Reviewerly/exHarmony huggingface dataset:
Impact of Information Technology (IT) on Education

Since mid-March 2020, information and communication technology has played an important role in the world of education. After the emergence of a circular letter (SE) regarding the implementation of education during the Covid-19 emergency, the learning system at all levels of education switched to an online learning model. Online is the translation of the term online which means connected to the internet network. While online learning is learning that is carried out online using learning applications and social networks, so that information and communication technology, especially laptops and cellphones (HP) are the main facilities to support the ongoing teaching and learning process. This study tries to present an overview of the role, impact and problems of using information technology in the world of education along with solutions to these problems. This study uses a descriptive qualitative approach whose purpose is to reveal facts, phenomena, variables contained in the research object. The source of the data for this study was from the author's observations at several schools and interviews with several teachers and took data from journals or literature from the internet related to the above theme. The results of this study are: 1) The role of information technology in the world of education, 2) The impact of information technology in the world of education, 3) The problems of using information technology in the world of education and their solutions.
Proactive Headcount and Suspicious Activity Detection using YOLOv8

Global population growth has resulted in more public gatherings, raising concerns on overcrowding and associated safety problems. Among these safety issues, crowd-smashing accidents stand out as unexpected and fast growing situations that pose serious risks to common people. Manually predicting and managing such chaotic situations leads to various challenges. To address these challenges, AI-based monitoring systems are currently used by integrating cutting-edge object detection algorithms such as YOLOv8. This advanced approach provides authorities with real-time crowd density evaluations and instant alerts, enabling immediate action and accident avoidance. Notably, the proposed model extends its capabilities beyond crowd counting by encompassing the detection of abnormal activities such as weapons, fires, falls, and smoke within the crowd. By identifying potentially dangerous crowd densities and quickly detecting abnormal incidents, the proposed system not only prevents disasters such as crowd smashing but also strengthens security measures, putting public safety at the forefront of the challenges posed by a growing global population and crowded public spaces.
Dynamic Ensemble Learning with Evolutionary Programming and Swarm Intelligence for Image Classification

Image classification is a fundamental and pervasive task in the field of computer vision, with profound implications across a wide array of applications. From the autonomous vehicles navigating complex urban environments to the critical diagnosis of diseases through medical image analysis, the accurate categorization of images plays a pivotal role in modern technology and society. As the world becomes increasingly digitized, the volume of image data generated daily continues to soar. This exponential growth necessitates the development of robust and adaptive image classification techniques capable of handling the complexity and diversity of this data. In response to these evolving demands, this work introduces a pioneering approach known as Dynamic Ensemble Learning with Evolutionary Programming and Swarm Intelligence (DEL-EPSI). DEL-EPSI represents a transformative paradigm shift in the realm of image classification, offering a multifaceted solution to address the multifarious challenges posed by this critical task. At its core, DEL-EPSI combines the strengths of dynamic ensemble learning, evolutionary programming, and swarm intelligence to provide a holistic, adaptive, and highly accurate image classification framework. In this study, the principles, capabilities, and implications of DEL-EPSI are studied and investigated.
Portable Modular Pool Prototyping for Military Training and Testing in Remote Environment

Military training and periodical assessment are essential in preparing a well-built soldier physically and mentally for the Malaysian Army Forces. One of them is the water survival training test. The test consists of a series of aquatic tasks conducted in a remote area. As a matter of fact, it requires a water-pool facility which is durable and portable. Therefore, this research aims to design and prototype a modular portable swimming pool for floating exercises and assessment for military personnel. The study is conducted in partnership with the Royal Army Engineers Regiment, with the objectives being to design, simulate, prototype, and build the modular portable swimming pool for military uses. The study output wastested, evaluated, and appraised on the swimming pool workability on site. The methodology is divided into 4 phases; (i) Content Studies and Design Profiling, (ii) Digital Prototyping, Evaluation and Testing, (iii) Physical Prototyping -Materials and Method; and (iv) Application and Measurement. The study seeks to maximise the training of military personnel and reduce the transportation time and resource costs of building a permanent pool by placing it with a modular portable swimming pool, which is more cost-efficient and user-friendly. The findings of Phase 1 prototyping required improvement on design structure where it indicated disadvantages in sustaining water volume ability at targeted level due to structural integrity. Upon improvement of Phase 2 prototyping using metal with bracing framethe pool are able to sustain the desired water volume at 5200 l3. Moreover, this study, also responds to the approaches of the Sustainable Development Goals (SDGs) 9, 16 and 17 towards harmonizing and interconnecting the three core elements of sustainability, economic growth, social inclusion, and environmental protection for human well-being.
Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods

Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a publicly accessible repository containing 2,346 images. Both methods were compared and evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was 6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a reference for further studies in developing new methods to enhance the accuracy of identification.
Development and Effectiveness Verification of a Group Counseling Program to Improve Mind-reading for Elementary School Students

This study was conducted to examine whether a group counseling program to improve mind-reading was effective in improving mind-reading and peer relationships in elementary school children. First, after implementing the mind reading improvement group counseling program developed in this study, dependent samples t-test was conducted for each of the experimental and control groups. As a result, the experimental group had statistically significant mean differences in interactive reading and mind reading, but the control group did not show significant results. Next, analysis of covariance and multivariate analysis of covariance were conducted to examine whether this program was purely effective in improving mind reading and peer relationships. As a result, the experimental group significantly improved than the control group in both mind reading and its subfactors, namely interaction reading and higher order thought reading, but no significant results were found in peer relationships. Implications and suggestions were presented based on the research results.
Qualitative Study on the Functions and Roles of Si/Gun/Gu Child Care Support Centers Related to Integration of Early Childhood Education and Childcare

ThisIn this study, 10 parents of infants and young children, child care teachers, and directors were surveyed to look back on the roles and functions of childcare support centers and to find out the direction for re-establishing the functions and roles of childcare support centers in the new era of integration of early childhood education and childcare. The analysis results are as follows. First, parents are most satisfied with the part-time childcare service for home childcare support, recognized it as a necessary project and expect that it will fulfill its role as a professional organization that can efficiently provide support to improve the quality of education and care of infant education institutions when early childhood education and childcare are integrated. Second, childcare teachers are satisfied with customized consulting support, healing programs, and substitute teacher support and recognize them as necessary projects. Third, directors are satisfied with substitute teacher and childcare staff training, parent education, and counseling projects, and perceived that childcare teachers and parents are growing through various supports. Integration of early childhood education and childcare requires a retraining process for qualification of existing childcare teachers by taking advantage of the geographical accessibility and convenience of si/gun/gu childcare support centers, and educational support to strengthen the expertise of childcare teachers is expected. This study is significant in that it suggests the direction of the functions and roles of childcare support centers so that various services can be efficiently delivered to infant families and infant education institutions in the region at the time when the integration of early childhood education and childcare began
PENGARUH MEDIA BALOK HURUF TERHADAP KETERAMPILAN MEMBACA PADA SISWA TUNAGRAHITA KELAS II SLB NEGERI PURWOSARI

The objective of the research is to identify disparities in the early reading proficiency of mentally challenged pupils when it comes to letter recognition using letter block media. The ultimate goal is to enhance their reading ability through targeted interventions. The employed approach is a quantitative experimental methodology, involving the gathering of data through observation, interviews, testing, and documentation on students with intellectual disabilities in class II SLB Negeri Purwosari. The data analysis technique employs a paired sample t-test. The research findings indicate that the utilization of letter block media is substantial, as demonstrated by the pretest mean score of 44.62 and the posttest mean score of 76.46. Pre- and post-application data analysis shown a notable enhancement in alphabet letter recognition and early reading skills among class II mentally challenged students at Purwosari State Special School. This conclusion can be drawn by comparing the Ttable value with the Tcount value, where the Tcount value is 0.003 and the Ttable value is 0.05, while employing letter block media
The Study on the Understanding and the Comprehension Strategies on Korean idioms by Undergraduate/Graduate Students Chinese learners in Korea

The purpose of this paper is to find out the recognition, understanding, and comprehension strategies of Undergraduate/Graduate Students Chinese learners' idiomatic expressions who are learning Korean language and live in korea. An idiomatic expression is an expression that is difficult to grasp the meaning of as a component and reflects the unique culture and social and cultural experience of the target language. Therefore, it is a difficult expression for foreign learners to understand. This paper was surveyed how Chinese learners recognitze the idiomatic expressions and how they understand them, and which understanding strategies they used. As a result of this survey, Chinese learners say that they lack of education or learning about idiomatic expressions, and among the four types of idiomatic expressions as the type of the same form but different meanings in Korean and Chinese has to have the lowest percentage of correct answers.And as the understanding strategy, a literal strategy or an inference strategy from the context is mainly used. Based on this, it is intended to suggest the necessity of idiomatic expression education targeting Chinese learners and to search for efficient educational methods.
A Study on the Higher Education Institutional Variables Affecting the Dropout of College Students

This study aims to identify variables related to higher education institutions that significantly affect the dropout rate in college students. To this end, 2022 data was collected from the college alert site operated by the Ministry of Education and the Korea Council for Higher Education, and two waves of multiple regression analyses were collected using the dependent variable “dropout rate” and seven independent variables (average tuition, scholarships per student, competition rate, number of students per full-time faculty member, secured teaching position, grade point average (percentage), and employment rate). The first analysis was conducted on all 219 universities selected for the study, categorized by institutional type (public, private) and region (metropolitan, non-metropolitan), and the second analysis was conducted by crossing institutional type and region, and reclassifying them into four categories. The findings are as follows. First, “grade point average (-)” was the only variable that significantly affected the dropout rate in college students in schools classified as public in the first analysis and those classified as public in the metropolitan area in the second analysis. Second, “competition rate” and “secured school site” were the variables that affected the dropout rate in college students in all analyses except in the case of public universities. Third, “scholarship per student” was found to be a negative factor related to the dropout rate in the case of private universities in the metropolitan area. Fourth, the variables “number of students per full-time faculty member” and “employment rate” did not show any statistical effect in this study. Discussion of the findings and suggestions for future research on college student dropout are presented in the conclusion.
NiaNetAD: Autoencoder architecture search for tabular anomaly detection powered by HPC

In the era of Industry 4.0, predictive maintenance (PdM) has become increasingly important for ensuring the efficient and reliable operation of machinery. However, the complexity and diversity of industrial datasets acquired from these sensors can make it challenging to detect anomalies and predict when preventative maintenance is necessary. The proposed NiaNetAD (Nature-Inspired Algorithms for Deep Neural NeEtwork creaTion for Anomaly Detection) method addresses this challenge by using nature-inspired algorithms (NIAs) to construct an auto-encoder (AE) neural network, which identifies anomalies in the machine’s operation. The anomalies in this case refers to unusual patterns in the machine state, which could indicate an eventual breakdown. By using NiaNetAD on industrial datasets, it can help identify potential failures and schedule maintenance before they occur, reducing downtime, improving equipment lifespan, and lowering maintenance costs. The results of the NiaNetAD experiment, which was conducted on a high-performance computing (HPC) platform with multiple NIAs, demonstrated the significance of an effective and dispersed search strategy when constructing an AE architecture for a specific task. When applied to unsupervised anomaly detection (AD) on a fault detection dataset, the results indicate that improving the AE’s reconstruction loss, reducing the bottleneck size, and avoiding excessive complexity in the topology can result in better outcomes.
Leveraging Programmability of Photonic Devices Towards Agile and Autonomous Optical Networks

The emergence and consolidation of increasingly programmable optical devices (transceivers, amplifiers, multiplexers), which allow their remote configuration and control into multiple modes of operation, is enabling the evolution towards more autonomous networks. Such networks leverage this programmability and are able to adapt and react to traffic and network condition changes, paving the way for the increased adoption of AI/ML models in support of network operation. In this paper, we review key elements in the control and orchestration systems of optical networks in support of autonomous networking, including configuration and optical telemetry aspects and we present some uses cases focusing on our evolving prototype of sliceable bandwidth variable transceiver, focusing on the design of the SDN agent.
ADDITION OF BENTONITE AND CHITOSAN FILLING TO POLYURETHANE COATING PAINT IN AN EFFORT TO INCREASE RESISTANCE AGAINST HEAT AND ANTIBACTERIA

The increasing market demand for polyurethane is due to its various functions, including being used as a coating material, adhesives, elastomers and hard foams.Polyurethane is a polymeric material that contains a functional group (-NHCOO-) in its main chain. In principle, polyurethane can be produced by reacting two reactive materials, polyol and isocyanate. Various researches are intensively carried out to synthesize polyurethane in order to improve the performance of polyurethane.Bentonite and chitosan are widely used as filler materials in polyurethane matrices to retain heat and improve the mechanical properties of polyurethane. The addition of bentonite and chitosan to polyurethane will affect the mechanical properties, thermal properties, and morphological structures which can be seen based on the FTIR test, TGA test, SEM test and Antibacterial test.In this study, variations of Bentonite and Chitosan 5.7.9% will be used, then the mixing process with Polyurethane will be carried out. The results showed that the results of the FTIR analysis showed that the mixing of Polyurethane and bentonite did not affect the wavelength absorption of the Polyurethane functional groups themselves. This was due to the mixing process which took place only physical bonds without chemical bonds. The results of thermogravimetric analysis (TGA) showed changes in the weight of the material when heated and it could be concluded that the best sample was B:K 9%, because it experienced less weight loss. Based on the results of the Morphological Structure test with a Scanning Electron Microscopy (SEM) chitosan chain dispersed well into the bentonite interlayer, as shown by testing using the SEM tool. The results of the antibacterial test showed that the more chitosan and bentonite added, the better the ability to inhibit the growth of Escherichia coli bacteria. In testing the activity of these bacteria it can be seen that chitosan and bentonite are better at inhibiting the growth of Escherichia coli than Staphylococcus aureus.
Automatically Identifying Sentences with Attack Behavior from Cyber Threat Intelligence Reports

In recent years, the automated extraction of attack behaviors from Cyber Threat Intelligence (CTI) reports has proven to be effective in rapidly responding to cyber threats. When extracting attack behaviors from CTI, it is crucial to identify the sentences that describe these behaviors. Identifying attack sentences from reports can be challenging due to semantic overlap between attack and non-attack sentences. To address this problem, We propose a sentence-level attack behavior identification method that integrates the semantics of sentences and labels, called BCC. It effectively identifies sentences in reports that describe attack behavior. Specifically, the BCC model uses the BERT layer to model sentences and labels by learning their similarity. The BCC employs a sliding window in the CNN layer to capture the interaction of IOC-level attack behaviors. During the model training process, we discovered that using one-hot labels leads to overconfidence. To address this issue, we introduced the LCM layer to learn the similarity between sentences and labels, capturing semantic overlap among labels. For experimental evaluation, we collected CTI reports from MANDIANT and UNIT 42 to construct a sentence-level attack behavior dataset. The dataset consists of 8,000 samples from CTI reports as the baseline corpus, and an additional 3,000 positive examples of IOC-level attack behavior sentences were generated using a customized template to prompt ChatGPT, ensuring a balanced distribution of positive and negative samples. On this dataset, the BCC model achieved significant results in identifying attack behavior sentences, with an F1 score of 94.70%. This outperformed baseline models such as EXTRACTOR (94.18%) and TTPDrill (90.98%). To validate the effectiveness of BCC in extracting subsequent attack behaviors, we conducted an experiment using five manually annotated CTIs. The results show that the BCC model significantly improves the accuracy of attack behavior extraction.
Towards Scale-Aware Self-Supervised Multi-Frame Depth Estimation with IMU Motion Dynamics

In recent years, self-supervised depth and ego- motion estimation have attracted extensive research attention. Self-supervised monocular depth estimation can effectively learn information from texture-less and non-Lambertian surfaces. However, the monocular methods' accuracy is limited owing to the depth ambiguity in the imaging principles of monocular cameras. On the other hand, multi-frame methods usually have higher depth accuracy benefitting from the geometric constraints of Multi-View Stereo (MVS). However, MVS is sensitive to non- Lambertian surfaces, moving objects, and texture-less regions, which often leads to wrong estimation. In addition, both methods are difficult to obtain the absolute scale result owing to the essential scale ambiguity of monocular images. To resolve the above problems, we propose the FusionDepth, a scale-aware method that fuses the information from monocular depth estimation, multi-frame depth estimation, and Inertial Measurement Unit (IMU) in this paper. FusionDepth fuses multi-frame depth estimation and monocular depth estimation through an uncertainty mask to realize complementary advantages. It further fuses IMU information with an Extended Kalman Filter (EKF) to learn an absolute scale metric. To run the model in real-time on resource-limited devices, we compact the framework by sharing the encoder and taking two lightweight decoders to lower the expense of pose prediction and the depth prediction of MVS. We verify FusionDepth's effectiveness on the KITTI benchmark and the comparison experiments indicate our method is efficient and accurate.