Reviewerly/exHarmony huggingface dataset:
Students’ and Lecturers’ Perceptions toward Interactive Multimedia in Teaching Academic Writing

Writing ability is expected to be achieved by all students because it dramatically affects academic achievement. However, some students have poor writing skills. The purpose of this study was to analyze data on student and lecturer perceptions of the use of media/multimedia in academic writing for Indonesian language courses. This type of research is development. Data collection methods in analyzing objective situations are questionnaires, interviews, and focus group discussions (FGD). The instruments used in collecting data are questionnaires and interview sheets. The technique used to analyze the data is descriptive qualitative and quantitative analysis. The research results are that students and lecturers feel that teaching and learning academic writing in Indonesian language courses do not apply interactive multimedia. Most of the teaching sessions use PowerPoint presentation slides and course books. Furthermore, they hope to use interactive multimedia in academic learning so that the sessions will be more exciting and engaging, not dull, use animations and videos, and can be accessed anytime, anywhere.
Implementasi MD5 dan Paillier Cryptosystem Untuk Membuat Tanda Tangan Digital

Of the several benefits of cryptographic techniques is data authentication. Before the digital era, signatures were used as a means to prove the authenticity of printed documents such as letters, certificates, certificates, books and so on. Documents that do not have a signature will be doubtful for their authenticity. One of the characteristics of these signatures is that all the signature patterns are the same, so this situation can easily be manipulated with signature forgery, if the related parties use a different signature on each document that is signed it will also have an impact on changing doubts on the parties who are signed. reserves the right to verify. To overcome the need, documents should be given a digital signature. The intended digital signature is not a manual signature that is scanned and then embedded in a digital document. Digital signatures will be built from cryptographic techniques. In this study, the signature that will be used is a signature with a combination of a hash function algorithm and a web-based public-key cryptography. The methods used are the MD5 algorithm and Paillier Cryptosystem. Paillier cryposystem is a public-key cryptographic algorithm which is believed to be difficult to attack because it uses n-residue class calculations so that it will be very difficult to find the key pair used, this is known as the Composite Residuosity (CR) assumption.
Workshop Marketplace System Using Rajaongkir API, Leaflet API and Midtrans Payment Gateway

The automotive shop is a business engaged in the automotive sector that serves vehicle repairs and sales of various kinds of vehicle spare parts. A marketplace is a place for buying and selling or a market that uses internet media. The current marketplace is still very common, making it difficult to find a marketplace that focuses on the automotive sector and difficult to find information about available automotive shops around. The automotive shop marketplace system can help to carry out the process of buying and selling vehicle spare parts and assist in finding various spare parts and information about available automotive shops based on the user's location. This system was developed on a web-based basis using the PHP programming language with the Laravel framework and integrated with the Leaflet API, RajaOngkir API, and Midtrans Payment Gateway as additional features in creating a marketplace. The leaflet API displays a map containing the location of the automotive shop listed on the marketplace. RajaOngkir API provides information about shipping costs to the buyer's place from the seller's place. Midtrans Payment Gateway bridges the payment process carried out by both parties so that it can take place properly. The method used in this research is to use the System Development Life Cycle (SDLC) waterfall method. The results of the research conducted were tested using the black-box method.
Optimisation design of distance education resource recommendation system based on hierarchical linear model

In order to solve the problems of large recommendation error and low efficiency in existing distance education resource recommendation systems, this paper proposes an optimisation design method of distance education resource recommendation system based on hierarchical linear model. The system is combined with hierarchical design, overall planning, design and realisation of design objectives, structure, logical structure, key technologies, etc. to realise the real-time data model updating of Hadoop distributed file system. The hierarchical linear model is used to construct the distance education resource recommendation model. According to the model, the optimisation objective function of distance education resource recommendation is set. The optimisation objective function is solved by the hybrid recommendation algorithm of content and collaborative filtering, and the optimisation design of distance education resource recommendation system is completed. The experimental results show that the proposed optimisation system has few recommendation errors and high recommendation efficiency, and the minimum recommendation error is only 0.11.
SVD based Image Actual Resolution Estimation

Image up-sampling is a fundamental operation in image processing, which enlarges the size of original image. Though the upsampled image may look better, it contains the same information as the original and requires more computation and storage. To accurately determine the actual resolution of the upsampled image is challenging with few previous studies to be investigated. Here, we proposed a method for estimating the actual resolution of an image based on Singular Value Decomposition (SVD). The proposed method generates multi-resolution images by SVD, and find the peak difference among each level image’s eigenvalues, where the level image below the actual resolution cannot keep enough feature information. The approach is model-free and does not rely on any user-defined parameters. We demonstrate its feasibility on a wide variety of datasets. Finally, we show how our method can be utilized to compress images effectively.
Development of 2-D augmented reality integrated physics e-book to improve students' problem-solving skills

This study aims to produce an augmented reality integrated e-book that is feasible to improve the problem-solving skills of 11th graders; identify the improvement of 11th graders problem-solving skills after using the augmented reality integrated e-book. Problem-solving as a way of obtaining solutions to difficulties is divided into four indicators, which are: understanding the problem, making plans, implementing plans, and reviewing solutions. Data collection techniques consist of tests, questionnaires, observations, and documentation using instruments that support the Research and Development (R&amp;D) technique with a 4D model. Research subjects were selected by simple random sampling with 34 students of 11th graders. In this study, questionnaires were used for validation, observations were made to determine the condition of students, and documentation was used as evidence in conducting research. The data were analyzed quantitatively based on the results of the validation and analysis of pretest and posttest items. The results showed that the augmented reality integrated e-book is feasible to use based on the results of validation using the average value conducted by a very high category validator, and the validation value obtained is 3.79. The improvement of problem-solving skills was analyzed using Normalized Gain, and the improvement of problem-solving skills got a score of 0.8 in the high category.
Artificial Intelligence Hand Spatial Position Predictor Based on Data Gloves and Jetson Xavier NX

Based on the functional design of the hand rehabilitation index evaluator for patients with clinical hemiplegia and hand dysfunction, as well as the design based on the function of gesture recognition and sign language translation for the deaf, this paper applies a wireless wearable data glove based on inertial sensors, combined with the current NVIDIA most advanced edge computing development platform Jetson Xavier conducts artificial intelligence development and applications. A database of sensor data and hand joint angle and spatial position information is established by using the sensor data obtained by the data glove. A recurrent neural network with continuous prediction function is then trained using deep learning based on this database and deployed on the Jetson Xavier development platform. Finally, the sensor data is transmitted to the platform through the wireless communication module for real-time reasoning based on deep learning neural network, and the role of artificial intelligence in predicting the angle and spatial position of hand joints is realized.
THE EDUCATIONAL GAME IN THE KINDERGARTEN THROUGH THE EYES OF THE CREATIVE TEACHER

The modern educational game is in line with the new needs of children to provide the necessary opportunities and abilities – knowledge, skills, understanding, way of thinking in a new communication and information environment. Our experience in the last 10 years shows that today’s education in kindergarten is not limited to the development of competencies for the use of modern digital technologies, but implies knowledge and widespread use of computer technology and platforms. The creation, storage and use of such resources in the educational process depends on the creativity and innovation of the children’s teacher. Knowing the main characteristics of the game, we have developed educational games that are fun in nature and are determined by the presence of predifferentiated components.
Endless Web

Endless Web is a game where a procedural content generator is deeply integrated into the mechanics, dynamics, and aesthetics. The game involves players exploring the generative space of its built-in content generator, by making decisions that influence the parameters to the generator. Players must build strategies around the choices they are making to ensure that levels are at an appropriate level of challenge for them, while simultaneously exploring to discover goals hidden in layers of the generative space.
Correlated Matching and Structure Learning for Unsupervised Domain Adaptation

For cross-domain tasks in real-world, a source domain and a target domain often have different marginal probability distribution and conditional probability distribution. To leverage the distribution difference between the source and target domain, domain adaptation has been applied in many fields. Unfortunately, as most of the existing domain adaptation methods only focus on eliminating the distribution discrepancy between the two domains, they do not make full use of the correlation information and data distribution structure between the two domains. In this paper, we put forward a novel domain adaptation method named correlated matching and structure learning (CMSL), which considers the association information between source and target domains, and extracts the feature representation and thus can learn the maximization correlation features between the two domains. Simultaneously, the class centroids of the source data are used to cluster the target data, and a local manifold self-learning strategy is introduced to the target domain to preserve the underlying structure of the data. Experimental results on six data benchmarks show that our proposed method achieves good classification performance and outperforms several state-of-the-art unsupervised domain adaptation methods.
Big data visualization through the lens of Peirce’s visual sign theory

Data from social media platforms, such as Twitter and Facebook, are generated by people who produce, spread, share, or exchange multimedia content. Such content may include text, images, sounds, or videos. To derive insight into the behavior of social media users, researchers often use open-source technologies to visualize data and generate models for data analytics. One of the most popular open-source applications for managing and analyzing social media data is the open-source R programming language. Friedman and Feichtinger (2017) created an R package termed ‘Peirce’s sign theory R package’ to analyze data using Peirce’s principles of discovery. Though Peirce semiotics have been introduced in the context of computer programming languages, so far, no previous work has applied Peirce’s sign theory to data modelling of social media data. In this paper, we use Peirce’s sign theory R package as an overall framework to gain insight into data collected from Twitter. We assembled the data using Twitter’s Analytics algorithm, examined the relationships between variables, and visualized the results. Subsequently, we assessed the feasibility of analyzing those graphics using the triadic model set out by Jappy (2013) and Peirtarinen (2012) for the interpretation of visual signs. The study results showed that Peirce’s sign theory R package effectively analyzes and visualizes Big Data from social media feeds. However, due to complexities in both the social media data feeds and Peirce’s interpretation of meaning, as outlined by Jappy (2013) and Peirtarinen (2012), we were unable to develop algorithms that generate or suggest an interpretation of visual signs.
Peningkatan Minat Dan Hasil Belajar Subtema Kegiatan Di Sore Hari Melalui Whatsapp Group Dengan Pendampingan Orang Tua

This classroom action research aims to increase interest and learning outcomes using the whatsapp group application in the subject of the activity sub-theme in the afternoon of first graders at SDn Kajar. The research subjects were 26 students. Data collection techniques using the method of test, observation and documentation. The data validation technique uses source triangulation. Data analysis used descriptive qualitative technique. The students' learning interest in the initial condition was 9 students or 34.62%, to 65.38% or 17 students, and 92.31% or 24 students in the second cycle. The average value of the initial conditions was 59.62 increased to 68.33, and 78.08 in the second cycle, while the level of mastery learning from 8 students or 30.77% to 16 students or 61.54% and 23 students or 88, 46% in the second cycle. This increase in learning outcomes can not be separated from the skills of teachers and students' interests. Referring to the research indicators, namely 85% of students experienced individual learning completeness 70, the research was declared successful.
Proposal of algorithms to make up for lost school and university years in post-conflict African countries in the face of Covid-19: case of the Central African Republic

The 12 years of politico-military crises in the Central African Republic (CAR), has created an educational social imbalance in the rural areas of CAR compared to the capital city of Bangui. In some rural areas of CAR, children have not attended school during the 12-year war. The situation has worsened with the Covid-19 pandemic. We modelled two algorithms and materialised them by developing an API Framework Rasa chat bot that uses the main components Rasa NLU, Rasa Core and the programming language Python for the remediation of lost school and university years in African countries in conflict, particularly CAR. It also allows for the orientation in normal, professional and specific training in each province of the CAR, according to the level required of the learner. It takes into account the social distancing imposed by the Covid-19 pandemic.
Curriculum Reform of Electronic Technology Training for Students’ Autonomous Learning Ability

Science and technology is rapidly developing in the direction of informatization, automation and intelligence. With the generation after 00s gradually being the subject of higher education, the development and popularization of electronic technology training is in urgent need of exploring new ideas. Under the impetus of the vigorous development of curriculum politics and the implementation of morality education in colleges and universities, this paper establishes and improves a new concept of practical teaching through curriculum reform practice, which is driven by project-based learning, aims to develop students’ individuality and to improve students’ autonomous learning ability. This paper also deeply studies the new system of electronic technology training which can cultivate students’ comprehensive practical ability and personal quality, aiming to provide a reference for engineering talent training and researching in related fields.
Direct-To-Consumer Genetic Testing: Privacy Issues &amp; Impacts on Biological Relatives

Numerous direct-to-consumer genetic testing services now offer individuals genetic sequencing, testing, and ancestry mapping services outside of the traditional healthcare infrastructure and regulatory barriers. The rise of direct-to-consumer testing services presents policy questions on how to best protect consumers while still promoting future innovation. From a data privacy perspective, concerns include who can share other people’s genetic information, what entities can use the information, what happens if the information is stolen, and whether law enforcement, or other entities, can request information from a third party. The overarching concern is that once disclosed, genetic information often cannot be unshared or returned. Further, genetic information from biological relatives can provide information on others who have not personally disclosed or authorized disclosure of their genetic information. In the United States, a patchwork of regulations has been created at the state level that exceeds the minimum requirements created by Congress. In other parts of the world, countries and regional networks have similarly created varying regulations to govern genetic information and its use. This comment reviews various regulations both in the United States and Europe, discusses the implications of the various regulatory schemes, and proposes a regulatory regime. This proposal aims at harmonizing rules across jurisdictions, distinguishing between “faceless” and individually labeled data, and seeks to allow for data use, testing, and development of new technology, while limiting exposure of individuals genetic information.
A study on the employment attractiveness of college students in Jinan, Shandong Province

Jinan, as the capital of Shandong Province, has introduced a series of policies to attract talents in recent years.Based on the research on the urban attractiveness of Jinan, this paper selects the young talent group of college graduates as the survey object, and deeply studies the employment attractiveness and influencing factors of Jinan through the combination of qualitative analysis and quantitative analysis, so as to provide reference for helping Jinan further deepen the reform of talent development system and mechanism, improve the ability to attract young talents, build a talent gathering highland in inland areas, and accelerate the construction of a national central city.
Design of prostate puncture system guided by 3D MRI and TRUS images

The incidence rate of prostate cancer in the world is increasing year by year. At present, the mainstream diagnostic method is puncture biopsy, the doctor holds an ultrasonic probe for imaging and manual puncture. There are many uncertain factors, such as ultrasound imaging effect, doctor's clinical level and psychological state. This paper designs a robot system including control system, image acquisition and processing system, probe feeding mechanism and mechanical arm. An automatic segmentation algorithm for prostate 3D MRI (Magnetic Resonance Imaging) and TRUS (Trans rectal Ultrasonography) images is proposed, which takes into account both real time and accuracy. A novel convolution module that integrates residual connection, dense connection and deep separable convolution is proposed for multi-scale feature extraction and fusion. At the same time, deep supervision mechanism and multiple attention mechanisms are integrated to improve training efficiency. The experimental results show that the real time and accuracy of the segmentation method proposed in this paper are better than the existing methods. The image-guided puncture experiment also shows that the puncture accuracy of the system designed in this paper is less than 1.5mm, The system and network designed in this paper have important clinical significance and practical value for image-guided biopsy.
N-Gram Feature for Comparison of Machine Learning Methods on Sentiment in Financial News Headlines

&#x0D; &#x0D; &#x0D; Sentiment analysis is currently widely used in natural language processing or information retrieval applications. Sentiment analysis analysis can provide information related to outstanding financial news headlines and provide input to the company. Positive sentiment will also have a good impact on the development of the company, but negative sentiment will damage the company's reputation. This will affect the company's development. This study compares machine learning methods on financial news headlines with n-gram feature extraction. The purpose of this study was to obtain the best method for classifying the headline sentiment of the company's financial news. The machine learning methods compared are Multinomial Naïve Bayes, Logistic Regression, Support Vector Machine, multi-layer perceptron (MLP), Stochastic Gradient Descent, and Decision Trees. The results show that the best method is logistic regression with a percentage of f1-measure, precision, and recal of 73.94 %, 73.94 %, and 74.63 %. This shows that the n-gram and machine learning features have successfully carried out sentiment analysis. &#x0D; &#x0D; &#x0D;
Birds in Boots: Learning to Play Angry Birds with Policy-Guided Search

In this paper we present Birds in Boots (BiB), a system that uses a sampling-based search algorithm to learn a neural policy for solving Angry Birds levels. Our learning procedure is based on the Bootstrap algorithm, which was previously used to learn heuristic functions for solving classic heuristic search problems. BiB starts its learning procedure with a policy given by a randomly initialized neural network. This initial policy is used to guide the search algorithm on a set of procedurally generated Angry Birds levels. The levels the search algorithm is able to solve are used to improve the neural policy. We repeat this procedure a number of times, until solving all levels or reaching a time limit. We perform several experiments with different instances of our method and show that it can solve more levels than other approaches, including learning-based and rule-based methods.