Reviewerly/exHarmony huggingface dataset:
Faktor Penyebab Ketidaklengkapan Rekam Medis Rawat Inap di RSUD dr. Saiful Anwar Malang

One of efforts to improve the quality of healthcare facilities is to improve the quality of medical record services including completeness, speed and accuracy in providing information for health care needs. A complete and accurate medical record can be used as a reference to health services, legal bases, supporting information to improve medical quality, medical research and be used as a basis to assess the performance of hospitals. The purpose of this research identifies the factors causing the incompleteness of the replenishment of inpatient medical record in the RSUD Dr. Saiful Anwar Malang. The type of research used is qualitative. Data collection techniques by way of document study, interviews and observation. Document studies are conducted on 100 inpatient medical record documents that have not been conducted assembling, while the interview is conducted to the IRNA 2 medical record officer to determine the factors causing the incompleteness of medical record documents. Observation is done to complete the interview data. The results of the analysis on 100 inpatient medical record files were obtained that the incomplete number of medical records was 79%, with the most incompleteness presentation on the responsibilities of the doctor which includes medical resumes, casemix sheets, and surgery reports. The interview and observation results show that the main causation factor for the filing of an inpatient medical record of a surgical ward is from the doctor's awareness and discipline in filling in the medical record documents. As a solution to improve the completeness of medical record filling is by spur motivation and increase the discipline of doctors in filling medical record documents by providing reward and punishment and to repair SOP filling medical record of hospitalizatio.
Reliability and Security of Extreme Parallelism

As we approach the end of Moore's law, current processors are reaching their physical limits. The focus has now shifted to multicore processors and distributed computational systems. Multicore computers are now the de-facto for most PCs and the use of heterogeneous hardware such as graphics processing units are no longer limited to just the field of high-performance computing. With the increase in cores, the chance that a single unit might fail also increases. In addition, side-channel attacks have been performed to steal data using parallel processors. Hence, the added features of parallel computing also bring new avenues for unintended errors and malicious attacks. In this article, we provide a systematic overview of current reliability and security concerns in the context of extreme parallelism.
Go Basic Features

In this chapter, we will drill down some of the important characteristics that make Go what it is. When we are done with this chapter, you should be able to characterize the finer Go language similarities and differences from Java.
Real-time stereo matching model based on deformable convolution

The current stereo matching network models have excellent performance in the aspect of matching accuracy, but most models have large number of parameters, which is difficult to meet the requirements of real-time performance, and reducing the model size will lead to the decline of their accuracy. In order to improve the accuracy and real-time performance of stereo matching, a real-time stereo matching model based on deformable convolution was proposed. In order to improve its accuracy, the model adopts 2-dimensional deformable convolution to extract more effective features, and adopts 3-dimensional deformable convolution to process the matching cost volume after processing, aggregates the image pairs from the disparity and spatial dimension more effective correlation, forms a better matching cost volume, and uses spatial propagation network to optimize the disparity. In order to improve its real-time performance, the model adopts the method of residual learning to reduce the amount of memory and computation consumed in forming matching cost volume. The prediction errors are divided into three stages. The endpoint errors of the three stages on the SceneFlow dataset are 2.99, 2.26 and 1.99, respectively.
A repair model to improve arm problem in semantic segmentation generation

Image visual try-on aims to transfer the target clothing image to the reference person, and it has become a hot topic in recent years. Current techniques usually focus on preserving the character of clothing images when warping it to arbitrary human pose. However, it is still a challenge to produce realistic try-on images when the reference person has a complicated pose. We propose a novel repair model to solve this problem. We train the network by simulating arm breaks and improper occlusion, so that it can be automatically repaired. Compared to the newest advanced techniques, we improve upon the inaccuracy in the predicted semantic segmentation, proposed a new generator model to obtain more detail from images.
Application of the COCOMO II method to estimate the labor costs of developing software products for various platforms

This article is devoted to the study of methods for assessing the complexity of the development of various software products. One of the most popular methods – COCOMO II – was selected for the study. In this work, a brief description of the COCOMO II method is given, and an assessment of the labor intensity is carried out on real projects. Based on the results of the analysis, it is revealed in which cases the selected method show the smallest forecast error, and the reasons for error indicators are explained. Based on the research results, it is concluded that applying COCOMO II for assessing labor intensity of mobile software gives significantly worse result than that of traditional personal computer application.
POSSIBILITIES OF APPLICATION OF UAV DATA IN CONSTRUCTION AND ECONOMY TASKS

This paper provides examples of data from UAV experiments carried out on the territory of Kyrgyzstan for the tasks of construction and economics. Examples of processing the obtained images for creating a 3D model, planning a road network, constructing transverse profiles, determining the amount of repair work, the state of roads, cracks and faults for research, designing road works are given. The possibilities of using such data for geodesy and mapping tasks are also shown.
Критический разбор работы "Эксперимент по созданию канала радиосвязи в морской среде"

The article "Experiment to create a radio communication channel in the marine environment" (authors: A.K. Tomilin, A.F. Lukin, A.N. Gulkov), published in the journal "Technical Physics Letters" (2021, vol. 47, Issue 11), from the standpoint of classical physics. The organization of the experiment in the mentioned publication, its interpretation and theory underlying the work are criticized. The proposed material is a methodological commentary and is intended to form consistent approaches in the study and interpretation of further work on the creation of radio communication channels.
Medical diagnosis in Alzheimer’s disease based on supervised and semisupervised learning

Dementia is one of the most common neurological disorders among the elderly. Identifying those who are of high risk suffering from dementia is important to the administration of early treatment to slow down the progression of dementia symptoms and help preserve some cognitive functions of the brain. To separate probably or possibly demented patients from normal persons, some medical data with features for describing symptoms are required. However, to achieve accurate classification, significant amount of subject feature information is involved and sufficient class labels are needed. Hence, identification of demented subjects can be transformed into a pattern recognition problem with high-dimensional nonlinear datasets, and conventional machine learning methods can typically be utilized for handling the problem. This chapter will discuss the applications of supervised and semisupervised learning methods on dementia diagnosis. The typical limitations of the current research are discussed in detail.
Sistem Pakar Diagnosis Penyakit Pada Ikan Bawal Bintang dengan Pendekatan Naive bayes

The star pomfret is a type of cultivated fish that has high economic prospects. The focus of the main problem in this study is the disease that attacks the star pomfret fish commodity. If this is allowed to continue, it will cause crop failure and cause the fishermen to lose money. Through this research, an expert system is one solution that can overcome these problems. The expert system built will apply the Naive Bayes method with the stages of entering the dataset into the database which will be used as training data, then the user inputs testing data to be processed into the Bayes method, in the final result the probability value of each disease will be displayed which will then be given recommendations on how to control it disease. From the symptoms selected by the user, namely: white or pale spots on the surface of the body, bleeding on the surface of the body, protruding eyes, the fish looks difficult to breathe, mucus production increases until the body runs out of mucus / roughness, fish lose their appetite, slow movement and slow growth get disease results Cryptocaryon with a value of 93.4. The results of tests carried out on 17 data obtained an accuracy value of 94% so that the expert system is suitable for use as a tool for diagnosing disease in pomfret
The impact of COVID-19 on digital currency

In modern society, science and technology are developing rapidly, and people's life has gradually become inseparable from scientific and technological products. In terms of transactions and payment, scientists have developed a digital currency. As a new currency, the digital currency has gradually affected the application of paper money in the market. With the outbreak of COVID-19, people have paid more attention to the protection measures of transactions, and digital currency and online payment can make people more secure to trade.
Co-Movements between an Asian Technology Stock Index and Cryptocurrencies during the COVID-19 Pandemic: A Bi-Wavelet Approach

This study investigates the co-movement patterns of Asia technology stock indices and cryptocurrencies during the COVID-19 pandemic. The analysis examines Bitcoin and Ethereum, China’s Tech index (XA90), and India’s Tech index (NSEIT) from 2017 to 2021, representing both before and during COVID-19. To visually explore the co-movement between these variables, a bi-wavelet method is employed. This approach allows for an examination of how these variables move together over time coherently. There were noticeable changes in the co-movement patterns between technology stock indices and cryptocurrencies during COVID-19 compared to before the pandemic. The duration of co-movements decreased significantly after the emergence of COVID-19. The previous financial crisis had a longer time horizon for joint movement, lasting 256 days. However, during the pre-COVID-19 period, XA90 exhibited a strong co-movement with Bitcoin over this extended period but weakened afterward when COVID-19 emerged. Conversely, NSEIT showed a significant co-movement with both Ethereum and Bitcoin in the initial stages of the pandemic. Before that period, NSEIT had muted price movements along with BTC. These changes in price co-movements suggest shifts in herding behavior due to the pandemic. Notably, cryptocurrency markets have demonstrated faster recovery compared to technology stock markets.
Free Speech Goes Online

The increasing omnipresence of the internet since the late 1980s and the more recent triumph of social media have irreversibly changed communication. The ability for speech to traverse the entire globe in an instant and be stored indefinitely has led to a social context under which the principle of freedom of speech has come into question. The rise of social media platforms to near-universal levels of participation coupled with a distinct lack of speech regulation has led to a myriad of issues that are causing tension and conflict in the modern world. Problems arising from this new online context include the emergence of filter bubbles, the influence over speech by corporations, and the destruction of privacy.
A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x

Pedestrian detection is a crucial task in computer vision, with various applications in surveillance, autonomous driving, and robotics. However, detecting pedestrians in complex scenarios, such as rainy days, remains a challenging problem due to the degradation of image quality and the presence of occlusions. To address this issue, we propose RSTDet-Lite (a robust lightweight network) for pedestrian detection on rainy days, based on an improved version of YOLOv5-x. Specifically, in order to reduce the redundant parameters of the YOLOv5-x backbone network and enhance its feature extraction capability, we propose a novel approach named CBP-GNet, which incorporates a compact bilinear pooling algorithm. This new net serves as a new backbone network, resulting in significant parameter reduction and enhancing the fine-grained feature fusion capability of the network. Additionally, we introduce the Simple-BiFPN structure as a replacement for the original feature pyramid module based on the weighted bidirectional feature pyramid to further improve feature fusion efficiency. To enhance network performance, we integrate the CBAM attention mechanism and introduce the idea of structural reparameterization. To evaluate the performance of our method, we create a new dataset named RainDet3000, which consists of 3000 images captured in various rainy scenarios. The experimental results demonstrate that, compared with YOLOv5, our proposed model reduces the network size by 30 M while achieving a 4.56% increase in mAP. This confirms the effectiveness of RSTDet-Lite in achieving excellent performance in rainy-day pedestrian detection scenarios.
A module lattice based construction of post quantum secure blockchain architecture

Abstract The Fiat–Shamir with Aborts paradigm of Lyubashevsky introduced efficient lattice based signatures. A lattice‐based signature is used to design post quantum secure blockchain architecture. But, a blockchain is always consisting of multiple nodes, and then we need an efficient aggregate signature to verify nodes in one‐step. We have designed a blockchain using module lattices. Blockchain security relies upon two assumptions, (1) module learning with errors and (2) module short integer solution. The proposed design ensures security against quantum attacks. The design is simulated in ns‐3 to capture latency and throughput, respectively.
ROLE OF MATHEMATICAL MODEL IN CLOUD COMPUTING

Various Mathematical Model plays an important role in the modeling of Cloud Computing. Cloud Computing environment is the task scheduling which plays major role of efficiency for variouscloud computing facilities.Task scheduling means the workspaceto allocate best suitable resources for the task to be executedwith the consideration of different kinds of parameters like timeliness, costfunction, scalability, reliability, availability,utilization of resource, etc..The proposed model algorithm considers reliability, utilization of resource and availability.Most task scheduling algorithms do not consider the some of the aspects like scalability, reliability and availability of workspace (Cloud Computing Environment) because of various parameters. There is one of the best model using Mutation of Load Balancing by Particle of Swarm Optimization (MLBPSO)which is based on task scheduling.MLBPSO is based on schedule and allocation for cloud computing that takes the task into account reliability, execution time, transmission time, round trip time, transmission cost and load balancing between tasks and virtual machine. A virtual machine (VM) is an Operating System(OS)or application environment that is installed on software, which imitates several dedicated hardware. The end user has the same experience on a virtual machine as they would have on dedicated hardware.Created MLBPSO can play a important role in reliability of cloud computing environment and also there are chances of rescheduling of various task
Can the AI-Generated Content be Protected as Work Under Copyright Law? ---Jurisprudential Developments in China

Recent Chinese cases of the copyright disputes on AI-generated content (AGC) reveal challenges to copyright jurisprudence by Natural Language Processing (NPL)-based AI systems. Some scholars argued from the perspectives of intellectual characteristics of the AGC. This paper, however, combines historical and doctrinal methods to show that the production process of AGC should be the defining factor of work. This paper argues that modern copyright law does not protect the AI-generated content from the aspect of copyright law in intellectual writings/ paintings/ music/ art as work. Work in the history of copyright law requires direct human intellectual labor, which the AI lacks. AI does not have physical and / or mental labor to the levels of humans, and the human physical and intellectual labors put into building the AI system is different from the AIs production of contents. These are two separate and consecutive processes: humans make AI, and AI automatically generates contents. The second process cannot produce work because it does not involve comparable labor to humans.
GENRE AND STYLE SPECIFICITY OF UKRAINIAN BOOKTRAILERS ABOUT THE WAR

The article examines a new genre of advertising media products – the booktrailer, and offers a refined definition of it: booktrailer is a genre of advertising media products that synthesizes verbal, visual, and audio elements to promote books through information technology. The genre and style specifics of contemporary Ukrainian booktrailers about the war are determined. The conditional chronological division of the studied booktrailerized texts about the war has been applied into those written before the full-scale invasion (S. Zhadan “Boarding School” (2017), T. Horikha Zernia “Dotsia” (2019)), and those created after it (N. Sukhorukova “Mariupol. Hope”, P. Vyshebaba “Just Don’t Write to Me About the War”, V. Stancyshyn “Emotional Swings of War”, O. Mykhed “Cat, Rooster, Locker”). Each booktrailer contains narrative elements. Their use depends on the content of the video and varies in length and nature of the narrative. The use of visual elements, such as color palette, graphics, photos, animation, or video, can help convey the style or mood of a book. Audio accompaniment – voiceover, audio recordings of interviews with writers, voiced fragments of the book’s text, music – carries both a semantic and emotional load. The popularity of booktrailers is driven by the surge in social media activity, as well as the sale of books in electronic format, which maximizes the logistics of the book to the reader: viewed the booktrailer → ordered the book → received it on an electronic medium. The linguistic specificity of booktrailers varies depending on the target audience and the genre of the book. The language used in book trailers for books about war is intended to convey the tension, emotionality, and tragic (or life-affirming) nature of events related to military conflict. The target audience of Ukrainian booktrailers for books about the war covers different categories of readers of different ages, interests, and social status, as the war is now affecting the entire population of Ukraine to some extent.
Study on the Development of a Beginner Korean Speaking App - Focusing on the Curriculum of D University -

This study aimed to present the development process of learning smartphone apps based on the D University beginner speaking smartphone app development and examine the learning app development research process in Korean language education. The D University's smartphone app was developed according to the following six criteria: (1) the content was composed based on the systematic curriculum regular course; (2) it pursued the learning excellence distributing it around core contents; (3) it was developed to enable self-learning without teacher guidance; (4) it was developed to enable interaction between learners and contents; (5) learners made self-directed learning possible; and (6) accurate feedback and evaluation analysis on learning activities were made. In this smartphone app, each unit is composed of three sub-units to ensure, the items of each sub-unit are studied individually. In addition to learning vocabulary and sentence patterns, sentence practice and conversation practice were suggested, and a function to present the degree of agreement with model utterances in numerical form and model when the learner uttered a sentence was added. Consequently, learners can self-practice to increase their communication the accuracy. In addition, practical conversations corresponding to utilization were presented to ensure learners could select and perform each role. Finally, self-directed learning is made possible by providing a review and evaluation process so that learners can check their own learning results and review the lacking parts. This study is meaningful in that the contents of the smartphone app are organized into a systematic curriculum based on the main textbooks for regular college courses and that the actual development process is presented.
Free Speech and the Redefinition of Harm

The question of what constitutes harming another person is central to how and why humans seek to regulate the actions of others. John Stuart Mill, in his championing of liberty as the essential component for the development of the self, recognised the role that unrestricted action can play in the harming of others. Because of this, it is critical to establish an understanding of what exactly constitutes harm insofar as it requires the restriction of human action. While debate appears settled in regard to physical actions that constitute harm (physical injury or deprivation of liberty), the question of mental and emotional harm is not so easily decided—especially as it relates to speech. As a result, there has been a resurgence of both academic and wider public interest in what exactly constitutes harm, as calls for greater restriction on speech have become both louder and more numerous.
IMPROVING STUDENTS’ READING COMPREHENSION IN ANALYTICAL EXPOSITION TEXT USING STUDENT TEAM ACHIEVEMENT DIVISION (STAD)

The problem with this research is the students found difficulties in reading comprehension, especially in finding the main idea of a text. The students also lack the motivation to read the text in English. Those difficulties made the students cannot fully understand the text they read. The research aims to improve students’ reading comprehension in analytical exposition text using Student Team Achievement Division (STAD). The subjects of this study were 36 students in eleventh grade at SMAN 103 Jakarta. This research only consisted of one cycle that consist of two meetings. Each cycle consists of planning, acting, observing, and reflecting. The data collecting technique used in this research was test, observation, and questionnaire. The result showed that there are improvements in the average score of the students. The score in the pre-test was 70.5 improved to 84.2 in the post-test. Using Student Team Achievement Division (STAD) also made students motivated and teamwork improved along with their individual skills. The questionnaire also showed that the students responded positively to Student Team Achievement Division (STAD), and it showed from the questionnaire result that 58% of students gave positive feedback. The result of this research indicates that the use of the Student Team Achievement Division (STAD) can improve students’ reading comprehension in analytical exposition text.