Reviewerly/exHarmony huggingface dataset:
MFGAN: A Novel CycleGAN-Based Network for Masked Face Generation

In the post-epidemic era, Masked Face Recognition (MFR) is of great significance to our daily life, but it confronts a severe challenge of lacking real-world large-scale masked face datasets with identity labels.Moreover, mask enhances the diversity of face images and further improves the requirements for datasets.To address the above problem, we propose a novel CycleGAN-based masked face generation method MaskedFace-GAN (MFGAN), which is able to generate correct, authenticlooking and type-diverse masked face while ensuring the invariance of facial features.We design a three-stage training pipeline for MFGAN, which corresponds to three modules, respectively.Specifically, a facial feature detector is adopted to guide the model to generate the correct mask in the correct position.Then, by utilizing a mask binary segmentation module, the authenticity of generated images can be guaranteed.Lastly, with mask style encoder, the model can be optimized towards generating typediverse masked faces.Finally, comparing with advanced masked face synthesis and generation methods comprehensively, our MFGAN achieves the best results.Then we apply the generated masked face datasets to MFR model training, which further proves the feasibility of training MFR models on generated datasets and the effectiveness and advancement of MFGAN compared with other state-of-the-art methods.
Learning Content Classification and Mapping Content to Synonymous Learners based on 2022 Augmented Verb List of Marzano and Kendall Taxonomy

Finding suitable learning content for learners with different learning styles is a challenging task in the learning process.Hence it is essential to follow some learning taxonomies to deliver learner-centric learner content.Learning taxonomies are used to express various learning practices and learning habits to be followed by the learner for a better learning process.The investigator has already classified the learners based on the 2022 augmented verb list of Marzano and Kendall taxonomy.The main objective of this paper is to minutely classify the tutordefined learning contents according to the domains as well as the subdomains of the considered taxonomy which is in text format.Providing personalized learning content could help the learners for a better understanding of learning content and their interrelationship which in turn produce better learning outcomes.Mapping the six levels of learning contents into the corresponding learner is a challenging task.Hence the investigator has chosen seven algorithms including Bagging, XG Boost, Support Vector Machine from Machine Learning and four algorithms including Convolutional Neural Network, and Deep Neural Network in Deep Learning algorithm to classify the learning contents.The experimental results indicate that Support Vector Machine performed well in machine learning and Deep Neural Network yields good performance in deep learning in the learning content classification process.These micro contents were organized using a property graph.Further, the micro contents were retrieved from the property graph using SPARQL for mapping the classified contents to the corresponding learners to achieve personalization in the learning process.
A Compressed Data Partition and Loop Scheduling Scheme for Neural Networks

Neural networks (NNs) have been widely adopted in various application domains, ranging from image and video recognition to natural language processing. Recent studies reveal that deeper NNs with more parameters greatly enhance the output accuracy. However, complex NNs incur intensive memory accesses. Since the weights of even a single layer can exceed the on-chip storage capacity, the data usually need to be partitioned. Compression can effectively reduce the storage space requirements. However, there is no research considering the partition of the spare matrix. In this paper, we propose a sparse NN data partition and loop scheduling scheme. We establish the compression efficiency model of the matrix sparse algorithm and design a partition selection method based on sparsity characteristics analyzed by the compression efficiency model. Finally, we design a loop scheduling scheme based on the proper partition size. The experiment results show that the average memory access of each layer can be compressed to 68% of the original; additionally, the throughput of the three networks is increased to an average of 1.66 times.
Meetings Suck—Some More Than Others [MicroBusiness]

Many of us have long considered meetings to be a necessary evil. Meetings are notorious for wasting our time. But we need to meet. We meet to coordinate what we do, assess projects, inform groups. Recent times have given us something even worse than the old-fashioned meeting—the hybrid meeting.
Finding One Common Item, Privately

Private set intersection (PSI) allows two parties, who each hold a set of items, to learn which items they have in common, without revealing anything about their other items. Some applications of PSI would be better served by revealing only one common item, rather than the entire set of all common items. In this work we develop simple special-purpose protocols for privately finding one common item (FOCI) from the intersection of two sets. The protocols differ in how that item is chosen—e.g., uniformly at random from the intersection; the “best” item in the intersection according to one party’s ranking; or the “best” item in the intersection according to the sum of both party’s scores. All of our protocols are proven secure against semi-honest adversaries, under the Decisional Diffie-Hellman (DDH) assumption and assuming a random oracle. All of our protocols leak a small amount of information (e.g., the cardinality of the intersection), which we precisely quantify.
Item Analysis of Korean Historyin the Test of Teaching Koreanas a Foreign Language

This study aimed to examine the Korean History items of the TOTKA (Test of Teaching Korean as a Foreign Language), determine whether they met the test's objectives, and, based on that, offer recommendations on improving the items on Korean history. For this purpose, the previous first sixteen tests were used to extract all the Korean history items, which were then assessed for content, item style, difficulty level, and degree of discrimination. The scope and importance of the Korean history knowledge covered in the test were confirmed through content analysis, and through item-type analysis, it was discovered that with only a few "knowledge utilization type" questions taking into account the relationship with Korean education. Through an analysis of the items' difficulty and degree of discrimination, it was confirmed that the items in the Korean history area had higher difficulty and a lower degree of discrimination than other areas of Korean studies. Based on this comprehensive analysis, this study concluded that the items in the Korean history section of the current Test of Teaching Korean as a Foreign Language was limited in its assessment of a candidate's Korean language proficiency. Therefore, recommendations were offered on how to improve items in the Korean history section in a way that fulfills the requirements of this test while enabling Korean language teachers to effectively teach the Korean language using their knowledge of Korean history.
High Performance Integer Multiplier on FPGA with Radix-4 Number Theoretic Transform

Number Theoretic Transform (NTT) is a method to design efficient multiplier for large integer multiplication, which is widely used in cryptography and scientific computation.On top of that, it has also received wide attention from the research community to design efficient hardware architecture for large size RSA, fully homomorphic encryption, and lattice-based cryptography.Existing NTT hardware architecture reported in the literature are mainly designed based on radix-2 NTT, due to its small area consumption.However, NTT with larger radix (e.g., radix-4) may achieve faster speed performance in the expense of larger hardware resources.In this paper, we present the performance evaluation on NTT architecture in terms of hardware resource consumption and the latency, based on the proposed radix-2 and radix-4 technique.Our experimental results show that the 16-point radix-4 architecture is 2× faster than radix-2 architecture in expense of approximately 4× additional hardware.The proposed architecture can be extended to support the large integer multiplication in cryptography applications (e.g., RSA).The experimental results show that the proposed 3072-bit multiplier outperformed the best 3k-multiplier from Chen et al. [16] by 3.06%, but it also costs about 40% more LUTs and 77.8% more DSPs resources.
Multi-layer Rotation Memory Model-based correlation filter for visual tracking

Object tracking technology is of great significance in laser image processing. However, occlusion or similar interference during visual object tracking may reduce the tracking precision or even cause tracking failure. Aiming at this issue, we propose a Multi-layer Rotation Memory Model-based Correlation Filter (MRMCF) for visual trackingin this paper. First, we establish a Multi-layer Rotation Memory (MRM) model, in which a set of three rotating concentric rings is used to simulate the three memory spacesand their updating processsimulate the memory spaces. Then we introduce the MRM model into the correlation filter tracking framework, which realizes realizing the dynamic updating of classifier parametersin the correlation filter. When the object is occluded or there is similar interference, the proposed tracker can use the Pre-occ classifier parameters stored in the memory spaces in the MRM model MRM memory spaces to retarget the object, thereby reducing the impact of these factors. The experimental results on the OTB50 dataset show that compared with trackers such as CNN-SVM, MEEM, Struck, etc., the proposed tracker achieves higher accuracy and success rate.
Improving Writing in English Using the Inquiry Method at Seven Grade Students SMP Islam Rengasdengklok

Abstract: IMPROVING WRITING ARTICLE IN ENGLISH USING THE INQUIRI METHODE AT SEVEN GRADE STUDENTS SMP ISLAM RENGASDENGKLOK, the purpose of this research is to improve students' ability to use articles in English. Talking about grammar, a special problem that students often face is students' ignorance in applying English articles. A condition where students easily make mistakes in applying English articles into a sentence. The main reason is because the teaching of articles is limited and often neglected. In fact, articles are important in terms of communication for Indonesian students. For junior high school students at MTs Rengasdengklok tend to have difficulty in obtaining articles because their mother tongue does not have the same language. So that the correct use of articles is a challenge for students at MTs Rengasdengklok. Then in this study the author uses the action research method which consists of 4 stages, namely planning, implementing actions, observing, reflecting, with a comparison of increasing values ​​from cycle 1 (45%) and cycle 2 (92%) using the inquiry method with an increase in value of 47 % of total success 100%.&#x0D; Keywords: Teaching, Articles, Junior High School.
Data Science: A Useful Tool for Understanding SARS-CoV-2 Information Facts

By the end of 2019, a local pneumonia outbreak in Wuhan, China, was determined to be caused by a novel form of coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which, since then, has spread worldwide thus becoming the most important public health issue of the beginning of the 21st century. As a response, governments and health organizations started collecting data about this disease for analyzing it, trying to draw conclusions that could lead to a better understanding of it, and eventually to alleviate its effects. Through the analysis of the available data, our area of interest has to do with finding possible correlations By the end of 2019, a local pneumonia outbreak in Wuhan, China, was determined to be caused by a novel form of coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which, since then, has spread worldwide thus becoming the most important public health issue of the beginning of the 21st century. As a response, governments and health organizations started collecting data about this disease for analyzing it, trying to draw conclusions that could lead to a better understanding of it, and eventually to alleviate its effects. Through the analysis of the available data, our area of interest has to do with finding possible correlations.&lt;br&gt;
RegionDrop: Fast Human Pose Estimation Using Annotation-Aware Spatial Sparsity

Convolutional neural networks (CNN) have been attracting attention for accurate scene parsing including a human pose estimation. However, CNN requires a massive amount of floating-point operations, so it is difficult to realize CNN on low-cost devices. Thus, we propose RegionDrop, an annotation-aware spatially sparse network, which skips computations of unnecessary spatial regions in activations. We present a novel loss that directly uses annotations so that important activation regions are retained. We also developed an efficient sparse GPU kernel to accelerate processing speed of both depthwise and general $$K\times K$$ convolutional layers. Our RegionDrop is evaluated by using two pose estimation networks, a modified stacked hourglass network, and an HRNet. RegionDrop using an hourglass network archived 3.2 times faster processing speed compared with a non-sparse network, and 1.8 times faster processing speed than a prior spatially sparse network, with no accuracy degradation. Moreover, the processing speed of RegionDrop using HRNet is increased by a factor of 2.0 with negligible accuracy loss.
Secure zero-effort two-factor authentication based on time-frequency audio analysis

Two-factor authentication (2FA) protects user's online account even if his/her password is leaked. Conventional 2FA systems require extra interaction like typing a verification code, which might not be very user-friendly. To improve user experience, recent researchers aim at zero-effort 2FA, in which a smart phone placed close to a client computer (browser) automatically assists with the authentication. In this paper, we propose SoundAuth, a secure zero-effort 2FA mechanism based on (two kinds of) ambient audio signals. We consider the comparison of the surrounding sounds and certain unpredictable near-ultrasounds as a classification problem and employ machine learning techniques for analysis. To evaluate the usability and security of SoundAuth, we study the effects of the recording duration and distance between two devices. Experiments show SoundAuth outperforms existent schemes for specific simulation attacks.
A variational image denoising model under mixed Cauchy and Gaussian noise

&lt;abstract&gt;&lt;p&gt;In this article, we propose a novel variational model for restoring images in the presence of the mixture of Cauchy and Gaussian noise. The model involves a novel data-fidelity term that features the mixed noise as an infimal convolution of two noise distributions and total variation regularization. This data-fidelity term contributes to suitable separation of Cauchy noise and Gaussian noise components, facilitating simultaneous removal of the mixed noise. Besides, the total variation regularization enables adequate denoising in homogeneous regions while conserving edges. Despite the nonconvexity of the model, the existence of a solution is proven. By employing an alternating minimization approach and the alternating direction method of multipliers, we present an iterative algorithm for solving the proposed model. Experimental results validate the effectiveness of the proposed model compared to other existing models according to both visual quality and some image quality measurements.&lt;/p&gt;&lt;/abstract&gt;
Hubungan Kreativitas Belajar dengan Prestasi Belajar Siswa Kelas V Gugus XVI Kecamatan Liliriaja Kabupaten Soppeng

The problem in this study is the lack of students' ability to develop answers or not being able to think creatively, tend to have poor learning achievements. However, it is possible for students who have high creativity to have good learning achievements, while students who have low creativity have high and low learning achievements. This study is a quantitative study with a correlation design that aims to determine the relationship between the implementation of creativity and the learning achievement of class V Cluster XVI students, Liliriaja District, Soppeng Regency. The results showed that there was a significant relationship between learning creativity and student achievement in class V Cluster XVI, Liliriaja District, Soppeng Regency with a tcount value (5.7855) greater (&gt;) ttable value (1.66792) which means H0 is rejected and H1 is accepted. Based on the analysis of the data above, it can be concluded that learning creativity will have an impact on students' learning habits in learning so that it will affect their learning achievement in the classroom. So the better the learning creativity, the better student achievement will be. From these conclusions, the impact of this research is that good learning creativity will increase student achievement.
Analisis Materi Bahasa Inggris Sebagai Pendukung Kurikulum Tematik di Kelas V SD 1 Jepang Kabupaten Kudus

This study aims to determine whether there is a relationship between the English language material and the thematic learning material and what are the similarities and differences between the English language material and the thematic learning material. This type of research is a qualitative research, the data in the study were obtained through interviews with principals, English teachers, students, parents, observation sheets, and documentation. The results of the research that has been carried out are that English material can support thematic learning of class V. The similarity between English material and class V thematic material lies in the subject or topic where English material and thematic material usually contain vocabulary related to events. as well as daily activities. The difference between English material and thematic material lies in the presentation of the material because the presentation of English material usually only contains an introduction to vocabulary related to simple things in everyday life. While the presentation of material in thematic learning which consists of themes and subthemes is usually presented in detail and is more complex.
Data Visualization Analytic for Understanding the Dynamics of Operating System Using Programming Language Paradigm

Evolution of Programming Languages lead to the emergence of new one while the old one slowly fade away. The choice of most developer on Operating System depends on the Programming Language to execute a project. This infer that there is a connection between Programming Languages and Operating System. Hence, this study proposes a data visualization analytic to observe the trend of programming language usages over the years and their effects on Operating Systems. Dataset containing the usage of Programming Languages and Operating System were retrieved from GitHub, Flourish and Stack overflow using text mining that employed regex techniques and Python was used to implement data analytic and visualization. Considering the first ten popular Programming Languages from 330,936 instances, our result similar to the result of other research output with Python leading the Programing Languages. The result of change over time of Operating System shows that Window OS is tending toward negative path while Linux OS and Mac OS are tending toward positive path. Also, Java and JavaScript are mostly used on Window OS, Objective C and Swift are mostly used on Mac OS and Python is mostly used on Linux. The overall results over the years showed that programmers are consistently shifting from Window OS to Linux OS and Mac OS
Pengembangan Media Pembelajaran Interaktif Berbasis Microsoft Power Point Untuk Kelas V Tema 3 Makanan Sehat Subtema 1 di Sekolah Dasar Negeri Giling

The objectives to be achieved in this research are the validity and feasibility of Microsoft PowerPoint for class V Theme 3 Subtheme 1 in Elementary School. This type of research is research development Research and Development (R &amp; D) is a research method used to make a product and test the effectiveness of the product only to a limited trial or only at stage six of the ten steps of the Borg &amp; Gall model, namely Research and information collecting, Planning, Develop preliminary form of product, Preliminary field testing, Main product revision, Main field testing. The subjects of this research are teachers and students of SD Negeri Giling 01. This research is based on the aim of developing interactive learning media based on Microsoft powerpoint that is suitable for use by elementary school students. The research method used is the Borg and Gall method with six steps out of ten research steps. The analysis technique in this study uses observation, interviews, and questionnaire responses. The results of the research, the media tested, is very feasible and efficient to be a learning medium in class V Elementary School.
Cloud Security Audit : A Necessity in the Cloud Computing Environment

The adoption of cloud computing by corporates has gained momentum over the last few years (Ismail and Islam, 2020). According to a report by Statista (www.statista.com) on the Global market size of cloud computing and hosting, the market has recorded a six-fold increase during the period 2010 to 2020. Cloud security is a major hindrance in the adoption of Cloud computing by the corporate sector. This article highlights the cloud security audit process. Cloud security auditing helps to reduce the cloud security issues and builds the confidence of the stakeholders.
Le tre fasi del discorso politico italiano in Twitter: una storia senza lieto fine?

The relationship between Twitter and Italian politicians started around 2010. Since then, it is possible to trace an evolution, and to identify three distinct phases that have characterised this relationship. This study describes some of the linguistic shifts that characterise the political discourse on Twitter, in its progressive move from the naive attitude to ʻbeing social’ of the beginnings, to the self-promotional monologue, up to the verbal excesses and forms of language aggression of the last three years. Through the analysis of corpora of tweets written by Luigi di Maio, Giorgia Meloni and Matteo Renzi, the focus is mainly on some of the lexical and discursive features that characterise this verbal aggression. The use of a rhetoric of simplification, through which politicians constantly tend to banalise and reduce reality to its extreme forms, strongly contributes to the diffusion of a highly polarised form of political discourse, as well as to our daily experience with social media.