Reviewerly/exHarmony huggingface dataset:
A Study of Flood Disaster Mitigation at the Tambak Anyar Traditional Polder in Banjar Regency South Kalimantan

The Tambak Anyar polder is one of the many traditional polders scattered in the Banjar Regency. Nevertheless, this polder is flooded almost every year. The purpose of this research is to evaluate dependable discharge that entering to polder system that can be supported by the river capacity inside the polder for flood mitigation. The method used to estimate the water availability is the Mock Model. Furthermore, the data needed are observed discharge, rainfall, and evapotranspiration data. The results of water availability analysis shown that the maximum dependable flow Q80% obtained is 4.89m3/s in the 1st period of January, and the minimum dependable flow Q80% is 1.37 m3/s in the 2nd period of September. The discharge entering the polder is quite large; the capability of Tambak Anyar River as a conveyor channel only can collect 29.74% from the dependable discharge, while the Rantau Bujur River only can accommodate 7.47% in April. In May, Tambak Anyar River's capability can only collect 7.17% from the dependable discharge, while the Rantau Bujur River can only accommodate 0.81%. The amount of discharge cannot be supported by the conveyor river inside the polder. The paddy fields in the Tambak Anyar polder often overflow caused by excess water. This excess of water caused floods that inundated the polder area and the other areas in Banjar Regency as of the beginning of January 2021. The amount of water that has access to the polder is not counterbalanced by watergates facilities and infrastructure management.
A Short Video Recommendation System

Abstract: With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngers. However, for many users, they spend a lot of time to get their favourite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Therefore, we propose a micro-video recommendation system. The recommendation algorithms are the core of this system. Traditional recommendation algorithms include content-based recommendation, collaboration recommendation algorithms, and so on. At the Big Data times, the challenges what we meet are data scale, performance of computing, and other aspects. Thus, we improve the traditional recommendation algorithms, using the popular parallel computing framework to process the Big Data. Slope one recommendation algorithm is a parallel computing algorithm based on MapReduce and Hadoop framework which is a high-performance parallel computing platform. The other aspect of this system is data visualization. Only an intuitive, accurate visualization interface, the viewers and producers can find what they need through the micro-video recommendation system. Keywords: Short, video, recommendation , machine learning
DEEP LEARNING APPROACH FOR DENOISING CONTOURS AND CLASSIFICATION OF AUTOMATIC HEALTH TISSUE IMAGE WAVELETS VIA CNN

Image classification is crucial in diagnostic scanning, notably when combining architectural pictures from CT, MRI, and operational pictures from photonic techniques or various new image analysis methods.Whenever combined using 3D photon transportation simulations techniques, picture separation additionally offers precise anatomical descriptions enabling quantified depiction of the therapeutic photon dispersion as in the body.Using five MRI face imaging files, we firstly employ post approaches like polynomial conducting to recover the precise outlines of distinct components including the cranium, cerebral liquid
An Analysis of Reading Teaching Based on SOLO Theory from Core Literacy Perspective——Taking Maupassant’s Necklace as an Example

While focusing on deep learning and cultivating students’ core Chinese literacy, Chinese reading teaching in primary and secondary schools can be designed based on the SOLO classification evaluation theory, and a layered teaching method can be adopted according to the differences in students’ thinking levels. Taking Maupassant’s famous Necklace as an example, from the perspective of teaching objectives and teaching process, the application of SOLO classification theory in teaching is analyzed. To innovate reading teaching evaluation methods is in order to promote the realization of students’ deep learning and the overall improvement of core Chinese literacy.
IMPROVING STUDENTS’ VOCABULARY MASTERY BY USING BEETLE DRIVE GAME AT THE SEVENTH GRADE STUDENTS OF MTS MUHAMMADIYAH LIMBUNG

This research aimed at finding out the improvement of students’ vocabulary mastery by using Beetle Drive Game. This research used a pre-experimental method. The population of this research was the second grade students of MTs Muhammadiyah Limbung. The total sample was 25 students and it used a purposive sampling technique. In collecting data, the researcher used vocabulary test that is administered of pre-test and post-test. The result of this research showed that the mean score in post-test was higher than the mean score in pre-test ( 78,56> 60,44). The result of t-test was greater than the t-table value (18,12 > 13,51) at the significance level of 0,05. Based on the result of data analysis, it is concluded that using Beetle Drive Game can improve the students’ vocabulary mastery at MTs Muhammadiyah Limbung.
A Study on the Standards of the Management of Segregated Biometric Data

This proposal of paper is the first international standard and research of the management technology of biometric data. By introducing and applying this international standards, everyone can acknowledge and approve are process for management of segregated biometric information. First, Safety requirement of segregated biometric information. Second, Growth and development of segregated biometric information. Third, Convenience promotion of financial transactions. The demand for biometric authentication service is on a trend of significant increase owing to the advances in new technology such as the Internet of Things (IoT), artificial intelligence and others as well as the expanded use of fin-tech and electronic financing arrangements.Biometric information is convenient because it is inherent and does not need to be remembered or stored,but leakage or misuse of the information could bring about serious problems because of its unique property. It is becoming more and more important to manage the information safely without the possibility of misuse or leakage. To store the information safely, it is effective to segregate the information into fragments in an un-usable form. Accordingly, we propose to standardize the international standard for Management of Segregated Biometric Information.The purpose of paper is to propose to standardize the international standard of the distributed management technology of biometric data such as ISO, IEC, ITU and UNCEFACT.
Modelling protocol gateways for cyber-physical systems using Architecture Analysis &amp; Design Language

Developing, configuring, and deploying legacy protocol gateways for existing cyber-physical systems to improve interoperability remains challenging. A possible solution is using model-driven engineering languages such as the Architecture Analysis & Design Language (AADL) that enables combined modelling of software, hardware, and communication for distributed systems. Experiences made while creating an OPC Unified Architecture (OPC UA) / Data Distribution Service (DDS) gateway indicate the suitability of AADL to model complex software artefacts. Moreover, a proposed process reduces the development and configuration effort for platform-specific gateway instances. A challenge to be addressed is the generation of executable code for resource-constraint devices.
Should Consumers Be Prohibited From Storing Card Data on the Internet?

In March 2020, the Reserve Bank of India's guidelines on Payment Aggregators and Payment Gateways prohibited merchants from storing data on cards used by customers. This paper argues that a total prohibition on card data storage is problematic as it affects the ease of transactions for consumers, and effectively tilts consumer preference towards other payment instruments. This runs the risk of technological choices in the industry being made or substantially shaped by the regulator. The documents released lack a cost-benefit analysis of this prohibition and do not demonstrate that the chosen intervention is the best one. This raises concerns in the light of emerging Indian jurisprudence on the standards of regulatory governance to be met by statutory regulatory agencies. We show alternative approaches to address concerns relating to breaches of card information stored by consumers on the internet. These include better security standards, tokenization, and liability frameworks.
Emotion Recognition Using Short-Term Analysis of Heart Rate Variability and ResNet Architecture.

Human emotions serve to have an important role in human behaviors. This study will identify and recognize emotions with the use of facial expressions for the convolutional neural network (CNN) using ResNet architecture and with the use of physiological signals, particularly, heart rate variability and electrocardiogram (ECG). The testing subjects’ images were taken, serving as an input to the CNN while the ECG device was attached to them. The emotions of the testing subjects would be the output on the resulting ECG signals and face emotion recognition. These emotions could be happy, sad, neutral, fear, and anger. The data gathered from the testing subjects with the use of the researcher’s device worked as intended. The accuracy’s obtained from the ECG based on the results was 68.42%.
Mobile Devices and Cybersecurity Issues Authentication Techniques with Machine Learning

Cybersecurity is the most important and hot research topic, especially at this time. As people are getting more dependent on digital systems and critical and sensitive information’s are stored on these digital devices. There is an advantage of using these devices and gadgets. Still, at the same time, there is a risk of getting attacks on these sensitive files, be it money or essential transaction information for business dealings like customer information for a business.  In this study, various security threats have been discussed, mainly for mobile devices. First, the harmful effects of using mobile phones concerning the attacks on these devices have been discussed. After that, authentication techniques are discussed, which show the various methods being applied to safeguard the mobile devices from intruders for malware attacks. There are four authentication techniques discussed in this work: authentication based on information, authentication based on biometrics, authentication based on behavioral, and authentication based on double factor. Each authentication technique is discussed with its advantages and disadvantages. Finally, the importance of machine learning for the detection of malware is discussed. There are various proposed techniques available that are designed using machine learning to fight malware attacks. Some of them are discussed here, which would help a new researcher in getting the proper research direction. At the end, recommendations are listed which can help to save the mobile device users from any attacks. These recommendations are categorized for users, researchers and government authorities.
How Online Reviews Affect Consumers Purchase Intention and Product Perception: A Qualitative Study

Online shopping has become one of the important resources of the internet, especially after the Covid-19 pandemic, due to which rapid development has seen in online shopping. The consumer search has shifted to online gradually. Researchers have shown that online shoppers view reviews before making a transaction online to make their final decisions before purchasing. As these online reviews are a necessary form of spreading awareness, these reviews impact consumer purchase buying behavior. The more the product has positive reviews, the more the product's chance to be sold out. Many businesses have taken this strategy to manipulate the consumers by providing a significant number of positive reviews of that product. Internet users around the world are more interested in reading positive online reviews so that it would help them to trust the product easily.Based on the literature, the perception of negative or positive online reviews, purchase intention, and product perception is considered in the present study. This study aims to confirm that online reviews affect consumer purchase intention and product perception. Moreover, buying attitudes influence behavior intention
Contracting for Algorithmic Accountability

As local, state, and federal governments increase their reliance on artificial intelligence (AI) decision-making tools designed and operated by private contractors, so too do public concerns increase over the accountability and transparency of such AI tools. But current calls to respond to these concerns by banning governments from using AI will only deny society the benefits that prudent use of such technology can provide. In this Article, we argue that government agencies should pursue a more nuanced and effective approach to governing the governmental use of AI by structuring their procurement contracts for AI tools and services in ways that promote responsible use of algorithms. By contracting for algorithmic accountability, government agencies can act immediately, without any need for new legislation, to reassure the public that governmental use of machine-learning algorithms will be deployed responsibly. Furthermore, unlike with the adoption of legislation, a contracting approach to AI governance can be tailored to meet the needs of specific agencies and particular uses. Contracting can also provide a means for government to foster improved deployment of AI in the private sector, as vendors that serve government agencies may shift their practices more generally to foster responsible AI practices with their private sector clients. As a result, we argue that government procurement officers and agency officials should consider several key governance issues in their contract negotiations with AI vendors. Perhaps the most fundamental issue relates to vendors’ claims to trade secret protection—an issue that we show can be readily addressed during the procurement process. Government contracts can be designed to balance legitimate protection of proprietary information with the vital public need for transparency about the design and operation of algorithmic systems used by government agencies. We further urge consideration in government contracting of other key governance issues, including data privacy and security, the use of algorithmic impact statements or audits, and the role for public participation in the development of AI systems. In an era of increasing governmental reliance on artificial intelligence, public contracting can serve as an important and tractable governance strategy to promote the responsible use of algorithmic tools.
Landscapes of Codes: rank distance codes and intersection problems in finite projective spaces

The aim of this thesis is to highlight once again how Geometry, and in particular Combinatorics, is visual knowledge.\\In this work the main results in my PhD research period are collected. More precisely, it is divided into three blocks. In the first, we investigate the theory of maximum rank distance (MRD) codes whose codewords are symmetric, alternating or Hermitian matrices. In the linearized polynomials setting, we explore how the already known classes of such codes can be seen as the intersection of an appropriate code with the restricted ambient in which ‘they live'. We solve the equivalence issues and we compute their automorphisms group. Moreover, we characterize these latters and present a new class of maximum symmetric codes.\\In the second part, we recall some notions about finite projective spaces and in this context we introduce the $q$-analogue of the Erd\H{o}s-Ko-Rado problem originally stated in set theory. After having retraced the known results in this topic, we study maximal families of $k$-dimensional subspaces in $\PG(n,q)$, $n \geq k+2$ and $k \geq 3$, pairwise intersecting in at least a $(k-2)$-space. We also give some upper bounds on the size of relevant families, exploring the largest examples.\\In the last part, we introduce the subspace codes theory as the geometrical counterpart of the intersection problems with assigned size arisen from the set theory. Finally, we generalize the concept of equidistant constant-dimension codes with the notion of SPID (\textit{Subspace Pre-assigned Intersection Dimensions}). The \textit{junta code}, i.e. a highly regular structure that extends the notion of \textit{sunflower}, is defined. In a vector setting, we analyze the space spanned by the elements of a SPID with two intersection dimensions and determinine a \textit{geometrical junta bound}. In particular for two consecutive assigned values of the intersection, we show that this threshold is sharp.
Absolute Distance Prediction Based on Deep Learning Object Detection and Monocular Depth Estimation Models.

Determining the distance between the objects in a scene and the camera sensor from 2D images is feasible by estimating depth images using stereo cameras or 3D cameras. The outcome of depth estimation is relative distances that can be used to calculate absolute distances to be applicable in reality. However, distance estimation is very challenging using 2D monocular cameras. This paper presents a deep learning framework that consists of two deep networks for depth estimation and object detection using a single image. Firstly, objects in the scene are detected and localized using the You Only Look Once (YOLOv5) network. In parallel, the estimated depth image is computed using a deep autoencoder network to detect the relative distances. The proposed object detection based YOLO was trained using a supervised learning technique, in turn, the network of depth estimation was self-supervised training. The presented distance estimation framework was evaluated on real images of outdoor scenes. The achieved results show that the proposed framework is promising and it yields an accuracy of 96% with RMSE of 0.203 of the correct absolute distance.
Twitter Opinion Analysis on Drug Usage by VADER Sentiment Analysis among Various Leading Countries

In Twitter people from all part of the planet will build their opinions detected and take feed from the opinions. Twitter produces around five hundred million of tweets daily that amounts to regarding 8TB of information. Data scrapped from twitter may be terribly helpful if analyzed as we are able to extract vital information via sentiment analysis. Opinions or comments regarding any news or launch of a product or a definite quite trend may be ascertained well in twitter information. Our aim is to analysis of tweets on use of drugs for the treatment of COVID-19. In twitter sentiment analysis, we have a tendency to categorize tweets into positive and negative sentiment. agglomeration could be a procedure of distinctive resembled objects, cluster along and type a pack or cluster. we have a tendency to conducted a study and located out that the utilization of agglomeration will quickly and efficiently distinguish numerous tweets on the idea of their sentiment scores and proportion and may notice weak and powerful positive or negative tweets once clustered with results of various dictionaries and establish a powerful support on our assumption. This paper surveys the polarity activity exploitation VADER sentiment analysis on the utilization of drug for of COVID-19 treatment.
PECULIARITIES OF INTRODUCTION OF DISTANCE COURSE “BASICS OF PEDAGOGICAL MASTERY” FOR PEDAGOGICAL SPECIALITY STUDENTS

The implementation of the Law of Ukraine “On Higher Education” has caused an increase in the requirements on the quality of specialists professional training, which is the main criterion in the assessment of educational institutions functioning on the one hand and an increase of educational requirements on the labour market – on the other. The main goal of professional education is higher qualification specialists training, able to work effectively on the world standards, ready for constant professional development and expansion of educational services, to be aware of scientific information, and have a desire to study not only in Ukraine but abroad. At the present stage of social development, the preference is given to Lifelong Learning; the development of mobile, competitive, high-technological educational environment; the implementation of innovative educational technologies; distance teaching techniques optimization for achieving a high level of professional competences. Taking into consideration all above-mentioned factors, under current conditions, there is an increase in distance learning, which is realized in the framework of distance education and is the key component of updating processes in modern higher education institutions and serves as a powerful integration bridge between the world and native educational spaces. Scientific resources analysis and the researches in theory and practice of the issue, taking into account professional competencies, the content, aim, objectives, evaluation criteria, level of students training under the conditions of distance education, are introduced in this article. The basic functions, principles, forms, methods and means of students’ individual work control are determined in the process of distance learning course “The principles of pedagogical skills” in pedagogical education institutions.&#x0D; Keywords: teacher; student; pedagogical education institution; distance learning; the principles of pedagogical skills; distance learning course; forms and methods of distance learning; Moodle.
PROBLEM-MODULAR APPROACH TO TEACHING PHYSICS TO CADETS OF A MILITARY UNIVERSITY

The article discusses a problem-modular approach to teaching physics to cadets of a military university. The relevance of the use of the problem-modular approach in obtaining professional knowledge, skills, mastering professional and personal competence by cadets is noted. The principles of the problem-modular approach in vocational training of cadets of a military university are highlighted: modularity, problematicity, integrativity. The tasks of the implementation of this approach in teaching have been determined. The purpose of the study is to analyze the features of the problem-modular approach in teaching physics at a military university. The research methods were analysis, generalization, systematization of the material on the research problem, the author's development of a problem-modular approach to teaching physics to cadets of a military university. It is concluded that the problem-modular approach in teaching physics to cadets of a military university at the level of the content and structure of the educational material is distinguished by integrativity, continuity, the presence of interdisciplinary connections, at the competence level, this approach is aimed at mastering both general scientific and professional knowledge and skills, the formation of a scientific worldview and professional competencies at each stage of education, the formation and development of a critical and systemic vision of the situation.