Reviewerly/exHarmony huggingface dataset:
Strategy to Develop a Domain-specific Pre-trained Language Model: Case of V-BERT, a Language Model for the Automotive Industry

최근 방대한 텍스트 데이터에 대해 사전학습을 수행한 모델인 사전학습 언어모델이 다양한 텍스트 분석에 활용되며 성과를 거두고 있다. 하지만 BERT와 같은 범용 사전학습 언어모델은 전문 도메인의 말뭉치에 대해서는 비교적 낮은 성능을 보인다는 한계를 가지며, 이에 따라 전문 도메인에 맞게 도메인 특화 사전학습 언어모델을 새롭게 개발하고자 하는 수요가 증가하고 있다. 하지만 이러한 수요에 비해 도메인 특화 사전학습 언어모델을 개발하기 위한 체계적인 전략 및 접근법에 대한 논의는 부족한 실정이다. 이에 본 연구에서는 도메인 특화 언어모델 개발 시 고려해야할 사항을 정리하여 제안하였으며, 이러한 개발 전략에 따라 자동차 분야 언어모델인 V-BERT를 개발한 사례를 소개하였다. 또한 자동차 분야의 전문 문서에 대한 실험을 통해 V-BERT 기반 CPC 분류 모델이 일반 BERT 기반 모델에 비해 우수한 성능을 보임을 확인하였다. Recently, pre-trained language models created through pre-training on vast text data have been used for various text analyses. However, general-purpose pre-trained language models, such as BERT, have a limitation as they show relatively low performance for a corpus of specialized domains. Therefore, the demand for newly developing domain-specific pre-trained language models suitable for specialized domains is increasing. However, compared to this demand, more discussion is needed on systematic strategies and approaches to develop domain-specific pre-trained language models. Therefore, in this study, we summarized the critical factors to be considered when developing a domain-specific language model, and introduced an example of developing V-BERT, a language model for the automotive sector, according to this development strategy. In addition, through experiments using patents and papers in the automotive field, we confirmed that the V-BERT-based CPC classification model performed better than the general BERT-based model.
Robust deep semi-supervised learning with label propagation and differential privacy

Semi-supervised learning (SSL) methods provide a powerful tool for utilizing abundant unlabeled data to strengthen standard supervised learning. Traditional graph-based SSL methods prevail in classical SSL problems for their intuitional implementation and effective performance. However, they encounter troubles when applying to image classification followed by modern deep learning, since the diffusion algorithms face the curse of dimensionality. In this study, we propose a simple and efficient SSL method, combining a graph-based SSL paradigm with differential privacy. We aim at developing coherent latent feature space of deep neural networks so that the diffusion algorithm in the latent space can give more precise predictions for unlabeled data. Our approach achieves state-of-the-art performance on the Cifar10, Cifar100, and Mini-imagenet benchmark datasets and obtains an error rate of 18.56% on Cifar10 using only 1% of all labels. Furthermore, our approach inherits the benefits of graph-based SSL methods with a simple training process and can be easily combined with any network architecture.
EVALUASI SISTEM INFORMASI AKUNTANSI PERSEDIAAN OBAT OBATAN PASIEN COVID 19 PADA RUMAH SAKIT MITRA MEDIKA TANJUNG MULIA

This study aims to analyze the Accounting Information System for Drug Inventory at Mitra Medika Tanjung Mulia Hospital. In this study, the author uses a descriptive technique, which is an analytical process that begins with collecting data, grouping the data and then compiling, analyzing and interpreting so that the actual picture of the problem under study is obtained. The technique of collecting data is through a documentation study, which is to obtain the necessary data from the hospital regarding the accounting information system for drug supplies. Based on research and discussion regarding the Evaluation of the Accounting Information System for Drug Inventory for Covid-19 Patients at Mitra Medika Tanjung Mulia Hospital, it shows that the accounting information system implemented has been implemented well and is able to assist leaders in making good decisions.
A consideration on causal structure and real time of FQHNN using Iris data

It is effective for promoting self-driving car society to grasp driver’s emotions in level 3 and to grasp remote observer ‘s ones in level 4 which are difficult for Convolutional Neural Network (CNN) which is leading the present third generation of AI because CNN has not clear causality. And so our object is to grasp these emotions by using Fuzzy quantification(FQ) theory aided Holographic Neural Network(FQHNN) because both of HNN and FQ theory have causality and HNN is highefficiency. But here we try to grasp the causal characteristics of FQHNN by using IRIS data which is often used for classification. As a result, it becomes clear that only HNN has not enough accurate but FQHNN is good accuracy in both learning and predict. And we get two causal characteristics from FQHNN.
高速投動作における手指のマーカーレスモーションキャプチャとその精度検証

In the motion capture systems of humans, reflective markers attached to the body have widely been used to track them by optical cameras. However, when the speed of motion increases, as the camera's angle of view and brightness are limited and the markers often fall off, especially for detailed body parts such as fingers in full-body movements, other parts of the body (palms) have been investigated instead. In this study, we attempted to acquire finger movements in high-speed throwing motion without attaching markers using automatic image recognition technology based on deep learning (DeepLabCut), and verified its accuracy comparing with conventional methods. As a result, the absolute distance between 3D coordinates obtained from the two motion capture systems was an average of 15.5 mm ~ 29.4 mm depending on tracked points and the correlation coefficients between them ranged 0.932 ~ 0.999. Therefore, the shapes of the time-series profiles of 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 it has been difficult to use conventional motion capture systems.
Supplemental Material: Vanished evaporites, halokinetic structure, and Zn-Pb mineralization in the world-class Angouran deposit, northwestern Iran

&lt;p&gt;Figure S1: (A) Raman spectra of anhydrite and dolomite inclusions in S1 sphalerite. (B) Raman spectra of dolomite and spherical calcite inclusions in double-terminated quartz. (C) Raman spectra of tabular dolomite inclusion and the host double-terminated quartz. Figure S2: Concentrations of Li, Na, and K versus Al of the double-terminated quartz at Angouran. Table S1: U-Pb data of zircons from Miocene tuffaceous pyroclastic rocks and volcanic clasts-bearing breccias in the Angouran deposit. Table S2: Sulfur isotope composition of sulfides in the Angouran deposit. Table S3: Quantitative LA-ICP-MS trace element data of double-terminated quartz in the Angouran deposit (concentrations in ppm). Table S4: Oxygen isotope composition of quartz from the sulfide ores and footwall schist in the Angouran deposit.&lt;/p&gt;
Score based Support Vector Machine for Spam Mail Detection

Nowadays, people are well aware of the essential role that social media plays in modern communication. Email is one of the most reliable and secure social media platforms used for making online contacts and exchanging data or messages over the internet. Free email and messages provided by strangers are relied on by many people nowadays. Because anyone may send an email or a message, spammers have a fantastic opportunity to write spam messages that cater to our varied interests. Mass emailing, sometimes known as “email spam,” is the practice of sending numerous messages at once using electronic mail. When emails are forwarded to unauthorized servers, spam is the usual result. Spam slows down internet connection, steals sensitive information like passwords and credit card details, and tampers with search results on computers. It's not simple to track down spammers and their content. Today, spam emails are mostly used to promote products and services, as this is where the money is. The proposed model allows us to categorize messages as spam or ham and determine what percentage of the dataset is comprised of spam or ham. In this proposed work, the machine learning method such as Support Vector Machine (SVM) are examined and how it is used to classify emails as spam or ham using proposed dataset. The accuracy of suggested model for determining the content of an email is significantly higher than that of earlier models.
Secure and Proficient Provable Data Procurity With Privacy Protection in Cloud Storage

Cloud technology is the distribution of physical servers in such processors, memory, networks, connectivity, applications, statistics, as well as smartness only via Online platform so,in addition to allow greater innovative technology, better versatile facilities, plus scalable enterprises.With this mechanism, the data owner’s maintenance and storage costs can be reduced. Simultaneously, when the data owner loses procurity and physical authority over the data, a security threat arises. As a result, data inspection is required to ensure data integrity over a server.However concern is now difficult for information owners because data authority must have been validated thereby preserving confidentiality. In the process of labelling these issues, this paradigm provides secure data acquisition as well as efficient privacy-preserving verification.Additionally, it now supports multipleownership, data complexities, also an unit identification. A majority intriguing aspect for the process in which examiner will validate ownership of data having little maintenance cost. The system demonstrates that a malware platform will present evidence that is adequate to carry third-party auditors authentication of the user. The scheme’s security is analysed and demonstrated at this system to protect data privacy from third-party public auditing while making it impossible for cloud service providers avoids saving the necessary blocks to ignore the result.So proposed scheme’s best intriguing feature is that it supports all of its crucial elements, such as blocking-free validation, security, process monitoring, as well as information behaviour.
Similarity- and Quality-Guided Relation Learning for Joint Detection and Tracking

Joint detection and tracking, which solves two fundamental vision challenges in a unified manner, is a challenging topic in computer vision. In this area, the proper use of spatial-temporal information in videos can help reduce local defects and improve the quality of feature representations. Although modeling low-level (usually pixel-wise) spatial-temporal information has been studied, instance-level spatial-temporal correlations (i.e., relations between semantic regions in which instances have occurred) have not been fully exploited. In comparison, modeling instance-level correlation is a more flexible and reasonable way to enhance feature representations. However, we have found that conventional instance-level relation learning that works for the separate tasks of detection or tracking is not effective in joint tasks in which a variety of scenarios may be presented. To try to resolve this problem, in this study, we effectively exploited instance-level spatial-temporal semantic information for joint detection and tracking via a joint relation learning pipeline with a novel relation learning mechanism called Similarity- and Quality-Guided Attention (SQGA). Specifically, we added task-specific SQGA relation modules before the corresponding task prediction heads to refine the instance feature representation using features of other reference instances in the neighboring frames; these features are aggregated on the basis of relational affinities. In particular, in SQGA, relational affinities were factorized to similarity and quality terms so that fine-grained supervision rules could be applied. Then we added task-specific attention losses for each SQGA relation module, resulting in a better feature aggregation for the corresponding task. Quantitative experiments based on several challenging multi-object tracking benchmarks showed that our approach was more effective than the baselines and provided competitive results compared with recent state-of-the-art methods.
Will Dynamic Foveation Boost Cloud VR Gaming Experience?

Cloud Virtual Reality (VR) gaming offloads the computationally-intensive rendering tasks from resource-limited Head-Mounted Displays (HMDs) to cloud servers, which consume a staggering amount of bandwidth for high-quality gaming experiences. One way to cope with such high bandwidth demands is to capitalize on human vision systems by allocating a higher bitrate to the foveal region of HMD viewport, which is known as foveation in the literature. Although foveation was employed by remote VR gaming, existing open-source projects all adopt static foveation, in which the HMD gamer gaze position is assumed to be fixed at the viewport center. In this paper, we construct the very first cloud VR gaming system that supports dynamic foveation. That is, the real-time gaze positions of gamers are streamed from eye-trackers on HMDs to cloud servers, which in turn adjust the foveation parameters, such as foveal region size/location and peripheral region quality degradation, accordingly. Using our developed cloud VR gaming system, we design and carry out a user study using a game called Fruit Ninja VR 2 to find the foveation parameters in static and dynamic foveation for maximizing the gaming Quality of Experience (QoE) in Mean Opinion Score (MOS). With the chosen foveation parameters, we found that, compared to cloud VR gaming without foveation, static foveation leads to a MOS increase of 0.60 and a bitrate reduction of 8.71%. Furthermore, adopting dynamic foveation results in an additional 0.60 increase on MOS while saving 9.81% bitrate, compared to static foveation. Our findings demonstrate the potential of dynamic foveation in cloud VR gaming, which dictates both high visual quality and short response time. The optimization techniques developed in this and follow-up work could benefit other cloud-rendered applications that typically have less strict requirements than cloud VR gaming.
A semantic study on the phenomenon of identical component noun phrase double extraction

본고에서는 동일 성분 명사구 중출 구문을 대상으로 생성어휘론의 틀을 이용하여 의미 해석 원리를 살펴보고 유형을 분류한다. 본고에서는 명사구가 중출되는 것은 병렬적으로 의미 결합이 이루어진 것이 아니라 생성적 차원에서 의미 해석이 이루어진 것으로 보고 생성어휘론적 입장에서 의미 합성과 해석 원리를 살펴본다. Pustejovsky(1998)의 생성어휘론에서 단어의 의미를 논항구조, 사건구조, 특질구조, 어휘계승구조로 기술하는데, 특히 특질구조는 대상에 대한 기본적 이해를 가능하게 하고 대상이 세상과 관계를 맺고 있는 방식을 보여주는 구조이다. 특질구조는 형상역, 구성역, 기능역, 작인역의 네 가지 의미특질로 구성되는데, 본고에서는 이 특질구조를 활용하여 동일 성분 명사구의 의미를 해석한다. 또한, 동일 성분 명사구 중출 구문에서 선행 명사구와 후행 명사구의 의미역이 동일한 경우도 있고 의미역이 다르다고 하더라도 의미역만으로는 구문을 설명하기 어려운 경우들이 있어 본고에서는 명사구의 특질 구조까지 포함하여 유형을 분류한다. 의미역이 동일한 경우는 특질구조가 동일한 경우와 동일하지 않은 경우 모두 동일명사 중출 구문이 형성되지만, 의미역이 동일하지 않은 경우에는 특질구조가 동일하지 않은 경우에만 동일명사 중출 구문이 형성된다. This paper examines the phenomenon of identical component noun phrase double extraction using the framework of generative lexicon theory. This paper classifies types and looks at principles of interpretation. It suggests that identical component noun phrases are not interpreted through enumeration or structural semantic combination but rather through generative dimensions. This paper uses Pustejovsky’s(1995) generative lexicon theory to describe how meaning is synthesized and interpreted. In particular, the qualia structure enables basic understanding of an object and shows how it relates to the world. This paper analyzes the semantic operation of identical component noun phrases using this qualia structure. Additionally, this paper categorizes identical component noun phrase extraction sentences based on their argument structure, event structure, qualia structure, and lexical inheritance structure.
A Model for Enhancing the Information Security Management Systems in Yemen Banks

Exposure of valuable assets of Yemen banking sector to various attacks and threats may violate the privacy, confidentiality, integrity and accessibility (availability) of their data, information or computer systems resulting in financial and valuable loss. Such a violation may adversely affect the continuity of the business and the competitive advantages they seek to achieve. The objective of this paper is to evaluate the current security performance of banking systems in Yemen and to introduce a framework for improving information security management based on the ISO 27002-2013 benchmark and standards. The proposed framework includes important security factors which are people, technology and process. IBM SPSS AMOS is applied for testing framework hypothesis on complex variable relationships and gain new insight. Such framework shows that the process variable has obtained the most direct positive effect on improving information security management system with value (0.990), while the technology variable has obtained the second direct positive effect on improving the information security management system with value (0.930). Finally, the people variable has obtained the least direct positive effect on improving the information security management system with value (0.740). The research methodology includes: data collection, data analysis, reliability testing and computing results. The model validated and compared with other similar models available in the literature and the results were in valid range. Also, the research hypotheses were significant and deferent results of the proposed model were very promising.
System Design of Vocal Music Teaching Platform Based on Virtual Reality Technology

Virtual reality technology-assisted vocal music teaching is a brand-new development path with the progress of my country's science. Its technical advantages can provide vocal music learners with a new method of vocal music art learning. The purpose of this paper is to study vocal music based on virtual reality technology. Optimized design of teaching platform. The significance of applying virtual reality technology to network education is pointed out; the specific application of virtual reality technology in network education is summarized. Based on the teaching design and system design of the courseware, the courseware is produced by using VRML technology. The optimization scheme of virtual reality system is provided, the influencing factors of system performance are expounded, and various optimization schemes are provided, such as the optimization of picture rendering efficiency and effect. The overall performance of the system is improved through the study of optimization principles and optimization schemes, and solutions to performance problems are provided for relevant R&D personnel. Through the statistics of the questionnaire results, it is concluded that 89.8% of the students expect VR technology to be used in music teaching.
PEMANFAATAN MEDIA PAPAN KOTAK DALAM MENINGKATKAN HASIL BELAJAR MATEMATIKA PADA MATERI SATUAN BERAT KELAS 2 MIN 2 BANGKALAN

Based on initial data in class 2 MIN 2 Bangkalan, problems were found in learning mathematics in the unit weight material, namely students' lack of understanding due to the absence of learning media, so students were not active, bored and did not even master the material. This factor resulted in low student learning outcomes. By applying a gridboard learning media is an alternative solution to overcome problems in learning mathematics unit weight class 2 MIN 2 Bangkalan. The purpose of this study was to find out 1) the process of using square board media in mathematics lessons on weight units and 2) improving student learning outcomes on weight unit material through the use of square board media.&#x0D; The type of research used is classroom action research (CAR), which has 2 cycles. Each cycle consists of planning, implementing, observing and reflecting. The research location was MIN 2 Bangkalan, Labang District, Bangakalan Regency which was conducted from February to March 2023. The research used a quantitative approach. Data collection techniques used are tests, observation and documentation. The research instrument was in the form of student learning outcomes test sheets. The subjects of the study were all 2nd grade students for the 2022/2023 academic year, totaling 28 students, consisting of 12 male students and 16&#x0D; female students.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Journal homepage: https://ejurnal.univamedan.ac.id/index.php/jkpm/index&#x0D; &#x0D; &#x0D; &#x0D; The results showed that the use of blackboard media can improve student learning outcomes that achieve scores above KKM (76) with a percentage of complete learning outcomes, pre-cycle 39.29% with an average score of 66.4, Cycle I 57.14% with an average score of 76 and Cycle 2 100% with an average of 90. Based on the research it can be concluded that learning mathematics in units of weight with the use of media unit weight boxes can be successful, so that it can be&#x0D; used as an alternative solution to improve mathematics learning in MIN 2 Bangkalan &#x0D;
Prediction of Solar Radiation Using Machine Learning Algorithms

Design of solar energy systems and its installation at any location critically depends on the Global Solar Radiation (GSR) available at that location. However, it is not practical to do GSR measurements at every location because of the instruments that will be used. This paper discusses the study, modelling and prediction of global solar radiation (GSR) using machine learning (ML) algorithms. The objective of this paper is to use the meteorological parameters like GSR, temperature and wind speed measured at a location and develop a mathematical model using Machine Learning Algorithms. Such models can be used to predict GSR when direct measurement is not possible. In this study, various Machine Learning algorithms such as Linear Regression, Random Forest Regression, Artificial Neural Networks (ANN) and Deep Neural Networks (DNN) are used for modelling. Among these models, the most suitable method is selected based on the Root Mean Squared Error (RMSE) and Coefficient of Determinant (R2) values. It is found from the study that prediction of GSR with good accuracy is possible using Machine Learning algorithms.
Erasable-Itemset Mining for Sequential Product Databases

Erasable-itemset mining has become a popular research topic and is usually used for product production planning in the industry. If some products in a factory may be removed without critically affecting production profits, the set composed of them is called an erasable itemset. Erasable-itemset mining is to find all the removable material sets for saving funds. This paper extends the concept of erasable itemsets to consider customer behavior with a sequence of orders. We consider the scenario that when an item (material) is not purchased, a product using that material cannot be manufactured, and clients will cancel all their orders if at least one such order exists. We propose a modified erasable-itemset mining algorithm for solving the above problem. Finally, experiments with varying thresholds are conducted to evaluate the execution time and mining results of the proposed algorithm considering customer behavior.