Reviewerly/exHarmony huggingface dataset:
Content-based Retrieval of 3D CAD Subassemblies Using 3D Radon Transform

We propose a content-based method for retrieving the 3D CAD assemblies which contain or are contained in the assembly given as a query from a database. The retrieval is based on ranking assemblies in the database according to their content rates which are different criteria from the similarities of shapes. The content rate is computed by comparing the projections of components constituting an assembly in the database with those of components constituting the query. We use the 3D Radon transform to obtain the projections. In existing methods for retrieving CAD models, projections of each model onto 2D planes, which are a set of two-dimensional data, are often used to compute the similarity between them. The proposed method simplifies the process of comparing the projections of components because the projections using the 3D Radon transform are a set of one-dimensional data. The method has the unique feature of identifying the component layouts, which reflect the technical know-how and information of the product designers. The components which have the same shapes but different properties such as material names are also distinguished based on their layouts without depending on the labels assigned to the properties. Other than our previous method, no existing methods possess such features. We show that the proposed method has better performance in the retrieval precision and processing time than the previous method.
Markerless motion capture of hands and fingers in high-speed throwing task and its accuracy verification

In human motion capture systems, reflective markers attached to the body have been widely used to track motion using optical cameras. However, when the speed of motion increases, because the brightness and angle of view of the camera are limited, and the markers often fall off, particularly of detailed body parts such as fingers in full-body movements, other parts of the body (palms) have been investigated. This study attempted to acquire finger movements during a high-speed throwing task without attaching markers using automatic image recognition technology based on deep learning (DeepLabCut) and verified its accuracy compared to conventional methods. As a result, the absolute distance between the 3D coordinates obtained from the two motion capture systems was an average of 15.5 to 29.4 mm depending on tracked points, and the correlation coefficients between them ranged from 0.932 to 0.999. Therefore, the shapes of the time-series profiles of the 3D coordinates obtained from the two motion- capture systems were similar. These results suggest that motion measurement using markerless motion capture is possible in environments where conventional motion capture systems are difficult to use.
Semi-supervised Video Object Segmentation Using Parallel Coattention Network

In this work, a novel method is proposed for video object segmentation. The temporal correlation is measured between subsequent frames in a video sequence to propagate a reference segmentation mask through the video sequence. The aim is to develop a novel deep learning based holistic method for video object segmentation. For this purpose, a parallel coattention module is used to capture the rich temporal information between two subsequent frames and mask. An autoencoder architecture with a siamese encoder module is applied with three inputs, and skipped connections are utilized between the encoder and decoder networks. No auxiliary information such as optical flows are used in this network. The method is validated on DAVIS16 and FBMS datasets with mean region similarity ( $$\mathcal {J}_{mean}$$ ) of 91.2 and 83.2 respectively.
A PCA-Based Keypoint Tracking Approach to Automated Facial Expressions Encoding

The Facial Action Coding System (FACS) for studying facial expressions is manual and requires significant effort and expertise. This paper explores using automated techniques to generate Action Units (AUs) for studying facial expressions. We propose an unsupervised approach based on Principal Component Analysis (PCA) and facial keypoint tracking to generate data-driven AUs called PCA AUs using the publicly available DISFA dataset. The PCA AUs comply with the direction of facial muscle movements and can explain over 92.83% of the variance in other public test datasets (BP4D-Spontaneous and CK+), indicating their capability to generalize facial expressions. The PCA AUs are also comparable to a keypoint-based equivalence of FACS AUs in terms of variance explained on the test datasets. Besides, PCA AUs can code at 30 fps on AMD EPYC 7402 24-Core Processor. In conclusion, our research demonstrates the potential of an automated coding system as an alternative to manual FACS, which could lead to efficient real-time analysis of facial expressions in psychology and related fields. To promote further research, we have made the code publicly available.
A bi-objective optimisation technique for scheduling repetitive projects

Repetitive projects are projects in which the same type of works/activities gets repeated in different locations or sites. For each of this type of projects, different crew options are available for each activity and selecting the best option corresponding to each activity is a difficult task. Project managers in these projects are often faced with the task of finding out the best schedule corresponding to the optimum project duration and expenditure which will satisfy different constraints. Since the computational complexity is very high in these types of projects, an ABC algorithm-based heuristic methodology is developed in this study, which can give good solutions for satisfying the above mentioned objectives with respect to different constraints. The proposed methodology's performance is analysed with exact solutions and the results show that an ABC algorithm-based methodology gives significantly good quality solutions.
Fine-Grained Attribute-Object Feature Representation in Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) is designed to recognize unobserved (unseen) compositions of given objects (guava, orange, pear, etc.) and their states (sliced, peeled, ripe, etc.). The CZSL is challenging because it is sometimes difficult to separate the visual aspects of objects and states from their context in the images. In addition, the detailing feature of a state may vary considerably depending on its composition. For instance, the state peeled displays distinct visual characteristics in the peeled apple and peeled guava compositions. Existing research uses linguistic supervision and word embeddings to better segment and composes attribute-object relationships for recognition. We emphasize the visual embedding space and propose a Fine-grained Compositional Learning (FgCL) method capable of separating attributes from object features. We integrate visual fine-grained and Siamese-based features with word embedding into a shared embedding space that is representative of unseen compositions to learn our model more effectively. Extensive experiments are conducted and demonstrate a significant improvement over existing work (SymNet, TMN) on two benchmark datasets: MIT-States & UT-Zappos50K.
Precise and Faster Image Description Generation with Limited Resources Using an Improved Hybrid Deep Model

We propose a model that performs image captioning efficiently based on entity relations, followed by a deep learning-based encoder and decoder model. In order to make image captioning more precise, the proposed model uses Inception-Resnet(version-2) as an encoder and GRU as a decoder. To develop a less expensive and effective image captioning model in accordance with accelerating the training process by reducing the effect of vanishing gradient issues, residual connections are introduced in Inception architecture. Furthermore, the effectiveness of the proposed model has been significantly enhanced by associating the Bahadanu Attention model with GRU. To cut down the computation time and make it a less resource-consuming captioning model, a compact form of the vocabulary of informative words is taken into consideration. The proposed work makes use of the convolution base of the hybrid model to start learning alignment from scratch and learn the correlation among different images and descriptions. The proposed image text generation model is evaluated on Flickr 8k, Flickr 30k, and MSCOCO datasets, and thereby, it produces convincing results on assessments.
A Subgraph Embedded GIN with Attention for Graph Classification

Graph neural networks (GNNs) have emerged as a powerful tool for analyzing graph data, where data are represented by nodes and edges. However, the conventional methods have limitations in analyzing graphs with diverse attributes and preserving crucial information during the graph embedding. As a result, there is a possibility of losing crucial information during the integration of individual nodes. To address this problem, we propose an attention-based readout with subgraphs for graph embedding that partitions the graph according to unique node attributes. This method ensures that important attributes are retained and prevents dilution of distinctive node features. The adjacency matrices and node feature matrices for the partitioned graphs go into a graph isomorphism network (GIN) to aggregate the features, where the attention mechanism merges the partitioned graphs to construct the whole graph embedding vector. Extensive experiments on six graph datasets demonstrate that the proposed method captures various local patterns and produces superior performance against the state-of-the-art methods for graph classification. Especially, on the challenging IMDB-MULTI dataset, our method achieves a significant performance gain of 27.87%p over the best method called MA-GCNN.
デジタル画像相関法とアンサンブル4次元変分法による材料モデルのパラメータ逆推定

The prediction accuracy of the deformation behavior of materials by finite element (FE) simulation depends on the parameters in selected material models. Although the parameters are conventionally identified from standard material tests (e.g., uniaxial tensile and multiaxial material tests) to characterize the deformation behavior, the identification process requires a large number of experiments. We develop a novel inverse methodology for estimating the material model parameters by combining digital image correlation (DIC) measurement and FE simulation coupled with an ensemble-based four-dimensional variational method (En4DVar). En4DVar incorporates the experimental data obtained from a material test into the FE simulation that reproduces the test and inversely estimates the parameters such that the simulation results follow the experimental data, allowing for the reduction of experimental effort. We use the proposed method to estimate the parameters of a strain-hardening law and anisotropic yield function from the results of uniaxial tensile test of a round bar of aluminum alloy. DIC measurement is conducted to obtain experimental data of the three-dimensional displacement and strain field over the surface of the specimen, including the post-necking range. The results demonstrate that En4DVar is a promising method for inversely estimating the parameters and characterizing the deformation behavior of a material from the results of a small number of tests.
Applying Agile Business Solutions as a Graduate Capstone Project

A graduate course was implemented to teach the fundamentals of Agile project management as part of the MS Technical Project Management curriculum. The course was designed to provide a solid foundation in Agile methodologies for project management practice to enable graduates to execute appropriate Agile techniques to plan, and deliver quality results that frequently deliver value to customers. During the course students are introduced to Agile processes which include developing user stories and story maps, estimate and plan for sprints, track project execution and releases using burn-down charts and conduct retrospective sessions. This paper discusses how a graduate student was able to successfully apply the Agile principles and processes learned in the graduate program to an Independent Study project designed to provide students with the opportunity to apply their knowledge and experience gained during the curriculum to addressing and solving a real business problem. The student chose to develop and implement an Agile framework solution to a software implementation and performance problem his business organization was facing.
Enhancing Quality of Experience (QoE) in IM-Tahfiz Framework for Predictive Acceptance Influence of User Screening Test

Tahfiz teaching has become progressively more conventional and is growing as an essential educational course. This significance is in line with the prospect above that is placed on the ability of tahfiz schools to produce Quran-memorising students who can thrive and improve the nation’s Islamic education. Due to this endeavour, the tahfiz learning management process must satisfy the user QoE related to the increasing student intake issues. These issues impact this education process due to insufficient tahfiz resources, infrastructure, and facilities. This paper proposes the Integrated Management Tahfiz (IM-Tahfiz) Framework’s suitability for selected education satisfaction at the empirical level. The primary purpose of this framework is about the Quality of Experience (QoE) based on user satisfaction for the screening test of stakeholder decision in selecting tahfiz centre to educate students. This framework design was implemented in three phases; data selection, where instruments and experiment setup gather resources from the mobile to the server to finalize data; user QoE setup, where the users’ data is obtained by using the Mean Opinion Score (MOS) to determine this matter’s solution regarding the suitability of tahfiz students to tahfiz centers; and execution phase, where both phases combine to finalize the data output. The final result illustrates the initial research question (RQ) was the factor that had effects on the problem of the tahfiz center that can be solved accordingly.
Calibration of Arduino-based Temperature Sensors for Parabolic Solar Collectors with Phase Change Material

The development of energy storage materials requires facilities for testing the materials' ability to store and release energy effectively. This study focuses on the development and calibration of a temperature measurement system for parabolic solar collectors equipped with heat storage media. We employed type-K thermocouples and MAX6675 modules with Arduino as our measurement tools. The calibration process involved comparing sensor measurements with those obtained from commercially calibrated instruments using seven different test materials. The calibration results demonstrated that our sensors exhibited an error rate ranging from 0.66 to 0.73, indicating their accuracy and suitability for monitoring temperature fluctuations in solar thermal collectors.&#x0D; Pengembangan material penyimpan energi thermal membutuhkan sarana untuk uji coba kemampuan bahan dalam menyimpan dan melepaskan energi. Penelitian ini fokus pada pengembangan dan kalibrasi sistem pengukuran temperatur untuk kolektor surya parabola yang memiliki media penyimpanan panas. Kami menggunakan sensor termokopel tipe-K dan modul MAX6675 dengan Arduino sebagai alat pengukuran. Proses kalibrasi melibatkan perbandingan hasil pengukuran sensor dengan alat komersil yang sudah terkalibrasi pada tujuh bahan uji yang berbeda. Hasil kalibrasi menunjukkan bahwa sensor kami memiliki tingkat kesalahan antara 0.66 hingga 0.73, menunjukkan bahwa sensor kami sudah akurat dan dapat digunakan untuk memonitor perubahan suhu pada kolektor surya termal.
China's campaign strategy during the Korean War armistice negotiations - Focusing on the entire Sangamryeong campaign and Geumseong campaign -

China realized the limitations of continuously waging war after the 5th large-scale campaign, and attempted to change the way it conducted the campaign. The purpose of this study is to examine changes in China's national security goals and military strategy as it participated in the armistice negotiations, and from what perspective it conducted major campaigns. In particular, the analysis focused on the Sanggamryeong Campaign in 1952 and the Geumseong Campaign in 1953, which were a period of stalemate in armistice negotiations.After the 5th large-scale campaign of the Korean War, China began to view the nature of the Korean War as an international war. At this time, China's national security goal was to keep North Korea alive and form a buffer zone with the United States. China's military strategy was an active defense strategy, and the goal was to secure the 38th parallel and end the war or ceasefire through negotiations. To this end, the Chinese and North Korean troops attempted to defend the 38th parallel and the east and west coasts through coordinated operations. The military means included the reinforced defense zone around the 38th parallel, defense units on the east and west coasts, and Chinese troops that could be deployed to the Korean Peninsula through rotation operations. The Sanggamryeong campaign and the Geumseong campaign were operational phases linked to these national security strategic goals and military strategy. According to the active defense strategy, the Sanggamryeong campaign was a campaign that expanded from a tactical level battle to an operational level. The operational goal was expanded to kill a large number of U.S. troops in the battle to secure the two hills occupied by the initial company size. This campaign was the first time in history that the Chinese military combined mine defense and counterattack to destroy enemy units, unlike before. The goal of the operation throughout Geumseong was to oppose Seungman Rhee's armistice agreement, and to force the Chinese military to force Seungman Rhee to agree to the armistice agreement through strong military action.
Edukasi Menemukan Ide Penelitian Melalui Pemanfaatan Aplikasi Berbasis Web Untuk Mahasiswa D3 RMIK

Students with Medical Records and Health Information have a short time to complete their final assignment, graduation demands are determined by an exit exam, so students must move quickly and efficiently in completing their final assignment. Therefore, this social service aims to increase student readiness in completing the final project by utilizing web-based applications. The targets of this activity are fourth and sixth semester students of D3 Medical Records and Health Information. The method in this activity is carried out by screening web-based applications that will be socialized and the implementation stage. The implementation of the activity was carried out on May 20, 2023. As a result of this activity students felt very useful by holding this activity and hoped that there would be activities to strengthen the preparation for the final project in future.
Development of E Rapport on Child Growth and Development in the Early Childhood Education (PAUD)

Every child has the right to monitor growth and development without exception, carried out at the family level; early detection of irregularities is carried out by trained health workers and early childhood/kindergarten educators. This activity has not yet reached the targeted figure, which is only 59.28% in 2020. Optimization of child growth and development can be developed through Paysandú integrated preschool. Based on this description, the authors consider it necessary to synergize the implementation of the SDIDTK program in the PAUD education program by making growth and development reports. The purpose of developing e-report cards is to increase efficiency and effectiveness and assist teachers, homeroom teachers, cadres, and health workers in managing the SDIDTK program so that valid, practical, and effective digital-based e-report applications can be obtained. Method: The research design refers to the development of the 4D model, including defining, designing, developing, and disseminating. The stages carried out include steps 1, 2, and 3, while step 4 is carried out in the following year. Result: The developed E-report card application is named SIDDIKA (Child health data and information application). The results of the material expert validation scored 73.5% (feasible). Media expert validation results scored 81.5% (very feasible). The test results on the child health data and information application design (SIDDIKA), which was carried out on users in the field, scored 84.1% (very feasible). Conclusion: The developed E-report card application is suitable for use in the field to assist technically in the implementation of SDIDTK at the PAUD unit level and to help increase the coverage of children receiving growth and development monitoring
Applying Whisper Game in Affecting Vocabulary Mastery at Eighth Grade of Junior High School

This aim of this research was to see the effect of using whisper game towards students’ vocabulary mastery at eighth grade of SMPN 3 Kota Jambi, whether it has significant effect or not. The design of this research was an experimental research that was done in pre-test and post-test. Before getting the sample, the population, VIIIA, VIIIB and VIIIC class, were previously given homogeneity test. After the class population achieving homogenous, the class samples were taken by using cluster random sampling technique. Then, the data of this research was collected by using vocabulary test. Type of the test was matching choice. The implementation of whisper game in teaching vocabulary at eighth grade of SMPN 3 Kota Jambi was conducted to two classes; they were experimental class and control class. The participant of this research was 37 students, there were 19 students in experimental class, and 18 students were in control class. In collecting the data, they were given the same test. The data from the test was analyzed quantitatively by using t-test formula. Based on the calculation of the t-test, the researcher found out that the value of t-test was 2, 3211 and t-table was 1,697 at the degrees of freedom (19 + 18 – 2) = 35, and at the level of confidence was 0.05. Due to the fact that t-test (2, 3211) was higher than t-table (1,697), so the alternative hypothesis (H1) was accepted. There was a significant effect of whisper game technique towards students’ vocabulary mastery at eighth grade students of SMPN 3 Kota Jambi.
Prediksi Financial Distress Menggunakan Model Artificial Neural Network pada Perusahaan Perhotelan yang Terdaftar di BEI Tahun 2017-2021

This study aims to determine early warning models, differences in calculation results, and prediction results of financial distress using the Artificial Neural Network (ANN) model on data testing of hotel industry companies listed on the Indonesia Stock Exchange (IDX) from 2017-2021. In this study, researchers used quantitative research methods where the results obtained from this study were in the form of numbers or data whose values were numbered. The population in this study are companies Hotels, Resorts &amp; Cruise Lines which are listed on the Indonesia Stock Exchange in 2017-2021 with a total of 17 companies. The data processing procedure in this study begins with calculating financial ratios using Microsoft Excel, then analyzing descriptive statistics, and testing the artificial neural network model using the PYTHON programming language to predict financial distress conditions in companies.