Reviewerly/exHarmony huggingface dataset:
PERANCANGAN DASHBOARD DAN PREDIKSI PENJUALAN UNTUK TOKO NAGAMASYURMART

The impact of Covid-19 has affected the economy individually, households, micro, small, medium and large companies, even affecting the country's economy with a scale of local, national and even global coverage. Although on the one hand the economy is weakening, on the other hand, the economy is also experiencing improvement, namely in the electronic commerce or e-commerce sector. Consumer needs for various kinds of products that can prevent and protect themselves from the transmission of Covid-19, such as masks, have increased. The increase in various types of masks from various manufacturers makes it difficult for Nagamasyurmart stores to monitor sales and make decisions in predicting future product sales. The purpose of designing this dashboard is to make it easier for store owners to find out sales information in visual form and make decisions easier. Sales data used in designing this dashboard is sales data from Nagamasyurmart Stores. The method used in designing this dashboard is the prototype method, while the prediction method used is the linear regression method. The results obtained are a sales dashboard with sales predictions to make it easier for store owners to make decisions for further sales.
Enhancing Barangay Household Registration with Google Maps

This study presents the creation of the "Enhancing Barangay Household Registration with Google Maps" system, which aims to enhance the efficiency and accuracy of household registration in the barangay. By incorporating Google Maps technology, the system improves data visualization and analysis for informed decision-making by barangay officials. The project follows Agile software development methodology and object-oriented analysis and design, ensuring a flexible and scalable system architecture. Implementation is done using the Laravel Framework, ensuring a robust and easily maintainable solution. User satisfaction is evaluated using the System Usability Scale (SUS), with positive feedback received on usability. The web-based registration system, integrated with Google Maps, effectively overcomes the limitations of the traditional paper-based approach, streamlining data collection and providing real-time information access
An Exploration of Core Competencies to Promote Professionalism in Directors of Home Daycare Centers

Objectives The purpose of this study is to elicit core competencies that are demanded for the promotion of professionalism in directors of home daycare centers.&#x0D; Methods To this end, the Delphi survey was conducted across three times targeting the panel of totally 19 experts comprising the professors of Early Childhood Education Department and the directors of child care centers. The primary survey consisted of the literature review and the draft in core competencies of child care center directors. The secondary survey was gathered the primary expert opinion, was arranged the draft of core competencies in home daycare center directors, and was examined the necessity and importance of this. The tertiary survey was finally confirmed the core competencies after looking through the representative nature, clarity, and inclusiveness of key competencies based on the draft that was formed in the secondary survey.&#x0D; Results First, the core competencies in home daycare center directors appeared to include job competency, interpersonal competency, leadership competency, problem- solving competency, and self-development capability. Second, the job competency as sub-capability in core competencies was indicated to include the achievement ability, leadership, the calling consciousness, the view of teaching, a strategy for job knowledge, skill and attitude, the resource management capability, and the ability to perform concurrent positions. The interpersonal competency was seen to include the attentive hearing, sympathy, intimacy, communication skill, caring others, positive personal relationship, creating an open atmosphere, and respect. And the leadership competency was shown to include leading by example, the ability to respond to a social change, the challenging spirit, the self directedness, tenacity, momentum, judgment, job performance, and providing the education opportunity. The problem- solving competency appeared to include problem- solving ability, collecting various opinions, a positive thought, forming horizontal relationship, and cooperation. Finally, the self-development capability was seen to include self- regulation ability, self-management ability, future-oriented goal, and information gathering capability.&#x0D; Conclusions This study has a significance with regard to having drawn the core competencies required by home daycare center directors. This is expected to be helpful for offering basic data in the event of a variety of the continuing education and the job training to promote professionalism in home daycare center directors, and for selecting the educational contents and seeking a support plan.
Investigating the Effectiveness of Collaborative Strategic Reading (CSR) In Enhancing Reading Comprehension of EFL Students Palu, Indonesia

This study aimed to determine whether implementing Collaborative Strategic Reading (CSR) could enhance students' reading comprehension. A quasi-experimental design employed pre-test and post-test assessments as measurement tools. The researcher employed purposive sampling and selected a sample of 40 students. The collected test data were then subjected to analysis using the test of paired samples in the SPSS v. 26 for Windows. The findings from the data analysis using the test of paired samples revealed a significant difference in scores between the pretest and posttest for the experiment class. The computed p-value (two-tailed) was 0.002, which falls below the predetermined significance level of 0.05. Consequently, the hypothesis has been accepted. In summary, Collaborative Strategic Reading (CSR) has been implemented, and results demonstrate that it has enhanced the reading comprehension skills of SMA Negeri 5 Palu's 10th-grade science students.
Penerapan Media Chat GPT pada Pembelajaran Manajemen Pendidikan terhadap Kemandirian Mahasiswa

The development of digital technology, especially Artificial Intelligence at this time has grown very rapidly and has penetrated into learning technology. One of them is the development of Chat GPT which has begun to be widely used by students and affects the level of student independence. This study aims to examine the effect of the application of Chat GPT on learning education management on student independence. The research method used is a quantitative experiment with One-Group Pre-test and Post-test Design. The population was 40 people and all of them became samples. Data collection techniques are observation and documentation. Data instruments use validity sheets, observation sheets, and documentation. The analysis techniques used are Validity Test, observation data analysis, Prerequisite Test, and Hypothesis Test. The results showed that the application of Chat GPT in learning educational management significantly influenced student independence. In the pre-test, student independence is still in the category of starting to be independent, while in the post-test, student independence increases to be very independent. So, it is concluded that student independence can be increased by the application of Chat GPT in learning education management.
Adoption of augmented virtual reality in higher education during Amrit Kaal: A systematic literature review

This systematic literature review aims to investigate the adoption of augmented virtual reality (AVR) in higher education during the Amrit Kaal. The Amrit Kaal is a period characterized by transformative advancements in technology, particularly in the field of virtual reality (VR) and augmented reality (AR). The purpose of this review is to provide a comprehensive analysis of existing literature on the topic, highlighting the current state of adoption, benefits, challenges, and potential applications of AVR in higher education.The review adopts a systematic approach, following established guidelines for literature search, selection, and analysis. A rigorous search strategy was employed, encompassing multiple academic databases, conference proceedings, and relevant literature.
Siamese-Network Based Signature Verification using Self Supervised Learning

The use of signatures is often encountered in various public documents ranging from academic documents to business documents that are a sign that the existence of signatures is crucial in various administrative processes. The frequent use of signatures does not mean a procedure without loopholes, but we must remain vigilant against signature falsification carried out with various motives behind it. Therefore, in this study, a signature verification system was developed that could prevent the falsification of signatures in public documents by using digital imagery of existing signatures. This study used neural networks with siamese network-based architectures that also empower self-supervised learning techniques to improve accuracy in the realm of limited data. The final evaluation of the machine learning method used gets a maximum accuracy of 83% and this result is better than the machine learning model that does not involve self-supervised learning methods.
Bayesian Inverse Problems andWell-Posedness

In this chapter we introduce the Bayesian approach to inverse problems in which the unknown parameter and the observed data are viewed as random variables. In this probabilistic formulation, the solution of the inverse problem is the posterior distribution on the parameter given the data. We will show that the Bayesian formulation leads to a form of well-posedness: small perturbations of the forward model or the observed data translate into small perturbations of the posterior distribution. Well-posedness requires a notion of distance between probability measures. We introduce the total variation and Hellinger distances, giving characterizations of them, and bounds relating them, that will be used throughout these notes. We prove well-posedness in the Hellinger distance.
Memory of formulaic sequences in L2 retention of chunks featuring inchoative uses of unaccusative verbs

This study investigates learners' processing of English unaccusative verbs in the inchoative frame (The door opened; Oil is spilling).Previous approaches explained L2 difficulty with the inchoative construction in terms of learners' L1 and their perception of discourse or semantic factors hypothesized to be responsible for common overpassivization errors (The door was opened).The purpose of the present study is to complement the extant inventory by proposing an additional factor instrumental in L2 processing.It is hypothesized that L2 use of unaccusative verbs is contingent on learners' familiarity with formulaic expressions exemplifying the inchoative construction.The study focuses on the entrenchment of frequent phrases like My jaw dropped in Czech and Polish learners.In a lexical decision task, their reaction times for frequent expressions were found to be faster and fewer mistakes were made than in the case of non-formulaic counterparts (My hair dropped).
Message from Program Chairs: NLBSE 2023

Welcome to the 2nd edition of the International Workshop on Natural Language-Based Software Engineering (NLBSE). The potential of Natural Language Processing (NLP) and Natural Language Generation (NLG) to support developers and engineers in a wide number of software engineering-related tasks (e.g., requirements engineering, extraction of knowledge and patterns from the software artifacts, summarization and prioritization of development and maintenance activities, etc.) is increasingly evident. Furthermore, the current availability of libraries (e.g., NLTK, CoreNLP, and fasttext) and models (e.g., BERT) that allow efficiently and easily dealing with low-level aspects of natural language processing and representation, pushed more and more researchers to closely work with industry to attempt to solve software engineers' real-world problems.
An Analysis of Product Liability for AI Entities with special reference to the Consumer Protection Act, 2019

An Analysis of Product Liability for AI Entities with special reference to the Consumer Protection Act, 2019
Towards Understanding the Instability of Network Embedding (Extended Abstract)

Network embedding algorithms learn a mapping from the discrete representation of nodes to continuous vector spaces that preserve node proximity. Despite recent efforts to design novel models, little attention has been given to understanding the instability of network embedding. In this paper, we define the stability of node embeddings as the invariance of the nearest neighbors of nodes in different instantiations. We find that existing embedding approaches have significant amounts of instability. In addition, network structures and algorithm models influence the stability of node embeddings significantly. We also examine the implications of embedding instability for downstream tasks and find remarkable impacts on performance.
ORGANIZATIONAL BEHAVIOR IN SMART ORGANIZATIONS

Background: Organizational behavior affects job performance, worker interaction, job commitment, leadership, and social control designs. Human behavior in an exceedingly work atmosphere determines its impact on job structure, performance, communication, motivation, leadership, etc. Aim: The research would like to understand and predict structure life, and get concerned to profit their organization may be a prime concern in reaching strategic goals by encouraging workers. Method: The study has completely captured a review of earlier studies relating OB by relating totally different international and national journals and studies conducted by different people at different periods. Findings: Companies that supply smart worker edges which have friendly conditions square measure a lot of profitable than those who square measure less people-oriented. The contemporary organizations notice that to achieve success and to stay competitive, it is imperative to rely on info technology (IT) and to use trendy management practices.
Transmedia Storytelling Website-Based Children's Learning

Children in Indonesia still show very little interest in reading.According to a poll by the United Nations Educational, Scientific, and Cultural Organization, Indonesian children's interest in reading is still extremely low at only 0.01% despite extensive indoctrination about the value of reading books (UNESCO).People are encouraged to use technology, particularly in the area of multimedia, by developments and developing technologies.This offers researchers the inspiration to develop a digital media application that tells a tale utilizing the idea of transmedia storytelling.With audience engagement so that users may choose their own plot, this application seeks to provide readers a new sense of enjoyment when reading stories.Children will find reading stories on smartphones, tablets, or computers interesting when combined with voice, text, and 2D animated pictures.This project attempts to provide engaging educational media that encourages kids to use their devices responsibly.Testing in this study was carried out by two methods, the first was done with black box testing to find out whether this application was functioning properly.the test results show the results that the application can work well.then proceed with usability testing using the usability scale (SUS) system and evaluated with the Net Promoter Score (NPS).the result is the final SUS score of the respondents is 73.00 and based on the NPS it indicates the application developed is in the passive category and needs improvement.
An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies

Evaluation campaigns provide a common framework with which the progress of speech technologies can be effectively measured. The aim of this paper is to present a detailed overview of the IberSpeech-RTVE 2022 Challenges, which were organized as part of the IberSpeech 2022 conference under the ongoing series of Albayzin evaluation campaigns. In the 2022 edition, four challenges were launched: (1) speech-to-text transcription; (2) speaker diarization and identity assignment; (3) text and speech alignment; and (4) search on speech. Different databases that cover different domains (e.g., broadcast news, conference talks, parliament sessions) were released for those challenges. The submitted systems also cover a wide range of speech processing methods, which include hidden Markov model-based approaches, end-to-end neural network-based methods, hybrid approaches, etc. This paper describes the databases, the tasks and the performance metrics used in the four challenges. It also provides the most relevant features of the submitted systems and briefly presents and discusses the obtained results. Despite employing state-of-the-art technology, the relatively poor performance attained in some of the challenges reveals that there is still room for improvement. This encourages us to carry on with the Albayzin evaluation campaigns in the coming years.
Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters. We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition (VMD)-Permutation entropy (PE) and long short-term memory (LSTM) neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data. The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components, which solves the signal decomposition algorithm’s end-effect and modal confusion problems. The permutation entropy is used to evaluate the complexity of the intrinsic mode function, and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling. Finally, we use the LSTM and stacking fusion models to predict and superimpose; the stacking integration model integrates Gradient boosting regression (GBR), Kernel ridge regression (KRR), and Elastic net regression (ENet) as primary learners, and the secondary learner adopts the kernel ridge regression method with solid generalization ability. The Amazon public data set experiment shows that compared with Holt-winters, LSTM, and Neuralprophet models, we can see that the optimization range of multiple evaluation indicators is 0.338~1.913, 0.057~0.940, 0.000~0.017 and 1.038~8.481 in root means square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and variance (VAR), showing its stability and better prediction accuracy.
Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract)

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs.
Single Image Haze Removal with Radiance Reflectance Minimization Using Illuminated Optimization and K-Means Clustering

Abstract: Haze removal is one of the important factors considered to avoid accident on roads during travelling. Most of the accident occurred in the snow and cloudy regions are due to the haze formation in road while driving. The view occurred on nighttime is differ from the view on daytime. During nighttime improper illumination will occur due to artificial light source with color distortion. In this paper the new method is developed to overcome de haze proper due to improper illumination and scattering of the light. Three step process is implemented in this i) Initially the radiance reflection optimization is performed ii) Once it is applied then to remove the haze the radiance reflection optimization with structured LO-NORM is performed iii) The haze in an image is removed from an image by applying the dark channel method and the illumination level will be estimated. The PSNR value of an image is calculated to check the quality of an image. Then the clustering for a de hazed image is performing using k-mean and fuzzy c-mean (FCM). The result show that the prospective process is better than current one and illumination balance is done effectively, and the image will be haze free
Mapping Land Field in Kampung Tua Nongsa using NRTK Method Based on PTSL Activites

Measurement and mapping of land parcels is a series of activities in carrying out land registration, this activity can be carried out by various methods, such as: photogrammetric methods, remote sensing, terrestrial and other methods. Complete Systematic Land Registration or commonly called PTSL is
Interview with Patrick Huck, on implementing FAIR for computed materials data

This week on Machine-Centric Science, I interviewed Patrick Huck, currently staff on the Materials Project at the Lawrence Berkeley National Laboratory in Berkeley, California. We talk about choices and considerations in implementing FAIR. LISTEN NOW » There are show notes at the link above. Also, I tried to summarize our discussion as a draft FAIR Implementation Profile (FIP). Talking Points Career paths for people that are scientists AND software engineers. The U.S. Department of Energy...