Reviewerly/exHarmony huggingface dataset:
Pengaruh Lingkungan Keluarga terhadap Kemampuan Literasi Anak Kelas 1 SDIT di Surakarta

ABSTRACT Background: Family is a place for children to grow and develop. The family environment is the oldest educational institution which is informal in nature, which is first and foremost experienced by children as well as natural educational institutions (Wahid, et al., 2020). Literacy is important in child development. Literacy is the absorption of information in the form of knowledge from text and orally, to develop cognitive abilities, through reading and writing (Arsa, et al., 2019). Parents must be able to increase their knowledge and abilities in creating a conducive family environment and conditions to support the child's development process (Framanta, 2020). Objective: This study aims to determine the influence of the family environment on literacy ability in 1st grade students at Islamic school in Surakarta. Method: This study used a cross sectional approach. The sampling technique in this study was purposive sampling. The collected data will be analyzed by univariate and bivariate analysis using the Kendall Tau test. Result: The results of bivariate analysis using the Kendall Tau test indicated a p value of 0.000 where p &lt;0.05 which means that the family environment influences literacy skills. Conclusion: The literacy ability of 1st SDIT children can be influenced by the family environment. Keywords : Family Environment, Literacy Skills, Family Environment Factors, Child Development tagName: - position: - top: - left: - right: - bottom: - display: - fontSize: - fontWeight: - textAlign: - color: - backgroundColor: - overflow: - boxSizing: - transform: - zIndex: - w 768 h 543.313 571.313 796 14 14 14 14
Making Worlds in the Pluriverse Panel at Congress: “Survival through the Pluriverse”

This is a select transcript from a moderated panel titled “Making Worlds in the Pluriverse” held as part of Congress of the Humanities and Social Sciences 2023 at York University in Toronto.
Making Worlds in the Pluriverse Panel at Congress: “Dolleen on the Pluriverse”

This is a select transcript from a moderated panel titled “Making Worlds in the Pluriverse” held as part of Congress of the Humanities and Social Sciences 2023 at York University in Toronto.
Report on the 9th ACM SIGIR / the 13th International Conference on the Theory of Information Retrieval (ICTIR 2023)

The 9th ACM SIGIR / The 13th International Conference on the Theory of Information Retrieval (ICTIR 2023) held in Taiwan co-located with SIGIR 2023. This brief report provides an overview of ICTIR 2023 and introduces the student reviewer program, which provides an opportunity for the students to understand and contribute to the conference review process. Date : 23 July 2023. Website : https://sigir.org/ictir2023/.
Report on the PhD Symposium at CIKM 2023

The PhD Symposium was held successfully at the 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023). A total of 22 people attended online or in person at the whole day event, which included two chairs, six mentors, nine students, and five panelists. Five people attended online, and 17 people attended in person. The sessions contain Chairs' opening and closing addresses, student talks, a panel session, and mentoring sessions. The feedback was very positive, especially about the lively discussion and the quality of work. Date : 21 October 2023. Website : https://uobevents.eventsair.com/cikm2023/phd-symposium-programme---saturday-21st-october.
Report on the 46th ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023): Reflections from the Program Co-Chairs

The ACM SIGIR Conference on Research and Development in Information Retrieval has been experiencing significant growth over the past few years. In 2023, SIGIR received a total of 822 full paper submissions and 1,787 papers/proposals. This has brought several challenges to the conference program organization. This article reports some of the main aspects of the work conducted by the program committee (PC) co-chairs for SIGIR 2023, and discusses emergent issues of the review process and program design. We expect that this document serve as a reference for future PC co-chars and as a starting point for further discussions towards improving the review process and overall conference experience. Date : 23--27 July 2023. Website : https://sigir.org/sigir2023/.
Deep Learning-Based Object Detection Techniques for Self-Driving Cars: an in-Depth Analysis

Self-driving cars are poised to transform transportation, but ensuring safe and reliable autonomous driving via robust object detection remains a critical challenge. This review comprehensively analyses object detection techniques within the self-driving car context. It highlights the significance of object detection focusing on the state-of-the-art deep learning (DL) based approaches. We investigate popular DL-based object detectors, assessing their strengths, weaknesses, and applicability to ensuring safety in self-driving scenarios. The paper explores the various network architectures, benchmark datasets, evaluation metrics and strategies to improve detection accuracy and efficiency. Moreover, we highlight unique challenges posed by self-driving environments and provide important research directions. Our findings underscore the ongoing, pivotal role of object detection as self-driving technology evolves. This paper equips researchers, practitioners, and policymakers with invaluable insights into shaping the future of transportation.
Python-Based Simulation of Non-Gaussian Stationary Random Process with Arbitrary Auto-Correlation Function

Simulation of stationary random processes (time series) is an essential engineering tool for system prototyping, design, and optimization. To create a simulation, a randomly generated time series must have a pre-defined distribution and autocorrelation function (ACF). It is challenging to model non-Gaussian distributions as using a linear filter can alter the target distribution. To address this issue, we wrote a Python package that implements one of the prominent methods in the field of random process simulation. To the best of our knowledge, it is the first publicly available implementation of such a simulator in Python.
Exploring the Effectiveness of Deep Learning Object Detectors for Multi-Class Road Target Detection in Autonomous Driving

This paper provides a comprehensive analysis of deep learning-based object detection algorithms for road target detection in autonomous driving. The study evaluates three popular algorithms - Mask R-CNN, RetinaNet, and YOLOv5 - trained on the BDD100K dataset. Performance is assessed based on mean average precision (mAP) and inference speed. Results show that YOLOv5 achieves exceptional accuracy and fast detection speed, making it suitable for real-time applications. Mask R-CNN demonstrates good accuracy but is computationally intensive, limiting its real-time feasibility. RetinaNet achieves good detection accuracy by focusing on hard samples during training. The study emphasizes the trade-off between accuracy and speed in object detection models. YOLOv5 strikes a balance between the two. The importance of diverse and well-annotated datasets, like BDD100K, for training robust models is highlighted. Future work could explore energy efficiency, model size, and the generalization ability of models across different driving conditions. Advancements in these areas will contribute to the development of accurate and efficient object detection algorithms for autonomous driving systems.
Research on the Evaluation of Teaching Modes of Internet Courses Based on Convolutional Neural Networks

This study presents a convolutional neural network-based evaluation method for Internet course teaching modes, aiming to optimize the teaching approach and enhance its effectiveness. The initial step involves determining the evaluation criteria for each subject and constructing a comprehensive evaluation index system for Internet course teaching modes. Teaching resources are then decomposed using mutual information and contrast angle principles, extracting essential features of Internet course teaching materials. These features are fed into a decision tree classifier to categorize the teaching resources. Leveraging convolutional neural networks with enhanced learning parameters, the evaluation scores for the Internet course are derived, ultimately establishing the teaching model's evaluation level. Experimental findings demonstrate the efficacy of this evaluation model. In the test dataset, the evaluation model's decision coefficient is 0.97, indicating a robust fitting effect. In the training dataset, the training error remains low at 0.26, and the evaluation accuracy exceeds 98.61%. The evaluated Internet course showcases commendable teaching outcomes.
Exploration of the Precise Management Mode of College Student Service Based on Ant Colony Algorithm

This article addresses the issue of unsatisfactory management effectiveness. Therefore, this article proposes an ant colony algorithm for service-oriented precision management analysis. Firstly, use positive feedback from information to evaluate the management model, and divide indicators according to the requirements of service-oriented precision management to reduce interference factors in service-oriented precision management. Then, feedback the information to service-oriented college students for precise management, forming a service-oriented precise management plan, and conducting a comprehensive analysis of the results of service-oriented precise management. MATLAB simulation shows that under certain evaluation criteria, ant colony algorithm is accurate in service-oriented and precise management of college student management.
Reconstructing Noised Images of Fashion-MNIST Dataset Using Autoencoders

In this paper, we delve into the potential of a structured latent space for enhanced data representation, particularly advantageous for tasks such as categorization with sparsely labeled data, effective label selection, and class morphing. Our research encompasses evaluations conducted on diverse benchmark datasets, including ImageNet, MNIST, Fashion-MNIST, DeepFashion2, and a collection of 3D human forms, demonstrating the versatility of our approach. Within the context of Denoising Autoencoder methodologies, our primary aim was to grasp and acquire input representations capable of disregarding inconsequential changes and comprehending the entire image. We introduced noise to the images and employed denoising autoencoders to reconstruct the original images, eliminating noise. Our findings show that a learning rate of 0.1 and 1000 hidden layer nodes were optimal for our model. The model excelled at reconstructing images from the test set when trained with 20% noisy images but struggled to generalize with 70% noise due to overfitting. Training with noisy images yielded improved testing accuracy, and increasing the number of samples for the classification layer was beneficial. However, our model's performance fell short in comparison to Support Vector Machines (SVM).
A Security Sharing Scheme for Personal Data Based on Block-Chain with Fine-Grained Access Control

In the AI-driven data governance era, privacy and open sharing are critical. Existing data sharing relies on centralized cloud servers, raising privacy and ownership concerns. Encryption and access control help, but they depend on third-party providers' trustworthiness. The BSSPD, a block chain-based security sharing system, addresses this. It uses block chain, cipher text-policy attribute-based encryption (CP-ABE), and the Interplanetary File System (IPFS). Data owners enhance decentralization by storing encrypted data on IPFS. CP-ABE encrypts data addresses and keys as per access policies. Block chain shares data, information, and keys. Only users meeting the criteria can decrypt data. BSSPD offers fine-grained access control and attribute-level revocation, preserving others' access. Cipher text keyword search bolsters user privacy. Security evaluation and EOS block chain simulations validate BSSPD's feasibility. It also performs well in terms of storage and computing overhead, addressing data security and privacy challenges effectively.
Encryption of Accounting Data Based on AES Algorithm

This article proposes an accounting data en-cryption scheme based on AES algorithm, aiming to protect the confidentiality and integrity of accounting data. This scheme uses AES algorithm to encrypt accounting data, ensuring the security of the data during transmission and storage. This article provides a detailed description of the principle, encryption process, and decryption process of the AES algorithm, and analyzes the advantages and feasibility of this scheme. The experimental results show that this scheme can effectively protect the confidentiality of accounting data, and has high security and reliability. Compared to traditional algorithms, the algorithm proposed in this article can more effectively protect data, and the algorithm efficiency is also improved to 99%.
Modal Logic for Relationships between Sets

In this article, we present a modal logic system that allows representing relationships between sets or classes of individuals defined by a specific property. We introduce two modal operators, [a] and &lt;a&gt;, which are used respectively to express “for all A” and “there exists an A”. Both the syntax and semantics of the system have two levels that avoid the nesting of the modal operator. The semantics is based on a variant of Kripke semantics, where the modal operators are indexed over propositional logic formulas (“pre-formulas” in the paper). Furthermore, we present a set of axioms and rules that govern the system and we prove that the logic is correct and complete with respect to Kripke models. In the final section of the article, we discuss potential future work. We consider the possibility of combining our operator with other modalities, such as necessity or knowledge. Additionally, as an example of the utility of our modal operator, we briefly analyze a conveniently adapted Barcan formula within the framework of our system. In summary, we propose combining our modal operator with other ones as a simpler, more compact, albeit less expressive way to address quantified modal logic.
Human Activity Recognition of Millimeter-wave Radar Based on DRCNet

The use of radar for human activity recognition has the characteristics of not being affected by external factors such as illumination and protecting user privacy, and millimeter wave radar as an important sensor has been widely used in attitude recognition. Based on this, a Doppler and Range decision-level Convergence Network model (DRCNet) is proposed in this paper. Firstly, the common human activity data is collected by millimeter wave radar, the doppler-time plot and range-time plot are obtained by preprocessing the original data. Then the convolutional neural network is built according to the characteristics of the generated human activity data set, and finally the final recognition result is output by decision-level convergence. The experimental results show that the recognition accuracy of DRCNet is improved compared with other models. The collected human activity can be effectively identified, and the recognition accuracy reaches 93.00%.
Tinjauan Pengelolaan Arsip di Bagian Unit Administrasi Rumah Sakit Bhayangkara TK II Sartika Asih Bandung

This study aims to determine the Management of Archives in the Administration Unit of Bhayangkara TK II Sartika Asih Hospital Bandung. The method used in this study is a qualitative method with a descriptive approach, while the data collection technique is carried out by observation, interview and literature study. In this study, researchers found problems that occurred in archive management such as (1) Accumulation of letters (2) Inadequate facilities and infrastructure (3) Lack of attention to the storage system (4) Unavailability of archive borrowing cards (5) Lack of maintenance and cleanliness of archives.&#x0D; Based on the problems that occurred above, the researcher tried to provide several suggestions as follows: (1) Minimize the scheduling of incoming mail because it can cause delays in the distribution of letters, (2) Increase the number of cabinets and the separation of the archive room itself, (3) Affirm the 1-door system policy and make SOP for the archive storage system, (4) Apply the procedure for borrowing archives so that archives are not lost, (5) Maintain archives to avoid dust and insects.
Copyright Page

Extract Copyright © 2023 by The University Press of Kentucky Scholarly publisher for the Commonwealth, serving Bellarmine University, Berea College, Centre College of Kentucky, Eastern Kentucky University, The Filson Historical Society, Georgetown College, Kentucky Historical Society, Kentucky State University, Morehead State University, Murray State University, Northern Kentucky University, Spalding University, Transylvania University, University of Kentucky, University of Louisville, University of Pikeville, and Western Kentucky University. All rights reserved. Editorial and Sales Offices: The University Press of Kentucky 663 South Limestone Street, Lexington, Kentucky 40508-4008 www.kentuckypress.com Maps courtesy of Mariann K. Nance. Cataloging-in-Publication data is available from the Library of Congress. ISBN 978-0-8131-9823-1 (hardcover : alk. paper) ISBN 978-0-8131-9926-9 (paperback : alk. paper) ISBN 978-0-8131-9824-8 (epub) ISBN 978-0-8131-9826-2 (pdf) This book is printed on acid-free paper meeting the requirements of the American National Standard for Permanence in Paper for Printed Library Materials. Manufactured in the United States of America. Member of the Association of University Presses
Basics Fashion Photo Editing Module for Fashion Design Students Based on Indonesian National Work Competency Standards

The limitations of the learning media on fashion photo editing materials become the main obstacle to achieving basic digital photo editing competence in the Fashion Photography course. Therefore, it is necessary to develop relevant modules. This study aims to develop an SKKNI-based basics fashion photo editing module for fashion students that can be used in the learning activities of fashion photography. This study is a research and development conducted using the ADDIE model. The subjects of this study were 30 students of the Fashion Design Study Program. The data were obtained from product evaluation by material experts and media experts as well as field trials involving research subjects. These data were analyzed using descriptive quantitative techniques. The result of this study is a basic module for fashion photo editing based on the basic digital photo processing competency sub-unit materials in the Indonesian National Work Competency Standards of Photography. From the material aspect, the developed module was considered valid (0.85) and reliable (0.742). The media aspect of the module was also regarded as valid (0.82) and reliable (0.714). Thus, it can be concluded that the module developed was of expert quality and feasible to use. Based on the percentage of students achieving the minimum passing grade, the developed module is categorized as effective (80%) as a learning media in the basic materials of fashion photo editing.
Peer Teaching for Early Reading of Elementary School Students: Classroom Implementation and Challenges

The purpose of this study was to analyze and describe the application of the peer teaching learning method to students' beginning reading skills. The research method used is qualitative research with exploratory descriptive design. Sources of data in this study were students and teachers in first grade of elementary school who had implemented the peer teaching learning method. Data collection techniques using documentation, interviews, and observation. Data analysis techniques used are data reduction, data display, and data verification. Test the validity of the data using technical triangulation. The results showed that the inclusion of students' initial reading skill aspects included being able to pronounce letters very fluently with no errors, being able to compose letters into words fluently even though there were a few structuring errors, understanding the meaning of words that were read quite fluently because there were several deficiencies in understanding the meaning of words, showing appropriate reading behavior quite fluently, there was activity of repeating words in reading even though not many words were repeated, and activities in decapitation were not quite right there were few errors, so that overall the coverage of reading skills through the peer teaching method analyzed was as large as fulfilling If the criterion is sufficient, in the future it is hoped that there will be a review related to the implementation of learning plans through the peer teaching method so that learning activities that refer to the development of students' reading abilities are achieved.
Document Validation for Cooperation Agreement Documents at The Undiksha Cooperation and Public Relations Agency (Badan Kerja Sama dan Kehumasan) using Local Binary Pattern (LBP) and YOLOv5 Methods

A cooperation agreement document managed by BKK Undiksha is a conventionally managed document. With the implementation of the Kampus Merdeka - Merdeka Belajar curriculum in 2021, the number of incoming cooperation agreement documents has increased rapidly, making the collection of document data much longer and inefficient. Therefore, there is a need for a scheme that can automatically validate collaboration documents. The document validation scheme was developed using the Local Binary Patterns and YOLOv5 methods. The data used in the development is primary data from BKK Undiksha in 2021, where two agencies collaborate. The development result is the document validation model collaboration using the Local Binary Pattern and YOLOv5 methods with the best accuracy results of 95.36%, and the ability of the model to detect the number of seal, stamp and signature components is 90.73%.
Accuracy Analysis of WP, AHP-WP, Entropy-Topsis Methods in Determining Majors

Determination of majors at SMK AR Rahma is still manual and Excel is only used to find average scores. On the other hand, the number of students is around 140 students, so the majors can cause inaccuracies. In this study, the accuracy of the WP, AHP-WP, and ENTROPY-TOPSIS methods was analyzed in determining the majors of SMK AR Rahma’s students. So that it will be known which method is more accurate in producing student majors. In the process of student majors, data are needed in the form of report cards, academic test scores, majors test scores and health scores. The result is that the majors produced by the AHP-WP method are more accurate than the majors produced by the other two methods, respectively by 70.71%, 64.29%, and 62.86%.
The Effect of the Smart Factory Adoption Level of Domestic Companies on Employee Satisfaction

[Purpose]Based on the Information System Success Model (ISSM), we investigated whether there was an average difference in employee satisfaction depending on the level of smart factory introduction. [Methodology]One－way ANOVA and the Scheffe method, a multiple comparison test method, were applied to verify the research hypothesis with a valid sample of 276 copies out of 300 returned questionnaires. [Findings]According to the hypothesis testing results, the level of smart factory introduction by domestic companies resulted in a difference in the average satisfaction of employees. In particular, it was confirmed that as the level of smart factory adoption increases, the employee satisfaction level of the adopting company can improve. [Implications]The results of this study are expected to provide the basic information necessary to secure the feasibility of supporting smart factory advancement projects promoted by the government in the future and to justify government support for smart factories.