorrect word choices. However, errors due to cognate pairs were not found.
Espirales primas como herramienta para el análisis de conjuntos en N

Within the area of mathematical games, spirals can be considered as sequences of natural numbers that are represented graphically, according to a drawing pattern. Initially, the most widespread is the Spiral of Ulam, which shows a very simple arrangement of the prime numbers in the spiral. In fact, the study of this type of spiral is usually limited to observing the arrangement of the subset of prime numbers in them. This article considers a type of spiral related to so-called? centered polygonal numbers?. The results found show the potential utility of such spirals as a graphic support in the analysis of a figure and its background (that is, for the visualization of a numerical subset and the gap it leaves). It is also shown how certain subsets of an initial set inherit graphic properties from the main set, in terms of the drawing pattern considered.
Hate Speech Proposals - A Consideration

This paper examines the Report of the Royal Commission of Inquiry into the 15 March 2019 terrorist attack in Christchurch, New Zealand and focusses on proposals to reconsider provisions of the existing law about “hate speech”. The paper considers the existing law contained within the Human Rights Act 1993 and then goes on the consider the critique of the existing law undertaken by the Commission. The paper also considers the Commissions proposals for a “hate speech” offence to be incorporated in the Crimes Act – a proposal which, in the opinion of this commentator is too wide and does unnecessary violence to the freedom of expression. An alternative and more limited offence is proposed which fulfils the objectives stated by the Commission yet maintains an environment for the robust level of discourse so vital in a free and democratic society.
Designing the Metaverse

The Metaverse, a term coined in science fiction, is now being discussed seriously as a new form of infra-structure. The Metaverse is intended to make possible thematically interconnected immersive experiences. In this paper, we conceptualize the Metaverse as a meta design space. Within this space, designers create var-ious interconnected design spaces. We highlight how the key dimensions of human experience (time, space, actors, and artifacts) each introduce tensions for making decisions in those design spaces, and we highlight the transitions between design spaces. This conceptual language opens up this novel and emergent phenomenon both to those wishing to design new disruptive technologies and those seeking to improve existing platform strategies. We conclude by highlighting how the Metaverse will not only comprise immersive virtual experiences but also transitions between physical and virtual experiences.
The Efficiency Misnomer

Model efficiency is a critical aspect of developing and deploying machine learning models. Inference time and latency directly affect the user experience, and some applications have hard requirements. In addition to inference costs, model training also have direct financial and environmental impacts. Although there are numerous well-established metrics (cost indicators) for measuring model efficiency, researchers and practitioners often assume that these metrics are correlated with each other and report only few of them. In this paper, we thoroughly discuss common cost indicators, their advantages and disadvantages, and how they can contradict each other. We demonstrate how incomplete reporting of cost indicators can lead to partial conclusions and a blurred or incomplete picture of the practical considerations of different models. We further present suggestions to improve reporting of efficiency metrics.
Application of Set theory: with Specific Reference to Uncertain and Vague Data

Decision making has always remained a problem for the professionals, finding solution in the cases where the set ofdata is not random and fulfills all the parameters of the universe is easier, But there are certain cases where the data isnot available in precise form i.e. it is either vague or uncertain, this makes the decision making more problematic. Todeal with such issues the researcher evaluates the theory of Soft Set and proves the authenticity of the same on thebasis of a real life problem.
Digital Citizenship in the Frame of Global Change

Digital Citizenship is the focus on the skills needed by all users of technology to work effectively in an ever expanding digital world.   This is especially important for children as they become exposed to digital tools, often at very early age.   Digital Citizenship provides a structure for the responsibilities, issues, as well as the opportunities that technology provides.  The research into this topic was collected through 15 years of international interactions, both qualitative and quantitative, to identify the framework and the subsequent creation of the Nine Elements and their structure within Safe, Savvy and Social constructs.  This definition was the first to organize Digital Citizenship into a single cohesive model.   From this work, an understanding of the skills necessary for all users of technology has been organized and defined.  The original research has been replicated around the globe and have exhibited that the elements are a solid foundation for the skills within Digital Citizenship. The current issue is the continued fracturing of the concepts within Digital Citizenship. This confusion has led to competing definitions identifying various organizations’ concept of Digital Citizenship.  There is a need for ongoing study of this topic reinforced by the implications of online learning caused by forced school closings.  The variance in skills and knowledge (especially Access) by parents, students and educators are evident with required social distancing requirements. The need of Digital Citizenship in a society that has turned to digital technologies to meet the needs of educational isolation is evident.
Nature Inspired Cluster Based Optimal Spectrum Sensing In Cognitive Radio Networks

The demand for spectrum has increased as the number of wireless devices has grown rapidly. However, despite the huge demand, the available spectrum is underutilized. To resolve issues regarding spectrum requirement and spectrum utilization Cognitive radio (CR) network has been introduced. The ability to share the spectrum among licensed and unlicensed users in the cognitive network gains more attention among researchers to reduce the spectrum scarcity. Specifically, cluster-based cooperative spectrum with optimal sensing introduces tremendous impact with its reliable sensing characteristics compared to cooperative spectrum sensing. Thus, in this research work, cluster-based optimal spectrum sensing is introduced to improve spectrum utilization and minimize sensing time. To achieve this, a simple but efficient K-nearest neighbor algorithm is employed for cluster formation and a bio-inspired firefly optimization algorithm is applied for optimal spectrum sensing. The optimal spectrum bands information is transferred to the fusion center and the final decision is obtained as a hard decision since it provides better sensing performance than soft decision detection.   To validate the performance of the proposed approach, conventional particle swarm optimization, genetic algorithm, and low energy adaptive clustering hierarchy algorithms are compared in terms of false alarm, detection probability, throughput, error rate, energy consumption, spectrum utilization, sensing time, and accuracy.
A Unified View of cGANs with and without Classifiers

Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions. Existing cGANs are based on a wide range of different discriminator designs and training objectives. One popular design in earlier works is to include a classifier during training with the assumption that good classifiers can help eliminate samples generated with wrong classes. Nevertheless, including classifiers in cGANs often comes with a side effect of only generating easy-to-classify samples. Recently, some representative cGANs avoid the shortcoming and reach state-of-the-art performance without having classifiers. Somehow it remains unanswered whether the classifiers can be resurrected to design better cGANs. In this work, we demonstrate that classifiers can be properly leveraged to improve cGANs. We start by using the decomposition of the joint probability distribution to connect the goals of cGANs and classification as a unified framework. The framework, along with a classic energy model to parameterize distributions, justifies the use of classifiers for cGANs in a principled manner. It explains several popular cGAN variants, such as ACGAN, ProjGAN, and ContraGAN, as special cases with different levels of approximations, which provides a unified view and brings new insights to understanding cGANs. Experimental results demonstrate that the design inspired by the proposed framework outperforms state-of-the-art cGANs on multiple benchmark datasets, especially on the most challenging ImageNet. The code is available at https://github.com/sian-chen/PyTorch-ECGAN.
Rancang Bangun Sistem Informasi Penjualan Online Untuk Penjual Dengan Studi Kasus 212 Mart Yos Sudarso Rumbai

212 Mart is a mini market that is engaged in selling basic food items and household appliances. Shopping transaction data collection is important, because it relates to the provision of stock of goods. For now, the data collection is still using Microsoft Excel, so it is possible for data collection errors, missing data or incorrect data collection to occur. By building a web-based inventory and sales system at the 212 Mart minimarket, it can assist minimarkets in recording buying and selling transactions. In this study, the FIFO (First In First Out) method was used to overcome the problem of inventory at 212 Mart. Meanwhile, to determine when the order is made, the Reorder Point (ROP) method is used. To minimize the occurrence of stock outs, a safety stock (SS) calculation is carried out. The purpose of this sales and inventory system was built to assist 212 Mart in providing stock of goods, reducing errors in data collection on stock items, report data on shopping transactions and also input sales transactions. The method used for functional testing of the system is black box testing and user acceptance test which is useful for knowing the system is in accordance with user needs or not. In building a sales information system at this seller the author uses the prototype method and uses the PHP programming language with MySQL as the database. Keywords: Inventory, System, Information, Sales.
LEXICAL COHESION ANALYSIS IN JACINDA ARDERN'S SPEECH AT CHRISTCHURCH NATIONAL REMEMBRANCE SERVICE

Taking into account how important the concept of cohesion in linguistics and how impactful a word from a political leader, this research aims to analyze the lexical cohesion usage in the speech of New Zealand’s Prime Minister, Jacinda Ardern at The National Remembrance Service. The speech is delivered on March 29th, 2019, to honor the victims of a terrorist attack in two mosques while worshipping. This research is conducted using a qualitative method. Furthermore, the cohesion theory by Halliday and Hasan (1976) is used to identify, classify, and analyze the data. 30 data has been chosen through simple random sampling as the representative to be analyzed. As a result, all the types of lexical cohesion devices are discovered within the speech. It consists of 37 repetitions, 20 synonyms, 8 superordinate, 7 general words, and 23 collocations. The repetition items are served to deliver impact and highlight the idea. It can be seen as a way to emphasize the information and establishing the integrity of the discourse. Choosing various words such as pain, suffer, anguish, and bereaved are functioned to evoke emotional responses from the audience and emphasize the connections between related ideas spoken by Jacinda Ardern. The superordinate items are operated to provide clearer information within the text so that the audience can comprehend the idea effortlessly. Additionally, the general words are functioned to support and integrate the information between the text and context in which the speech takes place. Lastly, collocation items served to achieve a rhetorical effect and highlight the idea of the discourse.
DESCRIPTION OF THE METHODOLOGICAL BASIS FOR ENSURING INTERDISCIPLINARY CONTINUITY OF THE SUBJECT "COMPUTER SCIENCE AND INFORMATION TECHNOLOGY" IN VOCATIONAL EDUCATION

The article discusses the importance of the development of professional training in teaching the discipline Informatics and Information Technology on the basis of interdisciplinary membership, the methodological significance of the problem of interdisciplinary communication, the necessary conditions for interdisciplinary communication and interdisciplinary training.
Penerapan Metode Fuzzy Mamdani dan Fuzzy Tsukamoto Dalam Menentukan Harga Mobil Bekas

The Mamdani method and the Tsukamoto method are the methods most frequently encountered when discussing fuzzy methodologies. In Tsukamoto's method, each consequent to the rule in the form of IFTHEN must be represented by a fuzzy set with a monotonous membership function as the result. Many problems can be solved by using these two methods, one of which helps in determining the price of a used car. The presence of cheap cars that are increasingly incessant also affects the business of selling used cars because people have perceptions and tend to trust the types and brands of cars that have been tested for durability. This condition is also a problem at CV. Citra Kencana Mobil to determine the price of used cars. The calculation results show that the fuzzy mamdani method estimates the price of used cars to be cheaper than the Tsukamoto method.
Learning to Find Translation of Grammar Patterns in Parallel Corpus

We introduce a method for assisting English as Second Language (ESL) learners by providing translations of Collins COBUILD grammar patterns(GP) for a given word. In our approach, bilingual parallel corpus is transformed into bilingual GP pairs aimed at providing native language support for learning word usage through GPs. The method involves automatically parsing sentences to extract GPs, automatically generating translation GP pairs from bilingual sentences, and automatically extracting common bilingual GPs. At run-time, the target word is used for lookup GPs and translations, and the retrieved common GPs and their example sentences are shown to the user. We present a prototype phrase search engine, Linggle GPTrans, that implements the methods to assist ESL learners. Preliminary evaluation on a set of more than 300 GP-translation pairs shows that the methods achieve 91% accuracy.
Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes after deployment in the real world. These changes are often spurious and unrelated to the underlying problem, such as background shifts for visual input agents. Unfortunately, deep RL policies are usually sensitive to these changes and fail to act robustly against them. This resembles the problem of domain generalization in supervised learning. In this work, we study this problem for goal-conditioned RL agents. We propose a theoretical framework in the Block MDP setting that characterizes the generalizability of goal-conditioned policies to new environments. Under this framework, we develop a practical method PA-SkewFit that enhances domain generalization. The empirical evaluation shows that our goal-conditioned RL agent can perform well in various unseen test environments, improving by 50% over baselines.
El e-learning y las Tecnologías de la Información y las Comunicaciones en la Universidad Libre

The article presents theoretical advances of the research Strategic plan for the incorporation of ICT at Universidad Libre, where the innovation of teaching-learning is one of the important factors in e-learning. Different conditions and variables for its implementation at the Faculty of Sciences of Education from Universidad Libre are described. Within a qualitative approach and descriptive research, the characteristics for the integration of ICT are specified. Through surveys, the categories: organizational, technological and academic were consulted, thus, recognizing aspects such as the planning strategies, the technological platform and the pedagogical model of the institution. As products, a vision for ICT and e-learning was outlined, and the virtual course was implemented for all first term students of the Bogota Section and the proposal for the formation of the interdisciplinary ICT committee.
The Prevalence of Cybersecurity Misinformation on Social Media: Case Studies on Phishing Reports and Zoom's Threats

Recently, threat intelligence and security tools have been augmented to use the timely and relevant security information extracted from social media. However, both ordinary users and malicious actors may spread misinformation, which can misguide not only the end-users but also the threat intelligence tools. In this work, for the first time, we study the prevalence of cybersecurity and privacy misinformation on social media, focusing on two different topics: phishing websites and Zoom's security & privacy. We collected Twitter posts that were warning users about phishing websites and tried to verify these claims. We found about 22% of these tweets to be not valid claims. We then investigated posts about Zoom's security and privacy on multiple platforms, including Instagram, Reddit, Twitter, and Facebook. To detect misinformation related to Zoom, we first created a groundtruth dataset and a taxonomy of misinformation and identified the textual and contextual features to be used for training classifiers to detect posts that discuss the security and privacy of Zoom and detect misinformation. Our classifiers showed great performance, e.g., Reddit and Facebook misinformation classifier reached an accuracy of 99% while Twitter and Instagram reached an accuracy of 98%. Employing these classifiers on the posts from Instagram, Facebook, Reddit, and Twitter, we found that respectively about 3%, 10%, 4%, and 0.4% of Zoom's security and privacy posts as misinformation. This highlights the need for social media platforms to dedicate resources to curb the spread of misinformation, and for data-driven security tools to propose methods to minimize the impact of such misinformation on their performance.
Controllable Image Dataset Construction Using Conditionally Transformed Inputs in Generative Adversarial Networks

In this paper, we tackle the well-known problem of dataset construction from the point of its generation using generative adversarial networks (GAN). As semantic information of the dataset should have a proper alignment with images, controlling the image generation process of GAN comes to the first position. Considering this, we focus on conditioning the generative process by solely utilizing conditional information to achieve reliable control over the image generation. Unlike the existing works that consider the input (noise or image) in conjunction with conditions, our work considers transforming the input directly to the conditional space by utilizing the given conditions only. By doing so, we reveal the relations between conditions to determine their distinct and reliable feature space without the impact of input information. To fully leverage the conditional information, we propose a novel architectural framework (i.e., conditional transformation) that aims to learn features only from a set of conditions for guiding a generative model by transforming the input to the generator. Such an approach enables controlling the generator by setting its inputs according to the specific conditions necessary for semantically correct image generation. Given that the framework operates at the initial stage of generation, it can be plugged into any existing generative models and trained in an end-to-end manner together with the generator. Extensive experiments on various tasks, such as novel image synthesis and image-to-image translation, demonstrate that the conditional transformation of inputs facilitates solid control over the image generation process and thus shows its applicability for use in dataset construction.
Covariance-Generalized Matching Component Analysis for Data Fusion and Transfer Learning.

In order to allow for the encoding of additional statistical information in data fusion and transfer learning applications, we introduce a generalized covariance constraint for the matching component analysis (MCA) transfer learning technique. After proving a semi-orthogonally constrained trace maximization lemma, we develop a closed-form solution to the resulting covariance-generalized optimization problem and provide an algorithm for its computation. We call this technique -- applicable to both data fusion and transfer learning -- covariance-generalized MCA (CGMCA).