Reviewerly/exHarmony huggingface dataset:
Analysis of well water feasibility as water consumption in Boarding House Area, Jember District Indonesia

Currently, as many as 45% of Indonesian people use well water to meet their daily needs. However, problems with the use of well water occur due to increasing population growth, resulting in increased water pollution. Waste generated by human activities can seep into the ground and contaminate well water. This research was conducted to determine the feasibility of well water as consumption water in the Kos area of ​​Jember Regency. The method used in this research is a descriptive observation method. The study used seven samples of well water originating from the Kos area of ​​Jember Regency and 1 sample of mineral water as a comparison. Samples of well mineral water were tested using physical, chemical, and biological tests. Physical tests were carried out on color, odor, temperature, and total dissolved solids (TDS). The chemical test was carried out in the form of a pH test. Biological tests were carried out by observing changes in color and odor due to bacteria after the samples were left for ten days. The results showed that three water samples did not meet the physics test, namely sample 4, sample 6, and sample 8. One well water sample did not meet the chemical test, namely sample 4. Five water samples did not meet the biological test: sample 2, sample 4, sample 6, sample 7, and sample 8.
A Novel Approach for Software Defect Prediction using CNN and GRU Based on SMOTE Tomek Method

Abstract Software defect prediction (SDP) plays an important role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to the methods that use historical defect data to build the relationship between software metrics and software defects. Several prediction models such as machine learning (ML), deep learning (DL) have been developed and adopted to recognize defect in software modules and many methodologies and frameworks have been presented. One of the most difficult problems that these models face in binary classification is the classes imbalance. When the distribution of classes is unbalanced, the accuracy may be high, but the model cannot recognize data instances in the minority class, this will lead to weak classifications. So far, few research have been done in the previous studies that address the problem of class imbalance in SDP. To address the class imbalance problem, we propose a novel SDP approach based on convolutional neural network (CNN) and gated recurrent unit (GRU) combined with synthetic minority oversampling technique plus Tomek link (SMOTE Tomek). To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets which obtained from the PROMISE repository and the experimental results have been compared and evaluated in terms of accuracy, precision, recall, f-measure, the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), mean square error (MSE). The average accuracy of the proposed models on the original datasets were 89% for CNN and 87% for GRU, while the average accuracy of the proposed models on the balanced datasets were 94% for CNN and 92% for GRU. The results showed that the proposed models on the balanced datasets improves the average accuracy by 5% for both models compared to original datasets. This indicates the positive effects of combining ML techniques with data balancing methods on the performance of defect prediction regarding datasets with imbalanced class distributions.
PERANCANGAN APLIKASI MEMBANTU MENCARI PELATIH DALAM BEROLAHARAGA BERBASISKAN MOBILE

Sport is an activity that is easy to do, but sometimes we need someone to direct us so that the exercise we do can provide maximum benefits. Along with the number of active smartphone users of more than 100 million people and internet users in Indonesia reaching 175.4 million with a presentation reaching 64% of the total population, this creates business opportunities on the internet, especially in terms of sports. This research discusses the design of applications engaged in sports, especially in the field of hiring sports trainers. The purpose of this research is to analyze, plan business, design and create applications based on mobile applications. This application sees an opportunity in the use of information and communication technology in the field of sports, to increase user interest in sports applications. In addition, this will also have an impact on increasing community participation in sports activities. The method that the writer uses to collect data is library research, questionnaires and interviews. The results of this study are that the application has features that make it easy for customers to carry out sports activities and also understand sports material.
IMPLEMENTATION OF CHATBOT CUSTOMER SERVICE FEATURES ON PT DIAN PRIMA JAYARAYA USING DIALOGFLOW

The customer service team functions to accommodate questions and answer questions experienced by application users. There are times when the number of questions that are accommodated is so large that the existing customer service team does not handle them. Ignored or unanswered questions can reduce customer satisfaction levels. For this reason, we need a system that can help automate customer service activities so as to avoid unanswered customer questions. A chatbot is a service powered by a set of rules and sometimes artificial intelligence, which interacts with users via a chat interface. Chatbot will be built within the iOS prototype application and will use the Natural Language Processing (NLP) platform, Dialogflow. This platform will later process and scan every question given by the customer so that by implementing this feature the customer service team can work more effectively because some of the manual activities have been automated. The results of this study are that the chatbot system created can reduce the frequency of questions by Solo application users by up to 92.82%. Thus, it is hoped that the customer service team can work more effectively and strategically, and can increase customer satisfaction.
HEART DISEASE CLASSIFICATION MODEL IN LEVEL II HOSPITAL MOH. RIDWAN MEUREKSA USING NAVE BAYES

Heart disease is the number one cause of death in the world. Heart disease is a disease due to blockage of the heart's blood vessels which causes a disturbance in the balance between blood supply and demand. Level II Hospital Ridwan Meureksa is a hospital that provides inpatient, outpatient and emergency services. One of the services at the Level II Hospital Moh. Ridwan Meureksa is dealing with matters related to the human heart. In January 2021 to December 2021 at the Moh. Ridwan Meureksa the number of people with heart disease has increased and it is necessary to anticipate the increase in the future. This study aims to classify based on probability or probability from previous data, as well as obtain information about accuracy, precision and recall obtained when testing patient data using the Naive Bayes algorithm. The data used is medical record data of 3970 patients with heart disease cases. The results of the analysis show that symptoms of shortness of breath, rapid pulse, palpitations, weight loss, fever and decreased consciousness can be indicators for diagnosing heart disease. The results of this study are 99.7% and 98.7%, accuracy, 99.8% and 99.3%, precision and 99.8 % and 99.3%.recall.
Emotion Recognition and Analysis Of Netizens Based On Micro-Blog During Covid-19 Epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies.
PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK MENGANALISA TRANSAKSI PENJUALAN JASA CETAK PADA UNIT PRINT ON DEMAND (POD) PERCETAKAN GRAMEDIA

Data mining is a method used to obtain valuable information contained in data banks. The information obtained can be used as input for determining the business strategy for the head of the company's agency. One of the most widely used data mining techniques is clustering using the K-Means method. Print on Demand (PoD) is one of the printing business units of PT. Gramedia which specifically provides printing services for various types of products, including: books, magazines, calendars, posters, promo materials such as product catalogs and brochures, as well as small-sized products such as business cards, tickets, coupons or vouchers and stickers. Currently every sales transaction data is stored in a SQL Server database, but until now the data processing is still done manually for reporting needs for company management. The purpose of this research is to perform K-Means Clustering analysis of the transaction data of sales of print services using the RapidMiner application to classify routine customer data based on the number of transactions made for each type of print product. The results of the application of the K-Means Clustering method resulted in 8 groups of customer data where the largest group consisted of 92.89% of the number of customers. The results of the analysis can be used by the company's management to determine various business strategies to increase the company's competitiveness.
Composing in virtual immersion: avatar and representation

This article presents a specific case study centered around the question of music composers in immersion, stemming from the EUR ArTeC project “VR Auditory Space, Sound Spatialization in VR Immersion.” How does creating a spatialization environment designed for virtual reality determine an electronic music composer’s way of interacting with this space? In-game programming and editing requires thinking about how to integrate a full-body or complete avatarial representation. This article seeks to address all of the problems we have encountered from a technical, philosophical, and critical point of view.
Smart Security Camera using Machine Learning

Numerous applications have been found for video surveillance. Smart video surveillance systems can improve situational awareness at various scales of time and space. It describes a remote control and surveillance architecture based on mobile devices. To record camera photos and detect intrusion using an image comparison technique, this project uses the Opency library. The streamed video is sent from the server to the remote administrator over the phone after the comparison is completed and an intrusion is discovered. Afterward, admin can take the necessary action. Automatic video analytics are used in smart surveillance to increase the efficiency of security measures. By automatically identifying the individual's deviant conduct, this system complements the existing video surveillance systems while also introducing intelligent analysis of single person activity to improve home security. By sending an SMS, the user is informed and the pertinent data is recorded. The user is able to watch the specific video. This system keeps the home secure, which lowers the frequency of burglary cases and improves social stability.
Precision Calibration of Omnidirectional Camera Using a Statistical Approach

Omnidirectional optoelectronic systems (OOES) find applications in many areas where a wide viewing angle is crucial. The disadvantage of these systems is the large distortion of the images, which makes it difficult to make wide use of them. The purpose of this study is the development an algorithm for the precision calibration of an omnidirectional camera using a statistical approach. The calibration approach comprises three basic stages. The first stage is the formation of a cloud of points characterizing the view field of the virtual perspective camera. In the second stage, a calibration procedure that provides the projection function for the camera calibration is performed. The projection functions of traditional perspective lenses and omnidirectional wide-angle fisheye lenses with a viewing angle of no less than 180° are compared. The construction of the corrected image is performed in the third stage. The developed algorithm makes it possible to obtain an image for part of the field of view of an OOES by correcting the distortion from the original omnidirectional image.Using the developed algorithm, a non-mechanical pivoting camera based on an omnidirectional camera is implemented. The achieved mean squared error of the reproducing points from the original omnidirectional image onto the image with corrected distortion is less than the size of a very few pixels.
How Well Do Self-Supervised Models Transfer to Medical Imaging?

Self-supervised learning approaches have seen success transferring between similar medical imaging datasets, however there has been no large scale attempt to compare the transferability of self-supervised models against each other on medical images. In this study, we compare the generalisability of seven self-supervised models, two of which were trained in-domain, against supervised baselines across eight different medical datasets. We find that ImageNet pretrained self-supervised models are more generalisable than their supervised counterparts, scoring up to 10% better on medical classification tasks. The two in-domain pretrained models outperformed other models by over 20% on in-domain tasks, however they suffered significant loss of accuracy on all other tasks. Our investigation of the feature representations suggests that this trend may be due to the models learning to focus too heavily on specific areas.
More efficient proof-search for sequents of temporal logic

The present paper deals with efficiency improvement of backward proof-search of sequents of propositional linear temporal logic, using a loop-type sequent calculus. The improvement is achieved by syntactic transformation of sequents into equivalent to them simpler ones. It is proved that some formulas can be removed from sequents with no impact on their derivability.
Facial expression using Histogram of Oriented Gradients and Ensemble Classifier

In this research, two methods were proposed to design a new system to recognize facial expressions. The first method relies on extracting features from the face area, and the second method relies on the process of extracting features on the parts of the face (eyes, nose, and mouth) where the histogram of oriented gradients (HOG) algorithm was used in the feature extraction process in addition to the principal component analysis algorithm to reduce feature dimensions in both methods. We have proposed a group classifier consisting of three basic classifiers: support vector machines, knn-algorithm closest to neighbors, and Naive Bayes in the classification stage. Our proposed algorithm was tested on japanese female facial expression (JAFFE) Dataset and Cohn-Kanade (CK) dataset. It was found that higher overall accuracy is achieved for F1-Score when using the second method of 93.82 % and 94.12% for CK and JAFFA, respectively.
Development of Discrete-Time Waterjet Control Systems Used in Surface Vehicle Thrust Vectoring

To create an autonomous surface vehicle, the microcontroller that will be responsible for the vessel’ s response will have access to both engine thrust controls and steering controls. In the case of a vehicle with waterjet propulsion, the term “controls” refers to engine RPM and nozzle angle. The latter allows the thrust vectoring. Having performed the mathematical modelling of the surface vessel, a method must be found such that the microcontroller actions on the controls create the desired outputs, based on predefined performance objectives. A commonly selected control algorithm considered an industry standard for control applications—at least from the perspective of classical control methods—is the PID control scheme. Many times, a PID control scheme is sufficient for achieving the required performance. There are also many methods that extend the functionality of the PID to robust control schemes, to accomplish more specific targets, such as noise rejection. The vast majority of all these implementations, will have to be implemented using a digital microcontroller or an FPGA. This paper assists theoretically in implementing PID controllers digitally, by elaborating on the discrete-time perspective of PID control. It also provides a test method for evaluating the algorithmic implementation.
BER Performance Improvement of Dual Chaotic Maps Based on STBC Communication System

Sensitive information of any multimedia must be encrypted before transmission. The dual chaotic algorithm is a good option to encrypt sensitive information by using different parameters and different initial conditions for two chaotic maps. A dual chaotic framework creates a complex chaotic trajectory to prevent the illegal use of information from eavesdroppers. Limited precisions of a single chaotic map cause a degradation in the dynamical behavior of the communication system. To overcome this degradation issue in, a novel form of dual chaos map algorithm is analyzed. To maintain the stability of the dynamical system, the Lyapunov Exponent (LE) is determined for the single and dual maps. In this paper, the LE of the single and dual maps have been computed numerically. Increasing the dynamical behavior of the system by using more complex chaotic maps leads to inferiority in the overall system performance. So, in this work, the BER performance for the dual and single chaotic maps by exploiting the benefits of a hybrid Chaos Shift Keying-Multiple-Input-Multiple-Output (CSK-MIMO) communication system has been investigated. The results show that the dual tent map has more randomness, whereas the single logistic map has the least randomness. As well as the CSK-MIMO gives an outstanding BER performance when it compared with the SISO system which helps in reducing the system’s inferiority.
MSA-GCN:A multi-information selection aggregation graph convolutional network for breast tumor grading

&lt;p&gt;This manuscript has been successfully submitted to IEEE-JBHI and is currently in the peer review stage.&lt;/p&gt;
Recovering in the post-pandemic era: the role of employee voice, knowledge sharing, employee innovation, and service recovery performance in the hotel industry

Purpose Covid-19 pandemic is perceived as one of the great disasters in history. It forced countries to impose travel restrictions and lockdowns, and it negatively influenced hotels’ performance. Therefore, this article aims to provide explanations of how hotels can survive, recover and succeed in the post-pandemic period. This article focuses on the role of employee voice, knowledge sharing, and employee innovation in promoting service recovery performance. Design/methodology/approach This is a conceptual paper that focuses on discussing the relationships between employee voice, knowledge sharing, employee innovation, and service recovery performance. Findings The study suggests that employee voice is a vital element that can enhance knowledge sharing at hotels. Furthermore, knowledge sharing is proposed to encourage employees to engage in innovative behavior, which, in turn, can promote service recovery performance. Originality/value The study provides a framework that explains a mechanism of how hotels can accelerate recovery efforts from Covid-19 repercussions by shedding light on the important role of employee voice, knowledge sharing, and employee innovation.
Survey on Blockchain Technology and its application in Electoral Voting System

Abstract: Blockchain technology can be used in the E-voting system to conduct a fair election and reduce injustice. The physical voting systems have many flaws in it as well as the digital voting systems are not perfect enough to be implemented on a large scale. This paper presents an overview of Blockchain based voting systems. The proposed platform will provide a framework that can be implemented to conduct voting activity digitally through blockchain. Our proposed system will use a flexible blockchain with consensus algorithms. The Chain security algorithm used in makes voting transactions more secure and integrated.
Detection of Malicious URLs Using Classification Algorithms

Malicious URL, a.k.a. malicious website, is a common and serious threat to cyber security. Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cyber security industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out important directions for future research.
Transfer Closure of Graphs Using Graph Augmentation &amp; Further Optimization of Euler Fast Augmentation Algorithm

A graph is a collection of nodes and edges. Transitive closure matrix is a matrix formed by the reach-ability factor, which means if one node A of the graph is reachable from another node B, then there exists a positive reach-ability between A and B, negative reach-ability otherwise. This can be easily denoted by using binary denotation of 0 and 1. Graph powering is a technique in discrete mathematics and graph theory where our concern is to get the path between the nodes of a graph by using the powering principle. In simple words, if we take the 𝒓𝒕𝒉 power of any given graph 𝑮 then that will give us another graph 𝑮(𝒓) which has exactly the same vertices, but the number of edges will change. In the powered graph 𝑮(𝒓) there will be a connection between any two nodes if there exits a path which has a length less than 𝒓 between them. This small intuition can help us in finding thetransitive closure of a graph in ( ) time complexity and ( ) space complexity.We can improve the time complexity of the above mentioned algorithm by using Euler's Fast Powering Algorithm to ( ).