Reviewerly/exHarmony huggingface dataset:
PERANCANGAN SISTEM INFORMASI POINT OF SALE (POS) BERBASIS WEB PADA TOKO ASKA OUTDOOR TANGERANG

Aska Outdoor is a business that sells sports and outdoor equipment in the Tangerang area. The transaction recording system at Aska Outdoor currently still uses manual recording in books which often causes various problems in managing purchase transaction data, sales transactions and inventory of goods such as inventory data which is often inaccurate, service becomes slow, goods are sold online through marketplace is only items with a lot of stock, the possibility of loss will not be caught immediately, and sometimes there is an accumulation of stock. To solve this problem, it is necessary to create a web-based point of sale (POS) information application that can improve and simplify the performance of employees at Aska Outdoor stores so that they can produce reports that are faster, more precise and accurate. The system design stages that will be proposed are using UML diagrams. The application is made using the PHP programming language and using the MySQL database. With the creation of "Designing a Web-Based Point Of Sale (POS) Information System at Aska Outdoor Tangerang Stores" it is hoped that it can assist sales admins (cashiers and online marketing) and warehouse admins in managing inventory data properly such as goods data, purchase transaction data, sales transaction data , supplier data, and customer data. Meanwhile, leaders can find out and get information about purchase transactions, sales transactions, and inventory of goods in the form of reports quickly, precisely and accurately.
Development of a Drone Simulator for Autonomous Flight Verification

This paper describes a drone simulator that we have developed for autonomous flight verification. Currently, interest in drones is increasing and their applications are expanding, but accidents are also occurring. In order to prevent accidents, it is necessary to thoroughly verify the control law, but there are risks such as crashing when using a real aircraft. Therefore, it is possible to reduce the risk by using a simulator in the testing phase of the control law. In this study, we constructed a simulator using MATLAB/Simulink and Simscape Multibody, and conducted controller adjustment experiment for a single drone and verification of autonomous control laws for multiple drones. From the first experiment, it was found that the simulator could not reproduce the behavior after adjustment. From the second experiment, we verified the control law and clarified its problems.
Development of a mobile robot grasping and traversing an unknown environment based on internal sensor information

There is a growing demand for mobile robots that can operate in inaccessible environments such as disaster sites. Such a robot that can move autonomously over unknown rough terrain needs to be able to acquire both its own position and the surrounding environment, but current robots rely mostly on external sensors. Since external sensors are susceptible to disturbances and cannot be used in some environments, a subsystem with internal sensors that are less susceptible to disturbances is necessary to operate under such circumstances. Therefore, in this research, we consider the acquisition of the surrounding environment by the internal field sensor. In this paper, we propose a method to convert the terrain information into maze information using the robot’s posture information, and to traverse the generated maze using the Adachi method. The effectiveness of the proposed method was confirmed by simulations and actual experiments.
Displacement derivative analysis using deep learning in digital holographic interferometry

In this article, we present deep learning approach to estimate displacement derivatives in digital holographic interferometry. The results show the capability of the proposed method on noisy experimental fringes.
Multistage pipelined architectures of Piccolo cipher for high-speed IoT applications

Lightweight cryptography has become an obsession in recent times because of the rising security concerns in the field of smart connected devices having low resource prerequisites for Internet of Things (IoT) applications. The number of connected devices in IoT applications has been increasing throughout leaving severe security concerns. Result comparisons have been made with different block ciphers using 6-input and 4-input LUTs of the proposed work, which has been implemented for high-speed IoT and radio-frequency identification (RFID) applications. On the field programmable gate array (FPGA) platform, this work features topologies that give a 257% increase in throughput per area and a 21.9% increase in area. To improve the efficiency of the architectures, a multistage pipelined technique is used. Piccolo-80 and Piccolo-128 proposed architectures have provided throughput speeds of 1288.4 Mbps and 1046.1 Mbps on FPGA platforms, respectively. The proposed architecture is also implemented in application-specific integrated circuit (ASIC) 0.18 μm UMC technology, and the frequencies of Piccolo-80 and Piccolo-128 are 714.28 MHz and 649 MHz, respectively.
The Authenticity of Fashion YouTuber’s Apology Videos for a Paid Advertisement and Viewer’s Reaction

<span lang="EN-US" style="font-size:12.0pt;line-height:107%;font-family:&quot;Times New Roman&quot;,serif;mso-fareast-font-family:바탕체;color:black;mso-themecolor:text1;mso-ansi-language:EN-US;mso-fareast-language:KO;mso-bidi-language:AR-SA">“Paid advertisement controversy,” which is not disclosing the paid advertisements while producing content with sponsorship, has become a recent issue. Controversy arose in various YouTube content such as hauls and mukbangs, and many YouTubers tried to calm the controversy down by posting apology videos. This apology strategy is the best action a company or organization can take in a crisis situation (Benoit, 1995), and today’s famous celebrities or influencers are also taking an apology strategy in order to overcome the situation. On the other hand, viewers’ reaction to YouTuber's apology for “paid advertisement controversy” is somewhat negative. The purpose of this study was to analyze the contents of the post by studying the content of fashion YouTuber's apology videos, examine the response of emotions expressed in the comments of the video, and examine what authentic apology posts viewers have accepted.</span>
Augmented Reality Kekarangan Bali dengan Natural Feature Tracking

The carving art that developed in Bali has very distinctive characteristics and can make Balinese carving easily recognizable, among others, from several combinations of leaf, fruit, and flower motifs in very beautiful convex and concave shapes. Balinese carving is generally divided into three parts, namely Keketusan, Pepatran and Kekarangan. The lack of information about Kekarangan Bali makes people less aware of the forms and meanings contained in the Kekarangan Bali due to the limited distance and time to be able to learn Kekarangan Bali directly. Because of this we need Augmented Reality (AR) as a solution to be used as props in helping to introduce Kekarangan Bali more specifically using the Natural Feature Tracking (NFT) method because it can directly apply feature detection and then give a descriptor at every angle detected on the target image that is visible in the real environment so that the application of Augmented Reality (AR) is not fixated on a predetermined target image to be able to display objects 3D.
Pengembangan Media Berbasis Android untuk Memfasilitasi Pembelajaran Speaking di Kelas VII SMP

This study aims to develop android-based learning media (android-based learning media) in English subjects, especially in learning speaking. This study uses a research and development approach (R&amp;D). The place of this research is SMP Negeri 21 Malang. The object of this research development is a smartphone application that is accompanied by various features developed from student textbooks. The data collection technique used in this research is the questionnaire method. The questionnaire was used to analyze the needs of students in learning English online, then the second questionnaire was used to collect data on the evaluation and validation of English learning media, including: material experts and media experts. From the research results, among others: the material expert test obtained a score of 72% in the good category, the media expert test obtained a score of 92% in the very good category, so that the android-based learning media in English subjects is suitable for use in teaching and learning activities
A hierarchical multilabel classification method based ON clustering relation

In multilabel classification, the problems of a large number of classification calculations and easy destruction of label relations are very common. To solve these problems, a hierarchical multilabel classification method based on clustering relations is proposed by mining the possible dependencies between labels. First, the algorithm adopts a local strategy to cluster labels multiple times. Then, the clusters of labels with hierarchical relation are formed, and the implicit relationships hidden in these clusters are analyzed. On this basis, a multilabel clustered clustering tree is constructed to train the local model. Finally, the clustering tree is constructed as a random forest classification model using the ensemble idea. The experimental results show that the method in this paper has a good classification performance, especially on datasets with a large amount of data, which is at least percentage points higher than the existing algorithms.
Functionality Testing in the Automation of Scientific Application Workflows in an HPC Environment

This paper presents the results obtained in performance tests of task automation in the supercomputing cluster of the University of Guadalajara (CADS Data Analysis and Supercomputing Center). The main objective was to design an automated workflow process to take advantage of high computational performance in scientific applications, routines in R, Python or shell that by nature consume only one core, and that by the volume of data to be processed could allow the execution of multiple tasks at once in a supercomputing cluster environment, or even in the cloud, for an efficient use of the infrastructure. In addition to using Singularity containers to encapsulate applications or scripts to be used in workflows. The following tools were tested: Snakemake as a tool for workflow automation and scaling, as well as Singularity container technologies for application encapsulation and SLURM for managing resource usage in the cluster. The results are presented as well as the experience gained in using these technologies.
Replacing Cookies in Online Advertising with API Conversion Tracking

The support of third-party cookies is coming to an end. Internet companies like Apple and Google have already started implementing new mechanisms for the cookieless online future. While these efforts, supported by new legislative restrictions, give more control to online users over their data, they significantly impact current targeting and performance monitoring techniques in online advertising. Advertising systems are introducing new tracking tools to compensate for the data loss caused by cookie consent banners. This article focuses on the Meta conversions API tracking introduced as a follow-up to the cookie-based Facebook pixel. We test the effectiveness of the Meta conversions API by comparing three advertising data sets collected under three different cookie tracking regimes: opt-out, op-in, and Meta conversions API. The advertising effectiveness of the 450 ads is evaluated regarding the individual stages of the customer journey: engagement, traffic building, and sales.
Email Security: S/MIME

S/MIME is a standard to encrypt and digitally sign e-mails, which is fully compatible with the MIME data format. It uses PKCS#7/CMS as cryptographic data format. E-mails are sent using the SMTP protocol and retrieved using either POP3 or IMAP (chapter 19 ). In this chapter, we will concentrate on e-mail data formats, starting with RFC 822.
Automation of Generating Obstacle Avoidance Trajectory for Redundant Manipulators

Redundant manipulators can be defined as robots that possess more than the required degrees of freedom to accomplish a specific task. This feature allows redundant manipulators to achieve its target trajectory while changing its configuration according to the surrounding environment. When introducing robots in the workplace, a process called "teaching" is necessary to make them learn what to do. However, it takes time and technological skills to program their jobs. To increase productivity in frequently changing environments, we propose a method that automatically generates a trajectory to reach the target position and posture according to the work environment. The effectiveness of the proposed method was demonstrated using a cylindrical obstacle to simulate whether the robot can avoid the obstacle and approach the target position and posture.
Nagereru-Kun: Design and Implementation of a Bowling Form Reflection Support Application for Beginners

In this research, our goal is to design and implement an application to support reflection on bowling form, targeted at bowling beginners. Learning correct bowling form is a vital factor for steadily achieving high scores. The influence of physical ability, weight of the ball, fatigue, and so on can easily cause deterioration of form. By using a video camera to record form at the moment of bowling a ball, it is possible to confirm visually how one is bowling, yetit is difficult to notice deterioration of form, or errors. The proposed application uses a camera to record the conditions of a learner when bowling a ball. The application has a function that enables comparison of the recorded bowling images, example images, images of when the learner bowled correctly, and so on, in the form of frame-by-frame images. In addition, there is a function that cues several comparison images for each moment in bowling form, such as the moment the ball is released. An evaluative experiment was conducted to compare the proposed method, which is the application we developed that uses evaluation functions, and a comparative method consisting of watching video of bowling. As a result, in a quiz, the average correct answer rate of subjects who used the proposed method was 45% higher than that of those who used the comparative method and a significant difference was observed when a two-sided Student’s t-test was applied at a significance level of 5%.
An Explorative Analysis of Using LMS (Lentera) in Writing Class at UIN Alauddin During Covid-19

This research aimed at finding out the students’ barriers in writing class and the advantages of using LENTERA as the LMS platform of Islamic State University (UIN) Alauddin Makassar in writing class during Covid-19 pandemic. This research employed a qualitative method with 9 participants from English and Literature Department students. The data were collected by using in-depth interview and documentation, then it was analyzed by using Miles and Huberman theory. The results of this research showed that English and Literature students who learned writing courses by using LENTERA application had several barriers such as network problems, difficulty in understanding the lesson, and inaccessible application. In addition, the advantages encountered by English and Literature students in writing course by using LENTERA application were facilitating the process of teaching and learning, facilitating the students in submitting the assignment, and providing accessible file storage. The researchers recommended that LENTERA features should be enhanced and the teachers should organize the material in attractive way so that during the teaching process the barriers can be reduced.
Face age estimation network based on improved residual blocks

To address the problem that age estimation accuracy is affected by face occlusion, uneven illumination, complex expressions and different postures under unconstrained conditions. In this paper, a new network model is constructed based on the pytorch framework and named as RFA network. The RFA network is implemented based on ResNet residual blocks.To reduces network over-fitting by introducing Dropout technique, and uses four improved residual blocks to extract more detailed age features of human faces. The maximum pooling and average pooling are also combined to overcome the problem of feature information loss during feature extraction, so that it can capture more subtle facial features to improve the classification accuracy. After the RFA network is trained and tested, the experimental results show that the average absolute error value of age has reached 3.95, which is lower than the classical networks such as VGG. The effectiveness of RFA algorithm is proved.
What is the Monte Carlo Method?—A Simulation with Random Numbers

The Monte Carlo method is a general term for computational methods that utilize random numbers (see e.g., [1]). Usually, it specifically means a class of algorithms that are guaranteed to give the right answer if we continue the simulation long time. Let us first see a few simple examples of Monte Carlo algorithms that are different from the “Markov Chain” Monte Carlo [2, 3]. (More nontrivial applications of “naive” Monte Carlo algorithms than those mentioned in this chapter can be found, e.g., in Ref. [4].) These algorithms are very easy, but there are significant limitations. By knowing the limitations of such “naive” Monte Carlo algorithms, we can understand the advantage of the Markov Chain Monte Carlo algorithms.
A Federated Learning Framework for Resource Constrained Fog Networks

Federated learning (FL) is a collaborative framework that aggregates multiple machine learning (ML) models and thus enables efficient handling of data collected from numerous and diverse IoT devices. However, traditional FL frameworks often do not consider the connectivity and the heterogeneity of the data sources and hence suffer accuracy imbalance among the individual (local) ML models and in turn, negatively impact the aggregated model. To mitigate such a shortcoming, this paper promotes a two-step optimization process. The first focuses on data offloading from devices to fog nodes with the objective of improving the accuracy of local ML models under resource and connectivity constraints. The second provides statistical distribution aware data offloading that trades off the communication cost and the accuracy of local ML models. We validate the advantages of our approach using a benchmark dataset.
Version Control

In Chapter 13 , “Package Development,” we will learn about the three application development models that Salesforce supports and where the metadata source of truth resides; namely, the Change Set Development Model, the Org Development Model and the Package Development Model. In this chapter we will explore version control systems as they are the source of truth for the Org and Package Development Models.
Compact Associative Classification for Up and Down Regulated Genes Using Supervised Discretization and Clustering

Gene Expression data are being collected from microarray. Several rule mining algorithms are used to generate rules on microarray datasets but it produces large number of rules. To produce effective class association rules, such rules have to be grouped and pruned for analysis. To solve this problem we propose a method to reduce the number of class association rules and clustering them, particularly for the colon microarray gene expression data. The main objective is classifying the colon gene expression data using class association rule. The proposed methodology identifies the up and down regulated genes, generating class association rules, pruning class association rules and clustering the class association rules. Aim of the proposed clustering of class association rules is for identifying the highly correlated genes. Experimental results show that the proposed method gives best accuracy for the colon cancer dataset.
Features and principles of the development of electronic training manual

Electronic training manual is a software and methodological training complex designed for self–study by a student of educational material in certain disciplines. Electronic training manual is a product that can be used fragmentally or completely instead of a textbook. We know that the electronic training manual is “not an electronic version of books, where all the information is translated from the printed version into an electronic one or there is a possibility of switching from the table of contents by hyper-linking to the desired chapter. Depending on the type of training session, the course of the lesson itself should be appropriately constructed to achieve the effect of using such a manual.” Before starting the development of the electronic training manual, it is necessary to highlight the criteria for comparing electronic training manual of similar subjects in order to understand at what level the problems of the chosen topic are now. Further, after analyzing the results obtained, and highlighting for yourself the advantages and disadvantages of already developed solutions, you can start developing a competitive product. We highlight the following criteria: 1) informativeness (availability of detailed and understandable information about information security threats and methods of combating them); 2) transparency of the interface (the interface should be clear to the user and harmonious); 3) ease of navigation on the electronic training manual (the ability to instantly switch to the section of interest, as well as the presence of hyperlinks); 4) interactivity (availability of means of user interaction with the system); 5) regular updating of information; 6) availability of a search engine (in order to provide quick access to the necessary information). When studying subjects related to information technology, of course, with the competent application of the electronic training manual, it is effective for the independent development of the discipline.
A primary study on U-bearing W-rich minerals in Juyuan large quartz vein type scheelite deposit, central Jiangxi Province

聚源钨矿是华南地区为数不多的大型石英脉型白钨矿矿床之一。在详细的野外地质调查基础上，本文利用α径迹蚀刻、电子显微镜、扫描电镜以及电子探针等实验手段，对该矿床含钨和含铀矿物开展了精细矿物学的研究工作，探讨了成矿过程中钨和铀的富集规律。研究显示，该矿床钨铀矿物的形成可分为四个阶段：第一阶段，钨铀主要进入富含Nb、Ti的氧化物矿物，形成铌铁矿、钇易解石等富钨矿物，另有极少量的钨进入黑钨矿和早阶段白钨矿；第二阶段，铌铁矿与钇易解石被后期流体交代，形成含钨富铀的骑田岭矿、铌锰矿以及钛-钇易解石；第三阶段，钨进入中阶段白钨矿，这一阶段也是钨最主要的矿化阶段；第四阶段，钨进入晚阶段白钨矿。最后两阶段白钨矿中铀含量不高。骑田岭矿(WO<sub>3</sub> 26.74%~29.68%)，是聚源钨矿中除白钨矿和黑钨矿之外钨含量最高的含钨矿物。该矿易解石族矿物WO<sub>3</sub>最高可达9.80%，极度富钨，是目前有文可查的钨含量最高的易解石。聚源钨矿中的含钨矿物大多数为白钨矿，但绝大多数的白钨矿却在骑田岭矿、易解石族矿物、铌铁矿族矿物、黑钨矿之后形成，说明成矿流体在演化过程中，绝大多数W首先进入富含Nb、Ti的含铀矿物和少量黑钨矿，之后才是白钨矿的大量结晶。
Localization using interior illumination reflected on cornea and inertial measurement unit

Corneal imaging has been studied to estimate the focused object and point-of-gaze by extracting the environmental information reflected from the corneal surface. However, it is also necessary to estimate the self-position as a daily-use device. In recent years, research has been conducted on self-position estimation using interior illumination patterns reflected on the cornea. However, localization based on illumination information was not accurate, and thus, an improvement is required. In this paper, we propose to improve the accuracy of localization by integrating SE-ResNet and IMU on the framework of Monte Carlo Localization.