Reviewerly/exHarmony huggingface dataset:
RADIO BASED INSTRUCTION: A MODULAR DISTANCE LEARNING TEACHER SUPPLEMENTARY MATERIAL

Radio Based Instruction (RBI) is a form of Distance Learning.It is still a superior alternative for those who want more convenience and flexibility for different modalities.One of the most significant advantages of distance education through RBI is that it allows students to use various learning materials while using minimum financial resources.This study aimed to analyze radio-based instruction as a MODULAR DISTANCE LEARNING (MDL) teacher supplementary material in English.Specifically, it sought answers to the following questions:(1) What is the level of radio-based Instruction in terms of objectives, content, learning activities, and assessments (2) What is the teachers' level of perception of RBI in terms of adaptability, appropriateness, consistency, usability, curriculum, alignment (3) What is the status of pupils the Learning Continuity with regards to engagement on academic tasks, cooperation, positive interaction, sense of competence (4) Do the teachers' perceptions on radio-based Instruction have a significant relationship to the learning continuity in English?The descriptive method of research was utilized in this study.The questionnaires were given to45 respondents who are public elementary teachers using radio-based instruction as supplementary study material in English in the District of Pagsanjan and other neighboring towns selected through a simple random sampling technique.The questionnaire was composed of three (3) parts: the level of radio-based instruction, 449 www.ijrp.orgIJRP 2022, 102(1), 449-484; doi:.10
PELATIHAN POWER POINT INTERAKTIF DENGAN ANIMASI DI SLB NEGERI 1 KABUPATEN GOWA

Teachers at SLB Negeri 1 Gowa are facing problems engaging interactive learning that is relevant to the current pandemic. They must present classes that are not only online-deliverable but also engaging, interactive, and entertaining for students. To address this issue, teachers must receive training that will assist them in creating engaging and dynamic learning materials that are also simple to create. The training's intention is for teachers to be able to develop interactive powerpoints with animation on their own. Participants can use animation to produce interactive power points as a result of this study. Some participants have done amazingly well. The conclusion is that after engaging in training on how to create interactive power presentations using animation, teachers in SLB Negeri 1 Gowa Regency improved their knowledge and skills. Given the teachers' output in the form of an interactive video created and posted to their YouTube channel, it is recommended that other schools enhance their teachers' knowledge and skills in creating interactive power points through training. The training is designed to provide children with special needs with engaging and interactive learning opportunities.
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification

Graph kernels are conventional methods for computing graph similarities. However, most of the R-convolution graph kernels face two challenges: 1) They cannot compare graphs at multiple different scales, and 2) they do not consider the distributions of substructures when computing the kernel matrix. These two challenges limit their performances. To mitigate the two challenges, we propose a novel graph kernel called the Multi-scale Wasserstein Shortest-Path graph kernel (MWSP), at the heart of which is the multi-scale shortest-path node feature map, of which each element denotes the number of occurrences of a shortest path around a node. A shortest path is represented by the concatenation of all the labels of nodes in it. Since the shortest-path node feature map can only compare graphs at local scales, we incorporate into it the multiple different scales of the graph structure, which are captured by the truncated BFS trees of different depths rooted at each node in a graph. We use the Wasserstein distance to compute the similarity between the multi-scale shortest-path node feature maps of two graphs, considering the distributions of shortest paths. We empirically validate MWSP on various benchmark graph datasets and demonstrate that it achieves state-of-the-art performance on most datasets.
THE XIONG’AN NEW AREA DEFORMATION MONITORING USING SENTINEL-1 SAR DATA BASED ON SBAS-INSAR TECHNIQUE

Abstract. In order to study the land subsidence trend since the construction of the Xiong’an New Area, 125 images of Sentinel-1 from January 2017 to September 2020 was obtained in this paper, and the average land subsidence rate and cumulative land subsidence of Xiong’an New Area is obtained by the SBAS-InSAR technology of multi-main image coherent target by GAMMA and the Self-developed software, and the causes of land subsidence from the geographical space in the Xiong’an New Area were analyzed by the divisional timing analysis method. The results showed that about 80% of Xiong’an New Area had slight land subsidence with the rate less than 1cm/yr and the cumulative subsidence less than 3cm, and the maximum subsidenc occurred in the northern of Xiongxian County with rate 7cm/yr and the cumulative subsidence was 30cm. Furthermore, the average subsidence rate and the cumulative maximum subsidence in the north and the northwest of Xiongxian County, the Baima Village and the Beifeng Village were studied. The research results showed that the land subsidence in Xiong’an New Area was related to the over exploitation of geothermal resources and groundwater, and the results could provide important reference values for keep abreast of the land subsidence status of the Xiong’an New Area, as well as the healthy development and long-term planning of the New Area.
Learner Performance-based Behavior Optimization Algorithm: A Functional Case Study

Abstract A novel algorithm called learner performance-based behavior algorithm(LPB) was proposed for single and multi-objective by Chnoor M. Rahmanand Tarik A. Rashid in 2021. LPB proved its ability to deal with complex optimizationproblems compared to the dragonfly algorithm (DA), genetic algorithm(GA), and particle swarm optimization (PSO). This paper presents and explainsthe implementation of the LPB algorithm, and it applies it as a model in a casestudy to maximize a fitness function. As a result, the LPB algorithm is successfullyimproved the initial population and achieved the optimal solution.
PEMBELAJARAN PENGOPERASIAN PINTU PERLINTASAN SEBIDANG KERETA API

The purpose of implementing this community service program is to increase expertise in operating crossing gates on barred and guarded railroads (service crossings). In this learning refresher activity, the demonstration method is carried out, which provides opportunities for learning participants to relate directly to the material being studied and demonstrate it. To find out the participants' understanding of the implementation of the trial, they immediately opened and closed the level crossing gates. The results from the implementation of refresh learning which are considered very effective can provide direct understanding and refresher regarding the correct operational standards of crossing gates.
EFFECT OF KNOWLEDGE MANAGEMENT ON FIRM PERFORMANCE AND COMPETITIVENESS

Purpose: The purpose of this study was to investigate the effect of knowledge management on firm performance and competitiveness. Theoretical relations are tested through an empirical study carried out on 5 companies.&#x0D; Results: The results showed how the ﬁrms that adopt knowledge management practices obtain better results than their competitors. The research limitations/implications were that the subject of principles has not been considered a dimension of knowledge management. New avenues of inquiry are opened considering this dimension.&#x0D; Unique contribution to theory, practice and policy: The practical implications were that the study only considered practices that have a positive incidence on ﬁrm performance. Further, the conceptualization of knowledge management practices represents atheoretical innovation. This scale can be used in other knowledge-intensive industries. The analysis concludes that knowledge management practices have a positive incidence on ﬁrm performance
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks

A striking observation about iterative magnitude pruning (IMP; Frankle et al. 2020) is that $\unicode{x2014}$ after just a few hundred steps of dense training $\unicode{x2014}$ the method can find a sparse sub-network that can be trained to the same accuracy as the dense network. However, the same does not hold at step 0, i.e. random initialization. In this work, we seek to understand how this early phase of pre-training leads to a good initialization for IMP both through the lens of the data distribution and the loss landscape geometry. Empirically we observe that, holding the number of pre-training iterations constant, training on a small fraction of (randomly chosen) data suffices to obtain an equally good initialization for IMP. We additionally observe that by pre-training only on "easy" training data, we can decrease the number of steps necessary to find a good initialization for IMP compared to training on the full dataset or a randomly chosen subset. Finally, we identify novel properties of the loss landscape of dense networks that are predictive of IMP performance, showing in particular that more examples being linearly mode connected in the dense network correlates well with good initializations for IMP. Combined, these results provide new insight into the role played by the early phase training in IMP.
Phishing URL Detection using Machine Learning

Abstract: As we have moved the majority of our monetary, business related, and other day by day exercises to the web, we are presented to more serious dangers as cybercrimes. URL-based phishing assaults are quite possibly the most widely recognized dangers to web client. In this kind of assault, the aggressor takes advantage of the human weakness rather than programming defects. It targets the two people and associations, instigates them to tap on URLs that look secure, and take private data or infuse malware on our framework. Diverse AI calculations are being utilized for the identification of phishing URLs, that is, to group a URL as phishing or real. Analysts are continually attempting to work on the presentation of existing models and increment their exactness. In this work, we expect to audit different AI strategies utilized for this reason, alongside datasets and URL highlights used to prepare the AI models. The presentation of various AI calculations and the strategies used to build their exactness measures are talked about and investigated. The objective is to make an overview asset for scientists to become familiar with the current advancements in the field and add to making phishing discovery models that yield more precise outcomes. Keywords: Phishing, Phishing websites, Machine Learning, anti-phishing, phishing attack, security and privacy, phishing approaches
Few-Shot Unlearning by Model Inversion

We consider a practical scenario of machine unlearning to erase a target dataset, which causes unexpected behavior from the trained model. The target dataset is often assumed to be fully identifiable in a standard unlearning scenario. Such a flawless identification, however, is almost impossible if the training dataset is inaccessible at the time of unlearning. Unlike previous approaches requiring a complete set of targets, we consider few-shot unlearning scenario when only a few samples of target data are available. To this end, we formulate the few-shot unlearning problem specifying intentions behind the unlearning request (e.g., purely unlearning, mislabel correction, privacy protection), and we devise a straightforward framework that (i) retrieves a proxy of the training data via model inversion fully exploiting information available in the context of unlearning; (ii) adjusts the proxy according to the unlearning intention; and (iii) updates the model with the adjusted proxy. We demonstrate that our method using only a subset of target data can outperform the state-of-the-art unlearning methods even with a complete indication of target data.
Phonological Awareness and Vocabulary Formation among Kindergarten Learners

This study aims to assess the Phonological awareness and Vocabulary formation among Kindergarten.Specifically, the study seeks to achieve the following objectives: level of Phonological awareness of Kindergarten learners in terms of: onset and rime, phoneme blending and phoneme segmentation; level of vocabulary formation of Kindergarten learners in terms of: word recognition, word invented meaning and word associated meaning; and significant relationship to the vocabulary formation of learners.The respondent of the study were thirty nine (39) kindergartens from Magdalena District who were 6 years of age.Learning activities given composed of onset and rime, phoneme blending and phoneme segmentation tasks and checklist.In addition, word recognition, word invented spelling and word associated meaning actual kindergarten word answer.The results are as follows in terms of objectives, significant relationship between the phonological awareness and vocabulary formation of the kindergarten learners.Specifically, it presents the relationship between the awareness on students on Onset and Rimes, Phoneme Blending, and Phoneme Segmentation with their abilities in Word Recognition, Word Invented Spelling, and Word Associated Meaning.Onset and Rimes is observed to have a significant strong relationship with Word Recognition (r=0.668,p=000) and a moderate relationship with the Word Invented Spelling (r=0.436) and Word Associated Meaning (r=0.561,p=000) with the respective p-values of 0.000, 0.006, and 0.000.Obtained p-values for the test were less than 0.05 hence the significance.Consecutively, Phoneme Blending is observed to have a strong significant relationship with Word Associate Meaning (r=0.619), a moderate relationship with Word Recognition (r=0.562), and a weak significant relationship with Word Invented Spelling (r=0.367) with the p-values of 0.000, 0.022, and 0.000 respectively.On the other hand, the Phoneme Segmentation is perceived to have no significant relationship with Word Recognition (r=0.073) and Word Associated Meaning (r=0.285)all the while having a significant moderate relationship with word invented spelling (r=0.525).Only the p-value for the latter was at 0.001 which was less than the significance alpha.Researchers tested the hypothesis rejected.This calls for the acceptance of the alternative which incites that there is a significant relationship.
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems

Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available.The stateof-the-art technology for neural dense retrieval or re-ranking involves deep learning models with hundreds of millions of parameters.However, it is difficult and expensive to get such models to operate at an industrial scale, especially for cloud services that often need to support a big number of individually customized dialogue systems, each with its own text corpus.We report our work on enabling advanced neural dense retrieval systems to operate effectively at scale on relatively inexpensive hardware.We compare with leading alternative industrial solutions and show that we can provide a solution that is effective, fast, and cost-efficient.
Research on evaluation Standard of Physical Literacy ability based on cloud Model

In order to better solve the ambiguity and uncertainty in the evaluation indicators of college students' physical literacy, the cloud model method is used to evaluate college students' physical literacy ability, and the evaluation system is constructed by combining the first and second level indicators such as sports cognition, sports ability and sports behavior. With the cloud theory, the index weight cloud and standard cloud are set up Based on information of each evaluation index and algorithm to get the final evaluation index of cloud, finally compared with the evaluation standard of cloud, determine the advantages and disadvantages of indicators of physical quality evaluation standards can comprehensive scientific assessment of college students' sports literacy ability, found that college students' literacy ability of sports based on cloud model sports ability is good, sports awareness and sports behavior ability needs to be strengthened The method is scientific and the results are more intuitive with the help of evaluation cloud map, which is helpful to improve the research of physical literacy in China.
Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce

One of the important aspects in e-commerce is how to recommend a product to users accurately. To achieve this goal, many e-commerce starts to build and research about recommender system. Many methods can be used to build a recommender system, one of them is using the collaborative filtering technique. This technique often experiences data sparsity problem that can impact to the recommender system prediction accuracy. To solve this problem, we apply improved collaborative filtering. This method predicts the missing values in the user item rating matrix. First, we do an initial selection to determine potential users who have the same characteristics with the active user. After that, we calculate the average distance between the active user and the other selected user. Next, we calculate missing values prediction. Missing values predictions is only done for items that have never been rated by other’s selected user but has been rated by the active user. We used Amazon electronic product with high sparsity level in this research to simulate the actual condition of e-commerce. We used MAE and RMSE to measure prediction accuracy. The methods we apply succeeds to improve the prediction accuracy compare to the conventional collaborative filtering method. The average MAE for method that we apply is 0.78 and RMSE 1.07
Peningkatan Keaktifan Belajar dan Kemandirian Belajar IPS melalui Media Pembelajaran Liveworksheet pada Peserta Didik Kelas VIIA di SMP

This study aims to increase learning activity and independence in social studies learning through live worksheet learning media for class VII A students in junior high school. This research is a classroom action research with the research subjects are students of class VII A in junior high school. This research will be carried out in two cycles, the first cycle consists of two meetings and the second cycle consists of two meetings. At the end of each cycle an evaluation is carried out. Data collection in this action research is done by observation, documentation and learning outcomes tests. True learning throughout the life of students, one of which is indicated by an attitude of activeness, independence in learning and an increase in learning outcomes. The activeness of students when learning is all learning activities that involve students, both physical and non-physical in the learning process that can encourage students to be more critical, express opinions in discussions, ask questions, and be able to solve a given problem. Independence is an attempt to break away from parents with the intention of finding oneself through the process of seeking ego identity, which is a development towards a stable and independent individuality. Based on the conclusion, there is an increase in learning activity and independence in social studies learning through live worksheet learning media for class VII A students in junior high school.
eDiscovery in the cloud-challenges and benefits

Cloud computing is a rapidly emerging area of the today’s technology industry. Moving towards cloud computing continues to gain importance and enable legal practitioners and law firms to expand their capabilities. The cloud provides the access to elastic computing that can fuel everything from traditional productivity applications like personnel management, presentation development and word processing, to sophisticated business application that includes content management, data mining and sales automations. Moving all or part of the firm’s e-discovery functions to the cloud may lead to navigating complex issues to effect firm’s security. This paper will focus on some of basics of eDiscovery, how cloud changes the way of its working. Here, we focus on the challenges of eDiscovery in the cloud.
ANALISIS KEMAMPUAN SISWA MENYELESAIKAN SIKLUS AKUNTANSI PERUSAHAAN JASA DI SMK NEGERI 8 PALEMBANG

Accounting is a compulsory subject given to accounting majors which contains material on the service company cycle. The pattern of learning which has implemented so far at SMK Negeri 8 Palembang has been optimal. Based on it, the problem in this study is how is the ability of students to complete the accounting cycle of service companies at SMK Negeri 8 Palembang. The purpose of this study was to determine the ability of students to complete the service company accounting cycle at SMK Negeri 8 Palembang. The research method that is used is descriptive statistical method. The results showed that the level of students' ability to complete the service company's accounting cycle in the cognitive domain of students' abilities "Good" in completing the general journal, students' abilities were "Very good", student's ability ledger was "Very good", the balance sheet of students' abilities was "Very good", verse student ability adjustment journal “Good”, student ability balance sheet “Good”, student ability financial report “Good”, student ability closing journal “Good”, trial balance after closing student ability “Good”, reversing journal student ability “Good”. In the affective domain, the student's ability is "Good". Furthermore, in the psychomotor domain, the students' abilities are "Good". Keywords : Analysis, student ability, accounting cycle, service company
IoT based SMEs shop management system

The Internet of Things (IoT) is an idea that intends to interface arranged data frameworks to actual items. The Internet of Things (IoT) has applications in pretty much every part of life in this day and age, and stock administration is no special case. IoT gives an answer for this issue by making it simpler to interface every one of the various organizations in a strategic framework utilizing Wireless Sensor Networks. An Interactive Shopping Model and an Automated Inventory Intelligent Management System that uses the Internet of Things to give constant item following, the board, and observing. A study and examination of the commonness of IoT among assembling SMEs is introduced, just as the current impediments and possibilities for permitting prescient investigation. The four examination capacities are depicted alongside an outline of the IoT empowering agents. Future patterns and difficulties in arising innovative work subjects are featured, for example, making IoT advances available to SMEs. The motivation behind this paper is to look at how the Internet of Things is changing our lives and work spaces, just as to feature probably the best strategic approaches, insights, and patterns. Considering the developing significance of big business IoT and the exploration hole in this field, an IoT design and the IoT administration industry will be examined. A model is needed to choose and send IoT administrations in different authoritative settings.