Reviewerly/exHarmony huggingface dataset:
SocialEcho: A Social Networking Platform with Community Guidelines Violation Pre-check

Social media has revolutionized the way people communicate, but it also comes with risks such as the proliferation of misinformation and cyberbullying and violations of community guidelines. To address these concerns, we propose the development of a social networking platform, called SocialEcho, which leverages a combination of advanced filtering techniques and third-party API services, to ensure that all user-generated content complies with established community guidelines. Our platform built using modern tools to create a regulated and safe space for online communication that promotes responsible and respectful interactions among users. In addition, we have implemented an NLP-based feature that automatically categorizes user posts into different channels, enabling users to easily find and engage with content that aligns with their interests. Through these features, SocialEcho enhances the user experience and promotes a more organized and structured community.
PENERAPAN METODE SURF DAN FLANN UNTUK MENDETEKSI TERBITAN SPAM PADA INSTAGRAM

Social media is a new media that utilizes the internet to share information, interact, participate and others, and to be used with each other. Currently there are many social media circulating, one of which is Instagram. At first Instagram was only used to share photos, then along with the development of technology and media, Instagram also developed into being able to share videos and shop on Instagram. Instagram is also one of the social media specifically used to upload images and videos. The growing use of Instagram in supporting promotion makes Instagram faced with various problems, one of which is the emergence of spam issues. For example, the publication of spam on Instagram is published by several sellers of products or the like continuously. It's good to promote a product. But on the other hand, it will interfere with other users if the spam often appears. This is exacerbated by the mass use of popular hashtags, done with the aim of getting more views. Popular hashtags are hashtags that are followed by many Instagram users. Based on these problems, it takes a computer program to detect spam issues based on certain hashtags on Instagram. In this final task, the Speeded-Up Robust Features (SURF) and Fast Library for Approximate Nearest Neighbor (FLANN) methods will be applied to detect spam publications on Instagram. The results of experiments that have been conducted on 12 images that produce 66 comparisons, the application of SURF and FLANN methods can be said to be very good in detecting the similarity of images between Instagram publications that indicate that the same image is a spam issue, which is with a maximum accuracy value of 100%.
Feasibility Study of Collaborative Education Model in Higher Education under the Background of “Internet+”

This study investigates the application of the collaborative education model in higher education within the “Internet+” context. It examines how rapid advancements in information technology have reshaped higher education, fostering the collaborative education model. The study combines literature review and empirical research to analyze the model’s core elements, implementation strategies, and impact on higher education. It delves into the transformations in teaching, learning, and management due to “Internet+”, and defines the collaborative education model’s concept, principles, and significance. Key elements like instructional design, student participation, and teacher roles are explored, highlighting interdisciplinary integration, teamwork, and teacher transformation. The study discusses implementation strategies such as planning, technological support, and faculty training. Empirical research, including case studies, interviews, and observations, assesses the model’s effectiveness. The paper concludes by summarizing findings, acknowledging limitations, and suggesting future research directions for enhancing the collaborative education model in higher education.
Klasterisasi Obat Berdasarkan Tipe dan Komposisi Sejenis pada Bagian Farmasi Rumah Sakit Queen Latifa

One important element in maintaining and improving the quality of healthcare services is the availability of adequate medication. Drugs are a crucial component used in the provision of healthcare services, and the expenses associated with them constitute a significant portion of overall healthcare costs. The implementation of data mining can aid in analyzing drug usage to obtain information that can be utilized for planning and controlling drug inventory, with one of the methods being the utilization of the K-Means algorithm. The K-Means algorithm is the most popular and widely used clustering method in data mining. This research aims to facilitate pharmacy personnel in identifying groups of drug types with similar characteristics and compositions. As a result, the categorization of a large number of drugs can be performed more efficiently and accurately. Moreover, with the grouping of drugs based on similar characteristics and compositions, pharmacy personnel can easily monitor the availability of specific medications and take appropriate actions in managing drug supplies at the hospital.
Membangun Aplikasi Augmented Reality Berbasis Android di Museum Sudirman Magelang

A museum is an institution intended for the general public. One of them is the Sudirman Museum, which is located at Magelang City, Central Java. This museum contains relics of General Sudirman that were used during his war. Each museum has different rules in terms of the interaction between visitors and objects on display in the museum. Sudirman Museum is one of the museums that do not allow not to touch objects directly and display information incomplete on each item in the collection. This study aims to overcome the limitations of interaction between museum visitors and objects on display in the museum. This study develops a mobile application based on an operating system based on Augmented Reality that can add to the attraction of the Sudirman museum for visitors so that it is expected to attract visitors to the museum. The results of this research are in the form of an Android application that simulates the contents of the museum and its information through a 2D display on a cellphone screen. In this way, visitors are expected to experience the experience of being in the museum through Augmented Reality. This Augmented Reality-based Android application is designed using the waterfall method and is made using the Unity3D, Vuforia SDK, and Blender applications
Embracing technology in EFL pre-university classrooms: A qualitative study on EFL learners’ perceptions of intensive and extensive reading approaches

This study attempts to explore the perceptions of Pakistani pre-university EFL learners about the intensive reading (IR) and extensive reading (ER) approaches and the use of technology in these two approaches. This is a qualitative study using a purposive sampling technique. The participants were 10 pre-university undergraduate students enrolled in the foundation program of a public sector university in Pakistan. Researchers used semi-structured interviews and reflective diaries as data collection tools. Thematic analysis and personal interpretation techniques were used to analyze the results. The results showed that after participating in the reading classes, the students had positive perceptions of the IR and ER approaches and found them useful and interesting. The results also showed that the learners felt that their language skills improved significantly after participating in the IR and ER-related activities. Students also found these reading approaches to be complementary to each other. The results also indicated that teachers’ use of intensive reading and extensive reading approaches could make learners skilled and autonomous readers. Ultimately, their reading problems could be solved with these approaches. Moreover, the learners also perceived the use of technology in these two reading approaches as of great benefit to them. The researchers recommend incorporating IR and ER activities into the EFL classroom using digital resources to maximize EFL learners’ reading abilities and other language skills.
The Contribution of the Surabaya State University Library in Promoting Open Access Information Sources

Background of the problem: Library, as a part of universities that create institutional scientific works bears responsibility for serving as both the central database for the scientific publications the library subscribes to and a platform for the management of institutional scientific works. Open access facilitates simpler access to and use of information while hastening the process of information distribution. Purpose: The purpose of this study is to determine how university libraries support open access information sources. Method: In this study, interviews were employed to collect data using a qualitative research methodology. Findings: The data indicated that libraries and librarians promoted the availability of OA information sources through the library page, which can be viewed at https://library.unesa.ac.id. Conclusion: To ensure that lecturers and students have access to a variety of scientific information sources, it is crucial for university libraries to develop and promote open access information sources.
Implementation of the library automation system in high schools in Surabaya City

Background of the study: High school-level school libraries in the city of Surabaya have begun to develop services and facilities, one of which is by building a library automation system. Problems arise about the orientation of the development carried out whether it aims to build benefits or only to meet the needs of the library. Purpose: The purpose of this study is to describe the implementation of the library automation system development that has been carried out by senior high schools in the city of Surabaya Method: The method used in this research is a quantitative approach focusing on the type of descriptive quantitative. The study used a sample of 60 respondents taken from a specified population of 266 high school library managers. The sampling technique used snowball sampling, then data collection using a questionnaire distributed via google form over three weeks. Findings: all variables used gave positive results because the value of the mode generated from each variable showed an answer which could mean that the respondents agreed with the variables that supported the implementation of the high school library automation system in Surabaya. Conclusion: This study resulted in the finding that the library manager has carried out the implementation because of the benefits generated by the library automation system.
Portable Perceptron Network-Based Fast Mode Decision for Video-Based Point Cloud Compression

In Video-based Point Cloud Compression (V-PCC), 2D videos to be encoded are generated by 3D point cloud projection, and compressed by High Efficiency Video Coding (HEVC). In the process of 2D video compression, the best mode of Coding Unit (CU) is searched by brute-force strategy, which greatly increases the complexity of the encoding process. To address this issue, we first propose a simple and effective Portable Perceptron Network (PPN)-based fast mode decision method for V-PCC under Random Access (RA) configuration. Second, we extract seven simple hand-extracted features for input into the PPN network. Third, we design an adaptive loss function, which can calculate the loss by allocating different weights according to different Rate-Distortion (RD) costs, to train our PPN network. Finally, experimental results show that the proposed method can save encoding complexity of 43.13% with almost no encoding efficiency loss under RA configuration, which is superior to the state-of-the-art methods. The source code is available at https://github.com/Mesks/PPNforV-PCC.
The Effect of Adult Participation Motivation for Lifelong Learning on Subjective Happiness through Self-Effect in China

The purpose of this study is to investigate the effect of Chinese adults' motivation to participate in lifelong learning on subjective happiness through self-efficacy. In this study, 798 Chinese adult lifelong learners were surveyed and the SPSS 25.0 program was used for analysis. As a result of the study, first, it was found that the motivation of Chinese adult learners to participate in lifelong learning affects subjective happiness through self-efficacy. This means that learners with clear motivation to participate in lifelong learning and learners with high self-efficacy have higher subjective happiness in lifelong education activities. Second, as a result of structural model analysis, the motivation to participate in lifelong learning had a positive effect on self-efficacy and subjective happiness. Through this, it can be seen that participation motivation is an important factor in fully expressing self-efficacy when adults participate in lifelong education activities. Third, it showed a mediating effect of self-efficacy in the relationship between motivation to participate in lifelong education and subjective happiness. Efforts were needed to improve self-efficacy as well as motivation for participation in order to improve the subjective happiness of adult lifelong learners. This study is meaningful in that it revealed the important factors of self-efficacy as well as the importance of motivation to participate in lifelong learning in the subjective happiness of Chinese adult learners.
Unveiling the Benefits of English-Speaking Club: Insights from Students at One Higher School in Palembang

This qualitative case study aimed to explore students' insights of practicing English speaking through participation in an English-speaking club. The three student participants in this research were selected using a purposive sampling technique. Research data were collected through semi-structured interviews using open-ended questions. Thematic analysis was then employed to analyze the data. The findings indicated that participating in the English-speaking club helps students get a supportive environment for practicing their English-speaking skills through conversation, presentation, speech, storytelling, debate, and other various speaking activities. They experience fellowship and friendship among club members, receive moral and emotional support for their views and opinions, gain more confidence and encouragement to speak English, have more opportunities to express their ideas and thoughts in English, develop their English vocabulary and expressions, receive feedback and corrections from fellow members, and have fun with their English speaking practices.
Embedding Meets Frequency: Novel Approaches to Stopword Identification in Burmese

In this study, we present a comprehensive exploration of stopword extraction techniques tailored for the Burmese language. Utilizing a manually segmented corpus of 212,836 Burmese sentences, we benchmark traditional methods such as term frequency and entropy against our novel proposals, word2vec_frequency and fasttext_frequency. Our findings showcase that these innovative embedding-frequency approaches not only align with the established behavior of Zipf’s law but also offer promising avenues for enhancing text analysis in Burmese and potentially other under-resourced languages.
COORDINATE STRUCTURE OF COMPOUND SENTENCES FOUND IN BRITANNICA.COM ABOUT TITANIC

In this study, the types of coordinating conjunctions used in sentences about the Titanic on Britannica.com are analyzed. This study's objective is to identify the different kinds of coordinating conjunctions used in compound phrases. In this study, the data were analyzed using a descriptive qualitative approach based on the theory put forward by Quirk and Greenbaum (1973). The 52 total data were broken down into three categories—syndetic coordination, asyndetic coordination, and syndetic and asyndetic coordination—that describe the three forms of coordinating conjunctions used in compound sentences. This study specifically discovered all three types of coordination in complex phrases. 27 data are coordinated synthetically, 18 data are coordinated asyndetically, and 7 data are coordinated syndetically and asyndetically. In syndetic coordination, there are 21 data using “and”, 5 data using “but”, and 1 data using “so”. While, in asyndetic coordination there are 15 data using “comma” (,), and 3 data using “semicolon” (;). The last 7 data are using both syndetic and asyndetic coordination.
Noisy Label-Resistant Deep Learning Methods for Cancer Diagnosis in Histopathology Images

Histopathology, one of the anatomical pathology tools, is one of the pathology branches specialising in the histological evaluation of diseased tissues. Histopathological images are examined by pathologists in the diagnosis of cancer and other diseases. Pathologists analyse the general tissue in detail in the diagnostic process, which requires a laborious workload and time. This study proposes a deep learning-based solution for anomaly detection using histopathology images, which is one of the most critical stages in cancer diagnosis. Thus, by developing deep learning techniques to speed up manual evaluations, more effective and close monitoring of sick people is possible. Additionally, the dataset used in this study shows that noisy and mislabeled data are a significant challenge in deep learning models. The accuracy was 0.35 with incorrectly labeled data, and 0.94 with correctly labeled data.
Enhancing South Korea’s Vocational Counselling Certification System: A comparative analysis with Australia

This study examines strategies to enhance South Korea’s vocational counselling certification system, drawing on the Career Development Practitioner scheme in Australia. An analysis is provided of Australia’s model, which involves practice-oriented competencies defined and updated by the Career Industry Council of Australia (CICA). This research recommends several key initiatives to improve provision in South Korea, including enhancing eligibility criteria, implementing maintenance and revocation mechanisms, instituting a centralised governing authority akin to the CICA, and introducing specialised undergraduate and graduate programmes focused on career counselling. These insights contribute to the advancement of South Korea’s vocational counselling certification system.
Multi-behavior Recommendation with Action Pattern-aware Networks

Predicting the next interaction based on an anony-mous short-term sequence is challenging in session-based rec-ommendation. Multi-behavior recommendations aim to capture effective user intention representations by considering session sequences with several action types. However, recent multi-behavior-based approaches for session-based recommendation still have limitations. First, the final prediction for most existing approaches is limited to the next item, ignoring which action the predicted item is associated with. Second, existing approaches consider item sequences and action sequences individually and thus do not explicitly model the action dependencies for a single item. In this paper, we propose a novel session-based recommendation algorithm with Action Pattern-Aware Networks (APANet), which could incorporate both historical item sequences and reformulated item-wise action patterns into the modeling process, and predict the next-best interaction (i.e., next-best item and its associated action) given a short-term anonymous multi-behavior sequence. Comprehensive experiments on three public benchmark datasets demonstrate the effectiveness of the proposed APANet.
Yoga Pose Detection and Identification Using MediaPipe and OpenPose Model

Yoga pose estimation is a crucial component of human pose estimation and it aids fitness freak in honing their yoga poses and avoiding harmful postures. This study describes a method for precise yoga posture detection using OpenCV and mediapipe. In the computer vision library OpenCv’ has variety of functions for video and image processing. And for estimating human posture many pretrained models are used and here MediaPipe is also one of the excellent machine learning frameworks that offers pre-trained models for estimating human posture. The suggested remedy combines the benefits of 2 techniques for creating yoga posture estimation system. The first input to the system takes a user's video and next it processes in MediaPipe to identify landmarks in the human body. To assess the user posture OnenCV is used to compute the angles between the identified landmarks. The technology offers the user immediate feedback on their posture and proposes corrective actions. The repetitions for bicep curls served as the use case for this study. The suggested system can be evaluated using a variety of challenging poses, including malasana, uktasana and jalasana. In various lighting situations, it can effectively estimate the user's position and is resistant to obstructions and background clutter. Fitness enthusiasts can use the method to identify their posture and improve their technique and form thereby lowering their chance of injury.
Enhancing the Performance of Facial Recognition Technology

Facial Expression Recognition became popular both in research and in industry. We’ll discuss the fascinating use of computer vision in this project. In the context of smart interaction between people and computers, the ability to recognize human emotion from facial expressions is extremely important. However, several characteristics, including inadequate lighting, quick actions, stances and perspectives, and occlusion influence the precision of facial emotion identification. Convolutional neural network and deep neural network models can be improved because they are continuously trained on fresh data sets, and are frequently more extensive and of more excellent quality. In this article, we offer a technique for feature extraction using the deep residual network ResNet-50, which integrates convolutional neural networks for the identification of facial emotions. Accuracy, precision, recall, and F1-score are just a few of the performance indicators that this micro document delivers a thorough examination of for every model. It accurately recognizes emotions and can help facial expression recognition algorithms become more accurate.
A New Approach Towards Deployment and Management of Machine Learning Models Using K-serve Platform for Building Robust Machine Learning Systems

Kserve, commonly referred to as KFServing, is an open-source serving framework for machine learning (ML) models that runs on Kubernetes. enables the deployment, servicing, and management of ML models in a production environment in a scalable and effective manner. Kserve resources make it easier to develop scalable and reliable machine learning serving systems by streamlining the deployment and management of ML models in production. With contributions from several organisations and developers in the ML community, it is a dynamic open-source project. The influence that Kserve’s work has had on the Data Science community is extensively covered in this research. This research also discusses the Kserve design, elements, and features. The additional Kserve platforms that aid in the deployment and administration of machine learning models for creating reliable machine learning systems have also been brought to light.