Reviewerly/exHarmony huggingface dataset:
Conventional Feature Selection Filter Based Techniques Used in Image Classification

With the increased volume of unstructured information coming from various sources, image classification has become substantially more applicable. Various image arrangement strategies have been developed. One of the set up issues in image arrangement is the high-dimensionality of element space. Highlight determination is one of the methods to lessen dimensionality. Highlight determination helps in expanding classifier execution, decrease over sifting to accelerate the grouping model development and testing and make models more interpretable. A review of experimental results examining the execution of few element choice methods (Chi-squared, Information Gain, Mutual Data, and Symmetrical Uncertainty) coupled with classifiers such as guileless bayes, SVM, choice tree, and k-NN. The purpose of this paper is to investigate the effects of component determination strategies on different classifiers on image datasets. The concentrate also enables contrasting the general exhibition of the classifiers and techniques. The assessment of element determination techniques for image grouping with little example datasets should think about arrangement execution, dependability, and productivity. It is, consequently, a numerous rule navigation (MCDM) issue. However, there has been little examination in include determination assessment utilizing MCDM techniques that think about various standards. Subsequently, we use MCDM-based strategies for assessing highlight determination techniques for image arrangement with little example datasets. Trial review of five MCDM strategies is intended to compare and contrast the proposed approach with 10 component choice techniques, nine assessment measures for paired characterization, seven assessment measures for multi-class ordering, and three classifiers with 10 little datasets. We propose strategies for highlight determination based on the positioned effects of the five MCDM techniques. This study indicates the effectiveness of the utilized MCDM-based strategy in evaluating highlight determination techniques.
An improved two-dimensional image encryption algorithm using Huffman coding and hash function along with chaotic key generation

Several picture encryption techniques have been developed in recent years. This work proposes a supported and advanced encryption technology and encryption, 2D encryption picture encryption method on paper. The sha-256 method removes picture components first, then generates a replacement key that matches the user’s key before inserting the original image. For every grey image size, we enable rows and columns in the same order by the same members created by the Logistic map. We determine the beginning values and parameters of complicated maps using the sha-256 hash of a clear image, making an important change in all encryptions without modifying the standard keys. Finally, the method is used to recover the encrypted picture. The system can accomplish good encryption results and security standards, according to test findings and security analyses. Data transmission over the Internet and personal networks has risen day by day as multimedia technology has advanced. As a result, today’s main focus is safeguarding data from unauthorised access and encrypting multimedia elements as they are stored on web servers and sent over networks. As a result, data encryption for multimedia is necessary. The multimedia encryption approach, on the other hand, is difficult to apply since it requires more time and memory.
Bounded-Cost Bi-Objective Heuristic Search

There are many settings that extend the basic shortest path search problem. In Bounded-Cost Search, we are given a constant bound and the task is to find a solution within the bound. In Bi-Objective Search, each edge is associated with two costs (objectives) and the task is to minimize both objectives. In this paper, we combine both these settings into a new setting of Bounded-Cost Bi-Objective Search. We are given two bounds, one for each objective and the task is to find a solution within these bounds. We provide a scheme for normalizing the two objectives. We then introduce several algorithms for this new setting and compare them experimentally.
Rise of Digital Forensics and its impact on criminal justice: A study of tools and techniques involved

With the technological advancement in various fields, the digital forensics technology has also been revolutionized and as a result, criminal justice system has been substantially changed. However, various information technologies has not just enabled the researchers to find out the criminals but also helped them to identify the root cause for crimes. Along with that, there has been a growth in justice system as a result of the technological advancements. In the study, various aspects of digital forensics have been identified and described effectively. In India, the numbers of crimes are growing faster and their techniques are changing as well. That is creating challenges to make justice against it without adequate evidence. In that case, which kinds of evidence can be collected and from which resources, have been discussed here with clear information. How the technological changes are impacting the criminal investigation procedures and bringing more efficiency, have been discussed as well. Keyword : Digital forensic technologies, Criminal activities, Criminal justice, Justice System, Evidence.
STUDENTS’ ABILITY TO IDENTIFY MAIN CLAUSE AND SUB CLAUSE ON REPORTED SPEECH BY FIFTH SEMESTER AT MUHAMMADIYAH UNIVERSITY OF MAKASSAR

This research aimed to aimed to find out students’ ability to identify main clause and sub clause on reported speech By fifth semester At Muhammadiyah University of Makassar. This study used a descriptive quantitative research approach. The sample size was 30 students in total. Purposive sampling was utilized by the researcher. The data gathering instrument was written test.Based on the findings, the researcher found tha students’ ability to identify main clause and sub clause. Total score of the main clause test was 2.212, and the total numbers of the students were 30 while mean score of the students’ ability to identify main clause was 73.73 (good). Total score of the sub clause was 2.212, and total numbers of the students were 30 while the mean score of the students’ ability to identify sub clause was 73.73 (good) means some students were still difficult to determine them, and some students were having good comprehension about them. However this score need to be improved because this category is still low for college’s students.From the findings above, it can be concluded that students’ ability in identifying main clause and sub clause have good comprehension. However, the researcher concluded this score need to be improved.
SCORING MODEL OF ONLINE COURSE AS A TOOL FOR EDUCATIONAL PROCESS QUALITY MANAGEMENT

2.5.5, 2.5.7, 2.5.8)
MANAJEMEN PEMBELAJARAN PONDOK PESANTREN DALAM MEWUJUDKAN PEMAHAMAN SANTRI TERHADAP KITAB KUNING

This study indicates that learning is carried out with a series of activities consisting of the preparation, implementation, and closing stages. Understanding student's level consists of (1) high, (2) moderate, (3) low. Assessment criteria (a) reading according to language rules, (b) the position of a word, (c) text understanding.
Reputation Mechanism Designed for Blockchain Empowered Dynamic Spectrum Sharing System

Blockchain-based dynamic spectrum sharing (DSS) is viewed as a robust measure to address the management and allocation of spectrum resources for Internet of Things (IoT) ecosystems in a secure and auditable manner. However, existing blockchain empowered DSS systems commonly assume the perfect communications among the nodes without any interference and throughput constraints, which will mislead the spectrum owner into making suboptimal decisions, especially under the time-varying and unstable channel situation in IoT. To overcome the influence of the imperfect channel situations on DSS, we propose a novel reputation mechanism-based blockchain empowered DSS system in this paper. By considering both the historical transaction successful rate and the communication throughput and the real-time channel situations, the reputation mechanism enables the spectrum owner to make the appropriate decisions even under the occurrence of the deep decay. Furthermore, the spectrum trading process is formulated as a Stackelberg game to encourage the participation of the spectrum owner and the requester. A pricing scheme is proposed by maximizing the utilities of all nodes jointly. Simulation results confirm that our proposed scheme is superior to existing works in revenue utility.
ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL SPLDV BERORIENTASI PISA DENGAN KONTEN CHANGE AND RELATIONSHIP PADA KELAS VIII SMP UNISMUH MAKASSAR

This study aims to determine student errors in solving PISA-oriented SPLDV questions with change and relationship content in class VIII SMP Unismuh Makassar based on the Newman error procedure. This type of research uses a qualitative approach with descriptive methods. Data collection techniques used were giving tests and interviews. The instrument used was a written test in the form of a description of two numbers adapted from the PISA questions and interviews of students who were selected as research subjects to ensure mistakes made when solving the questions. The researcher chose four students as research subjects from 19 students of class VIIIA 1 based on the most mistakes made to be interviewed. The data analysis technique was carried out by the stages of data reduction, data presentation, data verification and drawing conclusions. Student error analysis refers to errors with the Newman stage which consists of 4 categories, namely errors in understanding the problem, errors in transforming the problem, errors in processing skills and errors in writing the final answer. The results showed that the most errors were made by FQ students. The research subjects who were then interviewed were SM on the type of understanding error, ST on the type of transformation error, SP on the error type of process skills and SK on the type of error in writing the final answer.
Duração de sintomas por COVID-19 em pacientes pós-internamento em hospitais terciários no Ceará

Despite advances in knowledge about COVID-19, little is known about the course of the disease and its long-term outcomes. Therefore, post-pandemic research has focused on the sequelae and complications caused by the Coronavirus. Thus, the objective was to investigate symptomatological remains and their duration of discharged patients hospitalized for COVID-19 from three regional hospitals in Ceará. This is a descriptive, analytical cross-sectional study with a quantitative approach carried out from August to October 2021 through telesurvey. The final sample consisted of 49 patients in the first wave of the pandemic and 153 in the second, totaling 202 participants. Data were tabulated in an Excel® spreadsheet and analyzed according to descriptive statistics. There is a prevalence of male patients, 31 (63.3%) in the first period investigated, and 86 (56.2%) in the second wave. In the investigation of physical symptoms, fatigue/tiredness stood out, identified in 23 (46.9%) and 69 (45.1%) individuals according to the wave, followed by a lack of appetite in 13 (26.5%) and (40.5%). Shortness of breath was highlighted in the second period among 39 (25.5%) individuals. Symptoms such as memory loss, cough, and chest pain appear to be the most lasting, and were prevalent for up to 4 months after hospital discharge. While in the 2nd wave chest pain was cited lasting up to 6 months. In this context, this study exposes the main symptoms reported, pointing to the imminent need for public strategies for monitoring and intervention services with these patients.
A novel approach for verifying selective user identity attributes online using open banking APIs

In this work, we propose a novel workflow to tackle the problem of verifying individuals’ identities online assuming banks as a central source of truth. The existing process for this involves auth mechanisms from privately held companies, where the data do not have any physical backing by an authorized entity, reducing its credibility. Alongside, the online verification methods used are unsophisticated, like passwords and OTPs, and anyone can easily impersonate with access to one’s devices and credentials. Considering alternatives like national identity cards to prove data, one needs to verify original documents again and again physically, decreasing customer’s inspiration for the activity. One more issue to tackle is excessive data getting shared during authentication can serve maliciously against the users. Companies utilize this data for digital profiling of users and targeting advertisements. Hence, only data attributes of utmost necessity should get shared. We have tried to create a web app named Canfirm to demonstrate the possibilities, workflow and use of open banking APIs.
Review on deep neural networks of video inpainting

Inpainting term denotes the process of refill the holes created from the operation of removing a particular region in an image or video. It’s considered one of the important topics in computer vision and pattern recognition processing which may be used to enhance images or videos by removing unwanted objects. Video Inpainting is one of the most challenging tasks in computer vision due to the spatial and temporal information in video. In this survey various deep learning networks that dealing with video inpainting and the datasets that are used in these networks.
IMPLEMENTATION OF TEXT MINING FOR CLASSIFYING COMMUNITY QUESTIONS VIA WHATSAPP WITH THE NAÏVE BAYES CLASSIFIER METHOD

Text mining is the process of exploring knowledge based on specific patterns of textual data retrieval. There was an increase in the amount of text data from community questions on the Whatsapp information service at Surabaya Immigration Office which can be processed into detailed and complete information. Text data entered through Whatsapp question has also not been classified specifically, structured and also has not been published. This study aims to explain the characteristics of incoming messages provided by the public through the Whatsapp information service and to explain the process of classifying community questions according to the field of immigration public services namely WNI and WNA. The authors used the classification method with the Naïve Bayes Classifier (NBC). Obtained the value of classification accuracy with algorithms and methods using the Naïve Bayes Classifier on the training data equal to 93.5% and testing data equal to 95% that included in the excellent scale. Therefore, Naïve Bayes Classifier method is very well applied for classifying public questions and SIPESAN system.
CITRAAN DALAM ANTOLOGI “PUISI-PUISI CINTA” KARYA W.S. RENDRA

AbstractThe background of this study is analyzing and decriminalizing Citraan in the anthology of “Puisi-puisi Cinta” by W.S. Rendra. The imagery includes visual imagery, auditory imagery, touch imagery, taste imagery, olfactory imagery, motion imagery, and feeling imagery. The purpose of this study is to be able to describe Citraan in the Anthology of “Puisi-puisi Cinta” by W.S. Rendra. This research uses descriptive methods and qualitative research forms. The approach used in this research is structural. The data in this study is sourced from the Anthology of “Puisi-puisi C inta”by W.S. Rendra. Based on the results of analysis on poetry there are images of vision, auditory imagery, image of touch, imagery of smell, imagery of motion, and image of feelings in it then this study has a link to the learning of Indonesian Language and Literature class X odd semester curriculum 2013 refers to Basic Competence 3.17 and 4.17. The results of this study are expected to be useful for many circles, especially for teachers, learners, and for further researchers.Keywords: Imagery, Implementation, Poem
Market Basket Analysis Menggunakan Association Rule dan Algoritma Apriori Pada Produk Penjualan Mitra Swalayan Salatiga

Market Basket analysis is learning to manage associations in data processing in various fields. The main purpose of Market Basket analysis in the field of sales is to convey an important message to the company so that it can find out the behavior patterns of entering goods into the shopping basket by consumers so that partners can make a decision. In this study, the Apriori Algorithm is used to take into account changes that occur in the data. This study discusses data mining techniques in analyzing what items are most often purchased at the same time by consumers so that they can change the placement of items that are close together to increase the impulse buying effect. The results obtained are 5 rules where one of the rules obtains the highest confidence value when buying cigarettes, the dominant item is taken simultaneously, namely eggs by obtaining a confidence value that can meet the highest confidence requirements, namely 67%.
Pengaruh Pembelajaran Daring terhadap Motivasi Belajar Siswa pada Pembelajaran Matematika di Sekolah Dasar

This study aims to describe the online learning system at SDN 11 Ranomeeto, describe the level of fifth grade students' learning motivation when learning online, and analyze the effect of online learning on students' learning motivation in fifth grade mathematics learning. This research is a quantitative study with a survey approach. The research was conducted at SD Negeri 11 Ranomeeto with 22 students as respondents. Data collection techniques are observation, questionnaires, and documentation while data analysis techniques use descriptive analysis techniques and inferential analysis. The results of the study show that the online learning at SD Negeri 11 Ranomeeto V during the Covid-19 pandemic used the Whatsapp application. The level of student learning motivation is in the (moderate) category. The simple coefficient value (r) is 0.935 which means that there is a very strong relationship between online learning variables and learning motivation. The results of the coefficient of determination (KD) show that the influence of online learning variables on learning motivation is 87.5%, while 12.5% is influenced by other factors not discussed in this study.Keywords: Online learning, student’s learning motivation, elementary school
Professional Development Of The Teacher: History And Modern Approaches

Education is considered as a complex multi-process, including enlightenment, upbringing and training. Modern pedagogical activity faces two major problems: the expansion of the scope of this activity and the transition from subject-object relations to subject-subject.
Protocols for secure outsourcing of data storage in the cloud and analysis of their resistance to active and passive attacks

The paper explores new approaches to developing methods for storing big data using various cryptographic solutions, such as the Shamir secret sharing method, the Diffie-Hellman key distribution protocol, etc. Note that various researchers have proposed various methods for storing data in the cloud. In this paper, we explore methods and protocols for secure outsourcing for storing big data using secret sharing technology and analyze their resistance to active and passive attacks. Such problems are especially relevant in the context of the rapid development of the Internet of Things (IoT). Chips, smart cards, and other physically small devices tend to have significant memory limitations, so there is a need to use cloud storage as an auxiliary tool for secure data storage.
On the Efficiency of AdapTTA: An Adaptive Test-Time Augmentation Strategy for Reliable Embedded ConvNets

Test-Time Augmentation (TTA) is a popular technique that aims to improve the accuracy of Convolutional Neural Networks (ConvNets) at inference-time. TTA addresses a limitation inherent to any deep learning pipeline, that is, training datasets cover only a tiny portion of the possible inputs. For this reason, when ported to real-life scenarios, ConvNets may suffer from substantial accuracy loss due to unseen input patterns received under unpredictable external conditions that can mislead the model. TTA tackles this problem directly on the field, first running multiple inferences on a set of altered versions of the same input sample and then computing the final outcome through a consensus of the aggregated predictions. TTA has been conceived to run on cloud systems powered with high-performance GPUs, where the altered inputs get processed in parallel with no (or negligible) performance overhead. Unfortunately, when shifted on embedded CPUs, TTA introduces latency penalties that limit its adoption for edge applications. For a more efficient resource usage, we can rely on an adaptive implementation of TTA, AdapTTA, that adjusts the number of inferences dynamically, depending on the input complexity. In this work, we assess the figures of merit of the AdapTTA framework, exploring different configurations of its basic blocks, i.e., the augmentation policy, the predictions aggregation function, and the model confidence score estimator, suitable for the integration with the proposed adaptive system. We conducted an extensive experimental evaluation, considering state-of-the-art ConvNets for image classification, MobileNets and EfficientNets, deployed onto a commercial embedded device, the ARM Cortex-A CPU. The collected results reveal that thanks to optimal design choices, AdapTTA ensures substantial acceleration compared to a static TTA, with up to 2.21 $$\times $$ faster processing preserving the same accuracy level. This comprehensive analysis helps designers identify the most efficient AdapTTA configuration for custom inference engines running on the edge.
MEMBANGUN LITERASI DIGITAL SISWA ERA NEW NORMAL

The purpose of this research is to build students' digital literacy through digital literacy training. The research method uses a quasi-experimental method with a one group pre-test and post-test design. The results of the pre-test and post-test showed an increase in students' understanding of digital literacy skills as indicated by an increase in the average score from 50.8 in the pre-test to 96.2 in the post-test. From these statistical results, it can be concluded that this digital literacy training activity is able to increase students' understanding of digital literacy skills in schools.
Implementation of K-Means and Agglomerative Hierarchical Methods to House Clusterization

People in general will think that building a house with a larger building area will cost more than building a house with a smaller building area. This view is not always correct because one of the factors that affect the size of the cost depends on the size of the building and the ratio of the mixture between cement, sand and lime. The smaller the cement mixture for buildings, the smaller the costs will be. Based on this case, the researchers grouped the data (clustering) using Euclidean distance to measure the distance between points. Grouping 200 data based on 2 features, namely the amount of cement and the amount of costs that do not have a label. The results showed that clustering with the K-Means method was able to group 200 data into 3 groups with the results of group one as many as 50 data, group 2 as many as 50 data, group 3 as much as 100 data with a computation time of 0.444 seconds and silhouette 0.82 while the results of clustering research using the Agglomerative Hierarchical method with a single linkage shows 100 data in group 1,50 data in group 2, 50 data in group 3 with a computation time of 3.22 seconds and silhouette 0.51