Reviewerly/exHarmony huggingface dataset:
Landslides Susceptibility Mapping in R Program (Case study in Lima Puluh Kota Regency)

Landslide susceptibility zonation is necessary to be considered in land use planning at various scales., different approaches and analytical methods can be used to evaluate and zone the area and processed with GIS software. However, there are constraints in its use, such as the cost of the licenses of software and source code that cannot be accessed and evaluated by users. The recent development of open-source software that can integrate data, analysis, and graphs in a representation such as the R program, has opened up opportunities for researchers to reevaluate and modify interpretation further from available ones to address issues. In this regard, this study aims to create functions in R using the Weight of Evidence (WoE) method, a form of bivariate statistic approach to acquire the significant factors controlling landslides and generate a susceptibility map. The case study is located in Limapuluh Kota Regency, West Sumatra Province of Indonesia, a hilly and mountainous region where its districts are prone to landslides. Eight of eleven factors such as geology, landform, land cover, elevation, density of vegetation greenness, slope, rainfall intensity, and proximity to stream were regarded to control landslides which set up four classes of landslide susceptibility zone (very low, low, moderate, and high).
VIRTUAL LEARNING OF ELECTRONICS

The development of computer technology has enabled the emergence of new teaching opportunities in an artificial, virtual environment. It is then possible to work with electronic systems without the possibility of physical access to them and without measuring equipment. Such teaching and work with electronic systems was possible in a laboratory with computer stations with appropriate software. Only the development of network technology, and in particular the Internet, made it possible to fully virtual work at a distance. Until now, virtual learning has only been an alternative to teaching in a real hardware lab. It was also a supplement to the work that was carried out in the course of practical teaching with students. It was only the emergence of the Covid-19 epidemic that made this type of remote work learning a necessity, but also the only alternative in the event of school closings and quarantines. The necessity of distance learning inspired the use and development of many programs and information systems for this purpose. The paper presents selected programs that can be used and are used to learn electronics at a distance while working remotely. Each of these systems has its own advantages and disadvantages and can meet specific requirements.
TO COMBINATE THE CHEMICAL TASKS ON THE BASIS OF SYSTEMATIC ANALYSIS

The paper involves the construction of chemical tasks in chemistry in the general secondary education system and the use of systematic methods of analysis in their solution. Approaching combined tasks on the basis of systematic analysis and interpreting them on the basis of illustrative tools has a significant impact on increasing the level of mastery and interest of students.
Study and Analysis, the Partnership that Reveals the Best Solutions

Abstract The field of engineering is a complex and well-developed one and thus offers various opportunities to students. Design engineering is a branch of this field that has several categories. The field of engineering involves logic and reason and imagination. The strength of 3D and 2D design is the engineer’s ability to see in space. This skill is cultivated over time and requires practice and work. Time management in favour of effort is also a very important aspect of engineering tool. Design develops with technology because nowadays everything is designed in 3D/2D CAD and CAM space. Thus, technology is the basis of computer-aided design and manufacturing. Automotive design engineering involves continuous study and analysis of the CAD working environment (programs such as CATIA, NX Siemens), the processes to be performed (welding, marking and checking stations) and the materials that can meet the project requirements (such as C45 and PA6).
An Interdisciplinary Approach to the Evolution of Human Cognitive Ability

An interdisciplinary approach to evolutionary problems, in contrast to the more common method of taking a given set of cognitive abilities (usually from modern humans) and attempting to explain them by hypothesizing how they were present in our ancestors, is proposed. In this paper some implications of this approach for problems of evolution are explored. A discussion is provided on how the ideas expressed have changed in light of recent findings. It will be shown that while it may be premature to reject previous proposals on the basis that there are too many inconsistencies between data and theoretical predictions, it is also clear that several predictions about the functioning of non-human primate brains have been falsified.
The Power of Tests for Detecting $p$-Hacking

$p$-Hacking can undermine the validity of empirical studies. A flourishing empirical literature investigates the prevalence of $p$-hacking based on the empirical distribution of reported $p$-values across studies. Interpreting results in this literature requires a careful understanding of the power of methods used to detect different types of $p$-hacking. We theoretically study the implications of likely forms of $p$-hacking on the distribution of reported $p$-values and the power of existing methods for detecting it. Power can be quite low, depending crucially on the particular $p$-hacking strategy and the distribution of actual effects tested by the studies. Publication bias can enhance the power for testing the joint null hypothesis of no $p$-hacking and no publication bias. We relate the power of the tests to the costs of $p$-hacking and show that power tends to be larger when $p$-hacking is very costly. Monte Carlo simulations support our theoretical results.
Transformer-based Cross-Modal Recipe Embeddings with Large Batch Training

In this paper, we present a cross-modal recipe retrieval framework, Transformer-based Network for Large Batch Training (TNLBT), which is inspired by ACME~(Adversarial Cross-Modal Embedding) and H-T~(Hierarchical Transformer). TNLBT aims to accomplish retrieval tasks while generating images from recipe embeddings. We apply the Hierarchical Transformer-based recipe text encoder, the Vision Transformer~(ViT)-based recipe image encoder, and an adversarial network architecture to enable better cross-modal embedding learning for recipe texts and images. In addition, we use self-supervised learning to exploit the rich information in the recipe texts having no corresponding images. Since contrastive learning could benefit from a larger batch size according to the recent literature on self-supervised learning, we adopt a large batch size during training and have validated its effectiveness. In the experiments, the proposed framework significantly outperformed the current state-of-the-art frameworks in both cross-modal recipe retrieval and image generation tasks on the benchmark Recipe1M. This is the first work which confirmed the effectiveness of large batch training on cross-modal recipe embeddings.
Usability Analysis of Inlislite Website in Elementary School Library in Penajam District using Heuristic Evaluation Method

The purpose of this study was to see to what extent the quality of the system has functioned properly so that a system is able to achieve a certain goal so that the resulting output is effective, efficient, and achieves user satisfaction at the Elementary School Library in Penajam District Using the Heuristic Evaluation Method. The type of data used consists of primary data and secondary data. The research population includes all librarians from 40 schools, namely by distributing questionnaires to all elementary school librarian employees in the North Penajam Paser sub-district with a total of 90 people. The results showed that the level of Inlislite Website Usability Analysis actually had a positive and significant effect on users of the Elementary School Library Inslite website in Penajam District. In this case, the analysis of the test was carried out using the Cronbach Alpha's method with a total of 26 questionnaire items for the X variable and 5 for the Y variable with the Cronbach Alpha value 0.928 &gt; 0.60 for the X variable and for the Y variable the Cronbach Alpha value was 0.617 &gt; 0.60. it means that the questionnaire is reliable or consistent for use in influential research in Inlislite Website users at Elementary School Libraries.
Geographic Complexity: Concepts, Theories, and Practices

Geography is a fundamentally important discipline that provides a framework for understanding the complex surface of our Earth [...]
Abhyankar's Affine Arithmetic Conjecture for the Symmetric and Alternating Groups

We prove that for any prime $p>2$, $q=p^\nu$ a power of $p$, $n\ge p$ and $G=S_n$ or $G=A_n$ (symmetric or alternating group) there exists a Galois extension $K/\mathbb F_q(T)$ ramified only over $\infty$ with $\mathrm{Gal}(K/\mathbb F_q(T))=G$. This confirms a conjecture of Abhyankar for the case of symmetric and alternating groups over finite fields of odd characteristic.
MixFlows: principled variational inference via mixed flows

This work presents mixed variational flows (MixFlows), a new variational family that consists of a mixture of repeated applications of a map to an initial reference distribution. First, we provide efficient algorithms for i.i.d. sampling, density evaluation, and unbiased ELBO estimation. We then show that MixFlows have MCMC-like convergence guarantees when the flow map is ergodic and measure-preserving, and provide bounds on the accumulation of error for practical implementations where the flow map is approximated. Finally, we develop an implementation of MixFlows based on uncorrected discretized Hamiltonian dynamics combined with deterministic momentum refreshment. Simulated and real data experiments show that MixFlows can provide more reliable posterior approximations than several black-box normalizing flows, as well as samples of comparable quality to those obtained from state-of-the-art MCMC methods.
A tool for recommending keywords with more live and more attention.

Abstract In this paper, we propose a method to help authors to choose alternative Keywords that help their papers to gain visibility. These alternative keywords must have a certain level of popularity in the scientific community and, at the same time, be keywords that have fewer competitors. The competitors would be derived from other papers containing the same keywords. Having fewer competitors would allow the author’s paper to have a higher consult frequency. In order to recommend keywords, we must first determine an Attention-Survival score. The attention score is obtained by using the popularity of a keyword. The survival score is derived by the number of manuscripts using the same keyword. With these two scores, we created a new algorithm that finds alternative keywords with a high Attention-Survival score. We used ontologies in order to ensure that alternative keywords proposed by our method are semantically related to the original authors keywords that authors wish to refine. The hierarchical structure in an ontology supports the relationship between the alternative keywords and the input keywords. To test the sensibility of the ontology, we used two sources: WordNet and The Computer Science Ontology. Finally, we launched a survey to have human validation for our algorithm, by using keywords from Web of Science papers and three ontologies: WordNet, CSO and DBpedia. We obtained good results in all our tests.
Sentiment analysis using machine learning classification models

This paper is written to compare different classification models for sentiment analysis
When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it

Understanding longer narratives or participating in conversations requires tracking of discourse entities that have been mentioned. Indefinite noun phrases (NPs), such as 'a dog', frequently introduce discourse entities but this behavior is modulated by sentential operators such as negation. For example, 'a dog' in 'Arthur doesn't own a dog' does not introduce a discourse entity due to the presence of negation. In this work, we adapt the psycholinguistic assessment of language models paradigm to higher-level linguistic phenomena and introduce an English evaluation suite that targets the knowledge of the interactions between sentential operators and indefinite NPs. We use this evaluation suite for a fine-grained investigation of the entity tracking abilities of the Transformer-based models GPT-2 and GPT-3. We find that while the models are to a certain extent sensitive to the interactions we investigate, they are all challenged by the presence of multiple NPs and their behavior is not systematic, which suggests that even models at the scale of GPT-3 do not fully acquire basic entity tracking abilities.
PENGELOMPOKAN TWEETS PADA AKUN TWITTER TOKOPEDIA MENGGUNAKAN ALGORITMA DENSITY BASED SPATIAL CLUSTERING OF APPLICATIONS WITH NOISE

Social media has become a trend for Indonesian people to express opinions, socialize, and exchange ideas. Internet users in Indonesia in 2021 will reach 202.6 million, 84% of whom use the internet to access social media. Twitter is one of the popular social media in Indonesia. This phenomenon is an opportunity for companies to use Twitter as a marketing tool, one of which is a marketplace company in Indonesia, Tokopedia. This research is intended to cluster tweets uploaded by the @tokopedia Twitter account to find out the type of content that gets a lot of likes and retweets by followers of the @tokopedia Twitter account. Cluster formation is done by applying the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN). DBSCAN is a clustering algorithm based on density. The DBSCAN algorithm requires two parameters, namely the radius (Eps) and the minimum number of objects to form a cluster (MinObj). This research conducted several experiments with different Eps and MinObj parameters on 1.344 tweets that had gone through the stages of removing duplication, text preprocessing, and feature selection. The quality of the cluster formed is measured using the Silhouette Coefficient. Based on the highest average Silhouette Coefficient, the parameter values of Eps=5 and MinObj=3 with Silhouette Coefficient = 0.575 are determined as the best parameters that produce 2 clusters and 7 noise. The type of content that has the highest average number of likes and retweets is the WIB (Indonesian Shopping Time) campaign, so Tokopedia can use this type of content as a marketing tool on Twitter social media because this type of content is preferred by followers of the @tokopedia Twitter account. Keywords: Twitter, Tokopedia, Clustering, DBSCAN, Silhouette Coefficient
Scientific capital in the context of gender

This article reveals the impact of the gender image of school science disciplines on career choices by young people. The article is aimed at analysing the gender stereotype of school science among female and male students and the influence of gender science stereotypes on the career aspirations of young people.
Preface to the Special Issue on “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning”

Recent advancements in artificial intelligence and machine learning have led to the development of powerful tools for use in problem solving in a wide array of scientific and technical fields [...]
Quantum information scrambling in non-Markovian open quantum systems

In this paper we investigate the dynamics of a spin chain whose two end spins interact with two independent non-Markovian baths by using the non-Markovian quantum state diffusion (QSD) equation approach. Specifically two issues about quantum information scrambling in open quantum system are addressed. The first issue is that tripartite mutual information (TMI) can quantify information scrambling properly via its negative value in closed system, whether it is still suitable to indicate quantum scrambling in open quantum system. However we find that negative TMI is not an suitable quantifier of information scrambling in open quantum system in some cases while negative tripartite logarithmic negativity (TLN) is more appropriate. The second one is that up to now almost all the open quantum system effects on information scrambling reported were focus on the Markovian environment, while the effect of non-Markovian environment on information scrambling is still elusive. Significantly our results show that the memory effect of environment will be beneficial to the emergence of quantum information scrambling. Moreover, it is found that environment is generally detrimental for information scrambling in a long time, while in some cases it will be helpful for information scrambling in a short time.
Deployment of Medibot in Medical Field

During this pandemic situation, most of people's health care is in the need of medicine and doctors' suggestions to improve and protect their health.Also, have seen many such cases where many people have been infected by COVID.To reduce physical contact and help the people from the spread of diseases the proposed methodology is to implement the medibot in hospitals.A medical bot is a Chatbot that uses NLP (Natural Language Processing) by text format.The medibot is supported by AI and Deep Learning for Medical Diagnostics.The goal of the project is to create a medibot that overcomes the proposed methodology.Many people could not meet the doctors for simple problems such as cold and fever.To reduce these cases will implement the medibot.This medibot can communicate with the patients and understand the symptoms, it will also give them medicines.
3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds

3D deep models consuming point clouds have achieved sound application effects in computer vision. However, recent studies have shown they are vulnerable to 3D adversarial point clouds. In this paper, we regard these malicious point clouds as 3D steganography examples and present a new perspective, 3D steganalysis, to counter such examples. Specifically, we propose 3D-VFD, a victim-free detector against 3D adversarial point clouds. Its core idea is to capture the discrepancies between residual geometric feature distributions of benign point clouds and adversarial point clouds and map these point clouds to a lower dimensional space where we can efficiently distinguish them. Unlike existing detection techniques against 3D adversarial point clouds, 3D-VFD does not rely on the victim 3D deep model's outputs for discrimination. Extensive experiments demonstrate that 3D-VFD achieves state-of-the-art detection and can effectively detect 3D adversarial attacks based on point adding and point perturbation while keeping fast detection speed.
Continuous monitoring for noisy intermediate-scale quantum processors

We present a continuous monitoring system for intermediate-scale quantum processors that allows extracting estimates of noisy native gate and read-out measurements based on the set of executed quantum circuits and resulting measurement outcomes. In contrast to standard approaches for calibration and benchmarking quantum processors, the executed circuits, which are input to the monitoring system, are assumed to be out of any control. We provide the results of applying our system to the synthetically generated data obtained from a quantum emulator, as well as to the experimental data collected from a publicly accessible cloud-based quantum processor. In the both cases, we demonstrate that the developed approach provides valuable results about inherent noises of emulators/processors. Considering that our approach uses only already accessible data from implemented circuits without the need to run additional algorithms, the monitoring system can complement existing approaches. We expect that our monitoring system can become a useful tool for various quantum computers in the near-term horizon, including publicly accessible cloud-based platforms, and reduce resources that are required for their benchmarking and calibration.
IMPLEMENTASI METODE SCRUM DAN ANALISIS SWOT SEBAGAI STRATEGI FRAMEWORK CUSTOMER RELATIONSHIP MANAGEMENT(CRM) PADA PERUSAHAAN RENTAL MOBIL

The rapid competition of business, it is necessary to implement the right strategies to support its business processes, one of which is through customer relationship management, with the aim of creating customer loyalty. Loyalty is expected to be done, ranging from customers who are looking for information to finish getting the service. The application of Customer Relationship Management (CRM) is the right alternative solution to realize customer loyalty. Implement an e-CRM system by applying scrum methods and SWOT analysis. The SCRUM method is software that includes agile development that uses an object-oriented approach and includes planning, design, coding, and testing activities. Implementation of Scrum Modeling with three phases: pregame, scrum phase, post-game. After the application is evaluated with the SWOT method maximizes strengths and opportunities, but can simultaneously minimize weaknesses and threats, from the sprint process that has been completed reviewed, and changes are needed. Website-based e-CRM system application that facilitates customer service and mamou affects customer loyalty and can support business processes on S2 Trans Karawang service.&#x0D; Keywords: Customer Relationship Management (CRM), Business, Customer, SWOT Analysis, Information
Case Study on the Class Content Composition and Practice of Academy Writing Utilizing Digital Media

Undergraduate students in the 21&lt;sup&gt;st&lt;/sup&gt; century need to understand both the process of expressing their opinions and that of the critical acceptance of those opinions utilizing digital media. They also need to recognize the linguistic characteristics and linguistic expressions that are used in digital media in the language of their everyday lives. Since undergraduate students are both receivers of information utilizing digital media language and producers of new digital information, they should develop communication skills that better correspond to the language that they use in their offline lives. Hence, it is necessary to open liberal arts courses for the purpose of fostering more effective communication skills utilizing digital media. In keeping in line with the daily life and practical needs of undergraduate students living in the digital age, the writer developed a course (Digital Culture and Language) in the first semester of 2020, and has delivered lectures to the present date as part of a balanced (elective) liberal arts course (3/3). This thesis aims to suggest certain communication education content and to provide class cases utilizing the digital media that appeared in the Digital Culture and Language course, as taught in the second semester of 2021.
Quiz App Based On Android

Nowdays quizzes are very popular for education purpose as well as entertainment purpose. There are many quiz android apps available for android but they are different from each others in terms of content of the quiz, features of app, target users. Nowdays the gradually increase in number of Android phone users it is also increases the development of applications based on Android. This paper presents about android and the development of earning quiz android based app using android studio with Java as the programming language. This work deals with the development of an Android-based multiple-choice question examination system, namely: Quizo Reward App. Quizo Reward App is developed for practice of different entrance examination purpose with entertainment by providing features like interactive user interface, earn money, Leaderboards by which users can know their rank and wants to improve it. This app contains collection of previous year multiple choice based questions and their answer of different entrance examination.