Reviewerly/exHarmony huggingface dataset:
The Analysis of Support Vector Machine (SVM) on Monthly Covid-19 Case Classification

Covid-19 is disease caused by the new corona virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The effect of this virus usually causes infection on respiratory system. Covid-19 was rapidly spread globally. Experts said that the factor that caused this to spread rapidly is human mobility. Therefore, several countries create new rules so that it can suppress the spreading of this disease, by prohibiting a large scale gathering, keeping away distance with each other, mandatory rule of using mask, and the prohibition for the entry of their country. This research proposes a performance analysis of Support Vector Machine (SVM) to classify the monthly data of covid-19. The data used in this research is a series of covid-19 data of towns in Bandung from November 2020 until December 2021. From conducting this research It is found that the best accuracy was found on December 2021 with the accuracy of 100%, followed by July and August with the accuracy of 97%, and October with the value of 90%. We can conclude that Support Vector Machine (SVM), is a good method on classifying the monthly covid-19 data.
Task-Guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy

Colorectal cancer remains one of the deadliest cancers in the world. In recent years computer-aided methods have aimed to enhance cancer screening and improve the quality and availability of colonoscopies by automatizing sub-tasks. One such task is predicting depth from monocular video frames, which can assist endoscopic navigation. As ground truth depth from standard in-vivo colonoscopy remains unobtainable due to hardware constraints, two approaches have aimed to circumvent the need for real training data: supervised methods trained on labeled synthetic data and self-supervised models trained on unlabeled real data. However, self-supervised methods depend on unreliable loss functions that struggle with edges, self-occlusion, and lighting inconsistency. Methods trained on synthetic data can provide accurate depth for synthetic geometries but do not use any geometric supervisory signal from real data and overfit to synthetic anatomies and properties. This work proposes a novel approach to leverage labeled synthetic and unlabeled real data. While previous domain adaptation methods indiscriminately enforce the distributions of both input data modalities to coincide, we focus on the end task, depth prediction, and translate only essential information between the input domains. Our approach results in more resilient and accurate depth maps of real colonoscopy sequences.
Local-to-Global Structure-Aware Transformer for Question Answering over Structured Knowledge

Question-answering tasks over structured knowledge (i.e., tables and graphs) require the ability to encode structural information. Traditional pre-trained language models trained on linear-chain natural language cannot be directly applied to encode tables and graphs. The existing methods adopt the pre-trained models in such tasks by flattening structured knowledge into sequences. However, the serialization operation will lead to the loss of the structural information of knowledge. To better employ pre-trained transformers for structured knowledge representation, we propose a novel structure-aware transformer (SATrans) that injects the local-to-global structural information of the knowledge into the mask of the different self-attention layers. Specifically, in the lower self-attention layers, SATrans focus on the local structural information of each knowledge token to learn a more robust representation of it. In the upper self-attention layers, SATrans further injects the global information of the structured knowledge to integrate the information among knowledge tokens. In this way, the SATrans can effectively learn the semantic representation and structural information from the knowledge sequence and the attention mask, respectively. We evaluate SATrans on the table fact verification task and the knowledge base question-answering task. Furthermore, we explore two methods to combine symbolic and linguistic reasoning for these tasks to solve the problem that the pre-trained models lack symbolic reasoning ability. The experiment results reveal that the methods consistently outperform strong baselines on the two benchmarks.
TATA KELOLA REKAM MEDIS ELEKTRONIK BERBASIS TEKNOLOGI INFORMASI DALAM PELAPORAN PASIEN IGD DENGAN METODE AGILE

This system functions to record daily, monthly, and yearly censuses without changing the performance of the computer used because the data is stored using cloud storage. The purpose of this study was to determine the governance of information technology-based electronic medical records in reporting emergency department patients using the Agile method. The recording of daily, monthly, and yearly censuses in the emergency room at Hermina Arcamanik Hospital in Bandung is not optimal due to frequent problems with the local area network (LAN); computers often hang or freeze, and the system has not been updated. Currently, the recording of daily, monthly, and yearly sensors still uses the Visual Basic program, which has the concern that it requires a very large amount of memory during the installation process. Management of information technology-based electronic medical records in reporting emergency department patients using agile methods The agile method used in the development of this program is feature-driven development (FDD). The system that has been made is tested using the black-box testing method. This program only focuses on completing a certain program and produces medical record management for reporting emergency department patients based on information technology in order to speed up the work process and minimize the occurrence of device errors.
Transfer learning and data augmentation in osteosarcoma cancer detection

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.
Reading recall: A comparison of reading comprehension

Researchers query whether reading comprehension is the same, worse, or better when using e-books as compared with standard paper texts. This study evaluated this question in the elementary school population. Our hypothesis was that information would be retained equally whether read from paper or from an electronic device. Each participant read four stories, alternating between electronic and paper media types. After each reading, the participants completed a five-question test covering the information read. The study participants correctly answered 167 out of 200 comprehension questions when reading from an electronic device. These same participants correctly answered 145 out of 200 comprehension questions when reading from paper. At a significance level of p &amp;lt; 0.05, the results showed that there was a statistically significant difference in reading comprehension between the two media, demonstrating better comprehension when using electronic media. The unexpected results of this study demonstrate a shift in children’s performance and desirability of using electronic media as a reading source.
Assessing the Performance of Video Classification Models in Identifying Violent Actions

Abstract: Manual video surveillance takes time and is prone to human error. Thanks to machine learning and deep learning, video analytics automates these processes. Video classification is an important aspect of video analytics. It is critical to detect violent behavior in recordings for surveillance, crime prevention, and public safety. Video classification algorithms are capable of detecting violent acts in video data. This paper introduces readers to the many approaches to video classification and describes the underlying network structure of the 3DCNN, ConvLSTM, and LRCN models, which are commonly used for video classification. Additionally, the models' implementation results were compared in order to conduct a comparative performance analysis of the models for the task of violent action classification. When it comes to classification, F1-Score, AUC score, and accuracy are useful metrics for evaluating models, and they were compared. We discuss some of the difficulties in violent action classification, as well as some potential future opportunities and new perspectives on how to address them and improve the system.
Multi-Robot Task Planning to Secure Human Group Progress

Recent years have seen an increasing number of deployment of fleets of autonomous vehicles.As the problem scales up, in terms of autonomous vehicles number and complexity of their objectives, there is a growing need for decision-support tooling to help the operators in controlling the fleet.In this paper, we present an automated planning system developed to assist the operators in the CoHoMa II challenge, where a fleet of robots, remotely controlled by a handful of operators, must explore and progress through a potential hostile area.In this context, we use planning to provide the operators with suggestions about the actions to consider and their allocation to the robots.This paper especially focus on the modelling of the problem as a hierarchical planning problem for which we use a state-of-the-art automated solver.
Medical image classification of brain tumor using convolutional neural network algorithm

Brain tumor is a disease that is very dangerous for humans where this disease really needs faster and more accurate treatment. This disease requires early detection because it requires fast and accurate medical treatment. Machine learning helps solve problems by leveraging deep learning technology in the branch of machine learning. Deep learning is a technology that can detect, classify, and segment various problems in machine learning. One of the methods used in deep learning is the Convolutional Neural Network. This method is most often used in performing image processing where this method has various types of feature extraction. The purpose of this study was to test the accuracy of using the Convolutional Neural Network method in classifying brain images. The brain image used in this study is an image scanned by Magnetic Resonance Imaging. The dataset in this study was downloaded from the Kaggle website as many as 7023 data consisting of four classes of brain image data, namely glioma, notumor, meningioma, and pituitary classes. The results of this study obtained an accuracy value of 84% so that this research can be used by medical personnel to diagnose brain tumors easily, quickly, precisely, and accurately.
Flex Sensor-Based Hand Gesture Detector

The purpose of this project is to assist in the rehabilitation of those who have mobility problems, especially paraplegic stroke patients and silent people who find it challenging to interact with others in society. Voice assistants, virtual assistants, and other new technologies have been around for a while, yet many people still find it difficult to use them efficiently and realistically. Elderly, physically handicapped, and bedridden individuals are the main target audiences for the initiative. Hospitals and senior living facilities are the project's application areas. Our prototype can manage such scenarios successfully by identifying the user's hand gestures and delivering notifications to caretakers and hospitals during emergencies where the user is unable to use their cell phone. This system was designed to show built-in necessary commands based on finger movement to get over these difficulties. For detecting finger movement, it has flex sensors. Commands like "Emergency" can be conveyed by gesture from the person. An excellent way to deliver orders is by gestures since they can be utilized in noisy environments, they can be used to communicate in public settings, they can assess their own basic needs, and they do not need to rely on others to lead the patients. To translate hand movements into voice, multipurpose hand gloves are used.
Recruiting the Best Teacher Modality: A Customized Knowledge Distillation Method for if Based Nephropathy Diagnosis

The joint use of multiple imaging modalities for medical image has been widely studied in recent years. The fusion of information from different modalities has demonstrated the performance improvement for some medical tasks. For nephropathy diagnosis, immunofluorescence (IF) is one of the most widely-used medical image due to its ease of acquisition with low cost, which is also an advanced multi-modality technique. However, the existing methods mainly integrate information from diverse sources by averaging or combining them, failing to exploit multi-modality knowledge in details. In this paper, we observe that the 7 modalities of IF images have different impact on different nephropathy categories. Accordingly, we propose a knowledge distillation framework to transfer knowledge from the trained single-modality teacher networks to a multi-modality student network. On top of this, given a input IF sequence, a recruitment module is developed to dynamically assign weights to teacher models and optimize the performance of student model. By applying on several different architectures, the extensive experimental results verify the effectiveness of our method for nephropathy diagnosis.
PENCIPTAAN KOMIK DENGAN GAYA PIXEL ART

This study aims to create comics with a pixel art visual style by describing the elements and techniques used, to make pixel art style comics, especially in the form of digital comic strips, it requires knowledge of the techniques of making comics and the elements contained in pixel art itself. The method used is a descriptive qualitative approach with a creation methodology used in the making of this article, the creation method in question makes a work product the focus of research by going through three stages, namely (1) Exploration, (2) Improvisation and (3) Formation. The results showed that the technique of making comics used was to determine the story, determine the format, create characters, compose pictures and stories and determine the storyline. While the pixel art techniques or elements implemented are straight lines, dithering, jaggy style or not using anti-aliasing, and inline. Produce six panels with vertical positions measuring 260x150 pixels by paying attention to three visual elements (illustration, typography and color). This writing is expected to contribute ideas in enriching the results of studies on pixel artstyle and can popularize pixel art style as another part of art, especially as comic art.&#x0D; Keywords: comic, digital, pixel art
Technology for Continuous Cyber Monitoring of Offshore Assets

Offshore assets, such as vessels and oil rigs, are critical components of various industries, including oil and gas, shipping, and renewable energy. As these assets become increasingly connected and reliant on digital systems, the risk of cyber threats and attacks also escalates. To ensure the security and resilience of offshore assets, continuous cyber monitoring is essential. This paper presents an online platform that enables real-time tracking of cyber resilience and policy adherence for vessels operating in offshore environments. The platform utilizes advanced technologies to assess and mitigate cyber risks, allowing operators to proactively monitor and improve the security posture of their assets.
Making use of Digital Image Correlation to identify the true character of the applied load

Digital Image Correlation became one of the dominant optical full-field techniques used in experimental mechanics. It does not require sophisticated equipment to evaluate the full displacement vector field of the specimen under test with great accuracy. In this paper, we would like to put the reader's attention to the DIC technique as a solution that may identify the genuine character of the load applied to the object under test. Our experience with testing wind turbine blades using DIC proves that possibility. Full Text: PDF References E.M.C Jones, M.A. Iadicola, "A good practises guide for digital image correlation" International Digital Image Correlation Society, (2018), CrossRef B. Pan, "Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals", Meas. Sci. Technol. 29, 082001 (2018), CrossRef B. Pan, L.P. Yu, Q.B. Zhang, "Review of single-camera stereo-digital image correlation techniques for full-field 3D shape and deformation measurement", Sci. China Technol. Sci. 61, 2 (2018), CrossRef S. Sharma, N. Iniyan Thiruselvam, S.J. Subramanian, G.S. Kumar, "Computation of strains from stereo digital image correlation using principal component analysis", Meas. Sci. Technol. 32, 105201 (2021), CrossRef IEC 61400-5:2020 Wind energy generation systems - Part 5: Wind turbine, DirectLink IEC 61400-23:2014 Wind turbines - Part 23: Full-scale structural testing of rotor blades, DirectLink
Distance-Metric Learning for Personalized Survival Analysis

Personalized time-to-event or survival prediction with right-censored outcomes is a pervasive challenge in healthcare research. Although various supervised machine learning methods, such as random survival forests or neural networks, have been adapted to handle such outcomes effectively, they do not provide explanations for their predictions, lacking interpretability. In this paper, an alternative method for survival prediction by weighted nearest neighbors is proposed. Fitting this model to data entails optimizing the weights by learning a metric. An individual prediction of this method can be explained by providing the user with the most influential data points for this prediction, i.e., the closest data points and their weights. The strengths and weaknesses in terms of predictive performance are highlighted on simulated data and an application of the method on two different real-world datasets of breast cancer patients shows its competitiveness with established methods.
Pengaruh Penerapan Good School Governance Terhadap Efektifitas Pengelolaan Dana Bantuan Operasional Sekolah di Surabaya

This study aims to test the impact of implementing Good School Governance on the effectiveness of school operational assistance fund management, focusing on SDI Wachid Hasyim Rungkut Kidul, Surabaya. The research method used is a quantitative research approach with a causal associative method, gathering primary data through questionnaires completed by 90 respondents as the research sample, selected using a method called disproportionate random sampling. In this study, a multiple linear regression analysis is performed using SPSS version 23 software to determine the partial influence of the 6 principles of Good School Governance on the effectiveness of School Operational Assistance Fund (BOS) management. The study's findings show that, individually, the principles of Transparency, Accountability, Independence, Fairness, Participation, and Consensus Orientation do not have a significant impact on the effectiveness of School Operational Assistance Fund (BOS) management.
Discovering harmony: unravelling the work-life balance of hotel employees in Oman: an explorative study of luxury hotels in Muscat

Purpose- The main aim of this study is to analyse the work-life balance initiatives practised in Muscat hotels. The study also aims to examine the influence of work-life balance initiatives on employee career development and retention. Methodology- As the objective of this study was to assess and quantify several factors to provide findings, quantitative research was used. A descriptive research design was employed to collect extensive data by thoroughly detailing the topic or testing hypotheses. The sampling technique used in this study was purposeful sampling, with hotel employees in Muscat serving as the major target group. Responses from 164 individuals were gathered for the study using a questionnaire. Findings- Flexible Working Hours (4.27) was the work-life balance practise used in hotels to assist employees in finding a balance between work and family life. Career breaks (4.35) have the highest mean rating for work-life balance practises that motivate employees to stay with their employers. The results show that there is a positive and significant relationship between work-life balance and employees’ career development, with a Pearson's r value of 0.451 and a p-value of 0.000 &lt; 0.01. Further, the Pearson’s r correlation shows that work-life balance and employee retention are positively and significantly correlated, with the obtained Pearson's r value of 0.634 and a p-value of 0.000 &lt; 0.01. Conclusion- The results of this research indicate that hotels that promote work-life balance will reap benefits such as higher job satisfaction, lower turnover rates, possibilities for career advancement, better morale, and higher organisational productivity. Keywords: Work-life balance, luxury hotels, employees, career breaks, work-life balance initiatives JEL Codes: N30, J53, M54