Reviewerly/exHarmony huggingface dataset:
Gaming in Motion (Gameotion): A Web Portal for Physics-conceptualized Game

The research aimed to design and develop the Gaming in Motion (Gameotion): A Web portal for Physicsconceptualized games that consist of educational games which may solve the current problem of the Physics learners.Research shows that the impact of the current educational games is far from expected due to varying factors that go boring, inappropriate game design, and difficulty adapting the games to different educational contexts.To bridge this gap, this the study determined the acceptability of Gameotion in terms of content, accessibility, aesthetic, experience, and functionality; the overall level of acceptability of Gameotion as rated by the respondents; and the significant difference in the respondents' ratings on the acceptability of Gameotion.The study used the descriptive and developmental research method in designing the web portal.Also, a black box technique was used for testing accompanied by Technology Acceptance Model for the evaluation phase.This was conducted to 80 persons inside the Division of Laguna, composed of 40 Science Teachers and 40 I.T. experts.The selection of respondents was based on a non-random sampling technique.The study utilized Mean, Standard Deviation, and T-test to answer the research objectives.The findings show the following statistical results for the acceptability of Gameotion in terms: content (Teachers-M=4.70,SD= 0.52; Expert-M=4.57,SD= 0.60), accessibility (Teachers-M=4.68,SD= 0.52; Expert-M=4.40,SD= 0.67), aesthetic value (Teachers-M=4.72,SD= 0.50; Expert-M=4.52,SD= 0.60), experience (Teachers-M=4.69,SD= 0.53; Expert-M=4.44,SD= 0.62) and functionality (Teachers-M=4.75,SD= 0.51; Expert-M=4.53,SD= 0.63) which are all interpreted as Very High.In conclusion, the group of respondent results shows that the overall acceptability of Gaming in Motion (Gameotion): A web portal for Physics-conceptualized games in all indicators is verbally interpreted as "Very High."It can be therefore inferred that at a 0.05 level of significance, the null hypothesis stating that there was no significant difference in the respondents' ratings on the acceptability of Gameotion, a web portal for Physics-conceptualized games among the groups of respondents was partially rejected.Further, the analysis of the content and functionality indicators showed "not significant" relationship; while the indicators accessibility, aesthetic value and experience were "significant."The Gameotion, a web portal for a Physics-conceptualized game can be an additional learning material.Consultation and assistance of Computer/IT experts can greatly contribute to enhancing the Gameotion.
Comparative analysis of classification algorithm: Random Forest, SPAARC, and MLP for airlines customer satisfaction

The airline business is one of the businesses determined by the quality of its services. Every airline creates its best service so that customers feel satisfied and loyal to using their services. Therefore, customer satisfaction is an essential metric to measure features and services provided. By having a database on customer satisfaction, the company can utilize the data for machine learning modelling. The model generated can predict customer satisfaction by looking at the existing feature criteria and becoming a decision support system for management. This article compares machine learning between Split Point and Attribute Reduced Classifier (SPAARC), Multilayer Perceptron (MLP), and Random Fores (RF) in predicting customer satisfaction. Based on the data testing, the Random Forest algorithm provides better results with the lowest training time compared to SPAARC and MLP. It has an accuracy of 95.827%, an F-score of 0.958, and a training time of 84.53 seconds.
Web-Based Sales Information System Of Sengon And Chocolate Seeds

Sejahtera Jaya shop is a company that sells sengon seeds and cocoa beans. In the increasingly fierce competition, Sejahtera Jaya shop needs a good alternative marketing strategy that can attract and reach more consumers and fulfill their needs. The use of website information technology is a very good breakthrough. The sales strategy of Sengon and Cocoa Beans attracts more new consumers and reduces the very high risk of error by building a web-based online sales system. This strategy is very good. This fulfills consumers' expectations of satisfying their needs by enabling them to access quickly anytime, anywhere as the number of Internet users grows in almost every region, and at the same time serve more consumers. Because it can be done. And the company's goal is to increase company profits. From the results of this paper, the author can create a web-based sales information system whose truth has been checked by Internet media.
An Interactive System leveraging Automatic Speech Recognition and Machine Translation for learning Hindi as a Second Language

When English speakers are in the early stages of learning Hindi, formulating sentences in Hindi is often attempted by a verbatim translation of English words to corresponding Hindi words. Due to this reason, they are unable to learn Hindi sentences correctly. We have tried to overcome this problem by use of technology for second language learners. The use of Automatic Speech Recognition, and Machine Translation for second language learning, here learning Hindi by English speaker, has been illustrated by taking English speech as input and translating the given English sentences and words into Hindi and then displaying its equivalent construct in Devanagari script. The interactive system under study displays and speaks the same. It has been observed that a second language can be learnt faster by frequently listening to the vocabulary and sentences of the language. Thus the system furnishes the functionality of speaking the sentence in Hindi once it is represented in Devanagari script. The English sentences and words from the grammar tool books are given as input to the system for experimentation. We have observed that the critical problem encountered while doing so is the translation of English to Hindi. Another problem encountered at times is insertion error for letters (only surfaced). The system cannot translate sentences represented using continuous tense and perfect continuous tense correctly. The overall accuracy of the system, otherwise, is approximately 67% which can help the second language learners in the beginning.
Real-time Vehicle Detection, Classification &amp; Tracing Using State-Of-The-Art Dataset

In Today's era, real-time Detection of Vehicles has become highly challenging because, mainly every coming year, various new Vehicles of different models, different skeleton structures, and varied colours are produced. These vast differences make Real-time detections of the Vehicles Approach invalid and less accurate, thus causing an imbalance in the requirement. Furthermore, the real-time Detection of Vehicle Approach is not well trained on Day-Night images, further increasing a loss of response in the frame of time needed for Real-time Detection. The previously developed approach does not process or enhance the Day-Night Frames, which bring down the response time, and responding accuracy has been seen to go drastically down. We solely aim to improve the automobile classification accuracy at Daytime-Night-time & Night-Time and provide experimental results. We work on classifying the Vehicle type better to give a further matured accuracy after working on Vast Classified Vehicle Types. Detection of the object under Night-time conditions such as soft illumination, an excessive amount of Glare, non-visible contrast makes the process of classifying object during Night-time a challenging task. The development is distributed into several segments/steps, respectively casing various prerequisites and stipulations for the submission's efficacious accomplishment and disposition. We conducted a unique experiment on our Self-Generated Dataset (VEHICLE_BRL_DTU). This unique and novel experimentation helped us find that the accuracy of our approach is very high if the Real-time Videography per frame rate is run through our enhancing application, thus further pushed to our application.
An Intelligent System for hair coloring using UNET and ONNX

Hair is an essential feature of the face. It is often used in areas like biometrics, visage (the art of aesthetically matching fashion and medical accessories to the face region), and fashion. In this research paper, we mainly address the problem of hair segmentation and hair coloring in facial images using machine learning and deep learning techniques. The main objective of this paper is not to make people regret after coloring their hair and instead provide them with a sample photo of how they look after they apply the color they want. This model lets people know how they look with the colored hair before the hair coloring. In human-robot interactions, where real-time interaction is essential, the hair-segmentation model using a standard U-Net model has difficulty in real-time processing. This paper proposes a way to make it easier. We show that using U-Net CNN architecture can segment the hair in the actual world. We involved keras and pytorch for the segmentation, which is possible using Open Neural Network Exchange in short known as ONNX.
Pseudo-System Protocol for Information Transfer

The Pseudo-System Protocol for Information Transfer uses pseudo-clients and pseudo-servers to enable hosts to communicate through the internet while being completely isolated (offline) from the internet. It enables hosts to use the power of the internet while making the most of the safety of remaining offline and isolated, providing users with the best of both worlds.
Extraction of Logo from Real Time Document Images Using Masking and Median Filter Approaches

In this paper, we propose an efficient method for extracting the logo from real-time document images. A logo represent key perspectives and thoughts in a specific item, making it the most important choice for brands with a lot to say. These logos can include line-drawn symbols, shapes, patterns, or illustrations that use non-literal illustrations to communicate brand awareness. Frequently combined with a logotype. The proposed method extract the logo based on median filter and masking techniques. Experimentation is conducted with two stages namely logo extraction with enhancement and logo extraction without enhancement. Experimentation is carried out on our own data set containing 500 document images and have obtained 90% accuracy for logo extraction with enhancement and 86% for without enhancement.
Post-training 4-bit Quantization of Deep Neural Networks

In the past few years, there have been major advancements in the applications of machine learning in various fields. Deep Neural Networks(DNNs) being the most preferred machine learning technique, has evolved with higher implementational complexity and thus require substantial memory bandwidth and storage for intermediate computations, apart from significant computing resources. Network pruning to eliminate trivial connections, optimized DNNs designed for resource-constrained platforms, and reducing the model size of offloading precision requirements for weights and activations are some of the approaches to deploying DNN models on edge devices. DNN quantization has remarkable advantages in bringing machine learning and Artificial Intelligence(AI) from high computing devices to embedded platforms. Mathematical calculations involving integers are much more efficient than Floating-point operations but many existing solutions require a full dataset to regain the accuracy lost during quantization. Many researchers have contributed to the 8-bit quantization scheme that allocates a fixed number of bits for every Channel irrespective of its distribution, and one such implementation is the TFlite optimization toolkit provided by TensorFlow, But the domain of non-uniform quantization with fewer bits than 8 is an active area of research. In this paper, we propose a post-training 4-bit quantization scheme that decides the number of bits allocated to a channel based on its distribution. We also provide methods to calculate the bit requirement for the channel and overcome bias in mean and variance after quantization. Incorporating these two methods our proposed quantization scheme achieves float comparable accuracy. With the proposed quantization, depending on the architecture, there will be around 30-75% reduction in the model size and the accuracy loss will be around 0.9-5.2% as compared to the float model.
Solving IEEE CEC-2013 Real Parameter Optimization Problems using Harmony Search Algorithm

The Harmony Search Algorithm is an emerging meta-heuristic optimization algorithm. It mimics the behavior of a music orchestra for solving searching and optimization problems. The algorithm needs only a few control parameters like population size and the number of iterations. This paper presents an experimental implementation and analysis of the Harmony search algorithm on the IEEE Congress on Evolutionary Computation-2013 real parameter optimization problems test suite. This test suit contains 28 benchmark optimization functions including basic unimodal, multimodal and composition functions. The performance of the harmony search algorithm is analyzed and compared on all benchmark functions in lower dimensions (2 and 5), and in higher dimensions (10, 30, 50, and 100).
Sentimental Analysis of COVID-19 Tweets Using Semantic Approach

Sentiment analysis is an approach to determine individual’s emotional state by investigating text from web. Due to COVID-19 the entire worldwide population was driven by fear of erroneous information across the web. This article explores public opinion and examines people's attitudes and emotions using tweets during the pandemic. To analyze the changes in sentiments and emotions, tweets from Indian users in 2020 were collected during April 11-14, June 4-8, and August 20-24. The proposed work compares TextBlob and NRC Lexicon sentiment analysis methods. TextBlob performs better in sentiment classification whereas the NRC Lexicon technique evaluates emotional states in larger depth. The proposed work examines the sentiment variations from three different tweets time slots and compares the results from both experimental methods. The outcomes from both the methods indicate that neutral sentiments are the majority and positive sentiments overshadow negative sentiments
Interaction among Multiple Intelligent Agent Systems in web mining

In the current situation, the presence of Agent-based figuring is responsible to redesign the idea and exercise of arranging and executing the perplexing frameworks. This article presents the center information and capacity of the shrewd specialists and multi-specialist frameworks and accentuates a few uses of the multi-specialist framework in various areas like education, sports, business, Computer Science, Mechanical, Civil, Electrical, Electronics, etc. Semantic web techniques or control aware information, fail to give results in a specific format in complex structures. For that reason, agents and multi-agents are effective alternatives for designing and simulating real-world scenarios for example Corona disease where different entities interact in an uncertain environment. A multi-agent system is helpful in Corona’s uncertain environment. A knowledge base framework has been presented in this research paper, which can be useful in today's corona era, with the help of proposed algorithm in this framework, the knowledge base can be updated. This framework can prove to be very useful in the medical field.
Sentiment Analysis of company reviews using Machine Learning

Employees and job seekers today evaluate their experiences through online reviews of the company. These reviews can also be used by companies to understand their employee’s perception. This study aims to extract aspects of importance from the user reviews and the overall sentiment is classified. The suggested method entails pre-processing user reviews with a variety of methodologies, followed by sentiment score based on aspects and feature extraction. In the proposed system, sentiment is classified using Multinomial Nave Bayes (MNB), Decision Trees (DTs), Support Vector Machines (SVM), and Random Forest (RF) classifiers. Practical implementation is done on a glassdoor review dataset containing reviews from current and former employees of Google company is used. Results implicate that Random Forest produces best results as compared to the other algorithms.
Machine Learning based Offensive Internet Traffic Detection

Internet Traffic Classification is a vital focus in the domain of computer science. This paper emphasize on essential to study and recognize types of applications curving within network by that ISP or network operators can handle overall performance of a network. In this paper different strategy conventional method to order web traffic like Port and Pay-Load related along with Machine Learning Based strategy. The most well-known procedure utilized these days is Machine Learning (ML) strategy. In this paper, Un-supervised ML approach is applied to compare it with previously applied supervised ML approach with different datasets like textual, multi-media and cyber bullying on social media platforms as well. Expectation Maximization based clustering algorithm for unsupervised approach with more than 90% accuracy which is less than 10% more accurate than Naïve Bayes classifier for supervised approach. The earlier unknown applications and tools traffic is also determine using un-supervised technique.
Performance Analysis of Data Leakage Detection System

Data Leakage is the biggest issue, in this digital world. Big organizations keep consolidated data of personal information of employees, users, customers, clients, etc. Even there are many algorithms used for privacy preservation to protect the sensitive or confidential data, data leakage becomes an uncontrolled threat in the digital world. The purpose of this research is to analyze the performance of data leakage detection system used in the information retrieval system in web application. In this paper, we consider the models such vector space model and Interaction information retrieval model. The idea is to check the semantically similar documents in the Web for leaked data. The result inferred that Interaction Information Retrieval Model is better than Vector Space Model.
Adaptive F-SCA Method for Video Super Resolution

This work presents an adaptive fractional-sine cosine algorithm (F-SCA) optimization technique for noise reduction and video super resolution. Adaptive F-SCA technique is the combination of Sine Cosine algorithm and the fractional calculus to make the F-SCA algorithm adaptive for better video enhancement. This technique is used for converting low resolution videos to super resolution (SR) videos. The proposed method consists of alignment filter, three-dimensional convolution system, batch normalized modules to perform image filtering and also to reduce the blurriness issue in low resolution frames. The performance of proposed method is measured in terms of Peak-SNR and SSIM value. Experimental analysis was conducted through CamVid Database. The performance of the Adaptive F-SCA is evaluated using PSNR, and SSIM. The suggested technique gives a maximum PSNR, SSIM values of 29.182 dB, 0.9366 respectively. Indicating that it is superior to other existing methods. The proposed hybrid technique outdoes the prevailing methods with PSNR value of 33.5026 dB, SDME value of 41.1859 dB and a maximum of 0.6222, SSIM value. The proposed method gives maximum PSNR improvement of ≈ 18% in comparison with existing literature. This technique is used for enhancing resolution of noisy videos to super resolution videos.
Mineralogy, geochemistry and genesis of Temelieh Manganese ore Deposit (NorthWest of Selseleh, Lorestan Province)

Mineralogy, geochemistry and genesis of Temelieh Manganese ore Deposit (NorthWest of Selseleh, Lorestan Province)
Mineralogy and chemical variations of Sulfosalts of epithermal deposit of Ay-Qalasi (southeast of Takab, northwest of Iran)

Mineralogy and chemical variations of Sulfosalts of epithermal deposit of Ay-Qalasi (southeast of Takab, northwest of Iran)
Scaling and Selecting GPU Methods for All Pairs Shortest Paths (APSP) Computations

All Pairs Shortest Path (APSP) is one of the graph problems where the output size is significantly larger than the input size. This paper examines the issues in scaling GPU implementations for this problem beyond the memory limits. Because the existing (in-core) methods offer a complex trade-off between the overall computation complexity and the available parallelism, choosing the best out-of-core version for a given matrix is challenging. We develop three efficient out-of-core implementations, which are based on the blocked Floyd-Warshall algorithm, Johnson's algorithm, and the boundary algorithm, respectively. Next, we develop a methodology to select the best implementation for a given graph. Experimental results show that compared with an efficient multi-core APSP implementation, the out-of-core version achieves speedups of 8.22 to 12.40 for graphs with a small separator, and speedups of 2.23 to 2.79 for other sparse graphs, and our models can select the best implementation in most cases.
Rekomendasi Strategi Sosialisasi Program Studi Melalui Jalur Undangan Menggunakan Algoritma ID3 dan K-Means

Based on data obtained from SPAN-PTKIN registrants in 2018 and 2019, the number of interested people through the invitation path who chose the study program at UIN Alauddin as the first choice was 30523 records. Analysis using the ID3 algorithm found that those who interested in the study of religions were more dominant from vocational students. While analysis using the K-Means shows the regions / regencies from which those interested in study programs of religions are spread in 35 regencies / cities. It can be concluded that the socialization of study programs of religions through the invitation path is recommended to be more focused on SMAs that are located in 33 districts / cities as identified in cluster 3. The study programs of religions are prioritized, because these study programs experienced the lowest number of registrants. It is expected that by implementing this recommended strategy, the number of interested prospective new students will draw a significant increase in the future.
A quantitative comparison of automated cleaning techniques for web scraped image data of ‘Smart Cities’

This paper implements and compares four automated image cleaning techniques through the ResNet-34 Convolutional Neural Network, motivated by the need to reduce manual cleaning efforts of large image datasets. For each of these techniques, the relation with the literature on automated image cleaning is identified. Each of the four techniques uses a specific criterion to identify and remove unwanted images from datasets. The criteria range from identifying images with text, through identifying images with a specific size or tonal distribution, up to identifying images with a specific training loss value. In order to evaluate the four cleaning techniques, ResNet-34 was trained with web scraped images corresponding to 15 object classes of ‘Smart Cities’, and accuracy results were obtained through testing on the CalTech 256 dataset subset. The results show that manual cleaning outperforms automated cleaning techniques on all four criteria. However, analysis reveals that the individual automated techniques or a combination thereof can initially be deployed on large datasets before manual verification to reduce workload and increase dataset stability.