Reviewerly/exHarmony huggingface dataset:
Compute and Network Aware VM Scheduling using Reinforcement Learning in Cloud

Cloud computing has been used widely to provide a variety of services which include servers, databases, storage, networking, software, analytics, and intelligence. Scalability is increased and upfront IT infrastructure costs are lowered. In some scenarios, VMs are used extensively for an extended period of time which leads to higher infrastructure costs. Effective virtual machine (VM) management is necessary to make the most out of cloud infrastructures in terms of performance and cost. For scheduling VM effectively we have considered network and compute parameters. Machine learning algorithms are used to predict the best host for the VM placement. We have used reinforcement learning which further uses reward and penalty methods to schedule the VMs, and a methodology is proposed that consists of modules such as Scheduling Module, Host Detection Module, Learning Agent Module, and Consolidation Module. Resource utilization needs to be predicted in order for the system to learn and perform better. The reinforcement-learning-based and ARIMA-based Models are used for better understanding and result analysis. Further, on the basis of three parameters service level agreement violations, energy consumption, and the number of hosts shutdowns, the Reinforcement Learning, and the ARIMA-based Model are compared. The presented Reinforcement Learning Model, which integrates the Enhanced Max-Min algorithm to schedule the VMs and host load prediction, provides the best results by lowering energy usage and service level agreement violations.
Scientific Paper Recommendation System

Advancements in research and technology have resulted in a flood of information in scientific research areas. It is challenging to find research papers of interest in today’s scenario, where hundreds of articles are published daily. It makes it impossible for the researcher and other readers to keep abreast of the research in their domain. Previously keyword matching techniques and recommender systems were used for finding relevant research papers. But these approaches were time-consuming and failed in scaling for a large dataset containing millions of articles. In this research, we propose a scalable end-to-end content-based scientific paper recommendation system capable of recommending research papers from the abstract or the context of the research paper for which we want to find the recommendation. We also evaluate our system using the average co-citation metric from [1]. Our end-to-end system performs with a average co-citation score of 14.88 and has an average response time of 1.4 seconds.
Automated Handwriting Machine

The Automatic Writing Machine is a device used to automate the process of writing by eliminating the necessity of human aid for a physically challenged person. The proposed work aims to design a user-friendly automated writing machine with computer numerical control (CNC) along with Arduino and Raspberry Pi. The user input(image/text/audio) signal is converted into handwriting using Python programming to generate coordinates and drive your CNC machine through Arduino. The proposed work is a stand-alone system eliminating the requirement of high-end machines to process the coordinate data. The proposed work also eliminates the need of software such as Ben-box and G-code which restrict the user input to only images. The proposed work has increased the efficiency in handling user input over the work reported in literature along with reduced hardware and software requirements.
Yoga Pose Detection Using Long-Term Recurrent Convolutional Network

Yoga is an ancient art form originated in India that makes one fit physically and also provides mental peace/relaxation. Yoga has become popular all over the world because of the increase in stress in modern lifestyle. A model has has been developed which detects various yoga poses. An open-source dataset of 88 videos consisting of 6 asanas performed by 15 different volunteers have been used. Various approaches have been studied that are available for pose detection and the most suitable approach has been chosen to meet the requirements. A combo of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN), called Long-Term Recurrent Convolutional Network (LRCN) has been put to use. LSTM is used to make temporal prediction, whereas CNN is used to extract information from each frame. The proposed model differs from existing models as it does not use open pose or pose net for keypoint detection. Using the proposed model, accuracy of 81 achieved.
Quantum-Enhanced Support Vector Classifier for Image Classification

Machine learning plays an essential role in many fields, most commonly as a problem of approximation to determine the optimal value of an unknown function. In recent years, quantum-enhanced approaches have been widely studied to address machine learning issues. The quantum-enhanced approaches offer better classification results than some classical models. In this paper, we have proposed a Quantum Support Vector Classifier-based model for the binary classification of grey-scale images. We have applied a pre-processing model, including feature selection and state preparation, to improve the Quantum Support Vector Classifier method’s prediction rate to overcome the drawback of Noisy Intermediate-Scale Quantum systems. We have evaluated the performance of the proposed model using the publicly available Extended Cohn-Kanade, Gun, Knife, and FER2013 datasets. As a result, the accuracy of our proposed approach for binary classification of images is 95%, 83%, 87%, and 83% for the classical Support Vector Machine, meanwhile proposed Quantum Support Vector Classifier model obtained accuracy scores of 98%, 98%, 93%, and 92% on the Extended Cohn-Kanade, Gun, Knife, and FER2013 datasets, respectively.
Identifying Image Modifications using DCT and JPEG Quantization Technique

The volume of images and the videos are being shared on various social media platform are huge and there are cybercrimes happening in these areas. Hence, identification of the main source of social network, based on the images uploaded or downloaded from social network has become an important activity in the multimedia forensic analysis. When media shared on social network there are possibilities of exploiting different pattern embedding in image content by social network. To make it easier system is proposed with discrete cosine transform (DCT) method and JPEG Quantization to identify the changes in shared images on social networks applications. The quantization used in JPEG compression is used to help in separating the images that have been processed by software. DCT finds the pixel value of the blur images and it is easier in implementation. The combination of DCT and JPEG quantization can give the better accuracy rate which helps in finding the images shared or the source of the images.
Offline Handwritten Signature Verification using Image Processing Techniques

The use of signatures for personal identification and verification is quite common. Signatures are validated for many documents such as Bank cheques and legal transactions. The necessity for effective automated solutions for signature verification has grown as signatures are now a prerequisite for both authorization and authentication in legal activities. Two images—the original signature and the test signature—are used as input in this process. Prior to feature extraction, these photos are pre-processed. To determine whether the signature is fake or not, the characteristics that were extracted are compared, and the difference in error values between them is examined. The key advantages of signature verification systems (SVS) over alternative verification technologies include time and energy savings, a reduction in the risk of fraud during authentication, and a reduction in the likelihood of human error during the signature process.
An Effective Approach to Classify Fraud SMS Using Hybrid Machine Learning Models

Due to the increasing proliferation rate of fraud messages, authors build a more accurate and precise model to detect it. This model is split into two stages: the first is the classification of SMS (Short Message Service) messages using a hybrid model, and the second is the examination of URLs. Our method majorly focuses on the classification phase here we design a hybrid model to improve the accuracy and precision of the classification model. Our hybrid model used the Naive Bayes Classifier, Random Forest, and Extra tree classifier. Individually, the accuracy and precision of Multinomial Naive Bayes (MNB) are 95.9% and 100%, Random Forest (RF) is 97.0% and 99.0%, and Extra Class Classifier(ETC) is 97.7% and 99.1% respectively. In the analysis of the dataset, our model got an accuracy of 96.86% and a precision of 99.366%. It is noticeable that our hybrid model outperformed machine learning approaches in terms of performance.
Salesforce Technology: A Complete CRM Solution on the Cloud

With the generation of more and more data, companies need to leverage a platform that helps them make good use of their customer data, and Salesforce is one such Customer Relationship Management (CRM) platform. Salesforce is the leading on-demand Customer Relationship Management system that runs on the Force.com platform. It is a cloud-based CRM solution. In this article, we've covered the terms and technology utilized in Salesforce, as well as its core architecture and challenges. We've also covered how an on-demand solution is superior to an on-premise one. This article aims to highlight the significance of Salesforce.com and how it contributes to offering Customers a simple-to-use yet incredibly powerful Customer Relationship Management system.
The Role of Integrating Blockchain, Digital Signature, and Cloud Computing for Robust Smart Contract Structuring

Technology development has been metamorphosis due to blockchain technology being introduced in nearly the last ten years. Public acceptance remained a huge challenge due to security threats, by integrating a more secure decentralized form of ledger these issues can be addressed efficiently. Smart contracts are pre-programmed contract layouts that are self-executed and self-enforceable when the pre-set conditions are triggered without the intervention of any third party. This is non-empirical research conducted by reviewing various research work and gaps in their research to understand the role and challenges faced by smart contracts to become a working model and further, analyzed integration of various technologies namely blockchain, digital signature, and cloud-computing to build smart contracts to simplify the execution of traditional contracts which will entirely reform the present legal system. Issues such as rigidity from any alteration, uncontrollability, and difficulty to understand the coded language by ordinary prudent men of any smart contract are considered in this study. Finally, this research paper has elaborated on the process of execution of smart contracts and technology intervention at each stage, with a detailed explanation of the process of formation of smart contracts. It also aims to make some recommendations for further improvement of smart contracts. In future, the author aims to take this research ahead by examine the practical working model of smart contracts and their fallbacks.
Cloud Computing: Resource Management, Categorization, Scalability and Taxonomy

Cloud Computing well known for its services over internet. It provides a storage space to user for all kind of services. This model based on the resource sharing concept. In the Cloyd computing shared resources can be infrastructure based, software or Application Based over the network. All services provided by Cloud to user that is only requirement or demand based for single user or any organization. Over the internet, multiple user access internet at same time and Cloud Service provider works well to provide services to individual. Cloud Service provider applies Resource management scheme to all individual because after applying that each user can utilize the service benefits as per their requirements. Service Provider having focus on highest throughput obtained from the system as well as increase the profit. Different steps taken by the provider to optimize the system in proper way. Optimization technique used by the system if only if all available resources properly managed. Main reason to focus on resources Management because the resources are limited. In this paper authors focus on Classification and Optimization of cloud computing resources. Author mentioned a literature survey of Cloud Computing algorithms and a comparative study of various other resource management algorithms and has been provided.
Classification of Histopathological Images of Colon Cancer Using Convolutional Neural Network Method

Colon cancer is cancer with the most deaths in Indonesian society. Detection of disease through histopathological images of colon cancer still uses manual methods with readings by doctors. So it is necessary to do a system to detect and classify colon cancer. This study aims to create a colon cancer classification system to reduce the time in classifying the categories of colon cancer. In this study, a classification system for colon cancer was created into two classes, namely adenocarcinomas and polyps. Colon cancer data used in this study is data obtained online through the Kaggle website which consists of 2000 histopathological images measuring 768 pixels in jpeg format. The system is built using the Convolutional Neural Network (CNN) method with the MobileNet architecture. The design of this system is made by analyzing parameters that affect system performance based on the influence of image size, optimizer, learning rate, activation function, and batch size. Parameters used in evaluating system performance are accuracy, precision, recall, and f1-score. The results of testing the system based on parameters obtained the best model with image size 224x224 pixels, Adam optimizer, learning rate 0.0001, sigmoid activation function, and batch size 40. The best results of the best model are 100% accuracy value, 100% precision value, 100% recall value, and 100% f1-score with a loss of 0.000135.
Recommendation System Of Product Sales Ideas For MSMEs Using Content-based Filtering and Collaborative Filtering Methods

Looking for an idea to differentiate a product from other sellers is not easy. Sometimes sellers of MSME products need sales recommendations on what is trending among the public. A product recommendation can help users recommend a product that is interesting and needed by that user. Recommendation systems can help users come up with previously unknown or unthinkable information, which can directly aid user knowledge in their search results. In this research, a recommendation system will be built to search for product ideas. This study uses content-based filtering and collaborative filtering methods as well as the TF-IDF algorithm to assist users in recommending the products they are looking for to assist users in finding product-selling ideas they expect. Previous research has examined the recommendation system for Modern Musical Instrument Sales using the Simple Additive Weighing method but has the drawback that the weighting calculation must use fuzzy numbers. Therefore, the content-based and collaborative filtering methods are assisted by the TF-IDF algorithm used in this study to answer these problems. After implementation, we test accuracy by dividing the test data and training data differently. System testing is done by using a confusion matrix. The results that have been tested get an accuracy of 78%. Subsequent research suggests adding MSME product data in recommending product sales ideas to MSMEs so that recommendations are more optimal.
A Robustly Optimized BERT using Random Oversampling for Analyzing Imbalanced Stock News Sentiment Data

Stock news is one of the information sources that can used to monitor stock prices. The information from stock news usually contains positive and negative sentiments that can affect stock prices. Therefore, sentiment analysis is needed to process the sentiment of stock news. The stock news dataset is taken from Kaggle. From these data, there is an imbalanced class between positive and negative sentiment. This research proposed a method to solve the imbalance dataset with random oversampling which worked by randomly replicating several minority classes. This research presents several scenarios of pre-processing text with different stages, intending to get high accuracy. The classification method used in this paper is a robustly optimized Bidirectional Transformer Encoder Representation (RoBERTa). Besides that, this paper also compared with baseline of Machine Learning (ML) such as Multinomial Naïve Bayes, Bernoulli Naïve Bayes, Support Vector Machine, Random Forest Classifier, Logistic Regression and used two different text representation such as TF-IDF and Word2Vec. The best result in this research is obtained using RoBERTa method with the fourth scenario of pre-processing text, in which the stage of pre-processing in this scenario only removing hashtag, without removing punctuation, removing the number, converting number, stop word removal, and lemmatization. The performance result is 0.85 precision, 0,84 recall, 0,84 F1-score, and 86% for accuracy result.
Recognition of Real-Time BISINDO Sign Language-to-Speech using Machine Learning Methods

In this study, a sign language-to-speech system was developed to recognize and convert BISINDO's sign language into speech using a machine learning approach. The speech output will make it easier for the user to communicate with the other person and will make it easier for the other person to understand sign language and will improve the quality of communication. Using the dataset produced in this study and Mediapipe for feature extraction, the model accuracy was able to obtain a score of 98% using the Support Vector Machine method. However, the accuracy score of the model decreased drastically reaching 78% in trials conducted directly on users because the testing exceeded the system effective range. The results of the implementation of Sign Language-to-Speech succeeded in producing an output in form of audio speech without using an internet connection. The system was able to detect both dynamic and static gesture from the user in real-time.
Effect Difference Size of Tetrahedron Sun Tracker Based on Sensor for Energy Harvesting

This research is a new size of implementing a light sensor to detect sunlight on a dual-axis sun tracker based on tetrahedron geometry. The sun tracker is installed with different sizes of sun tracker based on tetrahedron geometry. The sun tracker can track the position of sunlight perpendicular to the solar cell which has the strongest light intensity. This study aims to energy harvesting and the Field of View generated. The method used to obtain voltage and current uses a control system with a proportional integral derivative (PID) algorithm and modifies the size of the sun tracker by adding a triangle height of the triangular tetrahedron. In addition, the light sensor used to track sunlight uses a phototransistor sensor. The result of energy harvesting is that the sun tracker using the proposed sensor collects 159.9% more energy than the previous sun tracker. The modification of the existing tetrahedron shape produces a Field of View (FOV) of 310° for the proposed sun tracker and FOV of 289.4° for the previous sun tracker.
Price Prediction of Non-Fungible Tokens (NFTs) using Data Mining Prediction Algorithm

Non-Fungible Tokens (NFTs) experienced a peak of popularity in Indonesia through content created and sold by an account at OpenSea called Ghozali Everyday in early 2022. Ghozali reportedly earned ± Rp. 1.3 billion from the content he has created. This sparked the curiosity of the Indonesian people to imitate what Ghozali Everyday did in the hope of getting similar benefits. The market price of NFTs is the same as stock prices, which will fluctuate depending on the price of the cryptocurrency because these NFTs can generally be purchased with the cryptocurrency, namely Ethereum. This research was conducted to predict the price of NFTs using the Data Mining Prediction Algorithm. Five algorithms are compared to find the best algorithm: Deep Learning, Linear Regression, Neural Networks, Support Vector Machines, and Generalized Linear Model. The methodology used is Knowledge Discovery in Databases. The NFTs price dataset is taken from the page coinmarketcap.com from 16 November 2021 to 16 November 2022. The results show that the best Data Mining Prediction Algorithm is a Neural Network with a value of The lowest Root Mean Square Error (RMSE) compared to other algorithms, namely 83.617 +/- 18.853 (micro average: 85.590 +/- 0.000). After the Neural Network is used in the Dataset, the graph results show no significant difference between the Closing Price and the Predicted Price.
Interpretability Vs Explainability: The Black Box of Machine Learning

To understand the complex nature of the Artificial Intelligence (AI) model, the model needs to be more trustable, transparent, scalable, understandable, and explainable. The trust of the AI model is concluded based on the decision taken by the AI model in its black box environment. Thus, Explainable AI (XAI) helps the developers to understand how the AI model behaves/performs while making a particular decision. With more complex AI models, scientists face difficulty in understanding the model outcome. Hence, XAI is required to explain the decision-making process of an AI model. However, to build trust-based AI models, organization embeds ethical principles in the AI processes. In our research paper, we studied the case of the banking sector where an inefficient onboarding process fails to establish a customer-based relationship. Due to the inefficient onboarding process, banks lose users’ faith which creates a gap in the customer-based relationship and hampers the onboarding process. To bridge this gap, we explain the decision-making process of the AI model through XAI.
Mandalika Modeling Topic on Social Media Using Latent Dirichlet Allocation

The Mandalika Circuit is one of the tourist attractions on the island of Lombok. The circuit is used for MotoGP events which can provide a positive trend for Lombok tourism. Many people provide information and comments on social media, especially Twitter, related to the Mandalika Circuit. The information and comments can be either positive or negative. This information can be used by certain parties to become a reference in improving the quality of services related to the Mandalika Circuit. Information and comments are obtained from Twitter with a total of 1,300 tweets. In analyzing the information and comments related to the mandibular circuit, this study used the Latent Dirichlet Allocation (LDA) method. LDA is used to find out topics that often appear. The topics are then tested for coherence. Based on the LDA, it was found that the topics that most frequently appeared were circuits, mandalika and others with a coherence value of 0.473. This study succeeded in displaying the visualization of the results of the topic modeling into the visualization of wordcloud and pyLDAvis the results of word distribution on topics related to the Mandalika Circuit. This can make it easier to understand and analyze trending topics.