Reviewerly/exHarmony huggingface dataset:
Pengaruh Kemampuan Berbahasa Inggris dan Pemahaman Dasar Akuntansi terhadap Hasil Belajar MYOB pada Mata Kuliah Komputer Akuntansi

MYOB (Mind Your Own Business) is a software program that is useful for users to make financial reports assisted by a computer system quickly, precisely and accurately. The purpose of this study was to determine the factors that influence MYOB learning outcomes in the Computer Accounting course, the factors used in this study were English language skills and a basic understanding of accounting. The approach used is a quantitative approach to the survey method. Sampling used a purposive sampling technique, namely sampling based on predetermined criteria. The sample results were 100 Computerized Accounting students at Mataram University of Technology to fill out a questionnaire. Data processing in this study used SPSS 26.00 by testing multiple linear regression. Hypothesis testing uses the F test, t test and R square test. The results obtained from the F test (6.202) with a significance level of 0.001 &lt;0.05. This means that all the independent variables used in this study have an influence on MYOB learning outcomes in the Computer Accounting course. The results of the t test concluded that H1 and H2 were accepted. The results of the R Square test show that the independent variables explain 35.2% of the dependent variable, while 64.8% are explained by other variables outside this research model.
Klasifikasi Kesiapan Anak Taman Kanak-Kanak Masuk Sekolah Dasar Menggunakan Metode Naive Bayes

In accordance with government regulations that prospective new students in grade 1 (one) elementary school must meet the age requirements of 7 (seven) years; or at least 6 (six) years on July 1 of the current year. In addition to the age factor, there are other aspects that affect the readiness of kindergarten children to enter elementary school, namely cognitive, gross motor, fine motor, language, and social emotional aspects. However, many students enter elementary school although there are still some aspects that cannot be fulfilled. Therefore, a classification is needed to find out whether these students have readiness to enter the elementary school level or not. The classification of children's readiness to enter elementary school in this study uses the nave Bayes method, with attributes used include age, ability to read, write, count, and independence. The results of this study are to determine the readiness of students to enter elementary school so that later students and teachers will not be overwhelmed in participating in teaching and learning activities. Based on the test results, obtained an accuracy value of 94.23%. From the test results, it can be concluded that the Naïve Bayes method can be used to classify students' readiness to enter elementary school.
TECHNICAL STUDY OF PT XYZ SHIPPING FLOW OF COAL BARGE TO INCREASE DWT (DEADWEIGHT TONNAGE) CAPACITY FROM 5,000 DWT TO 10,000 DWT

Coal is one of the important energy source commodities to carry out industrial activities, which require supporting infrastructure that uses shipping lanes as a means of activity in the delivery of coal from producers to consumers. PT XYZ, in this case as a coal producer, has allocated one of the areas used for coal loading facilities from one of the coal mining located in Gurimbang Village, Sambaliung District, Berau Regency, East Kalimantan Province by using the Berau river channel to the transshipment location in Muara Pantai, Pulau Derawan District, Berau Regency. PT XYZ has obtained a Terminal Permit for Own Interests in the Working Environment and Interest Areas of Tanjung Redeb Port to Support Business Activities in the Coal Mining Sector PT XYZ “TUKS” through the Decree of the Director General of Sea Transportation Number: BX-144/PP 008 concerning Approval Terminal Management for Own Interests in the Work Environment Area and Interest Area of Tanjung Redeb Port to Support Business Activities in the Coal Mining Sector with a capacity of 5,000 DWT. Based on the results of the study of the TUKS flow plan, the position of the general shipping lane, the bathymetry survey of the barge path, the depth of the TUKS pool, the planned loading and unloading volume, the dimensions of the ship that will dock and unload, the frequency of ship visits, the availability of Shipping Navigation Assistance Facilities (SBNP), as well as observations at several points that will become obstacles in shipping navigation, it can be concluded that the development/increase of the DWT TUKS Gurimbang capacity of PT XYZ to support operational activities is still safe enough to be increased to a payload capacity of 8,000 MT with a maximum draft of submerged ships/barges at a depth of -4.70 meters. Therefore, this research can be continued for the preparation of technical review documents as a requirement to fulfill PM 89 of 2018 concerning Norms, Standards, Procedures, and Criteria for Electronically Integrated Business Licensing for the Transportation Sector in the Sea Sector in the process of adjusting DWT (Dead Weight Tonnage) at the Terminal for Self Interest/TUKS PT XYZ in Gurimbang Village.
ANALYSIS OF THE SUITABILITY OF GRADE PARAMETERS, ANGLE OF TURN AND WIDTH OF PIT A MINE ROAD TO ENSURE THE SAFETY AND SECURITY OF THE MINE UNIT USING AERIAL PHOTOGRAPHIC DATA

One of the applications of survey science that exists in mining is aerial photo surveys. Data from aerial photo surveys can be used and utilized in various jobs, one of which is monitoring work including monitoring the safety of mining roads. Monitoring of mining roads aims to ensure the security and safety of workers in the process of mineral mining. In this study, the implementation of an aerial photo survey to provide an evaluation of the safety of mining roads based on the Decree of the Minister of Energy and Mineral Resources No. 1827 K/30/MEM/2018. The evaluation of the safety of mining roads is based on three parameters, namely road grade, road width, and road turn angle. Planning for the evaluation of the mine road was carried out through the data collection stage through an aerial photo survey on July 12, 2022, and data processing using Agisoft Metashape and ArcGIS. The output in data processing is in the form of a mine road safety map per parameter. Based on the results obtained in the classification of road grade values, a grouping was carried out with 4 classes, namely the very safe class, the safe class, the warning class, and the hazard class. For road grade parameters, 31% of mining roads at pit A site Alhasanie PT Equalindo are included in the dangerous category. In the width parameter of the mining road, 70% of the status is safe and has been in accordance with the Decree of the Minister of Energy and Mineral Resources No. 1827 K/30/MEM/2018. In the parameters of the turning angle of the mining road, 73% of the status is safe and has been in accordance with the Decree of the Minister of Energy and Mineral Resources No. 1827 K /30/MEM/2018.
ANALISIS PENULISAN TANDA BACA, HURUF KAPITAL, DAN KATA TIDAK BAKU (TATA EJAAN) PADA CHATTING WHATSAPP

Spelling is an important aspect of using the correct Indonesian language. This study aims to describe the writing of punctuation, capital letters, and non-standard words (spelling) in WhatsApp chats. This research is a qualitative descriptive study. The results of this study are in the form of punctuation errors, including periods, commas, and question marks. Mistakes in writing capital letters, including errors in writing capital letters at the beginning of sentences, writing capital letters as elements of people's names, and writing capital letters as month and day names. Errors in writing non-standard words, including words that are not standard, because of the variety of spoken language.
Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data

&lt;p&gt;This paper attempts at improving the accuracy of Human Action Recognition (HAR) by fusion of depth and inertial sensor data. Firstly, we transform the depth data into Sequential Front view Images(SFI) and fine-tune the pre-trained AlexNet on these images. Then, inertial data is converted into Signal Images (SI) and another convolutional neural network (CNN) is trained on these images. Finally, learned features are extracted from both CNN, fused together to make a shared feature layer, and these features are fed to the classifier. We experiment with two classifiers, namely Support Vector Machines (SVM) and softmax classifier and compare their performances. The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities. Experimental results on UTD-MHAD and Kinect 2D datasets show that proposed method achieves state of the art results when compared to other recently proposed visual-inertial action recognition methods.&lt;/p&gt;
Inertial Sensor Data To Image Encoding For Human Action Recognition

&lt;p&gt;Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework. These four types of activity images are Signal Images (SI), Gramian Angular Field (GAF) Images, Markov Transition Field (MTF) Images and Recurrence Plot (RP) Images. Furthermore, for creating a multimodal fusion framework and to exploit activity image, we made each type of activity images multimodal by convolving with two spatial domain filters : Prewitt filter and High-boost filter. Resnet-18, a CNN model, is used to learn deep features from multi-modalities. Learned features are extracted from the last pooling layer of each ReNet and then fused by canonical correlation based fusion (CCF) for improving the accuracy of human action recognition. These highly informative features are served as input to a multiclass Support Vector Machine (SVM). Experimental results on three publicly available inertial datasets show the superiority of the proposed method over the current state-of-the-art.&lt;/p&gt;
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks

&lt;p&gt;Screening mammograms is the gold standard for detecting breast cancer early. While a good amount of work has been performed on mammography image classification, especially with deep neural networks, there has not been much exploration into the confidence or uncertainty measurement of the classification. In this paper, we propose a confidence measurebased evaluation metric for breast cancer screening. We propose a modular network architecture, where a traditional neural network is used as a feature extractor with transfer learning, followed by a simple Bayesian neural network. Utilizing a twostage approach helps reducing the computational complexity, making the proposed framework attractive for wider deployment. We show that by providing the medical practitioners with a tool to tune two hyperparameters of the Bayesian neural network, namely, fraction of sampled number of networks and minimum probability, the framework can be adapted as needed by the domain expert. Finally, we argue that instead of just a single number such as accuracy, a tuple (accuracy, coverage, sampled number of networks, and minimum probability) can be utilized as an evaluation metric of our framework. We provide experimental results on the CBIS-DDSM dataset, where we show the trends in accuracy-coverage tradeoff while tuning the two hyperparameters. We also show that our confidence tuning results in increased accuracy with a reduced set of images with high confidence when compared to the baseline transfer learning. To make the proposed framework readily deployable, we provide (anonymized) source code with reproducible results at &lt;a href="https://git.io/JvRqE" target="_blank"&gt;https://git.io/JvRqE&lt;/a&gt;. &lt;/p&gt;
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors

&lt;p&gt;Multimodal fusion frameworks for Human Action Recognition (HAR) using depth and inertial sensor data have been proposed over the years. In most of the existing works, fusion is performed at a single level (feature level or decision level), missing the opportunity to fuse rich mid-level features necessary for better classification. To address this shortcoming, in this paper, we propose three novel deep multilevel multimodal fusion frameworks to capitalize on different fusion strategies at various stages and to leverage the superiority of multilevel fusion. At input, we transform the depth data into depth images called sequential front view images (SFIs) and inertial sensor data into signal images. Each input modality, depth and inertial, is further made multimodal by taking convolution with the Prewitt filter. Creating "modality within modality" enables further complementary and discriminative feature extraction through Convolutional Neural Networks (CNNs). CNNs are trained on input images of each modality to learn low-level, high-level and complex features. Learned features are extracted and fused at different stages of the proposed frameworks to combine discriminative and complementary information. These highly informative features are served as input to a multi-class Support Vector Machine (SVM). We evaluate the proposed frameworks on three publicly available multimodal HAR datasets, namely, UTD Multimodal Human Action Dataset (MHAD), Berkeley MHAD, and UTD-MHAD Kinect V2. Experimental results show the supremacy of the proposed fusion frameworks over existing methods.&lt;/p&gt;
Detection of Breast Cancer using Deep Learning Techniques

Breast cancer poses a global health challenge due to the high false-positive rates associated with traditional mammographic diagnostics.This study introduces a novel model using infrared digital imaging to exploit the thermal difference between healthy and tumorous breasts for early cancer detection.This study employed a Support Vector Machine model and a fine-tuned Deep Neural Network (DNN) to classify thermal images as cancerous or healthy, using a dataset of 67 diverse subjects.Images with confidence levels between 0.5 and 0.6 were directed to the SVM for further prediction, improving diagnostic accuracy.The model's utility was validated on a new set of 12 breasts.In this study, the results showcase the potential of infrared imaging in breast cancer detection and suggests improvements in thermal conduction levels and advocating for further model refinement research.
Big Data Service Design : From Collection to Analysis in the Cloud Computing Ecosystem

As one of the leading evolutionary trajectories in the information domain, big data innovation has profound applicability for information mining, data analysis, and data sharing within voluminous datasets.It leverages the latent value of data to generate substantial economic benefits while informing decision-making strategies for socio-economic development.Big data solution design, a novel service model that views data as a resource, orchestrates the collection and processing of data from diverse sources.This paper aims to provide a concise overview of the essential elements of big data solution design and its technical processing framework with a focus on data collection and storage.For this, this paper embarks on a comprehensive review of big data processing and analysis strategies, cognizant of the distinct service requirements that aim to provide invaluable information to solution consumers.It then presents an all-inclusive cloud computing solution system predicated on big data with a summary of a variety of big data application scenarios across disparate fields.
CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors

&lt;p&gt;Convolutional Neural Network (CNN) provides leverage to extract and fuse features from all layers of its architecture. However, extracting and fusing intermediate features from different layers of CNN structure is still uninvestigated for Human Action Recognition (HAR) using depth and inertial sensors. To get maximum benefit of accessing all the CNN's layers, in this paper, we propose novel Multistage Gated Average Fusion (MGAF) network which extracts and fuses features from all layers of CNN using our novel and computationally efficient Gated Average Fusion (GAF) network, a decisive integral element of MGAF. At the input of the proposed MGAF, we transform the depth and inertial sensor data into depth images called sequential front view images (SFI) and signal images (SI) respectively. These SFI are formed from the front view information generated by depth data. CNN is employed to extract feature maps from both input modalities. GAF network fuses the extracted features effectively while preserving the dimensionality of fused feature as well. The proposed MGAF network has structural extensibility and can be unfolded to more than two modalities. Experiments on three publicly available multimodal HAR datasets demonstrate that the proposed MGAF outperforms the previous state of the art fusion methods for depth-inertial HAR in terms of recognition accuracy while being computationally much more efficient. We increase the accuracy by an average of 1.5 percent while reducing the computational cost by approximately 50 percent over the previous state of the art.&lt;/p&gt;
Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning

&lt;p&gt;Facial expressions of emotion are a major channel in our daily communications, and it has been subject of intense research in recent years. To automatically infer facial expressions, convolutional neural network based approaches has become widely adopted due to their proven applicability to Facial Expression Recognition (FER) task.On the other hand Virtual Reality (VR) has gained popularity as an immersive multimedia platform, where FER can provide enriched media experiences. However, recognizing facial expression while wearing a head-mounted VR headset is a challenging task due to the upper half of the face being completely occluded. In this paper we attempt to overcome these issues and focus on facial expression recognition in presence of a severe occlusion where the user is wearing a head-mounted display in a VR setting. We propose a geometric model to simulate occlusion resulting from a Samsung Gear VR headset that can be applied to existing FER datasets. Then, we adopt a transfer learning approach, starting from two pretrained networks, namely VGG and ResNet. We further fine-tune the networks on FER+ and RAF-DB datasets. Experimental results show that our approach achieves comparable results to existing methods while training on three modified benchmark datasets that adhere to realistic occlusion resulting from wearing a commodity VR headset. Code for this paper is available at: &lt;a href="https://github.com/bita-github/MRP-FER" target="_blank"&gt;this https URL&lt;/a&gt;&lt;/p&gt;
A Pragma-Dialectical Study as a Directive Speech Act Pattern of a Lecturer to the Students in an Online Learning

This study aims to describe the patterns of directive speech acts of lecturers and students in online learning of Scientific Work courses. This research is a pragma-dialectical study using descriptive qualitative method. The research data are the utterances of lecturers and students of the Early Childhood Educator Teacher Education Study Program (PG-PAUD) of the Distance University in Borneo, Indonesia. All speakers are female with the age range of 25-40 years. Data were obtained from the online learning process using the Microsoft Teams in four webinar tutorial meetings. The data were collected using techniques of listening, note-taking, recording and screenshot. The results of the study found six patterns of directive speech acts contained in four critical discussions, namely requests, orders, questions, prohibitions, and persuasions, as well as invitations. The form of directive speech act patterns found is the most dominant pattern in speech. This form of speech pattern is widely used by speakers (lecturers) and speech partners (students) with the aim of testing students’ understanding or reminding students.
TEACHER'S COMMUNICATIVE LANGUAGE TEACHING (CLT) STRATEGIES IN IMPROVING THE SPEAKING ABILITY OF THE HOSPITALITY STUDENTS (A CASE STUDY AT SMKN 5 BARRU)

The descriptive study focused on investigating CLT strategies employed by the English teacher in hospitality classrooms to improve the hospitality students' speaking abilities. This research conducted a mixed method design with the quantitative analysis applied in small proportion only to measure the mean score of pre-test and post-test students speaking improvements. The participants in this study involved one teacher and one class from twelfth-grade (XII) students of hospitality at SMK N 5 Barru that consisted of 20 students. Observation with field notes and interviews were used as instruments for data collection. This research utilized Richards's (2006) three practices of mechanical practice, meaningful practice, and communicative practice as the basis in investigating the CLT strategies used by the teacher. The data were analyzed in three major steps such as data reduction, data display, and conclusion drawing proposed by Miles &amp; Huberman (1994). The findings indicated that the English teacher mostly used meaningful and communicative practices rather than mechanical practices. In applying CLT strategies to improve the hospitality students speaking abilities, the teacher mostly provided active discussion activities and worked in pairs or groups with the encouragement of integrating some additional tools such as the online platforms and applications. Moreover, cumulative score analysis from pre-test and post-test verified that the level of speaking proficiency in hospitality students demonstrated a 57.5% improvement, leading to the enhancement in their speaking score, which was classified as moderate categorization. The findings of this study concluded that CLT strategies that applied by the teacher helped improve the students speaking abilities gradually, as these applications also serve the purpose of engaging and captivating students in language learning by encouraging them with opportunities to utilize technological advancements in developing their language skills.