Reviewerly/exHarmony huggingface dataset:
Pengaruh Product Quality dan Brand Image terhadap Repurchase Intention Produk Kosmetik Halal di E-Commerce

The cosmetic industry is a potential market for companies. Ease of access to shopping using e-commerce makes competition between companies more competitive. The company tries to retain customers by improving product quality and brand image so that consumers choose to repurchase the product. This study seeks to determine whether product quality and brand image affect the repurchase of Wardah products at Shopee e-commerce. This type of research is quantitative research using the judgmental sampling method. The number of samples used was 200 respondents with data collection through an online questionnaire. The data analysis technique used multiple linear regression. This study found that product quality and brand image positively and significantly affect repurchase intention. Based on this research, cosmetic companies can improve product quality on various lipsticks and packaging designs that are easier and more comfortable. In addition, cosmetic companies must maintain their brand image as reputable and well-known brands to increase consumer intentions to repurchase and retain customers on halal cosmetic products.
Learning-Based Fake News Detector Using Multiple Features Using Machine Learning

Abstract: Lately, the ascent of Online Interpersonal organizations has prompted a multiplication of social news like item ads, political news, big name data, and so forth. Some of the Social media platforms such as Facebook, Instagram, and Twitter are influenced by their customers through with fake news. Shockingly, a few clients utilize dishonest means to develop their connections and notoriety by getting out the counterfeit word as texts, pictures, and recordings. Notwithstanding, the new data showing up on an internet-based informal community is suspicious, and much of the time, it misdirects different clients in the organization. Counterfeit word is gotten out purposefully to delude peruses to trust bogus news, which makes it hard for recognition systems to identify counterfeit news dependent on the shared substance. The approach of the Internet and the quick acceptance of online media platforms ready for data spreading that has never been watched in human being history in the past. With the existing use of online social media platforms, customers are producing and distributing other data place than any further time in latest celebration, several of which are untrue with no importance to the actual planet. Robotized order of a text paper as dishonesty or misrepresentation is a tricky responsibility. Really, just as a specialist in a particular region requires to examine various angles prior to doing a judgment on the integrity of an editorial. In this design, we plan to use a machine learning collection style for the automatic category of news report papers. Our analysis examines individual produced properties that can be used to split fake matter from honest. By developing those things, we prepare a mix of several AI sums employing various grouping approaches and evaluate their showcase on 4 real world datasets. The practice evaluation acknowledges the unparalleled showcase of our planned ensemble undergraduate methodology in distinction with different scholars.
Immersive feedback in fencing weapon practice using mixed reality

Providing athletes, during sports training, with real-time feedback based on automatic analysis of motion is both useful and challenging. In this work, a novel system based on mixed reality is proposed and verified. The system allows for immersive, real-time, visual feedback in fencing weapon practice. Novel methods are introduced for 3D blade tracking from a single RGB camera, creating weapon action models by recording actions performed by the coach and evaluating fencers' performance against these models. Augmented reality glasses with see-through displays are employed and a method for coordinate mapping between virtual and real environments is proposed, which allows providing real-time visual cues and feedback by overlaying virtual trajectories on the real-world view. The system is verified experimentally in fencing bladework practice, with supervision of a fencing coach. Results indicate that the proposed system allows novice fencers to perform the exercises more correctly.
Perspective Charts in a Multi-Foci Globe-Based Visualization of COVID-19 Data

The use of perspective projection in data visualization has been shown to potentially aid with the perception of small values in datasets with important variations at multiple scales. We integrate perspective charts, which use perspective projection in their designs, into a geospatial visualization application for global COVID-19 data. We perform an evaluation through Amazon Mechanical Turk to evaluate the readability of these visualizations compared to traditional methods, when tools such as interactive techniques are used. Results of our evaluation show that participants more accurately retrieved small values from perspective chart visualizations than traditional bar charts on the globe. The use of perspective projection in an interactive system allows for users to read data with important variations at multiple scales without affecting the overall perception of scale in datasets.
APPLICATION OF MULTIVARIATE ANOVA AND GENERALIZED DESIRABILITY TO OPTIMIZE THE COMPOSITION AND TECHNOLOGY OF TABLETS CONTAINING N-BENZYL-N-METHYL-1-PHENYLPYRROLO [1,2-A] PYRAZINE-3-CARBOXAMIDE

The creation of drugs with an anxiolytic activity, which do not have the main side effects characteristic of drugs of this group, is an important and socially significant task. For its implementation, within the framework of the development of an original drug with an anxiolytic activity, the composition and manufacturing of GML-1 tablets (N-benzyl-N-methyl-1-phenylpyrrolo [1,2-a] pyrazine-3-carboxamide) are being developed. The aim of this article is to study, using a four-factor analysis of variance, the influence of composition factors on the manufacturing properties of GML-1 tablets and the selection of the type, the amount, stage of the disintegrant addition and the type of lubricating excipients used in the technology of wet granulation of GML-1 tablets. Materials and methods. The materials used are: the substance – GML-1 (N-benzyl-N-methyl-1-phenylpyrrolo [1,2-a] pyrazine-3-carboxamide). Excipients: microcrystalline cellulose 101 (MCC 101); polyvinylpyrrolidone (PVP); crospovidone, croscarmellose sodium (CCS), sodium starch glycolate (SSG); magnesium stearate (MS), sodium stearyl fumarate (SSF). To obtain tablet mixtures, wet granulation and tableting with the study of their main pharmaceutical and technological properties was used. Results. Model compositions were developed and their pharmaceutical and technological properties were studied. These results have been analyzed, the degree of these factors’ influence and their interactions have been determined. In most of the cases considered, the interactions of the factors did not cause a significant change in the optimization criteria. With an increase in the amount of a disintegrant, the disintegration time decreased unevenly, so an increase in the amount of these excipients from 4 to 6 mg had a stronger effect than from 2 to 4 mg. Factor B affected the release degree non-linearly. Factor A influenced all the optimization criteria considered, especially a PS release. The best release and disintegration were observed with crospovidone, which was of a particular importance when processing the test results using a generalized desirability method. Conclusion. In view of the conflicting variance analysis results, for particular factors, the resulting values were additionally analyzed using the generalized desirability function. The use of this method made it possible to reduce the conflicting variance analysis results to the most optimal composition.
THE USE OF ORAL PRESENTATION IN TEACHING ENGLISH TO IMPROVE STUDENTS SPEAKING SKILL

Speaking is an ability that is considered quite important in learning English. The oral presentation is one activity that can be used in learning English to improve students' speaking skills. This study aims to examine the oral presentations conducted by students with the intention that students can improve their speaking also seeks to determine what obstacles students experience when making oral presentations. The researcher applied qualitative research as a research method to collect the data the researcher used observation and field notes as instruments. The data obtained were categorized and analyzed based on several related theories. Regarding the result, it was found that basically, students have a positive response to the implementation of oral presentation. Student perceives that it helps them to evaluate their ability to speak English, increasing motivation, as well as allowing them to think critically in the use of language. Although several aspects must be developed such as student pronunciation. This finding indicates that oral presentations are useful for improving students speaking skills. Keywords: Oral Presentation, Speaking Skill
Signification functions of Undomestic Goddess novel cover

Novel cover is like human appearance; it is the first thing that the people will take notice of. It turned out that novel cover is quite essential in influencing the opinions and impression of the reader regarding the novel itself. Novel�s cover using symbols, fonts, and colors, construct cover signs functions. The researcher intends to analyze the novel covers sign functions. The source data of the research is selected using purposive sampling following the standard of a novel cover. This research uses three (3) covers of the novel Undomestic Goods by Sophie Kinsella as the data source. The theory of signification by Rolland Barthes is the analytical tool of this research used to determine the function of the elements in the object of research. The results of the study are both covers have their own significations functions; the first edition (1) cover function emphasizes on promoting the novel by using signification content of the novel. Cover (2) emphasizes promotion function using the author popularity and best-selling author status and promoting the novel by using signification content of the novel. Cover three (3) the latest edition function emphasizes on �advertises� the novel by using the author popularity and best-selling author status.
Android-Based Application for Children’s Growth Monitoring as a Complement for Child Development Card

Posyandu is a community activity center to provide health services and family planning. Routine monthly weighing for toddlers is one of the activities carried out by the Posyandu. This activity aims to detect early childhood growth disorders to address these problems quickly and precisely. However, since the Covid-19 pandemic, the toddler Posyandu, held regularly every month, has been temporarily suspended until the pandemic ends. This situation causes monitoring of toddlers' growth, and development cannot be done. Therefore we need a mechanism for monitoring child growth and development that can be done online via a smartphone. In previous research has implemented several desktop, web, and mobile-based information systems, but they do not yet have complex features such as maternal and child health information management. The purpose of this research is to create an android-based application in the form of an information system for Child Development Posyandu, which will contain several features such as immunization schedules, monitoring of child growth and development, health news. This application development uses the design sprint method. The test results with the User Acceptance Test method of this android application to user respondents show that 80% of the applications are in a Good category and can be used at Posyandu.
Class-Incremental Learning on Video-Based Action Recognition by Distillation of Various Knowledge

Recognition of activities in the video is an important field in computer vision. Many successful works have been done on activity recognition and they achieved acceptable results in recent years. However, their training is completely static, meaning that all classes are taught to the system in one training step. The system is only able to recognize the equivalent classes. The main disadvantage of this type of training is that if new classes need to be taught to the system, the system must be retrained from scratch and all classes retaught to the system. This specification has many challenges, such as storing and retaining data and respending training costs. We propose an approach for training the action recognition system in video data which can teach new classes to the system without the need for previous data. We will provide an incremental learning algorithm for class recognition tasks in video data. Two different approaches are combined to prevent catastrophic forgetting in the proposed algorithm. In the proposed incremental learning algorithm, two approaches are introduced and used to maintain network information in combination. These two approaches are network sharing and network knowledge distillation. We introduce a neural network architecture for action recognition to understand and represent the video data. We propose the distillation of network knowledge at the classification and feature level, which can be divided into spatial and temporal parts at the feature level. We also suggest initializing new classifiers using previous classifiers. The proposed algorithm is evaluated on the USCF101, HMDB51, and Kinetics-400 datasets. We will consider various factors such as the amount of distillation knowledge, the number of new classes and the incremental learnings stages, and their impact on the final recognition system. Finally, we will show that the proposed algorithm can teach new classes to the recognition system without forgetting the previous classes and does not need the previous data or exemplar data.
Learning Mathematics through WhatsApp Groups in University Preparatory Program during the COVID-19 Pandemic

This study examined the impact of learning through social media (WhatsApp learning groups) on university students' mathematics achievements in the first-year university preparatory program when they engaged in some form of social media learning interaction compared with students who did the same module with a traditional face-to-face interaction learning approach. The study was undertaken using a quantitative research method. It employed a randomised Post-Test-Only with a Non-equivalent Groups design to investigate the only statistically significant difference between university students who studied through the traditional face-to-face lecturing approach and students who utilised a blended approach to learning mathematics. The study population comprised first-year university students enrolled for the university extended programme offered by all universities in South Africa. The sample consisted of 192 experimental groups and 341 control group students conveniently sampled from a university in South Africa. The main instruments used in this study were two standardised semester exams. These tests were checked and moderated by senior mathematics lecturers to ensure they conformed with the module content and satisfied all assessment policies of the university. The Cronbach alpha coefficient was used to measure the consistencies in these two-semester exams. The study's main finding showed no statistically significant difference in results between students who studied Mathematics through a face-to-face lecturing approach and students who studied mathematics through a blended learning approach. The study concludes that the latter performed slightly better than students in the former, confirming that a WhatsApp learning group can be a viable alternative to the teaching and learning at the university when face-to-face learning is not possible, as for an example in the Covid-19 era. The study recommends that more profound research be conducted to identify and analyse positive indicators when learning is done through social media interaction.
On a Bio-Inspired Method for Topology Optimization via Map L-Systems and Fractone Modeling

Nature has long been a source of inspiration to both engineers and designers alike.This chapter describes a bio-inspired, topology optimization method for engineering design optimization.The method is based on evolutionary developmental processes in biology and employs a cellular division model to develop topologies.Concretely, a Map L-System, a graph based, parallel rewriting method, is used to model the cellular division process that generates the topology of the design, and a genetic algorithm is then used to evolve a population of designs.In the developmental process of the structure, fractones, elements that play a fundamental role in the regulation of cellular divisions, are modeled and incorporated as an additional control parameter for the formation and optimization of topologies.The performance of the resulting method is illustrated with an aeroelastic flapping membrane wing optimization problem in which the supporting structure is optimized for power, lift, and thrust requirements.
A Semi-supervised Learning Approach with Two Teachers to Improve Breakdown Identification in Dialogues

Identifying breakdowns in ongoing dialogues helps to improve communication effectiveness. Most prior work on this topic relies on human annotated data and data augmentation to learn a classification model. While quality labeled dialogue data requires human annotation and is usually expensive to obtain, unlabeled data is easier to collect from various sources. In this paper, we propose a novel semi-supervised teacher-student learning framework to tackle this task. We introduce two teachers which are trained on labeled data and perturbed labeled data respectively. We leverage unlabeled data to improve classification in student training where we employ two teachers to refine the labeling of unlabeled data through teacher-student learning in a bootstrapping manner. Through our proposed training approach, the student can achieve improvements over single-teacher performance. Experimental results on the Dialogue Breakdown Detection Challenge dataset DBDC5 and Learning to Identify Follow-Up Questions dataset LIF show that our approach outperforms all previous published approaches as well as other supervised and semi-supervised baseline methods.
A weather-based forecasting system for the solar power plants in the Konya region

Increasing energy demand along with decreasing environmental resources necessitates looking for alternative energy sources.With this respect, solar power has gained considerable importance over recent years.This study analyzes the forecasting problem for the amount of electric power generated by solar power plants.The amount of electric power generated by solar power plants is not constant and changes depending on several variables such as the weather conditions, seasonal effects, t ype of solar panel, etc.On the other hand, to meet the electric power demand and minimize electric transfer cost, forecasting the electric power generated by solar power plants is critical.We test several neural network models with various weather-related input parameters.Among these parameters, we choose the most promising ones (radiation, humidity, hour, month) for further analysis to forecast the electric power generated by solar power plants located in the Konya region.Our test results over the past data show that it is possible to forecast the electric power generated by solar panels in the Konya region with less than 5% error.
Image data hiding with multi-scale autoencoder network

Image steganography is the process of hiding information which can be text, image, or video inside a cover image.The advantage of steganography over cryptography is that the intended secret message does not attract attention and is thus more suitable for secret communication in a highly-surveillant environment such as civil disobedience movements.Internet memes in social media and messaging apps have become a popular culture worldwide, so this folk custom is a good application scenario for image steganography.We try to explore and adopt the steganography techniques on the Internet memes in this work.We implement and improve the HiDDeN model [22] by changing the Conv-BN-ReLU blocks convolution layer with a multiscale autoencoder network so that the neural network learns to embed message bits in higherlevel feature space.Compared to methods that convolve feature filters on the row-pixel domain, our proposed MS-Hidden network learns to hide secrets in both low-level and high-level image features.As a result, the proposed model significantly reduces the bit-error rate to empirically 0% and the required network parameters are much less than the HiDDeN model.
Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models

Domain adaptation aims to leverage source domain knowledge to predict target domain labels. Most domain adaptation methods tackle a single-source, single-target scenario, whereas source and target domain data can often be subdivided into data from different distributions in real-life applications (e.g., when the distribution of the collected data changes with time). However, such subdomains are rarely given and should be discovered automatically. To this end, some recent domain adaptation works seek separations of hidden subdomains, w.r.t. a known or fixed number of subdomains. In contrast, this paper introduces a new subdomain combination method that leverages a variable number of subdomains. Precisely, we propose to use an inter-subdomain divergence maximization criterion to exploit hidden subdomains. Besides, our proposition stands in a target-to-source domain adaptation scenario, where one exploits a pre-trained source model as a black box; thus, the proposed method is model-agnostic. By providing interpretability at two complementary levels (transformation and subdomain levels), our method can also be easily interpreted by practitioners with or without machine learning backgrounds. Experimental results over two fraud detection datasets demonstrate the efficiency of our method.
Mapping Tree-Shaped Workflows on Memory-Heterogeneous Architectures

Directed acyclic graphs are commonly used to model scientific workflows, by expressing dependencies between tasks, as well as the resource requirements of the workflow. As a special case, rooted directed trees occur in several applications. Since typical workflows are modeled by huge trees, it is crucial to schedule them efficiently. We investigate the partitioning and mapping of tree-shaped workflows on target architectures where each processor can have a different memory size. Our three-step heuristic adapts and extends previous work for homogeneous clusters. In particular, we design a novel algorithm to assign subtrees to processors with different memory sizes, and we show how to select appropriate processors when splitting or merging subtrees. The experiments demonstrate that exploiting the heterogeneity reduces the makespan significantly compared to the state of the art for homogeneous memories.