Reviewerly/exHarmony huggingface dataset:
Automatic Identification of Poet in Arabic Poetry

Identification of a poet, for a given poem, is the task of distinguishing authority of poetic texts among different authors. It is a difficult process because it requires knowledge in Arabic language and poetry with semantic relation and poet fingerprint. In this thesis, a system was created that extract prosody style, the poetic meter and recognizes the poet (author) of a given Arabic poem automatically. For doing this, a collection of 2044 labeled poems to seven poets was collected using private crawler and private cleaning program. Firstly, the poem is transformed into Prosody styles using Prosody rules. Then the poem meter will be extracted using Naive Bayes and Decision Tree classifiers that learned from the data set. The process of identifying the poet is carried out in three methodologies: using content, features and combination of them with poem meter. The process of identification of poet of a given poem is done using four classification techniques; Naive Bayes, Support Vector Machine, k Nearest Neighbor, and Decision Tree.  The obtained results for identifying the poem meter were 0.723 and 0.735 using NB and DT classifier respectively. The best obtained result for identifying poets of poems, using combination of content and feature with support of poem meter, was 0.797, using NB, classifier. The results showed the identification of poet for a given poem is attainable with acceptable precision. Moreover, some algorithms are not suitable for this task.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU BARU PADA TK WALADUN SHOLIHUN MENGUNAKAN METODE TOPSIS

The process of selecting new teacher recruitment at Tk Waladu Sholihun generally still uses a manual system, where the determination is based on an assessment of 5 missing criteria, using a computerized system, resulting in inaccurate or less maximal problems arising by the school and in selecting recruitment new teacher.The system that will be used to perform analysis and research is using the TOPSIS method. The criteria used in determining decision making are work experience, ability test, interview test, latest education, age. The Decision Support System determines the acceptance of this new teacher based on the desktop using the PHP programming language and the MySQL database as data storage.This system produces the best choice of teacher selection from the alternative options produced. The final result of the TOPSIS method produces the best alternative choice that is determined. This system makes the process of determining the selection of new teacher recruitment computerized
In This Issue 75:2

The Reading TeacherVolume 75, Issue 2 p. 133-134 In this Issue In This Issue 75:2 Tanya S. Wright, Corresponding Author Tanya S. Wright Senior Editor tanya.sb.wright@gmail.com Correspondence tanya.sb.wright@gmail.comSearch for more papers by this authorPatricia A. Edwards, Patricia A. Edwards Co-editor edwards6@msu.edu Search for more papers by this authorShireen Al-Adeimi, Shireen Al-Adeimi Associate Editor aladeimi@msu.edu Search for more papers by this authorJungmin Kwon, Jungmin Kwon Associate Editor jkwon@msu.edu orcid.org/0000-0002-2151-5690 Search for more papers by this authorLaura Tortorelli, Laura Tortorelli Associate Editor ltort@msu.edu Search for more papers by this author Tanya S. Wright, Corresponding Author Tanya S. Wright Senior Editor tanya.sb.wright@gmail.com Correspondence tanya.sb.wright@gmail.comSearch for more papers by this authorPatricia A. Edwards, Patricia A. Edwards Co-editor edwards6@msu.edu Search for more papers by this authorShireen Al-Adeimi, Shireen Al-Adeimi Associate Editor aladeimi@msu.edu Search for more papers by this authorJungmin Kwon, Jungmin Kwon Associate Editor jkwon@msu.edu orcid.org/0000-0002-2151-5690 Search for more papers by this authorLaura Tortorelli, Laura Tortorelli Associate Editor ltort@msu.edu Search for more papers by this author First published: 08 October 2021 https://doi.org/10.1002/trtr.2055Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume75, Issue2September/October 2021Pages 133-134 RelatedInformation
A Web-Based Visual Analytics System for Traffic Intersection Datasets

The efficiency of a road network may be improved by making the traffic intersections more efficient. A smart traffic intersection is equipped with different sensors from which it is possible today ...
Peningkatan Pemahaman Karyawan Dalam Penggunaan Excel Untuk Pengelolaan Inventory

The obligations to each university as described in the Tridharma of Higher Education are education, research, and community service. One form of this obligation at the Nusa Mandiri School of Information and Computer Management (STMIK) is to carry out community service. This is carried out in relation to the concern of every institution or institution engaged in education by following technological developments and advances. This time, community service was carried out at PT. Jaya Persada Indonesia or JPI is a company that was founded with a spirit of togetherness, seeing opportunities for the development of its property and human resources, so the company is currently focusing on the business area carried out by PT. Jaya Persada Indonesia is a Property Developer, Property Agency (JPI Propertindo). Limited understanding of the Microsoft Excel program for office inventory, therefore the Nusa Mandiri College of Information and Computer Management conducted Community Service in the form of Excel Training Workshop Training for Inventory kator at PT. Jaya Persada Indonesia (PT. JPI) in East Jakarta. With this training, it can help management in managing inventory activities at PT. Jaya Persada Indonesia (PT. JPI).
Assessing and Ranking Cloud Computing Security Risks Based On a Hybrid Approach Based On Pairwise Comparisons

Assessing and Ranking Cloud Computing Security Risks Based On a Hybrid Approach Based On Pairwise Comparisons
Robustness of different loss functions and their impact on networks learning capability.

Recent developments in AI have made it ubiquitous, every industry is trying to adopt some form of intelligent processing of their data. Despite so many advances in the field, AIs full capability is yet to be exploited by the industry. Industries that involve some risk factors still remain cautious about the usage of AI due to the lack of trust in such autonomous systems. Present-day AI might be very good in a lot of things but it is very bad in reasoning and this behavior of AI can lead to catastrophic results. Autonomous cars crashing into a person or a drone getting stuck in a tree are a few examples where AI decisions lead to catastrophic results. To develop insight and generate an explanation about the learning capability of AI, we will try to analyze the working of loss functions. For our case, we will use two sets of loss functions, generalized loss functions like Binary cross-entropy or BCE and specialized loss functions like Dice loss or focal loss. Through a series of experiments, we will establish whether combining different loss functions is better than using a single loss function and if yes, then what is the reason behind it. In order to establish the difference between generalized loss and specialized losses, we will train several models using the above-mentioned losses and then compare their robustness on adversarial examples. In particular, we will look at how fast the accuracy of different models decreases when we change the pixels corresponding to the most salient gradients.
대용량 이미지 데이터베이스를 위한 부분 노이즈제거를 지원하는 윤곽선 매칭 시스템 설계 및 구현

In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.
The Impact of Knowledge Management on the Function of Employee Performance Appraisals in Industrial Companies-Case Studys

The study aimed at identifying the impact of knowledge management on the function of employee performance appraisals (it is one of the most important functions of human resources management) in Jordanian industrial public shareholding companies, relying on the descriptive analytical approach. A questionnaire has been developed and distributed on individuals of the study sample consisting of managers of departments and sections of human resources in each company. The number of questionnaire retrieved and valid for statistical analysis (294) representing (86.5%) of the distributed questionnaires. In order to analyze the study sample, reliance was placed on descriptive statistics, represented in the arithmetic means and standard deviations, in addition to the multiple linear regression analysis in hypothesis testing. The study reached a number of findings, most importantly, the presence of statistically significant impact at the level of (α=0.05) for the knowledge management including its dimensions (knowledge generation, knowledge storage, knowledge sharing, knowledge application) on the function of employee performance appraisals in Jordanian industrial public shareholding companies. The study has recommended that the Jordanian industrial public shareholding companies should follow an efficient evaluation system capable of identifying the employees’ weaknesses.
Union and intersection contracts are hard, actually

Union and intersection types are a staple of gradually typed languages such as TypeScript. While it's long been recognized that union and intersection types are difficult to verify statically, it may appear at first that the dynamic part of gradual typing is actually pretty simple.
Neural networks for long-term earthquake prediction using modified meta-learning

Neural networks have been successfully applied for modeling time series. However, the results of long-term prediction are not satisfied. In this paper, the modified Meta-Learning is applied to the neural model. The normal Meta-Learning is modified by time-varying learning rates and adding a momentum term to improve convergence speed and robustness property. The stability of the learning process is proven. Finally, two experiments are presented to evaluate the proposed method. The first one shows an improvement in earthquakes prediction in the long-term, and the second one is a classical Benchmark problem. In both experiments, the modified Meta-Learning technique minimizes remarkably the mean square error index.
Tailoring the Cyber Security Framework: How to Overcome the Complexities of Secure Live Virtual Machine Migration in Cloud Computing

This paper proposes a novel secure live virtual machine migration framework by using a virtual trusted platform module instance to improve the integrity of the migration process from one virtual machine to another on the same platform. The proposed framework, called Kororā, is designed and developed on a public infrastructure-as-a-service cloud-computing environment and runs concurrently on the same hardware components (Input/Output, Central Processing Unit, Memory) and the same hypervisor (Xen); however, a combination of parameters needs to be evaluated before implementing Kororā. The implementation of Kororā is not practically feasible in traditional distributed computing environments. It requires fixed resources with high-performance capabilities, connected through a high-speed, reliable network. The following research objectives were determined to identify the integrity features of live virtual machine migration in the cloud system: To understand the security issues associated with cloud computing, virtual trusted platform modules, virtualization, live virtual machine migration, and hypervisors; To identify the requirements for the proposed framework, including those related to live VM migration among different hypervisors; To design and validate the model, processes, and architectural features of the proposed framework; To propose and implement an end-to-end security architectural blueprint for cloud environments, providing an integrated view of protection mechanisms, and then to validate the proposed framework to improve the integrity of live VM migration. This is followed by a comprehensive review of the evaluation system architecture and the proposed framework state machine. The overarching aim of this paper, therefore, is to present a detailed analysis of the cloud computing security problem, from the perspective of cloud architectures and the cloud... [Abridged]
Fuzzy model for design decision-making based on interval analysis of heterogeneous information

The article proposes a methodology to select the preferred design solution at the early stages of the life cycle of radio-electronic systems.The technique takes into account heterogeneity and uncertainty of the source data.
Kemampuan Literasi Anak: Bagaimana Peran Serta Orang Tua di Masa Pandemi Covid-19?

Literacy skills in children include the ability to hear, read, write, and speak. Increasingliteracy skills in children can develop children's thinking patterns to be more critical and logical insolving problems, express new ideas, and be able to understand what information is obtained frombooks, audios, and other sources. However, currently, Covid-19 has appeared across the world,resulting in many institutions closing, including educational institutions, to break the chain of Covid-19 spread. The purpose of this study was to analyze how the role of parents in improving literacy skillsin children, especially during the pandemic. One of the guardians of grade one at MIS Paya BujokTunong, Langsa City became the subject of research using this type of qualitative research through anaturalistic approach that is descriptive and interview techniques as a research instrument. Theresults of this study indicate that parents' awareness and ways of improving literacy skills in childrenare very important to capture interest and disinterest in children. In addition, during this pandemic,the learning process is also carried out at home, thus a friendly way using soft intonation can maintainthe mood. children in the process of improving literacy skills in children.
Gender Exposure to Social Media of High School Students

The objective of the study is to determine the extent of exposure of the respondents to social media. Descriptive design was used in this study. Random sampling designs were used in selecting the respondents. The researcher randomly selected 150 respondents (75 male and 75 female) from grade 9 and 122 respondents (61 male and 61 female ) from grade 10 students in Dona Juana Chioco National High School in Lupao Nueva Ecija. Data were collected using the questionnaire. Male and female respondents were mostly 14 years old in grade 9 and 10, and Roman Catholics. The fathers of the respondents were farmers and their mothers were housewives with an average income of P14,000 per month. In terms of ownership of gadgets, almost the same percentage of both male and female respondents owned smartphones/cellphones, tablets, and laptops. With regards to an internet connection, it can be seen that 56.62% of male respondents have an internet connection while 56.62% of female respondents indicated that they have no internet connections. Among those male respondents who have an internet connection, 32% of it uses WiFi, 11% uses broadband and the remaining 8% uses mobile data. Female respondents, on the other hand, use wifi (56.52%), broadband (15.94%), and mobile data (27.54%). In ranking the frequently used media of the male and female respondents, data shows that they were visiting the same social media sites such as Facebook, Google, Youtube, and Yahoo. Among the male respondents, 44.17% of them admitted that they were using social media every day. For the female respondents use 47social media every day. In terms of the hours spent using social media, 41.04% of male respondents were using social media for one hour each day. Female respondents (33.08%), on the other hand, admitted that they were using social media for more than two hours each day.
Real-time Face Mask-Wearing Detection and Temperature Measurement based on a Deep Learning Model

In the context of the COVID-19 outbreak in a global scenario, mandatory mask-wearing and temperature control can effectively prevent its spread and realize self-protection. Therefore, real-time face-mask wearing and temperature measurement technology is of greater importance against the background of infectious disease prevention and control. The present study adopted MobileNet as the backbone of the single-stage RetinaFace framework for real-time face detection and mask-wearing detection. Moreover, the focal loss function of &#x3B1; dynamic value was adopted to avoid the class imbalance problem and improve the classification accuracy in the training stage. Regarding face temperature measurement technology, non-contact and uncooled temperature-sensitive elements were used for temperature measurement, but it was easily affected by environmental variables. Therefore, an SVR model was employed for temperature calibration with the constant temperature blackbody as reference. The alignment errors for the accuracy of face detection, mask wearing detection and temperature correction were 89.58%, 97.84% and 4.85%, respectively. The parameter quantity of the face mask wearing detection model reached 0.42 M, while the computation quantity arrived at 2.039 GFLOPs. The detection model proposed in this study combines real-time mask-wearing detection with face temperature measurement, which can help to quickly measure the body temperature and detect whether one wears face masks properly in the context of COVID-19, so as to reduce the risk of epidemic spread.
Study on Conversion of Relational Databases to Big Data (VoltDB)

We live in a data age, and there is a considerable measure of data streaming between systems, the internet, telephones, and other media. The data is being gathered and put away at remarkable rates.- The measure of accessible data has expanded by a gigantic sum in the previous few years in light of new friendly conduct and immense spread of social framework. Enormous data have assumed a vital part of development and growth. Relational database management systems (RDBMS), its utilization has been fundamentally reached out with new kinds of database systems. In particular, SQL has been discovered to be an incredible inquiry language in profoundly conveyed and versatile systems that cycle Big Data, i.e., datasets with high volume, speed, and assortment. While conventional relational databases address just a a tiny portion of the database systems scene, most database courses that cover SQL think about just the utilization of SQL regarding conventional relational systems.
Extracting Metadata from Jupyter Notebooks

Results from an ESIP FUNding Friday grant to automatically analyze Jupyter notebooks found on GitHub. We developed a Mongo Database to hold notebooks and created extraction jobs to find imported libraries, referenced URLs and the content of Markdown Headers. The presentation was given during the session Completing the Earth Science Cookbook at the Earth Science Information Partners (ESIP) Winter Meeting in January 2019.
FedMM: Saddle Point Optimization for Federated Adversarial Domain Adaptation

Federated adversary domain adaptation is a unique distributed minimax training task due to the prevalence of label imbalance among clients, with each client only seeing a subset of the classes of labels required to train a global model. To tackle this problem, we propose a distributed minimax optimizer referred to as FedMM, designed specifically for the federated adversary domain adaptation problem. It works well even in the extreme case where each client has different label classes and some clients only have unsupervised tasks. We prove that FedMM ensures convergence to a stationary point with domain-shifted unsupervised data. On a variety of benchmark datasets, extensive experiments show that FedMM consistently achieves either significant communication savings or significant accuracy improvements over federated optimizers based on the gradient descent ascent (GDA) algorithm. When training from scratch, for example, it outperforms other GDA based federated average methods by around $20\%$ in accuracy over the same communication rounds; and it consistently outperforms when training from pre-trained models with an accuracy improvement from $5.4\%$ to $9\%$ for different networks.
Inferring Offensiveness In Images From Natural Language Supervision

Probing or fine-tuning (large-scale) pre-trained models results in state-of-the-art performance for many NLP tasks and, more recently, even for computer vision tasks when combined with image data. Unfortunately, these approaches also entail severe risks. In particular, large image datasets automatically scraped from the web may contain derogatory terms as categories and offensive images, and may also underrepresent specific classes. Consequently, there is an urgent need to carefully document datasets and curate their content. Unfortunately, this process is tedious and error-prone. We show that pre-trained transformers themselves provide a methodology for the automated curation of large-scale vision datasets. Based on human-annotated examples and the implicit knowledge of a CLIP based model, we demonstrate that one can select relevant prompts for rating the offensiveness of an image. In addition to e.g. privacy violation and pornographic content previously identified in ImageNet, we demonstrate that our approach identifies further inappropriate and potentially offensive content.
Towards Demystifying Representation Learning with Non-contrastive Self-supervision

Non-contrastive methods of self-supervised learning (such as BYOL and SimSiam) learn representations by minimizing the distance between two views of the same image. These approaches have achieved remarkable performance in practice, but the theoretical understanding lags behind. Tian et al. 2021 explained why the representation does not collapse to zero, however, how the feature is learned still remains mysterious. In our work, we prove in a linear network, non-contrastive methods learn a desirable projection matrix and also reduce the sample complexity on downstream tasks. Our analysis suggests that weight decay acts as an implicit threshold that discards the features with high variance under data augmentations, and keeps the features with low variance. Inspired by our theory, we design a simpler and more computationally efficient algorithm DirectCopy by removing the eigen-decomposition step in the original DirectPred algorithm in Tian et al. 2021. Our experiments show that DirectCopy rivals or even outperforms DirectPred on STL-10, CIFAR-10, CIFAR-100, and ImageNet.