Reviewerly/exHarmony huggingface dataset:
Web-Based Inventory Application and Prediction Using Naive Bayes Algorithm (Case Study: Puskesmas Kecamatan Sawah Besar)

The development of information systems and technology affects various fields, including health, namely Puskesmas. The Sawah Besar District Health Center has several work units, one of which is a stock management unit. The stock management unit in carrying out its duties has problems including data management that still uses Microsoft Excel. When using Microsoft Excel, information about stock data not integrated between different work units, thus hampering the process of accessing documents. The process of approval and stock prediction is also still done manually using physical documents. PHP programming language with Bootstrap Framework and MySQL used to develop applications. While PHP-ML library which implement Naive Bayes algorithm is used for stock prediction. The features developed in this study can be used by related units to simplify the stock data collection process, assist in the stock approval process and speed up the preparation of stock inventory reports. In addition, there is a stock prediction feature that can help users so to support while making decisions in the procurement of drug stock at the Sawah Besar District Health Center to determine whether certain drugs need to be added or not.
IT Support Website Security Evaluation Using Vulnerability Assessment Tools

Vulnerability Assessment is one of the crucial stages that must be carried out to define and identify vulnerabilities in web systems so that they can be repaired and reduced. The XYZ institution is new, so the Vulnerability Assessment is to minimize attacks from irresponsible parties. In this study, a Vulnerability Assessment of the IT Support website was carried out on XYZ institution using the Nessus tool. This study used the Vulnerability Assessment Penetration Testing (VAPT) Life Cycle method, which has six stages: scope, planning, scanning &amp; vulnerability Analysis, exploitation, Privilege Escalation, and Generating Report. The results of this study obtained various vulnerabilities ranging from Low to Critical on the IT Support website at XYZ institution so that the IT Support party at XYZ institution to update PHP versions, JQuery and several other preventive steps reviewed in the discussion section.
Development of Early Grade Indonesian E-Modules Using the Kvisoft Flipbook Maker Application

This article aims to 1) Produce products in the form of e-modules for Early Class Indonesian courses using the Kvisoft Flipbook Maker Application; 2) To find out the feasibility of developing an e-module based on the Kvisoft Flipbook Maker Application in the Early class Indonesian language course; 3) To find out students' responses to the development of e-modules based on the Kvisoft Flipbook Maker application in the Early Grade Indonesian course. The type of research conducted is research and development (R&amp;D). The development model used in this study is a 4-D development model (four D Model), namely define activities, design activities, development activities and are validated by experts and see the practicality of e-module products. 1) The results of material expert validation obtained an average of 86% in the very feasible category. Second, media experts get an average of 93% in the very decent category. Third, linguists get an average of 87% with a very decent category. Then, in individual trials, students responded to this e-module in the very good category with an average percentage of 93%. In the small group trial, the students' responses were in the very good category with an average percentage of 92.25%. Meanwhile, in the limited field group trial, the criteria of "good" were obtained with an average score percentage of 87,08%. Based on this percentage, it can be concluded that this e-module is suitable to be used as teaching material for Indonesian language courses in the Early Class of HKBP Nommensen, Pematangsiantar University. Keywords: Kvisoft Flipbook Maker, E-Modules for Early Grade, Deveopment E-Modules
Challenges of higher education in the face of the pandemic: Universidad Autónoma Metropolitana

The document is an empirical study that shows the context in which virtual teaching was developed at the Universidad Autonoma Metropolitana as a result of the health emergency. The study discusses the response of the educational institution to the covid-19 pandemic; the challenge faced by the University in the transition from face-to-face to virtual modality; the resources available to the educational institution; the preparation of teaching staff; the adaptation of students to the change of modality and the effectiveness of virtual instruments in the learning process in terms of educational innovation. This is an analysis that inclu­des information from data and surveys collected by the institution from teaching staff and students from the educational center at different stages of application of the virtual model. The study is complemented by the contextual development of higher education in Mexico within the environment of the pandemic, in order to better locate the response of the UAM in front of the university community
Kombinasi Penjualan Kain Batik Menggunakan Metode Apriori

The existence of operational activities selling batik cloth every day will increase more and more, If left alone, the transaction data will only become meaningless garbage. is an individual company that manufactures and sells batik with various types of batik. The types of batik include Batik Insights, Color Insights, Cash Written Batik, Fine Written Batik, Fine Granite Written Batik, Night Print Batik, Stamped Batik, Combined Batik, Dagel Batik. In processing sales data, it is still done manually, which only uses records and receipts, so the data only functions as an archive. The data used in this study are sales transaction data for 2 years with a total of 215 transactions. The purpose of this study is to implement apriori algorithm system for the combination of batik fabric sales on Dewi Ratih batik so that it can help to find out the types of batik that are most often purchased. The results of this study are to determine which combination of batik fabric sales are most often bought simultaneously by determining a minimum support of 30% and a minimum confidence of 70% with a comparison of Weka and PHP MysQL applications with an accuracy rate of 100%
Implementasi Metode Location Based Services Pada Sistem Presensi Pegawai

The attendance system is a digital attendance record, this is needed to evaluate employee attendance data. During the corona virus pandemic, the employee attendance system having some problems related to physical distancing policies, it needs problem solving to solve the problem, it is namely by maximizing the attendance coordinates of each employee, which use Location Based Services (LBS) technology method and haversine formula that functions to calculate the distance of the attendance coordinates with the employee's position. Creating the system using Software Development Life Cycle (SDLC), Waterfall, Node.js, Express.js, Flutter and MySQL as well as a VPS server. The final result of the employee attendance system can run on two platforms, the frontend version on android mobile devices and the backend version on the website. The test results on the employee attendance system received a "reasonable" predicate with an achievement range of 70-74 %. The conclusion of this research is creating the creation of an employee attendance system use location coordinates which is answered the problems on this research.
Sistem Penunjang Keputusan Penerimaan Siswa Baru Berprestasi Di SMP IT Surakarta Menggunakan Metode Nearest Neighbor Dan Simple Additive Weighting

SMP IT Nur Hidayah Surakarta, was established in 2004/2005 academic year. SMP IT Nur Hidayah has been graduated 3 generation of graduation, on the academic year of 2006/2007, 2007/2008 and 2008/2009. In the new student admission program there is an acceptance limit for students who are eligible for scholarships. The criteria used in the selection process include: average grades of report cards, written tests, parents' income and achievements. In decision new students, the school wants quality selection results by predicting new prospective students and then decide new students. The purpose of this study was to design a decision support system for new students using the Nearest Neighbor (NN) and Simple Additive Weighting (SAW) methods. This method was chosen because it is able to carry out a prediction process and a ranking process to determine the best alternative. The technique used in this research is observation, interview and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. The application is made using the PHP programming language and the database uses My SQL. The final results are in the form of student data reports, prediction reports, selection reports and reports on the best selection results. System testing is done by testing the functionality which shows the accepted results and the validity test which shows the valid results. The results of system testing with the MAPE test showed an accuracy of 88.7%.
Productive Home Arrangement for Brick Processors Based on Community Empowerment (Case: South Bontolangkasa Village, Bontonompo Sub-district, Gowa Regency)

The Brick processing in Bontolangkasa village, Bontonompo sub-district, Gowa district is very much so that the housing arrangement of the community is irregular. The use of the house as a residence as well as a business (productive house) so that there is a lack of a circulation system, the ventilation system to the dwelling is disturbed because of the heat from burning red bricks, irregular placement of traded red bricks. The solution to the problem of structuring the housing of the brick processing community in Bontolangkasa village, Bontonompo sub-district, Gowa district, requires community empowerment, namely providing information in data collection so that the drafting of the concept of structuring productive housing for the brick processing community can be implemented. The community service applied is the provision of information in the form of data that is compared to produce distances for the placement of brick processing systems in the residential area of the community. The arrangement of productive houses based on community empowerment in the brick processing houses of Bontolangkasa village is divided into 3 types of houses: stilt houses (balla rate'), stilt and modern houses (balla tingka') and modern houses (balla batu ), brick processing containers utilizing under the house and yard.&#x0D;
MRI and CT Image Based Breast Tumor Detection Framework with Boundary Detection Technique

Image segmentation is vital in image processing and computer vision, and it is also regarded as a bottleneck in image processing technology development. Picture segmentation is the process of dividing an image into a group of disjoint sections with uniform and homogeneous characteristics. Before proceeding with various statistical methods of analyzing segmentation of tumor, one has to understand the labels consisting of brain MR image. Because of the high inconstancy in tumor morphology and the low sign to-commotion proportion characteristic to mammography, manual characterization of mammogram yields a critical number of patients being gotten back to, and consequent enormous number of biopsies performed to decrease the danger of missing malignant growth. The convolutional neural networks (CNN) is a mainstream profound learning build utilized in picture arrangement. This procedure has accomplished huge progressions in enormous set picture arrangement challenges in later a long time. In this examination, we had acquired more than 3000 excellent unique mammograms with endorsement from an institutional survey board at the University of Kentucky. Various classifiers dependent on CNNs were manufactured, and every classifier was assessed dependent on its exhibition comparative with truth esteems created by histology results from biopsy furthermore, two-year negative mammogram follow-up affirmed by master. In this paper, a method for classifying traffic signs is proposed that is based on training the convolutional neural networks (CNN). Furthermore, it shows the preliminary classification performance of using this CNN to automatically learn and categorise RGB-D images. For this four-class classification job, the method of transfer learning known as fine tuning technique is proposed which involves reusing layers learnt on the ImageNet dataset to discover the optimal design.
Chaos Based Pseudo Random Bit Generator Design and Its Application in Secure Image Encryption

Security and privacy problems in communication systems and social media platforms where digital image/video are shared almost always, have attracted researchers interest on information security. Starting from this point of view, a novel one-dimensional chaotic maps based pseudo random bit generator is proposed to make a significant contribution to the literature about protection of personal data in any network. Electronic circuit realizations of Logistic and Tent maps as entropy source are designed on Orcad-Pspice environment and state variables are inputted to novel post-processor algorithm. The rest of main blocks of proposed pseudo random bit generator design are built up fixed-point binary conversion algorithm, XOR processor and H function post-processor. The generated pseudo random bit series are tested by using NIST 800.22 statistical test suite and applied to color image encryption in order to show the effectiveness of proposed design. Cryptanalysis processes such as histogram, NPCR-UACI and correlation analysis are demonstrated. Analysis results show that the proposed design can be used successfully in many secure communication and media transmission applications.
Hodson Assessment of Phonological Patterns Criterion HAPP-3 Translation and Arabization

and phonological disorders.The measurement has been introduced to a panel of referees to ascertain its validity.Moreover, researchers have applied the measurement to a sample of 100 ( 72 ) males and females( 28 ) children aged from 3 to 6 years old .The average age is 4.43 and a standared deviation 1.39 .Children's intelligence quotient (IQ) ranges from (90-110) .The children in the sample were in nurseries and centers for children.Results indicated the validity, reliability, and practicality of the test In Egypt.
Data Compression and Noise Reduction in Smart Grid Using Discrete Wavelet Transform

This paper proposed wavelet-based design using Discrete Wavelet Transform to compress smart grid electrical signals and to reduce noise. For the Smart Grid’s smooth functioning, the power signal must be monitored, and proper actions must be taken quickly for any abnormality. The compressed data takes less time to communicate the disturbances. The proposed design is tested for the phasor measurement unit, which monitors and records the status of the smart grid hence circulating extensive data to utilities, control centers, etc. It is also tested for load voltage data. Effective data compression can reduce the cost of data storage and transmission. Noise dramatically affects the effectiveness of the techniques detecting the disturbances. Hence data compression and denoising the data with minimum distortion is essential. The proposed design is simpler as it uses fewer filters and less number of decomposition level as compared to the existing design. Simulation results show that compression ratio and signal-to-noise ratios are increased as compared to existing design.
A Neighborhood Impact Driven K-Medoid Clustering and Fuzzy Logic Blended Approach for High Density Impulse Noise Detection and Removal

In the field of image processing, removing impulse noise has been regarded as one of the most important tasks, primarily because of the noise pattern it presents. Existing filters used the effect of only those non-noisy pixels which were present inside the specified windows ignoring the effect of the non-noisy pixels present in the surrounding windows. So, the least distant non-noisy pixels in the present window as well as in the surrounding windows may have an influence on the present window's noisy pixels. Hence, considering the above factors, in this paper, a two-step technique named KMDCIFF (K-medoid clustering identified fuzzy filter) is proposed for removing impulse noise from digital images. In the proposed KMDCIFF algorithm, the first step is noise detection using K-medoid clustering, followed by a fuzzy logic-acquainted noise reduction strategy that utilizes the least distant local and non-local non-noisy pixels for removal operation. The detection process involves the application of K-medoid clustering on all 5×5 windows produced by centering each pixel of the considered image. In order to remove noise, a 7×7 window is constructed with each detected noisy pixel in the center. Analyzing the impact of the least distant local and non-local pixel on each noisy pixel, the same is replaced by an estimated pixel’s intensity value obtained from the most influential non-noisy pixels. KMDCIFF is evaluated using well-known metrics for diverse types of images. At a high noise density of 80%, KMDCIFF exhibited significant peak-signal-to-noise-ratios (PSNRs) of 26.97 dB and 29.67 dB and structural similarity indexes (SSIMs) of 0.8045 and 0.9288 on random and fixed valued impulse noise impacted Lena image, respectively. Comparing the results of the contemporary study to those of previous studies of a similar kind in this sector, the results are unswervingly astounding.
Scanning the Issue

<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Vision-Based Semantic Segmentation in Scene Understanding for Autonomous Driving: Recent Achievements, Challenges, and Outlooks</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K. Muhammad, T. Hussain, H. Ullah, J. D. Ser, M. Rezaei, N. Kumar, M. Hijji, P. Bellavista, and V. H. C. de Albuquerque</i> Scene understanding in autonomous driving utilizes sensory data for contextual information extraction and decision making. Related research directions include person/vehicle detection/segmentation, their transition analysis, lane change, and turns detection, among others. Unfortunately, these tasks seem insufficient to completely develop fully autonomous vehicles, i.e., achieving level-5 autonomy, traveling just like human-controlled cars. This statement is among the conclusions drawn from this review paper: scene understanding for autonomous driving cars using vision sensors still requires significant improvements. Specifically, the authors define, analyze, and examine current achievements in scene understanding, covering the generic scene understanding pipeline, investigating state-of-the-art performance levels, informing about the time complexity analysis of avant-garde models, and highlighting major triumphs and limitations identified by recent research efforts. The authors also include a thorough discussion on available datasets and challenges that, even if lately confronted by researchers, remain open to date. Finally, research directions are offered to welcome researchers/practitioners to this exciting domain.
Outlier elimination method of GPS data points based on Hough transform

The electronic map of railway track can realize the monitoring and evaluation of line status, so as to reduce the workload of manual operation. In order to improve the fitting accuracy of the existing railway track electronic map, the measured data of the railway line are obtained by Global Positioning System (GPS), and the Gaussian coordinates of the measured data points are obtained through coordinate transformation. Based on the analysis of GPS data, the Hough transform is improved to eliminate the outliers in the straight track and curved track scenarios respectively. With the help of the idea of taking the average value of multiple measurements to eliminate errors, the processed data are corrected and the track line is fitted. The results show that the track route electronic map generated by the outlier elimination method based on Hough transform has high accuracy and has important practical significance and practical application value.
Research on the plane intelligent design of homestays based on deep learning

Abstract The design of the homestay exposed the problems of single-function layout and poor living experience. Therefore, the purpose of this article is to efficiently grasp the design requirements of the country house and to construct an algorithm for automatically generating the floor plan of the house to improve the design output efficiency. This paper takes the homestays surrounding the Paifang Street scenic spot in Chaozhou, Guangdong Province as an example, crawls the comment data of tourism websites through Python, classifies and constructs a portrait of the living needs of the country homes, analyzes the current pain points and opportunities of the country homes, and generates the subsequent plane personalized Plan the data research work; then combine the preliminary research and field research to put forward the principles of the graphic design of the homestay. The paper proposes a method of the automatic layout of architectural functions based on a generative confrontation network, which can automatically layout the architectural functions of rooms according to the design requirements of different homestays, forming a more complete floor plan.
Aspect Based Sentiment Analysis - Twitter

&lt;p&gt;Due to the increased use and popularity of social media platforms in the most recent technological period, sentiment categorization has emerged as an important research area among those platforms. When it comes to Twitter, the main problem of previous research is, they all did a sentiment classification on the document level (Tweet level). It cannot classify the sentiment for any particular aspect. When it comes to the review of any multifunctional product and service, gathering an overall positive or negative mood may not be helpful to the firms as it is more crucial to ascertain precisely what their customers are happy or upset about, to bring the updates and changes on that particular product and service. &lt;/p&gt; &lt;p&gt;In addition to this, what if someone wants to know the sentiments about recently generated data or tweets? What if someone wants to know the sentiment for data between a particular date range? What if users want to get sentiment of the tweets regarding current ongoing events and happenings? Along with this, very few of them performed aspect-based sentiment analysis on other platforms and they are using the same data set for training purposes as well as analytics purposes. So here we come up with the idea of Aspect based sentiment analysis on twitter, in which we train our model with a publicly available dataset, and then the user will give a particular hashtag and aspects. Our system will get tweets related to that specific hashtag from publicly available daily search twitter API and our model will take those tweets as input for analytics. Then machine learning operations will be performed on those tweets to find sentiment analysis for that hashtag’s tweet and its aspects with the best accuracy. In that way, we can get responses from people on any event, feedback or national issue, or matter of people's support. The experimental findings also showed that our method beats current state-of-the-art approaches.&lt;/p&gt;