Reviewerly/exHarmony huggingface dataset:
Production Stock Monitoring System at PT Yamaha Indonesia Using the Scrum Method

Digital transformation brings changes to the industrial sector to become Industry 4.0. PT Yamaha Indonesia is a piano-manufacturing company that also uses technology to help monitor the production of piano parts. Production stock monitoring is carried out in order to meet the company's production targets. As a result, the company's stock can meet customer demand, keeping inventory costs to a minimum. Furthermore, it has the potential to optimize operational production time. Previously, the production monitoring procedure was done manually. Recordings are made every few hours or so to keep track of the data. Due to the manual recording of each step of the piano's part-making process, productivity and the ability to perform other tasks are slowed down. To address these issues, PT Yamaha Indonesia, through this research, developed a production stock monitoring system. This system was built using the Scrum method and implements data visualization using chart.js to facilitate monitoring. The research contribution is analyzing user needs until the production stock monitoring system is built so that monitoring the manufacturing process can be simplified and more efficient, and it can be done in real-time.
A Study on "pecunia Kongso" the Origin of the ‘denarius cultus’ in Korean Catholic Church

Canon law states that christians have a duty to support the needs of the church, but it does not mention of "denarius cultus". It is known that the "denarius cultus" system originates from "pecunia Kongso"(公所錢). In September 1931, Korea"s first council established "pecunia Kongso" as a "denarius cultus". "pecunia Kongso" is an important meaning as the origin of the "denarius cultus", but there are few studies on its origin. 『Coutumier de la Mission de Coree』 states that "pecunia Kongso" is money that believers donated to missionaries in the form of donations. However, it is not clear whether this originated from the Korean church or whether it was influenced by outsiders. In September 1803, a synod was held in Sichuan, China, and the Ministry of Propaganda approved the Sichuan Synod Decree on June 29, 1822. The Sichuan Synod Decree mentions refers to the donations offered by christians. Of course, The Sichuan Synod Decree did not specify this money as a "pecunia Kongso". However, through the Sichuan Synod, it can be confirmed that the charitable funds were used for charity work and pastoral activities of the church. On May 26, 1912, F. Demange published the 『Directorium Missionis Taikou』, which include more detailed regulations are mentioned than those of the 『Coutumier de la Mission de Coree』. As this regulation was accepted in the 『Directorium Missionis de Seoul』 in 1923, the process of "pecunia Kongso" being settled in the Korean church is confirmed. On September 26, 1932, the 『Directorium Commune Missionum Coree』 was published, and "pecunia Kongso" was established as a "denarius cultus". The 『Directorium Commune Missionum Coree』 provides detailed guidelines for "denarius cultus", establishing as a culture in the Korean church that uses the "denarius cultus" as financial aid for the diocese and for pastoral activities in the parish.
Ten Percent of Indonesian Publications with the Highest Citation, Period of 1945-2021: Bibliometric Dataset

This data article presents an essential bibliometric dataset of ten percent Indonesian publications with the highest citation, 1945-2021. The highest ten percent of citations from publications written by authors from Indonesia are grouped based on the periodization of the president, who ruled from 1945 to 2021. There are seven Periods of government in Indonesia. President Sukarno Era (1945-1965), there were 24 publications out of a total of 239 publications by Indonesian authors. President Suharto Era (1966-1998) had 578 publications out of 5,784. President B.J Habibie Era (1999), there were 65 publications out of 654. President Abdurrahman Wahid Era (2000-2001), there were 144 publications out of a total of 1,449 publications. President Megawati Soekarnoputri Era (2002-2004), there were 263 publications out of a total of 2,635 publications. President Susilo Bambang Yudhoyono Era (2005-2014), there are 3,098 publications out of 30,984 publications. President Joko Widodo Era (2015-2021) has 32,311 publications out of 323,115. Using Scopus database search analysis, this data article collects data on documents per year, subject area of documents, top author of documents, top source title of documents, the top affiliation of documents, top funding sponsor of documents, leading countries of author collaboration, most cited publications, and authors.
Simultaneous Use Dyad Design Model for User-Centered Design: A Case Study of Kangaroo Care Garments Developed for Use in NICU Environments

<p class="MsoNormal" style="margin-bottom:0in"><span style="font-family:&quot;Times New Roman&quot;,serif">User-centered design has been conducted in various fields, including computer science, engineering, medicine, and textiles and apparel. These fields have primarily focused on designing products for one user at a time. However, there are circumstances in which a pair or dyad is defined as the main user. User dyad shave been addressed in the field of medicine as caregiver-patient, where caregivers can be medical providers or even parents. In these dyads, the user roles are primary users and support users suggesting that the product is used by one individual at a time with the support of another. However, yet to be explored within the context of user-centered design, is the simultaneous use dyad (SUD). Meaning, circumstances in which two users are simultaneously using a product, and both are defined as the primary user. We have conducted a case study that examines the SUD of mother and infant through the exploration of creating a kangaroo care garment to be utilized by the dyad (mother-infant) in the NICU (neonatal intensive care unit).&nbsp;<o:p></o:p></span>
8 Face mask detection and crowd size estimation using IoT platform

Due to the rise in coronavirus disease (CoVID-19) cases, it has been a significant concern for governments all over the world to control the spread of the virus. We aim to aid in controlling the spread of the CoVID-19 virus. The face mask detection and crowd counting model with the Internet of things (IoT) platform (Thingspeak) can be deployed in situations where there is a lack of workforce, and additional assistance is required. Using a camera, an area can be remotely monitored, restricting contact between people and the frontline workers, promoting their safety. The research conducted to date about this topic is solely on detecting facemask or doing crowd counting, respectively. We included both face mask detection and crowd counting, connecting both to the IoT platform, enabling us to access the results online. In previous research work, traditional/wired cameras were used. But in our research work, we have used the concept of a Wi-Fi camera, i.e., wireless camera, by which the camera can be positioned in places where the placement of wires is difficult. For face mask detection, we have created two data sets which include images of faces with masks and without a mask, and this data set is used to train the model. For training and testing the model, we have included TensorFlow, Keras and SKLearn libraries. We have included different deep learning layers in training the model like Dense, Dropout and AveragePooling2D. In the test model, python code is connected to a wireless camera, which can capture live video feed and detect whether the person is wearing a mask or not. We have used OpenCV Histogram of oriented gradients (HOG) descriptor in crowd counting, often used to extract features from image data. Using an OpenCV HOG Descriptor, we can count the number of people present in a crowd. Face mask and crowd count is then sent to the IoT platform. Whenever the person is found wearing a mask, a green box appears around the face, and whenever the person is not wearing a mask, a red box appears around the face. When lots of people are detected by a crowd counting model, a green color box enclosing the entire body appears. This study helps in getting face mask detection and crowd size estimation on a single IoT platform, which helps monitor the data with great ease.
Generalized Poisson--Dirichlet Distributions Based on the Dickman Subordinator

We study exchangeable random partitions based on an underlying Dickman subordinator and the corresponding family of Poisson--Dirichlet distributions. The large sample distribution of the vector representing the block sizes and the number of blocks in a partition of $\{1,2,\dots,n\}$ is shown to be, after norming and centering, a product of independent Poissons and a normal distribution. In a species or gene sampling situation, these quantities represent the abundances and the numbers of species or genes observed in a sample of size $n$ from the corresponding Poisson--Dirichlet distribution. We include a summary of known convergence results concerning the Dickman subordinator in this context.
1 OpenCV libraries for computer vision

From the last few decades, researchers are enthusiastic to develop machines that work and act as human beings. Computer vision (CV) is the most excellent field for researchers to fulfill their dreams. CV is a subfield of artificial intelligence. CV works with visual inputs like digital images and videos. Mainly it focuses on creating systems, which process and analyze the visual input data, to get meaningful information from those inputs. It aims to perform different types of operations on supplied visual inputs. Object classification, object identification, object tracking, edge detection, feature extraction and human identification are few applications of CV. To perform different types of operations, special algorithms with minimum computation time are required. Various libraries are developed to perform CV operations. Among all open sources, computer vision library (OpenCV) is the most popular and widely used open-source library for developing CV applications. OpenCV is designed to develop CV applications using inbuilt methods with less computational time. The major goal of OpenCV library is to provide a classy view to researchers with an optimized code mechanism on basic infrastructure. In OpenCV, optimized code is developed for a lot of applications like filters and edge detection. Using this optimal code, researchers can develop their applications. OpenCV supports various operating systems, including mobile operating systems. This chapter discusses developing computer visual applications using OpenCV. This will be useful for researchers to understand and use OpenCV for their applications.
3 Image caption generating system using convolutional neural networks and long short-term memory networks

Human beings have the ability of easily determining the environment and the situations happening around them. Making a computer to automatically understand the surroundings can be done by training it with the images and ensuring that it describes the content of images. In this way, a computer will be able to learn the surroundings. Image caption generation is a process of creating appropriate captions for an image which is linguistically suitable and semantically precise for a scene and is also same to the understanding of humans. It is gaining attention because of its wide applications in scene recognition systems, human-robot communication systems, information retrieval systems and assistance systems for visually disabled persons. In this research, an image caption generating system is implemented using state-of-theart CNN model InceptionResnetV2 for image feature extraction (transfer learning), and long short-term memory networks for image caption generation. The model is trained using Flickr8k dataset and it is evaluated using metrics like BLEU and METEOR scores.
Soft clustering based on high- and low-level features for image smoothing

Edge-preserving image smoothing is essential for computational imaging. Traditional filters are based on low-level image gradients; however, the edges defined on gradients do not align with the contours of human perception, thus suffering from certain limitations. We propose an image filter based on a soft clustering model that combines high-level semantics (derived from instance segmentation) with low-level features (intensities). The proposed filter works by first performing soft clustering on the input image according to the high- and low-level features to derive the affinity matrices, which are then fused for image smoothing. Experiment results indicate the advantages of the proposed filter in a variety of applications, including image smoothing, flash/non-flash fusion, detail enhancing, image dehazing, and depth upsampling. Furthermore, the proposed filter is efficient, and it takes 1.75 s to process a color image with 1 megapixel on a modern desktop.
10 Computer vision-based approach for alphabetic recognition and Indian Sign Language recognition system

Purpose: Sign language is a communication media for hearing impaired people. Although sign language can be effectively used for communication between two disabled persons, the challenge is higher when they need to communicate with the outside world which rarely has the understanding toward sign language. The aim of this work is to develop computer vision based Indian Sign Language (ISL) fingerspelling identification system which will help in recognizing alphabetic signs as well as emergency words in the sign language without any additional equipment. It will thus improve the existing communication gap between the disabled and abled people. Methodology: We surveyed the literature using Prisma methodology. The study shows that deep learning techniques like convolutional neural network (CNN) can promise better accuracy than any other neural network architecture with feature extraction. We have also used data aggregation between two data sets and prepared one dataset of around 50 K images. Our idea is to implement a CNN which can be employed for real-time applications to detect hand gestures. The input of this system can be then forwarded to long short-term memory (LSTM) model (sequence to sequence learning algorithm) which will encode the gesture and then decode it into text format. Findings: Many research works have been proposed on American Sign Language (ASL) recognition. The method used for ASL cannot be used for ISL, as it differs from Indian Sign Language (ISL) recognition. As in ISL, two hands are used for communicating, whereas ASL uses one hand for communication. This will lead to overlapping of hands making ISL unclear and difficult to understand the features. The major problem of all related research works was lack of available dataset. There are very few works done on creation of such datasets. Values: A system which will help in automatic recognition of sign language and will help people with hearing impaired to communicate with the real world. Originality: Similar systems have been developed on only alphabet recognition with sign language. We also aim to leverage this dataset by augmenting with frequently used emergency words and gestures that can help hearing impaired people to convey important messages in emergency as well as routine activities.
More than a body of water

In 2016–2019 I worked as a commissioned artist exploring Calgarians’ emotional attachment to their water system. I was taken to many of the key sites of water management including for drinking water, sewage and storm water, and spent time with engineers, managers and leak locators. In this essay I share my research in the meter-shed and travelling around the city with the leak location service, through which I created a multi-form public artwork that sought to give the public the experience of listening for leaks.
Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework

Skewed evolving data stream (SEDS) classification is a challenging research problem for online streaming data applications. The fundamental challenges in streaming data classification are class imbalance and concept drift. However, recently, either independently or together, the two topics have received enough attention; the data redundancy while performing stream data mining and classification remains unexplored. Moreover, the existing solutions for the classification of SEDSs have focused on solving concept drift and/or class imbalance problems using the sliding window mechanism, which leads to higher computational complexity and data redundancy problems. To end this, we propose a novel Adaptive Data Stream Classification (ADSC) framework for solving the concept drift, class imbalance, and data redundancy problems with higher computational and classification efficiency. Data approximation, adaptive clustering, classification, and actionable knowledge extraction are the major phases of ADSC. For the purpose of approximating unique items in the data stream with data pre-processing during the data approximation phase, we develop the Flajolet Martin (FM) algorithm. The periodically approximated tuples are grouped into distinct classes using an adaptive clustering algorithm to address the problem of concept drift and class imbalance. In the classification phase, the supervised classifiers are employed to classify the unknown incoming data streams into either of the classes discovered by the adaptive clustering algorithm. We then extract the actionable knowledge using classified skewed evolved data stream information for the end user decision-making process. The ADSC framework is empirically assessed utilizing two streaming datasets regarding classification and computing efficiency factors. The experimental results shows the better efficiency of the proposed ADSC framework as compared with existing classification methods.
PENERAPAN GAMIFICATION PADA SISTEM INFORMASI MANAJEMEN RUANG BACA

The Reading room is a room that is used by visitors to search and study the library materials. The reading room has an important role in the learning and teaching process on campus. The Faculty of Engineering Studies of Udayana University have a reading room that is managed in a conventional way. In addition to these problems, students ' interest in visiting the Reading room is also very low. Using information technology with the Gamification method can be used to resolve the issue. This system can handle reading room management and provides convenience for users to access the reading room. The form of Gamification to be applied is the point given to the user against interactions with the Reading Room management information System. Users will be given problems that have the form and difficulty in each level, correct answers will get points and wrong answers will get a reduction of points. The points gained will be accumulated so that the level of users can be found in the leaderboard, with the mechanisms in the system expected to borrow and read books in the Reading room can increase. The Reading Room Management Information System uses 3 tests: Black Box, System usability scale, and the last of Gamification testing. Based on the test results using the black box method, the system has a function that is declared successful. Based on the test result system usability scale obtained score 70.12 expressed acceptable. In addition to the Gamification testing, a score of 3.98 is categorized by agreeing with the application
Integrated Wind-Solar-Hydro Power Prediction Method Based on Deep Neural Network

With the increase of the proportion of renewable energy in the power system, the randomness and volatility of wind power and photovoltaic output and the seasonality of hydropower output have increasingly prominent impacts on the security and reliability of the power system. In order to guide the day-ahead generation plan of wind-solar-hydro complementary system, it is necessary to accurately predict the future output of wind-solar-hydro, analyze the generation benefit and output reliability of distributed power supply side, and finally realize the economy and reliability of power grid operation under high-density distributed new energy integration. In this paper, an ultra-short-term power prediction method based on deep neural network for regional wind-solar-hydro integration is proposed. Considering the spatio-temporal correlation of the stations, the prediction accuracy is improved. Historical wind-solar-hydro power series are decomposed by empirical mode and reconstructed into high frequency and low frequency components. On this basis, based on LSTM deep neural network, the integrated prediction models of the high frequency and low frequency power series of the wind-solar-hydro are constructed, respectively. By fusing the prediction sequences obtained from the two models, the integrated prediction of the ultra-short-term power of the regional wind-solar-hydro is realized.
Construction and Application of Radiologic Interpretation Knowledge Graph Based on Three-layer Structure Model

The medical knowledge graph is the cornerstone of intelligent medical service and is expected to bring more efficient and accurate medical services. However, due to the diversity of medical knowledge terms, the complexity and professionalism of knowledge structure, the existing knowledge graph construction technology cannot meet the application requirements. To solve this problem, this paper proposes a three-layer structure model of “the lesion area - lesion features-detail features” by simulating the thinking of radiologists. The method is to first observe the area where the lesion belongs, then judge the features of the lesion in the area, and finally determine the detail features and location of the lesion features. Finally, taking chest X-ray as an example, the paper elaborates the construction process and application of radiologic interpretation knowledge graph based on three-layer structure model.
Semi-Transparent Encryption to Prevent Leakage of Sensitive Information

With the continuous progress and development of science and technology, the application of computers has been popularized in all aspects of human life, providing great flexibility for human work and study. But at the same time, computer data security is also a big problem for this booming industry. The purpose of this paper is to study semi-transparent encryption to prevent the leakage of sensitive information. A database storage encryption mechanism is discussed, and the data security storage method at the bottom of the database management system is analyzed in detail. The encryption algorithm information used by objects encrypted by the proposed semi-transparent database storage encryption (SSE) method is stored by the database management system, and the key is provided by the user connecting to the database. The storage encryption method introduced in this article allows each user to easily encrypt sensitive data, and provides users with a safe and easy-to-use database storage encryption method. In the test, semi-transparent storage encryption has basically no effect on system function and performance.
Joint Generative Network for Abnormal Event Detection in Surveillance Videos

Abnormal events can be seen as spatiotemporal objects. Recent methods attempt to extract features from spatial information and learn abnormal events with convolutional neural networks. However, such representations lack motion information to model abnormal events. In this paper, we propose an architecture based on generative network structure to learn and detect abnormal events. Spatial and temporal encoders are employed to extract features from images. Shortcut Inception Modules (SIM) are used in encoders to keep meaningful information during training the neural network, which also reduces the network parameters. Experimental results on benchmark datasets show that the architecture performed comparative results in terms of detection accuracy and processing time compared to learning-based methods.