Reviewerly/exHarmony huggingface dataset:
Pemodelan Arsitektur Enterprise Sistem Informasi Rencana Kerja dan Anggaran Rumah Sakit dengan Zachman Framework

Raden Mattaher Jambi Regional General Hospital is required to submit a Work Plan and Budget every period. The preparation of the RKA report starts from collecting proposals from each work unit to the approval process by the Board of Directors on the proposals of each work unit. In an effort to streamline business processes that occur, analyze data needs, processes and also actors involved in organizational activities, an Information System is needed to streamline processes that occur, collect all required data and create proposal reports. In addition, to unravel the complexity of the processes that occur, a framework is needed that can be used as a guide and reference in modeling the organization's business processes. This study uses the Zachman Framework as a reference framework that includes what, how and who columns and rows in the perspective of planners, owners and designers. From the results of the analysis carried out with the Zachman Framework, then the information system architecture modeling is carried out by utilizing tools in the form of flowmaps, use cases and class diagrams. With modeling that is designed to be a reference for developing information systems and unraveling the complexity of the process and knowing the parties involved and the required system interfaces.
Benefits And Approaches Of Artificial Intelligence

Artificial Intelligence (AI) is going to undoubtedly very soon go to the facility of every significant technical devices on earth, featuring remittances, conformity, economical markets, protection as well as also self-defense, health care along with Internet of Things (IoT). A specific work indicates that the layout of the inputs as well as additional outcomes is known.
ATM Fraud Detection System using HMM &amp; SVM Algorithm

The banking sector has long been a critical institution that contributes significantly to a country's economic sustainability and maintenance.When bank transactions are tampered with by intruders or fraudsters, the results can be disastrous.This article aims to examine the current system of Electronic Fund Transfer (EFT) ATM activities in terms of cash withdrawal, fund transfer, password hacking, pin misplacing, and biotechnology.The article will look at many types of frauds and try to come up with a solution for solving and detecting frauds on ATMs, as well as a more advanced machine that can accept security technology.Fraudsters have untiring times making illegal moneys while the proposed algorithm in this work will combat most efforts of illegalities regarding funds by electronic data processing (EDP) in the Banking sector; this will be achieved by data mining the bio data though biometric combinational operations at the initial opening of the accounts and as such will conform with the algorithm proposed; the paper worked carefully using the existing literatures and systems to combine the approaches of biometric to the already existing ones and making a complete proposal for a design of ATM engine that will be having on it an incorporated thumbprint capture area and the possibility of the eye scanners and also make sure it doesn't slow down the process to unacceptable speed.
AI Chatbot Using Dialog Flow

In this article, conversational AI takes on increasing importance as it enables an easy interaction interface between humans and computers. Thanks to its promising potential and enticing corporate values to serve as virtual and/or social assistants chatbots, major AI, NLP and research & amp; Mining conferences explicitly require conversational studies contributions.The main purpose of the app is to help unassisted customers while minimizing staff distraction.The chatbot responds to the consumer with textual messages.This is accomplished by Natural Language Processing (NLP).Natural language processing (NLP) is a popular method for building Chabot apps, and we're utilizing it to build a versatile customer-specific Chabot.NLP recognizes the purpose of the user entity with a high degree of accuracy by analysing user input against three distinct engines.Customers can get help from this chatbot for their specific requirements.Furthermore, the purpose of this research is to create a knowledge system with a high potential for application specificity.
What is Open-Source Intelligence and How it Can Prevent Frauds

Abstract: This paper introduces the concept of Open Source Intelligence (OSINT) and How OSINT can prevent Frauds .The Estimated Data Consumption from 2021 to 2024 by finances online could increase from 74 zettabytes to 149 zettabytes [1] and most of this data is Publicly available. OSINT is an intelligence that is Produced by collecting, processing, analysing and correlating the information available publicly. Crimes such as fraud, illicit trade, and security are developing in the digital era of the twenty-first century, and new methods and procedures are emerging that can make them tougher to detect and investigate. OSINT investigations gather insights from open source data (OSD), revealing information on possible business partners, clients, suppliers, and workers. With the assistance of next-generation OSINT tools, corporate investigations teams and anti-fraud specialists can make the rapid and correct choices necessary to avoid reputational and financial harm
Teaching Coagulation Using Bio Coagulant from Mango Skin Against Cement Wastewater to Vocational School Students through Experimental Demonstrations

Abstract: The purpose of this study was to determine the effect of video learning on the coagulation process using a mango peel-based bio-coagulant on cement wastewater for vocational school students. The bio-coagulant fabrication is done by drying the mango peel in an oven at 60 degrees Celsius, mashing it in a blender, then uniforming the powder particle size using a 200 mesh sieve. The mango peel bio-coagulant powder was then applied to cement wastewater that had been conditioned to an acidic atmosphere with variations of 100, 150, 200, 250, and 300 mg/L. Cement wastewater was tested through pH, temperature, and total dissolved solids (TDS) tests before and after adding the bio-coagulant powder to determine the effect of the bio-coagulant application. This research was also conducted by providing pretest questions, learning videos, and posttest questions to vocational students. The results showed that giving bio-coagulants to cement wastewater could increase the pH value, had no effect on temperature changes, and could decrease the TDS value. Then, the learning outcomes for vocational students show that the use of video learning media has a low effect on students' understanding of the coagulation process. Keywords: bio-coagulant, coagulation, mango peel, teaching, students
Development of Android-based Multimedia to Improve Student Learning Outcome in Crystal Structure Subject of Engineering Materials Course

Abstract: The study aims to develop and evaluate the android-based multimedia for Crystal Structure subject in the Engineering Materials course. A Crystal Structure is one of the materials contained in the Engineering Materials course. The concept must be mastered well so that students can understand the subject matter being taught. Meanwhile, without being facilitated with adequate media, students experienced difficulties in the learning process. This is because the material of this Crystal Structure subject is abstract and complex. The multimedia development used the Analysis, Design, Development, Implementation, Evaluation (ADDIE) model which consists of five stages. The multimedia developed was validated by experts to assess its feasibility. Furthermore, it was implemented with experimental methods on the control group and the experimental group to find out the impact on improving student learning outcomes. The samples used in this study were 18 DPTM class A students as a control group and 18 DPTM class B students as an experimental group, with an age range of 17-20 years. The results confirm that the android-based multimedia that was developed received a very good response from users and experts. Android-based learning multimedia can facilitate students in understanding the Crystal Structure subject. This can be seen from the increase in learning outcomes in the experimental class using android-based learning multimedia with an N-gain value of 0.94 and categorized in the high category. Keywords: android-based multimedia, crystal structure, engineering materials, learning outcome.
Adaptive pruning threshold based convolutional neural network for object detection

In this paper, it is proposed that the redundancy in convolutional neural networks of object detection can be effectively removed via an adaptive pruning threshold method (APTCNN) which is associated with scaling factors in batch normalization layers. In this way, the channel pruning can be done iteratively with varying pruning threshold until the satisfactory performance is obtained. The method is also useful for identifying the unimportant convolutional layers. Therefore it can be applied for layer pruning. The experiments are conducted on three benchmark object detection datasets. APTCNN is verified for pruning the backbone network of object detectors YOLOv3 and YOLOv3-spp. It is shown that the importance of channels and layers are accurately ranked by the proposed adaptive threshold. For the channel pruning, our method reduces the size of YOLOv3 and YOLOv3-spp by 32× and 48× respectively, and accelerates 1.7× and 1.9× respectively. However, the accuracy suffers only 0.77% and 1.32% loss, respectively. As a result, the redundancy in the network architecture can be efficiently removed yielding a slimmed model that has lower computing operations, reduced size, and without compromising accuracy.
Opportunity Solution Tree

Based on your research and current understanding, articulate the main aim of your initiative in the form of a single outcome.
Detection of object analysis with data classification in confidence order

Abstract Object analysis with the detection of an image, in order to specify the classification might be distributed in the coordinate order for rendering the data movement in the order distributed from the classified image. To distribute the image variable in an coordinate sequence that acquire the order to be achieved in the refinement of data that attain the image, with the distribution order that render the sequence in movement from the other object. Object with the classification order that position the movement with the coordinate relation that distribute the movement order with the image that is notated for classification. With the image that classify the model under the distribution data that analyze the order, with the relation mapped in the specific coordinates, the image that order the function in relation with the coordinate that analyze the image in position from the order. Image that analyze the model under distribution for the order mapped into function. In the Existing system with the use of different probabilistic features to calculate the uncertainity data image in computation. In our Proposed model might require the huge integration of a analyzed image data in the probabilistic data in the order metric
Encoding High-Level Features: An Approach To Robust Transfer Learning

Transfer Learning (TL) plays a vital role in image classification systems based on Deep Convolutional Neural Networks (DCNNs). Systems employing such technique may be susceptible to distortions on images, motivating the development of robust DCNNs capable of facing these problems. Unfortunately, changes in the architecture of DCNNs are sometimes specific to a kind of distortion and result in models that need to be retrained from scratch. This work proposes the use of autoencoders as intermediaries between pre-trained DCNNs and classifiers, delegating the denoising task to this architecture trained to encode feature maps. The classifiers are then trained to map the inputs from the autoencoder latent spaces to their respective classes. Models employing this approach achieved 3% to 4% increase in accuracy and 50% to 70% reduction in loss on the CIFAR10 and CIFAR100 datasets. The results also showed an up to 80% reduction in loss and up to 15% increase in accuracy for images with unseen distortions compared to the classical TL approach. This work improves classification results and increases robustness to distortions in a straightforward manner.
Secret Numerical Interval Decision Protocol for Protecting Private Information and Its Application

Cooperative secure computing based on the relationship between numerical value and numerical interval is not only the basic problems of secure multiparty computing but also the core problems of cooperative secure computing. It is of substantial theoretical and practical significance for information security in relation to scientific computing to continuously investigate and construct solutions to such problems. Based on the Goldwasser-Micali homomorphic encryption scheme, this paper propose the Morton rule, according to the characteristics of the interval, a double-length vector is constructed to participate in the exclusive-or operation, and an efficient cooperative decision-making solution for integer and integer interval security is designed. This solution can solve more basic problems in cooperative security computation after suitable transformations. A theoretical analysis shows that this solution is safe and efficient. Finally, applications that are based on these protocols are presented.
Attacking Random Forest Classifiers based on Shortest Path Algorithm

Though learning-based models have shown high performance on different tasks, existing efforts have discovered the vulnerability of classifiers to evasion attacks. Recent work has shown that individual models are more vulnerable than ensemble models in adversarial settings. However, we have empirically demonstrated that ordinary integration methods do not always improve the robustness against black-box attacks, which is more common in the physical world. In this paper, we prove that random forest does not effectively defend against adversarial attacks, even if it is highly discrete. The proposed non-gradient based algorithm can be fast implemented and receives binary feature inputs. We experimentally compared the robustness of random forests and SVMs using white-box and black-box assessments respectively, and show that random forests and decision tree are consistently worse than SVMs.
Optimization of Processing Power by Improving the Performance of Application Service

This article discusses the problems of optimizing computing power by improving the performance of web applications. The shared responsibility model for the Azure cloud is considered and the area of responsibility of the user and the provider of cloud services for such models as IaaS, PaaS and SaaS is shown. The article discusses methods for optimizing loops and provides the results of performance tests in Microsoft Visual Studio using Performance Profiler and C# language. Methods for optimizing such constructs as algebraic expressions are described and ways to increase performance from caching are considered. Optimization methods are described when using the String type. The article also looks at how boxing/unboxing objects of reference and value types improve application performance. The practical steps given in the article are aimed at writing safe and efficient code. Please, note that the tests and optimization designs carried out and described in the article will help to write code that will not cause poor application performance in the future. These steps will help to avoid buying software to detect optimization problems and, accordingly, significantly reduce material costs for the entire project as a whole.
Development of a Depression Ontology as a Necessary Step for the Effective Implementation of IT in Psychopathology

Informational technologies have been used in medicine and biology for decades. Machine learning algorithms have proven effective for the analysis of data of all possible types and disorders diagnoses. Decision-making support systems, which were based on ontologies developed at the turn of the century, have also been widely disseminated. However, in psychopathology the situation is different: only a few attempts to implement the IT are presented and their efficiency remains questionable. Furthermore, in recent years, there has been lower progress in the research of mental disorders, despite the growing role of the latter in modern society. In this article, we try to explore the reasons for mentioned problems. From our point of view, the main cause is the absence of a satisfying formalized representation of mental disorders area. Therefore, we assume that it is necessary to create an ontology of mental disorders as a comprehensive and noncontradictory model and propose our approach to the development of the ontology of a more specialized data domain such as Depression, as an initial step. In the article, we also consider already existing approaches to the formalization of the depression and mental disorders data domains and identified possible perspectives.
PERCEIVED CHALLENGES AND SATISFACTION OF EDUCATION STUDENTS IN ONLINE DISTANCE LEARNING

The study aimed to determine the perceived challenges and satisfaction levels of Bachelor of Elementary Education in online distance learning. The descriptive-correlational design was used in this study. The investigations were conducted at Notre Dame of Midsayap College on fifty (50) BEED students who are enrolled in online distance learning for School Year 2021-2022 and the majority of them were female students. A researcher-modified survey questionnaire was used in data gathering, Frequency and percentage distribution, mean and standard deviation, t-test and ANOVA, and Pearson-r correlation were used in treating the data. The study revealed that the students have challenges in online distance learning. The study found out that the highest challenges encountered by the students in online distance learning are slow internet connection due to their location and facing many interruptions when learning at home such as household chores and other errands. The students are highly satisfied with how teachers showed interest in helping students to learn and the teacher’s accessibility and availability. Moreover, the respondents are least satisfied with how timely teachers give feedback on their performance. The study also revealed that there was a significant difference in the satisfaction level of the students in terms of their sex.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0369/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
HOME AUTOMATION USING EYE BLINK SENSOR FOR PARALYZED PEOPLE

The constant demand to improve daily living standards for paralysis patients serves as a motivation to develop newer technology. The taskonce performed by big traditional computers is now solved with smaller smart devices. Paralysis isdefined as the complete loss of muscle function in any part of the body. It occurs when there is a problem with the passage of messages between the muscles and the brain. The main objective is todesign a real time interactive system that can assistthe paralysis patients to control appliances such aslights, fans etc. The eye blink sensor is able to detect an intentional blink from a normal blink, which is useful for the paralysis patients especially Tetraplegic patients to regulate their home deviceseasily without any help.
Experimental and Numerical Evaluation of Mechanical Properties for Carbon Fiber Reinforced Epoxy LY5052 Composite for Prosthesis Structures

Carbon Fiber Reinforced Epoxy is a material that is widely used in the manufacture of prosthesis structures. In this research, the carbon fiber used is plain weave-type carbon, while the matrix used is LY5052 epoxy, LY5052 epoxy has advantages such as low viscosity and long pot life making it possible to manufacture large-sized prostheses. The first step in this research is making specimens using the vacuum infusion method and then cutting the specimens with dimensions referring to ASTM D3039. Furthermore, tensile test experiments were carried out with the results obtained that the maximum stress () was 537.15 MPa. As an evaluation, a tensile test simulation was carried out with Finite element analysis using Abaqus software, the material properties used were the results of a tensile test experiment, in the process the mesh selection was carried out by input sizing control many times this was done to determine the accuracy of the results. The tensile test simulation results with abaqus software are 523.3 MPa when compared to the experimental, the difference is only 2.58%. based on experimental and simulation results, the mechanical properties of the Carbon Fiber Reinforced Epoxy LY5052 Composite material are almost the same, so that in the manufacture of prosthesis the mechanical properties data in this study can be used when performing numerical analysis in the prosthesis design process.
The Role of English and Technology on Human Performance in International Business

Our aim is to investigate concepts in previous research regarding English Language Education through Human Performance and International Business. We used Content Analysis to compile scientific articles from Emerald, Science Direct and Korea Science as well as books written by experts in the field of English Education. We find that teachers and students alike must acquire sensitivity, emotional awareness, understanding of the teaching and learning process, and understanding of practical learning experiences. English can improve students' academic achievement and can improve their job prospects after graduation. English can also have a social and cultural impact. Culture can play an important role in life in various disciplines, not only in education but also in the world of work; culture has a significant direct impact on work motivation and performance of human resources. English also plays an important role in international business relations because English is an international language. Improving the quality of education, international business and human performance can also be affected by technology. One of these technologies is Artificial Intelligence helps the need for translation of English into the mother tongue used by teachers and students in learning English. Artificial intelligence has a significant impact on business performance.
Facial Recognition Attendance Scheme on CCTV Cameras Using Open Computer Vision and Deep Learning: A Case Study of International University of East Africa (IUEA)

There are plenty of home security cameras currently on the market, many allowing you to stream live footage over the internet and receive alerts whenever someone wanders past. This paper discusses the facial recognition attendance scheme on CCTV Cameras using OpenCV and Deep learning. Generally, Face Recognition is a method of identifying or verifying the identity of an individual by using their face. Various algorithms are there for face recognition but their accuracy might vary. In this paper, we discuss how we can do face recognition using deep learning. We used face embeddings to perform deep_metric_learning and the development steps of the scheme were; face detection, feature extraction, and lastly comparing faces. The legacy system used at IUEA Was firstly studied in more detail. More requirements for the proposed system were obtained and the system was developed. The interfaces for the new system were implemented using HTML, Bootstrap, and Django which is a high-level Python web framework. After the implementation, the new system was then tested, validated, and deployed for use.