Reviewerly/exHarmony huggingface dataset:
AuthenticTap: A Behavioral Biometric Tap-Based User Authentication Method for Mobile Application

Significant security and privacy concerns of the use of mobile devices and mobile applications user authentication methods have been an increasing concern for mobile device users. The purpose of this study is to use the behavioral biometrics of the users, particularly the tap patterns, as an alternative approach to biometric user authentication. Using the newly developed authentication method, the study conducted two user studies. First user study addressed the appropriate tolerance rate of the method through examining all the 960 tap patterns gathered. Second user study analyzed the robustness of the method in real world scenarios. The optimum tolerance rate of the method has been found within the range of 0.02 percent to 0.06 percent. Within the range, the user authentication method is robust against the scenarios as it correctly classifies the user's tap pattern. The study provided the creation of a more robust user authentication method entirely resistant to attacks in real world scenarios that users may employ to ensure the security and privacy of their mobile devices and specifically, mobile applications. The study may help standardize behavioral biometrics as a stand-alone authentication method device which can be used in institutions and establishments.
Analysis Sentiment of President Vladimir Putin on Twitter Social Media Using Multinomial Algorithm and Bernoulli

Sentiment analysis is one of the most critical factors influencing people's perspectives when judging a person's appearance or character. Russian President Vladimir Putin is one of the world's most powerful figures. Since the conflict between Russia and Ukraine, many people have been drawn to the figure of Vladimir Putin. The opinion that develops in the community about Vladimir Putin is both positive and negative sentiments. Many people have an opinion about using social media. Social media that is often used by people is Twitter. Opinions contained on Twitter can be used to assess the public image of figures by using sentiment analysis techniques. Data was retrieved from Twitter data with the keywords “Vladimir Putin” and search “Presiden Rusia” which is used to filter data taken from Twitter and to limit the data taken from as many as 1600 data sets. The method used in this study is multinomial Nave Bayes with Bernoulli Naïve Bayes. This study will compare the level of accuracy between the two. From the results of testing and this research, the value of the multinomial Nave Bayes algorithm gets an accuracy rate of 76%, while the Bernoulli algorithm gets an accuracy rate of 74%.
Comparison of ResNet50 and SqueezeNet1.1 for Plastic Bottles and Cans Classification

Waste problem is one of crucial global issue. Population growth, government regulations, people behavior and many other factors affect the waste management problem. This has a huge impact on the livability of the people in the neighborhood and maintaining a clean environment as well as managing waste is particularly challenging. Plastics and cans are the types of waste that are very difficult to recycle, so by using a reverse vending machine (RVM) these problems can be overcome. The critical point of RVM is the ability to recognize the type of waste. Deep learning has been successfully overperforms in many computer vision tasks, more specifically image recognition. Squezeenet and ResNet are two convolutional based deep learning algorithms. This study aims to compare the performance of two methods for plastic bottles and cans classification using SqueezeNet 1.1 and ResNet-50. From this research results, highest accuracy of SqueezeNet 1.1 is 92,5% with mean performance 1.518 sec and 97.5% for ResNet-50 wih 6.298 sec mean performance.
Internet of Things and Machine Learning Banking Business Model Using Neuro Fuzzy Technique

Traditional banking methodology is changing due to the Internet of Things (IoT) and Machine Learning (ML). The Internet of Things and Machine Learning has given birth to the thought process of smart home and smart city. Bank must come up with respective changes in its working, analysis and services phenomenon to meet the smart customer needs. This article aims towards the implementation and utilizations of the IoT and ML business model in the banking ecosystem with a neuro-fuzzy tool. The business model addresses security, digital marketing, strategies to reach to offline customers, advisory board, quality assurance, product analysis and hiring or outsourcing external services in a collaborative manner. Banks have to redesign the business model with respect to meet the opportunities of IoT and ML. With the help of ML, the bank finds new ways of pre-approved lending with its products and earns profits. IoT and ML will suggest those customers who can be offered any banking product. These kinds of offerings will open new sources of income for banks and its sister concerns. Also, it will help in bad debt reduction. The ML model is fed with 957 random data and the model is performing well with an accuracy level of 73.5% on the platform of python.
Prediction of Water Quality Index using Deep Learning in Mining Company

The quality of mine water at the mining company XYZ already has an Internet-of-things-based measuring tool. The quality of mine water is a standard that must be complied by the company to the regulator. The impact of passing the quality standard is the temporary suspension of mining activity. An effective process for implementing mine water quality is by predicting mine water quality based on historical data. The data is obtained every two minutes in areas in the company. Forecasting using ARIMA and Support Vector Regression (SVR) have used for years. In recent years, Recurrent Neural Networks (RNN) have shown more correct prediction results than ARIMA. Long Short-Term Memory (LSTM) is a RNN model that uses past data (long term) to predict current data (short term). The results of this research are comparison model of water quality using ARIMA, SVR, and LSTM. It shown that LSTM algorithm gave the best result with lower error. The model from the LSTM method can be used to make predictions such seven days prediction of the pH value in next day whether it is following the rules or needs to be controlled. Because of this preventive maintenance, the company will not be penalized.
Two-stage classification of pap-smear images based on deep learning

After years of discovery, the cancer cervix is still a significant worldwide threat that can be detected early using the pap-smear test. The pap-smear is a screening procedure to find a candidate or positive cancer cell. Recently this process has been done using deep learning, especially Convolution Neural Networks (CNN). The Herlev dataset with seven class data is one of the public datasets that has been researched. Since the high similarity of pap-smear images, previous research has modified the data into two large categories to provide a good result. However, they still require some improvement in the original seven-class classification. We proposed the two-stage classification based on deep learning on pap-smear images to specifically classified the data into their original categories. This method classified the dataset into five classes, then reclassified them into three. In the end, the dataset has been classified into seven classes following the original dataset. This research uses various types of CNN, such as VGG types, ResNet, MobileNet, and EfficientNet. As a result, this proposed method gives 78.73% accuracy in test data. This result increased by 26.77%, better than using the one-stage classification. Due to the data augmentation technique, this method provides an accuracy of 90.98% by combining three data types. In further research, this method could be an idea to classify other high-similarity data cases, such as medical images.
CNN Pre-Trained Model Performance Analysis For Vehicle Detection On Low Illumination

In this study, we compared and analyzed the performance of CNN pre-trained model for vehicle detection in badly illuminated situations and using moving cameras. The comparison was performed to analyze the ability of the benchmarking CNN pre-trained architecture to perform under low quality images. We conducted experiments using five CNN architectures, namely Faster R-CNN, SSD-ResNet, YOLOv3, YOLOv4, and EfficientDet. The dataset used is a dataset that is taken independently by using a camera mounted on the observer's vehicle (data taker). The results of our experiment show that YOLOv4 used with a mean average precision (mAP) of 0.37 has the best performance compared to the other architectures. While the lowest performance is shown by Faster R-CNN and SSD-ResNet with mAP value of 0. YOLOv4 has good results because its architecture includes steps to recognize objects in various degradation conditions.
An Integrated E-Learning Fuzzy Expert System for Acute Pharyngitis Diagnosis

A sore throat, or acute pharyngitis, is an inflammatory disease caused by viral and bacterial infections. Clinically, the causes of acute viral and bacterial pharyngitis are difficult to distinguish. In addition, there are differences in treatment for viral and bacterial acute pharyngitis. This study integrates expert and e-learning systems to diagnose acute pharyngitis. The Fuzzy Mamdani method is used for the disease diagnosis process in the expert system. System integration aims to optimize the database of disease diagnosis history results in fuzzy expert systems. Thus, the results of the history of disease diagnosis can be generated into test questions that will be retested on students. The results of testing the fuzzy expert system on 25 samples give it an accuracy of 84% The test results are strongly influenced by establishing a rule base between the premise and the consequent that is consistent and interrelated.
The Influnce Stemmer Truncating and Statistical in Mapping Students Research Trends with Latent Dirichlet Allocation (LDA)

The application of the LDA model in finding trends in student research topics needs to be done by testing the effectiveness of the application of stemmer truncating and statistics by looking at the coherence score. In this research stemmer truncating uses literal algorithm while stemmer statistic uses N-grams. There are 4 scenarios that will be carried out in this study, namely the first model with the application of stemmer truncating and statistics, the second model with the application of stemmer truncating but without the application of stemmer statistics, the third model without the application of stemmer truncating but applying stemmer statistics and the fourth model without applying stemmer truncating and statistics. From the results of the study, the first model that applied stemmer truncating and statistics had the largest total and average coherence score compared to other models and it was found that in the first model and the selection of the best number of topics was number 12. So this proves that the application of stemmer truncating and statistics has a big influence on the LDA model with case studies looking for trends in student research topics.
Recommendation News For Joumalist Based on Twitter Data Using CNN

Getting the update news fast is important for journalist. This study uses Twitter data which is fast and rich information for get the update news and useful for journalist. This study starts from collecting data from Twitter, then predicting the category of tweet using convolution neural network (CNN) algorithm. With convolution neural network data, Twitter can categorize 10 news categories that mention news to official Twitter media on Twitter social media. Scenario data in predicting news by dividing the data based on 70% training, 15% validation, and 15% testing. Then the data is converted into vector form where the convolution neural network predicts the data in vector form. The results show that, the convolution neural network is able to conduct the good performance based on the values of accuracy, precision, recall and f1 score. It also compares by the other methods.
An AHP Analysis of Correlations Among Project Performance, Agile Core Values, Principles, and Practices

Agile Software Development (ASD) has greatly influenced the community of software developers. Extreme Programming and Scrum are the two most known and widely used agile practices in handling the challenges of a software project. However, insufficient knowledge and understanding of the practices may affect the project's performance. Therefore, this study analyzes the correlation between agile values, principles, and practices to enhance project performance. We use the Analytic Hierarchy Process (AHP) to investigate how factors such as project performance, agile values, agile principles, and agile practices are related to one another. A total of 35 respondents from academics and practitioners provided judgment on the correlation priority. The result indicated the highest rank of priority associated with correlation among each variable. The final result of this study suggests which practices adopting when the project performance needs enhancement and which moderator can affect the decision-making process.
Improvement of GAN-LCS Performance with Synonym Recognition

Automatic Essay Scoring (AES) is an automatic scoring system that compares answer keys with answers given by students and calculates the similarity of answers using intelligent system algorithms. The advantage of automatic essay scoring is that a computerized scoring system can provide faster assessments than manual scoring. This study will use the GAN-LCS (Geometric Average Normalized-Longest Common Subsequence) and MMR (Maximum Marginal Relevance) combined with Synonym Recognition in the text pre-processing section to improve the system performance. Synonym Recognition will be processed after the stemming part. Our proposed methods resulted in an accuracy of 86.77%, a correlation of 0.891, RMSE of 1.434, and MAPE of 13.23%. In terms of accuracy, our proposed method has improved from the original GAN-LCS results. However, there was a sharp increase in time performance resulting in 87.26 seconds of time execution. This is due to the large number of words scanned and converted to the main word form. There is also the ambiguity of words after changing the word in the answer sentence to the main word based on the Thesaurus Dictionary.
Optimized LightGCN for Music Recommendation Satisfaction

With the large amount of information available on the internet, recommendation tasks have grown to be more crucial than ever. Businesses that store digital media on the internet such as video streaming and music streaming platforms, benefit a lot from recommendation systems. A simple yet powerful recommendation system that can give better recommendation performance is always being sought after. Light Graph Convolution Network (LightGCN) is a simplified version of Graph Convolution Network (GCN) for collaborative filtering in recommendation systems. LightGCN architecture includes only the most essential part of GCN for collaborative filtering that is the neighborhood aggregation, it removes the feature transformation and nonlinear activation because both of them contribute little to no effect to the recommendations. The focus of this research is to optimize LightGCN by tuning the hyperparameters using exhaustive search (grid search). The optimized LightGCN model is able to out-perform LightGCN by more than 140% in music recommendation
Sentiment Analysis of Bandung Tourist Destination Using Support Vector Machine and Naïve Bayes Algorithm

Bandung is the capital of West Java, also known as a famous tourist destination in Indonesia. Many places have been visited by many tourists. After their visit, they usually write their opinions about the place they visited on their favorite social network. People's opinions towards a particular object may contain any emotion, it can be positive or negative. Sentiment analysis is a process that analyzes people's opinions of the entity from the text pattern. Sentiment Analysis classifies an opinion into positive or negative sentiment using the Sentiment Analysis System. Analyzing people's sentiments about tourist destinations in English has been carried out by many researchers, but only a few in Indonesian. The aim of this study is to compare Support Vector Machine and Naïve Bayes performance using the synthetic Minority Oversampling Technique (SMOTE). SMOTE is a method used to handle data that is not balanced between classes. Support Vector Machine is an algorithm used to classify big data by maximizing margins using a hyperplane. And Naïve Bayes is a simple and efficient algorithm for handling classification data, Naïve Bayes has well performance for handling data with few features. The results show that the Support Vector Machine with SMOTE has a higher accuracy of 88.51% and 71.51% for Naive Bayes with SMOTE.
Electric Wheelchair with Avoiding Obstacle Feature using LiDAR and CNN Method

Wheelchairs are one of the important medical instruments for the mobility of patients with disabilities to carry out several activities. Wheelchair has been significantly developed from the structures to features such as electric motors, controllers, and location detection system to improve the quality of safety and comfort. However, aspects of the smart obstacle avoider are currently being developed. In this study, an Advanced Driver-Assistance System (ADAS) has been developed using 2D LiDAR as an area mapping sensor, and Convolution Neural Network (CNN) to control a wheelchair embedded in a Raspberry Pi single board computer. The experimental results show that the success rate in avoiding obstacles automatically is between 76.6% to 100%.
Does User Satisfaction Play a Role in the Sustainability of Contact Tracing Application Intention?

Various countries have developed contact tracing applications to prevent the spread of COVID-19 outbreaks. For example, Indonesia launched a government surveillance technology named PeduliLindungi as a contact tracing application for COVID-19. This empirical paper aims to assess the explanatory power of the Expectation-Confirmation Model (ECM) by adding three new constructs, perceived security, perceived privacy, and effort expectancy, concerning the users' continuance usage of contact tracing applications. A survey instrument was developed for PeduliLindungi application users, with survey participants (N=535) recruited from Indonesia. The study tested the model using partial least squares structural equation modeling. The results showed that effort expectancy is the most significant factor in continuance intention mediated by satisfaction. Meanwhile, perceived privacy does not affect continuance intention mediated by satisfaction.
The Comparative Study of Adding Edge Information to Pix2pix Architecture for Face Image Generation

The image-to-image translation is part of image generation, which aims to transform the source image into the target image. The benefit of the image-to-image translation method in the forensic field is to recognize the face of the perpetrator of a crime through facial sketches. Pix2pix is a deep learning architecture for image-to-image translation that implements the Conditional Generative Adversarial Network (CGAN). Pix2pix can generate images well, although the resulting image is blurry in many cases. Modification of the Pix2pix architecture through the addition of edge information has been reported to improve the sharpness of the resulting image generation. This study aims to compare the performance of the modified Pix2pix architecture using various edge extraction methods, namely Laplacian, Sobel, and Prewitt. The comparison of the performance of the Similarity Structural Index (SSIM) on the Chinese University of Hong Kong (CUHK) faces student dataset shows that the modification of the Pix2pix architecture with the addition of Prewitt edge information produces the highest average value of 81.4%.
A Security Analysis of Labeling-Based Control-Flow Integrity Schemes

Secure and transparent policy enforcement by a cloud provider is crucial in cloud infrastructures. Particularly, enforcement of control-flow integrity (CFI) policy has been widely accepted for stopping software-induced attacks. Using low-level hardware metadata to encode CFI policy is a fairly recent development. Besides moving enforcement out of the software and into the hardware for performance benefit, tagging metadata also offers other benefits in the precision of defenses. We evaluate several different metadata layouts for CFI policy enforcement, and examine the layouts' effects on the number of valid forward edges remaining in a RISC-V binary after policy enforcement. Additionally we look at related work in tag-based tools that provide CFI policy enforcement in order to get a sense of their performance and the design trade-offs they make. We evaluate our policy and the related works in terms of space and precision trade-offs for forward- and backward-edge CFI, finding that some trade-offs have a higher impact on the number of remaining forward edges, notably return address protection. Additionally, we report that existing backward edge protections can be highly effective, reducing the number of remaining backward edges in a protected binary to an average of 0.034% over an equivalent coarse-grained CFI.
Towards Data Gravity and Compliance Aware Distributed Deep Learning on Hybrid Clouds

To store large volumes of data concurrently from a diverse set of sources, data stores such as data silos, lakes, and warehouses, have been widely embraced by various organizations. Thanks to data fabric architectures, such scattered data (both structurally and geographically), can be accessed transparently at scale while adhering to various administrative regulations (e.g. governance, privacy, compliance, etc.). However, modern workload schedulers and distributed deep learning (DDL) runtimes are oblivious to the uneven data distribution across different storage services and compliance regulations, leading to sub-optimal resource utilization and training completion times. Al-though state-of-art workflow schedulers such as Apache Hadoop Yarn, Horovod, etc. exploit data locality, they require application developers to explicitly map data and resources available across various cloud services during job submission. These approaches are redundant and counterproductive for next-generation data fabric architectures that feature automated transparency and compliance abstractions for accessing disparate data sources with uneven data distribution. To this end, we propose an algorithm based on greedy programming that leverages the meta-data catalog of data fabric to efficiently determine training schedules based on data gravity, compliance, and resource availability. Our simulations based on synthetic data and resource distribution profiles demonstrate significant improvements in execution times and resource utilization compared to traditional DDL scheduling approaches in hybrid multi-cloud environments.