Reviewerly/exHarmony huggingface dataset:
Improved Residual Dense Network for Large Scale Super-Resolution via Generative Adversarial Network

Recent single image super resolution (SISR) studies were conducted extensively on small upscaling factors such as x2 and x4 on remote sensing images, while less work was conducted on large factors such as the factor x8 and x16. Owing to the high performance of the generative adversarial networks (GANs), in this paper, two GAN’s frameworks are implemented to study the SISR on the residual remote sensing image with large magnification under x8 scale factor, which is still lacking acceptable results. This work proposes a modified version of the residual dense network (RDN) and then it been implemented within GAN framework which named RDGAN. The second GAN framework has been built based on the densely sampled super resolution network (DSSR) and we named DSGAN. The used loss function for the training employs the adversarial, mean squared error (MSE) and the perceptual loss derived from the VGG19 model. We optimize the training by using Adam for number of epochs then switching to the SGD optimizer. We validate the frameworks on the proposed dataset of this work and other three remote sensing datasets: the UC Merced, WHU-RS19 and RSSCN7. To validate the frameworks, we use the following image quality assessment metrics: the PSNR and the SSIM on the RGB and the Y channel and the MSE. The RDGAN evaluation values on the proposed dataset were 26.02, 0.704, and 257.70 for PSNR, SSIM and the MSE, respectively, and the DSGAN evaluation on the same dataset yielded 26.13, 0.708 and 251.89 for the PSNR, the SSIM, and the MSE.
Feature Extraction With Forest Classifer To Predicate Covid 19 Based On Thorax X-Ray Results

Coronavirus 19 (COVID-19) is a highly contagious infection caused by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a new virus for which no cure has been found, marked by the increasing death rate worldwide. Coronavirus disease which can cause pneumonia which attacks the air sacs of the lungs with symptoms of dry cough, sore throat to acute respiratory distress (ARDS) that occurs in COVID-19 patients. One of the ways to detect the virus is by detecting chest X-rays in the patient. Over the past decade's mechine learning technology has developed rapidly and is integrated into CAD systems to provide accurate accuracy. This research was conducted by detecting thoracic radiographs using feature extraction Hu-Moments, Harralic and Histogram and detecting the best accuracy with a classification algorithm to detect the results of COVID-19. The study was conducted by testing the dataset obtained from the Kaggle repository which has images, namely 1281 X-rays of COVID-19, 3270 X-rays Normal, 1656 X-rays of pneumonia, and X-rays of bacteria-pneumonia 3001. In general, this research is included in the Good category because it produces the highest accuracy by the Random forest classification algorithm where the accuracy result is 84% and the standard deviation is 0.015847. In addition, the research also produced Kappa of 0.713. The results of this accuracy are carried out in several stages, namely by feature extraction in the form of hu-moments, Harralic and histogram. In this study, the best results were given by the Random forest algorithm with feature extraction Histogram and Hu-Moment.
Classification of $(q,q)$-biprojective APN functions

In this paper, we classify $(q,q)$-biprojective almost perfect nonlinear (APN) functions over $\mathbb{LL} \times \mathbb{LL}$ under the natural left and right action of $\mathrm{GL}(2,\mathbb{LL})$ where $\mathbb{LL}$ is a finite field of characteristic $2$. This shows in particular that the only quadratic APN functions (up to CCZ-equivalence) over $\mathbb{LL} \times \mathbb{LL}$ that satisfy the so-called subfield property are the Gold functions and the function $\kappa : \mathbb{F}_{64} \to \mathbb{F}_{64}$ which is the only known APN function that is equivalent to a permutation over $\mathbb{LL} \times \mathbb{LL}$ up to CCZ-equivalence. The $\kappa$-function was introduced in (Browning, Dillon, McQuistan, and Wolfe, 2010). Deciding whether there exist other quadratic APN functions (possibly CCZ-equivalent to permutations) that satisfy subfield property or equivalently, generalizing $\kappa$ to higher dimensions was an open problem listed for instance in (Carlet, 2015) as one of the interesting open problems on cryptographic functions.
ADVANCED PEDAGOGICAL EXPERIENCE AS AN EFFECTIVE MEANS OF FORMING REFLECTIVE STUDENTS 'SKILLS

The article is devoted to the problem of formation of students reflective skills in the process of gaining knowledge based on the advanced pedagogical experience.
HiJoNLP at SemEval-2022 Task 2: Detecting Idiomaticity of Multiword Expressions using Multilingual Pretrained Language Models

This paper describes an approach to detect idiomaticity only from the contextualized representation of a MWE over multilingual pretrained language models.Our experiments find that larger models are usually more effective in idiomaticity detection.However, using a higher layer of the model may not guarantee a better performance.In multilingual scenarios, the convergence of different languages are not consistent and rich-resource languages have big advantages over other languages.
Neural Retriever and Go Beyond: A Thesis Proposal

Information Retriever (IR) aims to find the relevant documents (e.g.snippets, passages, and articles) to a given query at large scale.IR plays an important role in many tasks such as open domain question answering and dialogue systems, where external knowledge is needed.In the past, searching algorithms based on term matching have been widely used.Recently, neural-based algorithms (termed as neural retrievers) have gained more attention which can mitigate the limitations of traditional methods.Regardless of the success achieved by neural retrievers, they still face many challenges, e.g.suffering from a small amount of training data and failing to answer simple entity-centric questions.Furthermore, most of the existing neural retrievers are developed for pure-text query.This prevents them from handling multimodality queries (i.e. the query is composed of textual description and images).This proposal has two goals.First, we introduce methods to address the abovementioned issues of neural retrievers from three angles, new model architectures, IR-oriented pretraining tasks, and generating large scale training data.Second, we identify the future research direction and propose potential corresponding solution 1 .
CYBER CRIME AWARENESS AMONG UNDERGRADUATE STUDENTS

Medicinal products have been used by human beings since ancient times.The fact that medicines are not safe and have the potential to cause adverse reactions is known for a long time.Various disasters over the last few decades highlight the damage that the medicines can cause during their usage in patients.The two significant disasters that are commonly referred to during pharmacovigilance history and can be considered as having a significant impact on the development of drug safety are the SULFONAMIDE and the THALIDOMIDE DISASTERS.
Adjustment of Online Learning: The Problem Experienced by Parents, Teachers, and Students during the Covid-19 Pandemic

This study aims to identify problems experienced by teachers, parents, and students in online learning during the COVID-19 period and examine students' completeness in learning without physical interaction with teachers. This research was conducted at MIN 2 and MI al-Amanah Bailang Manado. There were 16 informants: 4 teachers, 6 parents and 6 students. Interview was conducted directly and by telephone. The results showed that while students were at home learning during Covid-19 some parents paid attention and help their children to study. The parents find it difficult to accompany their children to study at home. It is due to their busyness and their inability and knowledge to explain their children’s subjects. Completeness of teaching and learning in online learning without physical interaction make the teachers must be concerned about the condition of their students. The students are 'forced' to learn through online media while they do not have adequate facilities at home. Teachers find teaching using online media difficult. It is due to limited ability in using applications such as zoom, e-learning, cloud, whatsapp, and others. Some teachers are not ready to conduct online learning. During the covid-19 period, the student will study if the teachers give an assignment. The parents’ supervision makes their children uncomfortable to study at home.
Group privacy for personalized federated learning

Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among others. The goal is the optimization of a statistical model's parameters by minimizing a cost function of a collection of datasets which are stored locally by a set of clients. This process exposes the clients to two issues: leakage of private information and lack of personalization of the model. On the other hand, with the recent advancements in various techniques to analyze data, there is a surge of concern for the privacy violation of the participating clients. To mitigate this, differential privacy and its variants serve as a standard for providing formal privacy guarantees. Often the clients represent very heterogeneous communities and hold data which are very diverse. Therefore, aligned with the recent focus of the FL community to build a framework of personalized models for the users representing their diversity, it is also of utmost importance to protect the clients' sensitive and personal information against potential threats. To address this goal we consider $d$-privacy, also known as metric privacy, which is a variant of local differential privacy, using a a metric-based obfuscation technique that preserves the topological distribution of the original data. To cope with the issue of protecting the privacy of the clients and allowing for personalized model training to enhance the fairness and utility of the system, we propose a method to provide group privacy guarantees exploiting some key properties of $d$-privacy which enables personalized models under the framework of FL. We provide theoretical justifications to the applicability and experimental validation on real datasets to illustrate the working of our method.
WEB Based Social Assistance Recipient Selection Application Based on Community Education Level With C4.5 Algorithm

The Koramil 1408-02/Tallo office is in charge of distributing social assistance recipients. The social assistance is in the form of basic food assistance for the community and MSMEs for people who have micro-enterprises, where each aid has different criteria in determining the recipient. Regarding the existing social assistance and having different criteria in determining acceptance, including the level of education, of course, the staff of the 1408-02/Tallo Koramil Office will find it difficult to carry out the selection process. Based on existing data mining, beneficiaries can be made to assist beneficiaries who will assist the Koramil 1408-02/Tallo staff. Classification can be done using the C4.5 algorithm. This study aims to determine the recipients of social assistance and apply it in the development of the recipient system of social assistance, including the level of community education. This makes it easier to determine recipients of social assistance and the results of this study are applications for social assistance recipients, so that they can overcome the problems that exist in the current system. Based on the results of the research that has been done on the design of this application, it has succeeded in helping the Koramil Office 1408-02/Tallo in selecting social assistance recipients based on the recipient's education and functioning as expected.
Image Style Transfer Using Deep Learning

Abstract: In order to increase the quality of the composite image throughout the image style transfer process. This study presents an improved style loss function-based image style transfer method: the improved Gram matrix calculates the inner product of the feature map and the spatial transformation map, and then the new style loss function. At the same time, the weighted algebraic sum of the two loss functions is utilized as the neural network's total loss function, which is merged with the content loss function. To generate the style-transferred image, the gradient descent algorithm is employed to iteratively optimize. Keywords: CNN (convolutional neuralnetwork), neural style transfer, deep learning.
THE DEVELOPMENT OF TEACHER’S PROFESSIONAL IMAGE IN A MODERN SCHOOL

The relevance of the problem is caused by the fact that modernization of education is impossible without creation of a positive teacher’s image, formation of an active teacher’s personality, building a successful career. A modern teacher with a positive image is a highly educated, spiritual, creative personality who is able to generate new ideas, solve current problems in an original way, successfully realize himself/herself in professional activities, make a good impression on others, make a positive impact on pupils and be an example to them. The aim of the article is to theoretically analyze and empirically investigate the peculiarities of the development of the professional image of a teacher in a modern school. Research methods applied: comparative, typological, analytical, questionnaires, generalization, induction, deduction. Teachers’ opinions on the factors that most influence the formation of a pedagogical image in a modern school as well as the duration of its formation, the appearance of a modern teacher, the role of a pedagogical image in teacher’s professional activity were determined. Based on the analysis of the results of survey, the following peculiarities of the formation of the teacher’s image were identified: reputation and authority, professional skills, behavior, appearance, self-presentation skills and work experience. Methodological recommendations provided would help future teachers to form a positive pedagogical image and avoid difficulties in their professional activities. Adherence to them contributes to the professional and personal growth of a teacher, enrichment of professional and general culture, strengthening authority, development of a creative personality.
Contributor-Aware Defenses Against Adversarial Backdoor Attacks

&lt;p&gt;Deep neural networks for image classifi cation are well-known to be vulnerable to adversarial attacks. One such attack that has garnered recent attention is the adversarial backdoor attack, which has demonstrated the capability to perform targeted misclassifi cation of specifi c examples. In particular, backdoor attacks attempt to force a model to learn spurious relations between backdoor trigger patterns and false labels. In response to this threat, numerous defensive measures have been proposed; however, defenses against backdoor attacks focus on backdoor pattern detection, which may be unreliable against novel or unexpected types of backdoor pattern designs. We introduce a novel re-contextualization of the adversarial setting, where the presence of an adversary implicitly admits the existence of multiple database contributors. Then, under the mild assumption of contributor awareness, it becomes possible to exploit this knowledge to defend against backdoor attacks by destroying the false label associations. We propose a contributor-aware universal defensive framework for learning in the presence of multiple, potentially adversarial data sources that utilizes semi-supervised ensembles and learning from crowds to fi lter the false labels produced by adversarial triggers. Importantly, this defensive strategy is agnostic to backdoor pattern design, as it functions without needing—or even attempting—to perform either adversary identifi cation or backdoor pattern detection during either training or inference. Our empirical studies demonstrate the robustness of the proposed framework against adversarial backdoor attacks from multiple simultaneous adversaries.&lt;/p&gt;
A Decentralized Asset Tokenization System using Ethereum

Abstract: Real estate is one of the most trusted investments that people have preferred. It provides a steady source of income which can be in the form of rent as well as lease. There are a number of advantages, but one of the key disadvantages of real estate investments is illiquidity. Although the global real estate investments are twice the size of investments in the equity markets, the number of investors in the real estate market is remarkably lower. Blockchain technology has real potential to solve the issues of illiquidity and transparency, and most importantly, it opens this very market to the retail investors as well. By creating Security Tokens, which are backed by real-world assets, real estate can be made more liquid with the help of Special Purpose Vehicles. These security tokens, which represent fractional ownership of the real estate can be traded by a trader / investor and these tokens can be listed on legitimate secondary exchanges. The robustness of Smart Contracts can be very efficient in transferring of tokens and also provide a seamless distribution of earnings amongst the investors / traders. This work describes Ethereum blockchain based solutions has the potential to make the existing Real Estate investment system much more efficient and reliable. Keywords: Tokenization , Blockchain , Fungible Assets, Tokens, Special Purpose Vehicle, Smart Contracts, Liquidity, Security Tokens,
FOOD RECALL MOBILE APPLICATION DESIGN AS A MEASURING TOOL FOR INDIVIDUAL AND FAMILY FOOD CONSUMPTION

The information technology development has been used to create SITAMARI (daily food record system) that can be used as an assessment that correlates with a daily intake of food or the eating habit of the society that has been designed to be easy to use, efficient, and precise. The goals of this research were to design the SITAMARI application as the learning media of the Food Consumption Survey practice of the nutrition major's students. For now, the SITAMARI application is still in the development stage and thus hasn’t been launched online for a larger audience. This research used the quasi-experimental design of one group pre-test-post-test. Research subjects are 25 3rd-grade Diploma III Nutrition students, aged 18 – 25 years old, who have already understood 24 hours food recall method, and have a well-working Android smartphone. A pre-test questionnaire was given, followed respectively by the treatment and the same questionnaire as the post-test. Lastly, the Likert questionnaires about their perception after using the application were filled out. The statistical analysis using the paired sample t-test showed that t-calculated t-table with a significant probability value of 0.00 0.05. It was concluded that the use of the application is effective in the students' learning. The user satisfaction survey showed the application was attractive in design and easy to use and increased knowledge in Food Consumption Survey. However, the results of the food recall calculation still need to be validated comparatively with the manual food recall format.
Dynamic DNNs Meet Runtime Resource Management on Mobile and Embedded Platforms

Deep neural network (DNN) inference is increasingly being executed on mobile and embedded platforms due to low latency and better privacy. However, efficient deployment on these platforms is challenging due to the intensive computation and memory access. We propose a holistic system design for DNN performance and energy optimisation, combining the trade-off opportunities in both algorithms and hardware. The system can be viewed as three abstract layers: the device layer contains heterogeneous computing resources; the application layer has multiple concurrent workloads; and the runtime resource management layer monitors the dynamically changing algorithms' performance targets as well as hardware resources and constraints, and tries to meet them by tuning the algorithm and hardware at the same time. Moreover, We illustrate the runtime approach through a dynamic version of 'once-for-all network' (namely Dynamic-OFA), which can scale the ConvNet architecture to fit heterogeneous computing resources efficiently and has good generalisation for different model architectures such as Transformer. Compared to the state-of-the-art Dynamic DNNs, our experimental results using ImageNet on a Jetson Xavier NX show that the Dynamic-OFA is up to 3.5x (CPU), 2.4x (GPU) faster for similar ImageNet Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higher accuracy at similar latency. Furthermore, compared with Linux governor (e.g. performance, schedutil), our runtime approach reduces the energy consumption by 16.5% at similar latency.
Penggunaan Rekam Medis Berbasis Komputerisasi Terhadap Pelayanan Pasien Rawat Jalan di Puskesmas Cisitu

Service is the process of meeting needs through the activities of others directly. Service to outpatients at the puskesmas is the main task of the medical record in charge of the registration section, to provide the best service. At the Cisitu Public Health Center already using a computerized system, the service is easier, so that patient data is automatically entered into the computer at the polyclinic he is aiming for. To find out the use of computer-based medical records for outpatient services. The research method uses data collection methods, namely field studies, field studies include direct observation or observation methods, and direct interviews with parties related to the problems studied. The results of the research are the lack of human resources and the lack of supporting infrastructure that can affect the outpatient service at the Cisitu Public Health Center which is already computerized. More attention should be paid, because if the implementation continues, there will be several obstacles, for example, there will be a buildup of patient queues and staff will be overloaded with work. It should be with the existence of a computerized system that makes the work of officers easier and the service faster.
FORMATION OF MUSICAL AND AESTHETIC FLAVORS OF STUDENTS OF PEDAGOGICAL UNIVERSITIES IN THE PROCESS OF ARTISTIC AND CREATIVE ACTIVITY

The purpese of this article is the opening of the main aspects in the problem of formation musical and aesthetic tastes in the process of creative of students from Pedagogical Institute’s.
Hardware Designs of Cyclic Redundancy Check Code with Calculation Time Trade-Off Strategy

This paper will discuss the design of cyclic redundancy check code (CRC), a most popular error detecting scheme in intelligent communication. At first, the concepts of CRC are given in detail with mathematical model and software simulation in python scripts. And mainly, with calculation time trade-off strategy, we provide the CRC hardware design with three architecture models serial, parallel, hybrid serial and parallel. Keywords: Error Detection Scheme; Cyclic Redundancy Check; Python; Verilog HDL; Digital System Design