Reviewerly/exHarmony huggingface dataset:
Development of Corporate Digital Archives in the Industrial Age 4.0

CV. Ziefa Karya is a company in Labuhanbatu which is engaged in construction contractors who have experience in working on national projects. CV. Ziefa Karya has used computer applications in daily data processing. However, archive storage and management is still done manually. The current filing system can be said to be still less efficient and effective because the incoming and outgoing mail data is often not in accordance with the actual incoming and outgoing mail data. For that we need a solution to overcome this problem. This study aims to build an information system that can be used to manage digital-based archives in CV. Zief Karya. The information system built is web-based. System development using the waterfall model. This research has produced an archive management information system on CV. Ziefa Karya web-based. The result of this research is a website-based mail filing system on CV. Ziefa Karya which has various uses such as data collection of incoming and outgoing letters, details of incoming and outgoing letters, classification of types of letters, disposition of incoming letters, reports of incoming and outgoing letters, and printing of reports of incoming and outgoing letters, as well as printing disposition sheets. Records management information system on CV. Ziefa Karya is able to provide convenience for admins and users in managing digital-based archives. To build a web-based archive information system on CV. Ziefa Karya, development must be carried out by applying the stages in the Waterfall method.
Optimasi Penentuan Menu Makanan Pendamping Air Susu Ibu Menggunakan Algoritma Genetika

Indonesia has a bad situation in stunting. Stunting is a problem because of the consumption of less nutrition. This is caused by giving food that is not in accordance with the nutritional needs of infants. Infants 0 until 24 months old need attention in choosing a weaning food to support the baby’s growth. The purpose of this research is to find an optimized way to improve the menu of weaning food with the genetic algorithm method. It is expected to be able to reduce stunting suffering in Indonesia. The process of the genetic algorithm method uses the fitness function of a baby’s daily nutritional needs. The selection is Roulette wheel selection method, the crossover is whole arithmetic crossover with 0.5 crossover rate, and the last is the mutation process with 0.6 mutation rate. The research used 100 generations and the result is menus for breakfast, lunch, and dinner for 6 days. The genetic algorithm can determine optimal weaning food’s menu.
Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy

Large pre-trained neural language models have supported the effectiveness of many NLP tasks, yet are still prone to generating toxic language hindering the safety of their use. Using empathetic data, we improve over recent work on controllable text generation that aims to reduce the toxicity of generated text. We find we are able to dramatically reduce the size of fine-tuning data to 7.5-30k samples while at the same time making significant improvements over state-of-the-art toxicity mitigation of up to 3.4% absolute reduction (26% relative) from the original work on 2.3m samples, by strategically sampling data based on empathy scores. We observe that the degree of improvement is subject to specific communication components of empathy. In particular, the cognitive components of empathy significantly beat the original dataset in almost all experiments, while emotional empathy was tied to less improvement and even underperforming random samples of the original data. This is a particularly implicative insight for NLP work concerning empathy as until recently the research and resources built for it have exclusively considered empathy as an emotional concept.
Tinjauan Tata Ruang Unit Rekam Medis dalam Menjaga Keamanan dan Kerahasiaan Rekam Medis di RSUD Kembangan

In the Medical Record Unit of the Kembangan Regional General Hospital, the work space of the medical record unit is directly related to the active medical record storage room, the door of the medical record unit at the Kembangan Regional General Hospital does not yet have a secret key or additional security such as a fingerprint. The purpose of this study was to determine the layout of the medical record unit in maintaining the security and confidentiality of medical records at the Kembangan Regional General Hospital. This study used quantitative descriptive methods with observation and interview data collection techniques, as well as direct measurements in the workspace and storage of the medical record unit of the Kembangan Regional General Hospital using a measuring instrument in the form of a building meter, then to determine the need for storage space obtained from the calculation of shelf requirements. medical record storage using the formula according to the International Federation of Health Information Management Associations. The results of the research are in the medical record unit of the Kembangan Regional General Hospital already have standard operating procedures related to the security and confidentiality of medical records but in practice it has not been implemented optimally. Inadequate facilities and infrastructure can be seen from the current work area of 10.27 m3, it should takes 15 m3, the storage space is still minimally lit and does not meet the standards. Suggestion: it is better if standard operating procedures have not been implemented to be implemented immediately.
Hybrid Feature Selection based Classifier for Non Functional Requirements

Feature Selection aims at finding the most relevant features of a problem domain. It is very helpful in improving computational speed and prediction accuracy. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. In this paper, we introduce a hybrid FS method which combines two FS methods – the filters and the wrappers for NFRs classification. Candidate features are first selected from the original feature set through two filter techniques then the union of these two sets are further refined by more accurate wrappers. This hybrid mechanism takes advantage of both the filters and the wrappers. The mechanism is examined by primary dataset based on NFRs. Experimental results show that better prediction accuracy can be achieved with a smaller feature set.
High Performance Consensus without Duplication: Multi-pipeline Hotstuff

The state-of-the-art HotStuff operates an efficient pipeline in which a stable leader drives decisions with linear communication and two round-trips of message. However, the unifying proposing-voting pattern is not sufficient to improve the bandwidth and concurrency performance of the modern system. In addition, the delay corresponding to two rounds of message to produce a certified proposal in that scheme is a significant performance bottleneck. Thus, this study developed a new consensus protocol, Multi-pipeline HotStuff, for permissioned blockchain. To the best of the authors' knowledge, this is the first protocol that combines multiple HotStuff instances to propose batches in order without a concurrent proposal, such that proposals are made optimistically when a correct replica realizes that the current proposal is valid and will be certified by quorum votes in the near future. Because simultaneous proposing and voting are allowed by the proposed protocol without transaction duplication, it produced more proposals in every two rounds of messages. In addition, it further boosted the throughput at a comparable latency with that of HotStuff. The evaluation experiment conducted confirmed that the throughput of Multi-pipeline HotStuff outperformed that of the state-of-the-art protocols by approximately 60\% without significantly increasing end-to-end latency under varying system sizes. Moreover, the proposed optimization also performed better when it suffers a bad network condition.
The Relation of Indonesian Language Learning to the Environment

Language is a medium of communication to convey the contents of thoughts and goals and objectives clearly to the people who are invited to communicate both orally and in writing. Language functions as a means of interaction and communication in society. In addition to being a means of interaction, language can also be a sign of identity from the form of human behavior or the culture of a nation. Therefore, language has a very important role in life, especially Indonesian. However, the reality that has happened is that there are still many Indonesian people who are not fluent in using Indonesian properly and correctly because they are influenced by regional languages and lack of interest in learning Indonesian. For this reason, Indonesian language learning can be taught from an early age through its environment, both in a formal and informal environment.
Dynamics of Panic Disorders in the Study of Psychological Readiness of Personality for Professional Activity

The article highlights the results of the applied study "Development of psychological readiness of teachers for professional activities in the New Ukrainian School", conducted at the Ivan Ziaziun Institute of Pedagogical and Adult Education of NAES of Ukraine. The main tendencies and influence of anxiety states on the psychological readiness of the individual for professional activity during the implementation of the NUS Concept under the conditions of quarantine and war are analyzed. The peculiarities of the dynamics of panic disorders due to being under the conditions of hostilities are highlighted.
VIRTUALLY THROWING BENCHMARKS INTO THE OCEAN FOR DEEP SEA PHOTOGRAMMETRY AND IMAGE PROCESSING EVALUATION

Abstract. Vision in the deep sea is acquiring increasing interest from many fields as the deep seafloor represents the largest surface portion on Earth. Unlike common shallow underwater imaging, deep sea imaging requires artificial lighting to illuminate the scene in perpetual darkness. Deep sea images suffer from degradation caused by scattering, attenuation and effects of artificial light sources and have a very different appearance to images in shallow water or on land. This impairs transferring current vision methods to deep sea applications. Development of adequate algorithms requires some data with ground truth in order to evaluate the methods. However, it is practically impossible to capture a deep sea scene also without water or artificial lighting effects. This situation impairs progress in deep sea vision research, where already synthesized images with ground truth could be a good solution. Most current methods either render a virtual 3D model, or use atmospheric image formation models to convert real world scenes to appear as in shallow water appearance illuminated by sunlight. Currently, there is a lack of image datasets dedicated to deep sea vision evaluation. This paper introduces a pipeline to synthesize deep sea images using existing real world RGB-D benchmarks, and exemplarily generates the deep sea twin datasets for the well known Middlebury stereo benchmarks. They can be used both for testing underwater stereo matching methods and for training and evaluating underwater image processing algorithms. This work aims towards establishing an image benchmark, which is intended particularly for deep sea vision developments.
The Relationship between Indonesian Language Learning and the Environment

In communicating, a language is needed. Language plays an important role in human life because language is a tool of human communication in everyday life (Iskandarwassid andDadang, 2009: 226). Language is a medium of communication, so language is a mirror of one's personality, meaning that through language one can know one's personality or character (Pranowo, 2009: 3). Thus, language is one of the fields that plays an important role in shaping one's character.
Improvement of garbage classification using pretrained Convolutional Neural Network

Recycling waste is necessary for the sustainability of a good social life. The recycling process for waste is generally carried out manually and with many types of waste categories. For the waste sorting process to run more effectively and efficiently, automatic waste sorting is required. One example is by using an image-based waste type classification system using deep learning or machine learning algorithms. In previous research, AlexNet and SVM were used as classification algorithms for waste types in 6 different waste categories. The results of this study show that SVM performance is better with an accuracy of 63% compared to AlexNet's performance with an accuracy of 20%. Whereas in general, the CNN algorithm's performance should produce better accuracy than SVM. Based on this, we propose to re-examine the classification of waste types in the same dataset as previous research using the latest CNN algorithm architecture, namely ResNet. More completely, the architectures used are AlexNet, ResNet-18, and ResNet-50, respectively with and without pretrained, so that a total of 6 types of CNN algorithm architecture are used in the training. In this study, the AlexNet architecture is used with a different configuration from previous studies. The test parameter is the number of epochs of 50 epochs with attention to how many epochs the training results have shown convergent. From the training results, the highest accuracy obtained by the ResNet-50 architecture with pretrained is 91.16% and shows convergent results since the 14th epoch. Then for the lowest accuracy obtained by the AlexNet architecture without pretrained, which is 58% and shows convergent results since the 21st epoch.
EYE BALL CURSOR MOVEMENT USING OPENCV

In this study, a specific human computer interaction system using eyeball movement is presented. Conventionally, computer system uses mouse as one of the data input devices. But in this system, we use eyes instead of mouse which provides a unique way of operating the computer with the help of eyeball movements. The implementation work underlying this system for pupil identification uses OPENCV library to control the cursor of the personal computer and moreover Eye Aspect Ratio technique is ascertained along with Dlib to detect the pupil. This system tracks the eye movements of the user with an IP cam (Internet Protocol camera) and simulates the eye movements into mouse cursor movements on screen and also detects user’s eye staring on icon and will translate it into click operation on screen. The main aim of this system is to help the user to control the cursor without the use of hands and is of great use especially for the people with disability.
Fixes of permutations acting on monotone Boolean functions

We present a few algorithms and methods to count fixes of permutations acting on monotone Boolean functions. Some of these methods was used by Pawelski \cite{P} to compute the number of inequivalent monotone Boolean functions with 8 variables.
Improving the Availability of Space Research Spatial Data*

The rapid development of space technology and the increased interest in space exploration have resulted in the intensive observation of celestial bodies, mostly in the solar system, over the past decade with the prospect of an upward trend in the future.Large amounts of collected data on space bodies impose the need to develop the Spatial Data Infrastructure of Celestial Bodies at the general level to enable standardized organization and storage of these data, and their efficient use and exchange.To approach the development of such an infrastructure, it is necessary to investigate what data, as well as how and to what extent, are collected through space observation.It is also necessary to investigate how this data can be obtained.This paper provides an overview of planetary spatial data archives, data storage and retrieval methods, and their shortcomings in the context of easy search, download and interpretation of data, all with the aim of establishing Spatial Data Infrastructure of Celestial Bodies that would make space data more accessible to the public and non-planetary scientists.
Efficient Neuro-Fuzzy Identification Model for Electrocardiogram Signal

This paper addresses the performance of the Artificial Neural Networks (ANNs), Fuzzy inference systems (FISs) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for the identification of some nonlinear systems with certain degree of uncertainty. The efficiency of the suggested methods in modeling and identification the responses were analyzed and compared. The Back-propagation algorithm and Takagi-Sugeno (TS) approach are used to train the ANNs, FISs and ANFIS, respectively. In this study we will show how ANFIS can be put in order to form nets that able to train from external data and information compared to ANNs and FISs. In order, it is proposed forms of inputs that can be used along with ANNs, FISs and ANFIS to modeling nonlinear systems. Two nonlinear systems with an electrocardiogram (ECG) signal in the form of simulation and complexity were used to test the identification of the structure presented. Because ANFIS has an inherent capacity to approximate unknown functions and to adjust the changes in inputs and parameters, it can be used to identify the proposed systems with a very high level of complexity. The results show that the ANFIS technique can provide the most ideal approximation when the right structures are employed.
approach to temporal concept localization in videos

Localizing moments in the videos has been a new challenging task in the field of Computer Science to provide faster search time for video retrieval, query processing and also behavioral analysis. The process involves stages such as video understanding, video segmentation, query processing using NLP and generation of localization of the concepts in the video. Though there have been many attempts to Video understanding in the field of NLP and Computer Vision in past years, they lack to cover the large untrimmed videos in current real-life scenarios. We propose the deep learning-based solution with the use of Random Forest and Bi-LSTM approach to localize the labels in segments and also the time at which they pertain to the particular segments. We used the YouTube 8M dataset provided by YouTube in Kaggle's challenge to train our frame-based model and use it to classify the segments using sliding windows of size 5. Our approach tries to provide a naïve and robust approach to model this concept and provide a way to tackle this large problem. Further improvements in the Bi-LSTM based models and Random Forest models with VLAD would lead to better results.
A Robust Color Image Watermarking Scheme Based on Discrete Wavelet Transform Domain and Discrete Slantlet Transform Technique

Watermarking is primarily used to prevent unauthorized copying of digital data by inserting a watermark as a symbol of ownership in the digital data. The purpose of this work is to increase the imperceptibility and robustness of watermarked image by utilizing the Discrete Wavelet Transform (DWT) domain and Discrete Slantlet Transform (DST) technique. In compared to existing methodologies, the strategies used in this work considerably increased the robustness, imperceptibility, and protection of the watermarked image against JPEG compression and other types of noise attacks. The proposed method's robustness was determined by comparing the image's Normalized Cross-Correlation (NCC) value before and after the watermarking operation. The proposed method increased the NCC value to greater than 0.7 and the Peak Signal-to-Noise Ratio (PSNR) value to greater than 59 decibels (dB). Additionally, the proposed approach does not require knowledge of the original image in order to extract the watermarks.
Overparameterization Improves StyleGAN Inversion

Deep generative models like StyleGAN hold the promise of semantic image editing: modifying images by their content, rather than their pixel values. Unfortunately, working with arbitrary images requires inverting the StyleGAN generator, which has remained challenging so far. Existing inversion approaches obtain promising yet imperfect results, having to trade-off between reconstruction quality and downstream editability. To improve quality, these approaches must resort to various techniques that extend the model latent space after training. Taking a step back, we observe that these methods essentially all propose, in one way or another, to increase the number of free parameters. This suggests that inversion might be difficult because it is underconstrained. In this work, we address this directly and dramatically overparameterize the latent space, before training, with simple changes to the original StyleGAN architecture. Our overparameterization increases the available degrees of freedom, which in turn facilitates inversion. We show that this allows us to obtain near-perfect image reconstruction without the need for encoders nor for altering the latent space after training. Our approach also retains editability, which we demonstrate by realistically interpolating between images.
Pengaruh Pengendalian Internal, Penerapan Standar Akuntansi Keuangan (SAK), Dan Bukti-Bukti (Kecukupan dan Kelayakan) Audit Terhadap Opini Laporan Keuangan Wajar Tanpa Pengecualian “Studi Penelitian Di Kantor Akuntan Publik Di Jakarta Selatan

The purpose of this study was to determine the effect of internal control, the application of financial accounting standards (SAK), and audit evidence (adequacy and feasibility), either simultaneously or partially. The population used in this study are auditors who work at the Public Accounting Firm (KAP) located in South Jakarta. The sampling technique in this study used the purposive sampling method. This study uses primary data in the form of a questionnaire distributed at the public accounting firm in South Jakarta. Hypothesis testing in this study using multiple linear regression. The results show that the magnitude of the coefficient of determination is 0.535 or internal control, the application of financial accounting standards (SAK) and audit evidence (adequacy and feasibility) can explain the unqualified opinion on financial statements by 53.5%, while the rest (100%- 53.5% = 46.5%) is explained by other variables that are not used in the study such as limitations on audit scope, accounting systems and procedures, client survival factors, and company integrity, and the results of the hypothesis show that internal control, application of accounting standards Financial Statements (SAK), and audit evidence (Sufficiency and Eligibility) have a significant effect simultaneously or partially on the unqualified opinion on the financial statements.
Improvement of practical training of professional teachers in commodity science

The subject of research: the need to improve the professional competence of the applicant by introducing new components of the curriculum and acquiring skills during practical work. The purpose of the study is to identify the main aspects and directions of professional competence, as well as its formation, which should be carried out in the process of training a teacher of vocational training in commodity science in higher education from the standpoint of modern practical approach. The field of application of results. The results of the study can be used in the activities of higher education institutions to increase the effectiveness of practical training of teachers of vocational training in commodity science. The results of the research and conclusions. On the basis of the conducted research, the improvement of the formation of professional competence of future teachers of professional training in commodity science is revealed, which is deepened and consolidated during the internship. Professional and practical training of bachelors is one of the main elements of the educational process and a prerequisite for their successful employment.