Reviewerly/exHarmony huggingface dataset:
Improving Students’ Writing Skill in Recount Text by Using Picture Series Medium

This research aimed to describe how picture series medium can increase the students’s writing skill for eighth grade at state junior high school 1 of Bintan. Classroom action research was the research design. The research comprised of two cycles with four meeting in the two cycles. A test was the main data of this research. Twenty students in VIII B at state junior high school 1 of Bintan, in the academic year of 019/2020 was the subject of this research. The outcome appeared there were upgrades in students’ writing skill. It tends to be seen from the outcome of each post-test in the two cycles. Students’ test average in cycle 1 was 63.57. Students’ test average in second cycle was 68.87. It means picture series medium could help students in improving their writing skill.&#x0D;
Legalization of Central Bank Digital Currency under Blockchain Industry

In recent years, private digital currency based on blockchainindustry has caused many doubts, such as privacy infringement, money laundering tools, consumer protection and financial stability. However, as digital currency has gradually become the important issue, the central banks of various countries have already started to study the central bank digital currency (CBDC). In this paper, firstly, the concept of private digital currency and its derivative issues are explained. Secondly, based on the two chains scheme of the blockchain, a CBDC system is established to facilitate supervision, which stores and accesses transaction information and verification information separately to balance the user privacy security and the convenience of supervision. Meanwhile, the consortium blockchain is settled to the public chain to protect the reliability of the data. Moreover, although some countries have started to develop CBDC, laws and regulations which regulate various aspects of it are still deficient. Therefore, in this paper, in addition to proposing a general outline of the legal system regulating the CBDC, it also illustrates separately the monetary rights and obligations of the central bank, merchant banks and the public, which will be helpful for the future legal construction.
Effective Service Discovery based on Pertinence Probabilities Learning

Web service discovery is one of the most motivating issues of service-oriented computing field.Several approaches have been proposed to tackle this problem.In general, they leverage similarity measures or logic-based reasoning to perform this task, but they still present some limitations in terms of effectiveness.In this paper, we propose a probabilistic-based approach to merge a set of matching algorithms and boost the global performance.The key idea consists of learning a set of relevance probabilities; thereafter, we use them to produce a combined ranking.The conducted experiments on the real world dataset "OWL-S TC 2" demonstrate the effectiveness of our model in terms of mean averaged precision (MAP); more specifically, our solution, termed "probabilistic fusion", outperforms all the state of the art matchmakers as well as the most prominent similarity measures.
Modeling of Information Support for the Development of Creative Abilities in Students in the Context of Philosophy Education

Creativity is defined as a general ability that combines general intelligence, personality traits, and the ability to think productively. The creativity of psychology students is a mandatory characteristic of a holis- tic personality because of the appropriate psychological and pedagogical conditions and the high level of formation of his professional and personal qualities. The purpose of the article is to systematise the process of increasing the creativity of psychology students with the help of mechanisms and elements of the func- tional model of IDEF0 in the context of the philosophy of education. It allows us to improve the process of developing students’ creativity by using the modern management model IDEF0, which is unique and in- teresting. For the specification of the methodology for increasing creativity among psychology students, it has been chosen the methodology of functional modelling and a graphical description of processes (IDEF0). The article considers the specifics of students of higher educational institutions and psychologists in particular. In the future, the IDEF0 model should be applied to other students in the context of the phi- losophy of education.
Relationship between Wood Traits and Sound Frequency at Different Pitch Levels of Talking Drums (Gangan) Manufactured from Aningeria robusta Wood

Abstract Wood is a unique material for making musical instruments and is used to make West African talking drums, whose pitch can be regulated depending upon how the player strikes the head of drum and changes its tension. Additionally, talking drum manufacturers have certain wood preferences, and in the absence of preferred species, they use supposedly unsuitable woods. Therefore, there is a need to examine wood traits in relation to the pitch of a talking drum. This study was designed to determine the existing relationship between selected wood traits and the frequency of talking drums made from A. robusta wood. Three A. robusta trees were obtained from Onigambari Forest Reserve. From each tree, three bolts of 50 cm in length were obtained from the base, middle, and top of the tree to determine selected properties (moisture content ( MC ), wood basic density ( WBD ), modulus of elasticity ( MOE ), modulus of rupture ( MOR ), and manufacturing of talking drums). A spectrum analyzer was used to analyze the sound frequency of the talking drums at three pitch levels (high, medium, low). A completely randomized block design was used and the data obtained was analyzed using descriptive statistics, ANOVA , and correlation analyses at α 0.05 . Sound frequency was not significant along sampling height, but was significant at pitch levels. Additionally, the correlation analysis between wood traits and sound frequency was not significant. Thus, wood cannot be recommended for talking drums’ optimal acoustic performance based on wood traits alone.
Searching Pattern in DNA Sequence Using ECC-Diffie-Hellman Exchange Based Hash Function: An Efficient Approach

In this paper, the efficient Elliptic Curve Cryptography with Diffie-Hellman exchange is implemented in order to perform the searching pattern effectively from DNA sequences. The elliptic curve cryptography is defined as public key cryptography which utilizes the elliptic curve properties in terms of finite field. For key exchange process, ECC is combined with Diffie-Hellman algorithm (EC-DH) which is defined as technique for exchanging cryptography keys securely through the public channel. It ensures the security and it highly resistive against the brute force attack. Lesser computational time is obtained and effective security with power consumption achieved. Experimentation results are carried out by using the publicly available dataset. The effectiveness is proved from the comparison results of the proposed and existing study.
Computer – Aided RGB analyses of images

Computers have long been applied to physics and education. Considering that computer technologies are affecting science in many ways, special attention is payed to images and image analyses. RGB processing is used to analyse several computer images and presented with this papper. Furthermore, numerical data from images are used to tables and graphs, to motivate students devoted to science and research.
Light People: Professor Aydogan Ozcan

In 2016, the news that Google's artificial intelligence (AI) robot AlphaGo, based on the principle of deep learning, won the victory over lee Sedol, the former world Go champion and the famous 9th Dan competitor of Korea, caused a sensation in both fields of AI and Go, which brought epoch-making significance to the development of deep learning. Deep learning is a complex machine learning algorithm that uses multiple layers of artificial neural networks to automatically analyze signals or data. At present, deep learning has penetrated into our daily life, such as the applications of face recognition and speech recognition. Scientists have also made many remarkable achievements based on deep learning. Professor Aydogan Ozcan from the University of California, Los Angeles (UCLA) led his team to research deep learning algorithms, which provided new ideas for the exploring of optical computational imaging and sensing technology, and introduced image generation and reconstruction methods which brought major technological innovations to the development of related fields. Optical designs and devices are moving from being physically driven to being data-driven. We are much honored to have Aydogan Ozcan, Fellow of the National Academy of Inventors and Chancellor's Professor of UCLA, to unscramble his latest scientific research results and foresight for the future development of related fields, and to share his journey of pursuing Optics, his indissoluble relationship with Light: Science & Applications (LSA), and his experience in talent cultivation.
Information Flow Control for Serverless Systems

Security for Serverless Systems is looked at from two perspectives, the server-level security managed by the infrastructure company and the Application level Security managed by the tenants.The Trusted computing base for cloud systems is enormous as it encompasses all the functions running on a system.Authentication for systems is mostly done using ACL.Most Serverless Systems share data and thus, ACL isn't sufficient.IFC using appropriate label design can enforce continuously throughout the application.IFC can be used to increase confidence between functions with other functions and cloud provider and also mitigate security vulnerabilities making the system safer.A survey of the present IFC implementations for Serverless Systems is presented and system designs which are relevant to Serverless Systems and could be added to Serverless Systems Architecture and, an idea of an IFC model that could be effectively applied in a decentralised model like serverless systems.
Application Of The CAI Method Of Learning Signs-Traffic Signs For Early Childhood Based On Android

Traffic signs are part of road safety equipment that contains symbols that serve to convey messages to road users or traffic in an effort to bring order, make it comfortable and safe for road users and the activities of the people around them. Obeying traffic signs for safety and traffic order is very important to avoid accidents and violations of traffic rules. Learning and awareness of the importance of traffic signs needs to be instilled in the community from an early age. Learning is more interesting with the CAI method, which makes early childhood happy in learning the material presented. Early childhood is able to understand the signs presented in the android application.
Mounting and support for pseudo biaxial Scheimpflug focusing for unity-magnification, high-speed particle velocimetry

A camera mount that can support both heavy cameras and heavy optics allowing a total of seven degrees of freedom shared between them has been designed. This allows for Scheimpflug focusing along one or two axes. A paper proposing a solution to two- axes Scheimpflug focusing has been examined and a new nomer is proposed for two-axes Scheimpflug focusing. The newly designed mounts allow for a broader range of solutions for combinations of positioning and alignment than traditional Scheimpflug mounts.
An Analysis of Students’ Learning Interest in Programming Language Based on Data Mining with Fuzzy C-Means Method

&lt;p class="0abstract"&gt;Students' learning interest and motivation in programming language to date are determined by their scores and abilities to create applications. However, this is not sufficient to identify students' learning interest in programming language because some students got low scores. This study aims to identify students' learning interest in Dipanegara school of informatics management and computer (STMIK), Makassar, in Java programming language. The samples were 65 technical information students and 63 information system students. The data collection technique of this study was questionnaire and processed with data mining technique. Additionally, Fuzzy C-Means clustering method was applied on Java Netbeans programming language to classify the level of students' interest in studying Java programming language. The result of the study was a web-based application that could determine students’ learning interests. It was obtained through questionnaires and resulted as follows: 47 students had high learning interest, 45 students had moderate interest, and 36 students had fair interest out of 128 samples.&lt;/p&gt;
Comparative Analysis of Machine Learning Algorithms Based on Variable Importance Evaluation

 Machine learning algorithms may provide useful insights on variable importance via effective tuning. The cubist algorithm can present predictive results on property value assessments. The cascade of machine learning and statistical methods are powerful tools for the property valuation.One of the main goals in machine learning studies is to determine the most significant variables on a specific research problem.Various algorithms have been developed to achieve this goal.Random forest, Cubist, and MARS algorithms are the most common ones among these algorithms.Although classical statistical algorithms have been useful to obtain the importance level of the effective variables on the output in a certain amount, the machine learning algorithms may provide clearer and more precise results.In this study, the estimation results of Random Forest, Cubist, and MARS algorithms have been presented comparatively in terms of some performance criteria like mean squares error, the coefficient of determination, and mean absolute error by using a real data set.The results show that the performances of Random Forest and Cubist are similar amongst themselves but better than MARS.Additionally, the rank of the most important variables varies according to the type of algorithm.The concordance between algorithms is investigated from a statistical perspective and found satisfactory.Consequently, Random Forest, Cubist, and MARS can be considered effective and reasonable algorithms for both estimation performance and variable importance evaluation.Figure A. Fitting results of whole data set based on each algorithm
An Improved Tool for Detection of XSS Attacks by Combining CNN with LSTM

Cross-Site Scripting (XSS) is still a significant threat to web applications. By combining Convolutional Neural Networks (CNN) with Long ShortTerm Memory (LSTM) techniques, researchers have developed a deep learning system called 3C-LSTM that achieves upwards of 99.4% accuracy when predicting whether a new URL corresponds to a benign locator or an XSS attack. This paper improves on 3C-LSTM by proposing different network architectures and validation strategies and identifying the optimal structure for a more efficient, yet similarly accurate, version of 3C-LSTM. The authors identify larger batch sizes, smaller inputs, and cross-validation removal as modifications to achieve a speedup of around 3.9 times in the training step.
Snowball Throwing: An English Learning Method to Teach Descriptive Writing

&lt;span&gt;Writing is a powerful multipurpose tool that helps us discover and explore more fully what we are thinking so that we learn as well as express our feelings and thoughts. One of reason that students lazy to written something because the methods are used by their teacher. Most of them still used direct learning method (extracurricular learning), the students were bored and they are did not enjoy when teaching learning process. So, that is why, in this study the writers try to use snowball throwing method to help students in learning descriptive writing. The data collection methods which used in this study are observation checklist and questionnaire checklist. Observation used to find out the implementation snowball throwing on students writing in descriptive text. The questionnaire was used to know the students’ responses after being taught descriptive text by using snowball throwing method. This study reveals that the highest aspect score of observation checklist in first observation is less, but in the second meeting, the writers found that highest aspect score of observation was good. Based on data questionnaire checklist, the writers found that the highest students’ responses were good. Based on this result data resource above, the writers concluded that the students could master writing after they were taught using snowball throwing in writing descriptive text. Finally, the writers suggested that the students had to practice their writing skill in many times. Besides, teacher had to have an interesting method in teaching writing descriptive text.&lt;/span&gt;
Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN

Scalable and sustainable AI-driven analytics are necessary to enable large-scale and heterogeneous service deployment in sixth-generation (6G) ultra-dense networks. This implies that the exchange of raw monitoring data should be minimized across the network by bringing the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying traffic patterns and channel conditions, making thereby the data collected in different points non independent and identically distributed (non-IID), which is challenging for FL. To sidestep this issue, we first introduce a new a priori metric that we call dataset entropy , whose role is to capture the distribution, the quantity of information, the unbalanced structure and the “non-IIDness” of a dataset independently of the models. This a priori entropy is calculated using a multi-dimensional spectral clustering scheme over both the features and the supervised output spaces, and is suitable for classification as well as regression tasks. The FL aggregation operations support system (OSS) server then uses the reported dataset entropies to devise 1) an entropy-based federated averaging scheme, and 2) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to show the superiority of these novel approaches.
Development of Mobile Learning Application Android-Based for English Subject to Support Blended Learning Model in The Department of Guidance and Counseling State University of Medan

The purpose of this study was to determine the shape of the android based Mobile Learning needed by students majoring in guidance and counseling Unimed in learning English. To find out how the students' interest towards online learning application android-based language.This research has been conducted using ADDIE development model.This media development begins by analyzing some of the necessary requirements. These needs include: the election materials, the determination of the user (user), eligibility criteria, and software to develop android applications online learning. After that the next stage isdraft structured in a flow diagram that contains forms and display features contained in the application. The development stage is the stage of manufacture of mobile learning applications in accordance with the design plan. Media experts give excellent ratings for throughout the item with the total amount of 82.50 with an average value is 4.10 and the results of the calculation for the item throughout the expert assessment of as many as 20 items of material obtained a total of 86 ratings with an average value is 4.30 so that it can be concluded that the application and the material is very viable for use in learning English. This application is tested on 30 students of the 1st half of Guidance and Department of the Faculty of Education Unimed.The results of calculations throughout the student questionnaire responses obtained 87.15%% percentage of aspects of good feelings, 86.57% of the student's interest aspects of the use of the application, 85.85% of aspects of attention, and 88.24% of aspects of student involvement. Viewed from a fourth aspect of the students showed a very positive response, so it can be seen that the interest of students towards learning using the application English For Counseling very high.
Playfair with Multi Strata Encryption

Playfair cipher is a substitution scheme. The classical playfair scheme has a limited matrix size containing only uppercase letters, so it is prone to hackers and cryptanalysis. To increase the resistance of playfair cipher, a new encipherment and decipherment method is proposed in this work, which depends on the permutation and its inverse, respectively. In addition, a modified key matrix is utilized, which includes capital and small Alphabets, numbers, and 38 special characters collected from ASCII codes. In the proposed method, both substitution and transposition schemes are used, where the first stratum of the cipher is a substitution by using key matrix and the second stratum is a transposition by using permutation key which provides multi strata resistance to brute force and other cryptanalysis attacks. A comparison between the traditional playfair scheme and the proposed method demonstrates that the encoded text is hard to recognize by cryptanalysts, which improves the security of the encryption process.
Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model

In this paper, we propose an improved algorithm based on the active contour model Mumford-Shah model for CT images, which is the subject of this study. After analyzing the classical Mumford-Shah model and related improvement algorithms, we found that most of the improvement algorithms start from the initialization strategy of the model and the minimum value solution of the energy generalization function, so we will also improve the classical Mumford-Shah model from these two perspectives. For the initialization strategy of the Mumford-Shah model, we propose to first reduce the dimensionality of the image data by the PCA principal component analysis method, and for the reduced image feature vector, we use <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math> -means, a general clustering method, as the initial position algorithm of the segmentation curve. For the image data that have completed the above two preprocessing processes, we then use the Mumford-Shah model for image segmentation. The Mumford-Shah curve evolution model solves the image segmentation by finding the minimum of the energy generalization of its model to obtain the optimal result of image segmentation, so for solving the minimum of the Mumford-Shah model, we first optimize the discrete problem of the energy generalization of the model by the convex relaxation technique and then use the Chambolle-Pock pairwise algorithm We then use the Chambolle-Pock dual algorithm to solve the optimization problem of the model after convex relaxation and finally obtain the image segmentation results. Finally, a comparison with the existing model through many numerical experiments shows that the model proposed in this paper calculates the texture image segmentation with high accuracy and good edge retention. Although the work in this paper is aimed at two-phase image segmentation, it can be easily extended to multiphase segmentation problems.