Reviewerly/exHarmony huggingface dataset:
Smart Household Object Detection Using CNN

Object detection is one of the most basic and central tasks in computer vision. object detection is a subset of object recognition. Its task is to find all the interested objects in the image, and determine the category and location of the objects. Object detection is widely used and has strong practical value and research prospects. Applications include face detection, pedestrian detection and vehicle detection. In recent years, with the development of convolutional neural network, significant breakthroughs have been made in object detection. This work aim to detect objects in the video frames. It detects household objects and predicts the object where it may be present. Convolutional Neural Networks (CNN) is used to detect objects in the environment. Then Resnet50 is used to classify the images into objects. Then Support vector machine (SVM) is used to train objects and stored in object database. It will be retrieved whenever neural networks sent object for verification.
CGX: Adaptive System Support for Communication-Efficient Deep Learning

The ability to scale out training workloads has been one of the key performance enablers of deep learning. The main scaling approach is data-parallel GPU-based training, which has been boosted by hardware and software support for highly efficient point-to-point communication, and in particular via hardware bandwidth overprovisioning. Overprovisioning comes at a cost: there is an order of magnitude price difference between "cloud-grade" servers with such support, relative to their popular "consumer-grade" counterparts, although single server-grade and consumer-grade GPUs can have similar computational envelopes. In this paper, we show that the costly hardware overprovisioning approach can be supplanted via algorithmic and system design, and propose a framework called CGX, which provides efficient software support for compressed communication in ML applications, for both multi-GPU single-node training, as well as larger-scale multi-node training. CGX is based on two technical advances: \emph{At the system level}, it relies on a re-developed communication stack for ML frameworks, which provides flexible, highly-efficient support for compressed communication. \emph{At the application level}, it provides \emph{seamless, parameter-free} integration with popular frameworks, so that end-users do not have to modify training recipes, nor significant training code. This is complemented by a \emph{layer-wise adaptive compression} technique which dynamically balances compression gains with accuracy preservation. CGX integrates with popular ML frameworks, providing up to 3X speedups for multi-GPU nodes based on commodity hardware, and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.
Meta-Analisis Praktikalitas Penggunaan E-Learning Berbasis Edmodo Oeh Guru Dan Peserta Didik Dalam Pembelajaran

The progress of IPTEK, especially in the field of education, have made the use of tools in the form of learning media more widespread and interactive. One of the learning media that can be developed by teachers is Edmodo-based e-learning. E-learning is a web-based learning medium that can be accessed by the internet. Meanwhile, Edmodo is a simple electronic learning media tool that is used to present lesson content, can be accessed using a smartphone, and is useful for students and teachers to interact online outside the classroom anywhere, anytime. This study aims to analyze the practicality of using Edmodo-based e-learning for teachers and students. Practicality is the degree of ease of use of the product that has been developed. Meta-analysis is done by collecting similar articles by searching for articles from several journals via the internet. The sample used in this study included as many as eight articles related to the use of Edmodo-based e-learning. The results of the study found that the average practicality level of using Edmodo-based e-learning by teachers was 89.15 in the very practical category, while the average practicality level by students was 87.02 in the very practical category. Based on the results of the analysis of the use of Edmodo-based e-learning that was developed, it can be categorized as suitable for use in the learning process.
Recognizing Handwritten Digits Using Multi-Dimensional Recurrent Neural Networks Intelligent Character Recognition (ICR) with Improved F-Score Measures

The goal of this study is to create a model that can recognize digits using Novel Recurrent Neural Networks (RNN) with LSTM cells and provide an F score comparison for optical character recognition versus Support Vector Machines (SVM) with Linear Kernel on the MNIST dataset. The sample estimation is done using the GPower statistical software with a pre-power test of 80%. The type-I error rate (alpha error rate) of 0.05 is considered. The dataset has 70K samples of handwritten digits, of which 60K are used as training samples and the remaining 10,000 are used as testing samples. In this research work, the digits are classified using RNN and linear SVM algorithms. The RNN attains an accuracy of 99% with a significance value of 0.171 (p 0.05), whereas the Linear SVM attains an accuracy of 87.75%. The results proved that optical character recognition using Novel RNN with LSTM cells performed much better than Linear SVM.
Implementation of UI based on Gesture Recognition for Hospital Enquiry System

Abstract: To think accessible movement affirmation structure and perceive how classifier changes over hand movements into huge orders. Cultivated a program for treatment of gotten picture. To sort out some way to made UI using Django. To focus on different steps of picture taking care of and execute them in Python. Touchless marks of communication are substantially more essential in the hour of COVID-19. We're cautious with regards to contact like never before, a hatred at risk to proceed until a reasonable counter acting agent opens - and potentially past. The system will grant customer to enquire concerning different wards, availability of the trained professional/master, etc. Uses of PC information development are at this point facilitated with center and activities. Clinical facility records are modernized; imaging gadgets like MRI, PET, and CT, to give a few models, are totally dependent upon PC advancement. Clinical gadgets that exactly recuperate and inspect data have saved lives. In any case, PC information development's normal use in Operating Rooms and Intensive Care Units isn't totally extended since PC advancement presents a couple of issues that may opportunity patients' prosperity.
INTEGRASI DATA BARANG PADA SISTEM INVENTORY CV. XYZ MENGGUNAKAN WEB SERVICE

In various fields the industry has used as well as implemented the concept of web services that provide message interoperability and data exchange interfaces. One of the uses for sharing data on data integration of one system with another system in this case study was carried out to manage iron supply goods data, in CV. XYZ Pratama Manufacture Engineering uses research methods of collecting data structures, architectural design data, building application web services, building Rest APIs, testing, and measuring and securing JWT HS 256 tokens. Building integration can formulate how to use CV Goods data. XYZ, becomes an Inventory system built using Web Service technology by managing item API data of json data type on the Rest Server so that it can be used by Client Rest. This research resulted in a Web Service through testing stages with different services with the total data requested and received which is 62.48 kb and the total Response time from data retrieval 2.2 seconds with an average execution time of Request data of 177 milli seconds.
Development of Novel Ensembled Boosting Model (EBM) for Fall Detection

Mountain biking is an extreme sport with unpredictable terrain and several dangerous risks associated with it. Even the soundest minds might need external stimuli to alert them to be more careful at a particular moment of a potential fall. The proposed work involves developing an algorithm capable of detecting falls in mountain biking activity. Machine Learning classifier algorithms are used for fall detection. The existing fall detection algorithms are used to detect falls in environments with limited movements. Fall detection through the use of cameras causes invasion of privacy and is done in fixed environments with predictable dangers, another type is through sensors attached to the human body which acts as an obstruction to the activities of the person. The proposed Ensembled Boosting Model (EBM) classifier involves overcoming these pitfalls and developing a high accurate system to detect falls in open and unpredictable environments. The algorithm proposed in this paper aims to detect falls through real-time data such as acceleration, gyroscopic values, for any user. In the future, this algorithm can be used as a precursor to implement a real-time fall prediction device to be used by anyone and in any environment.
Smart System Pendeteksi Keberadaan Narapidana di Lapas Kabupaten Kotawaringin Timur

– The problems regarding prisons are almost endless. The government should pay attention to this. The problems that arise today, almost all correctional institutions in Indonesia exceed (over) capacity, including those in the East Kotawaringin district . Indoor Positioning System IPS is a technology that can be used to locate people or objects in a building, usually through a mobile device such as a smartphone or tablet. Although this technology is newer than GPS, services utilizing IPS are quickly gaining ground in indoor locations such as shopping malls, hospitals, airports and other indoor venues. By utilizing an iBeacon mounted on a prisoner's wristband, the iBeacon will always transmit a signal that can be picked up by the receiver. Devices that can become receivers are bluetooth repeaters (signal boosters) and prison officers' android phones. To be able to know the estimated position of the receiver on the iBeacon, a map is needed locally, which is to determine a certain location, then map it in the form of coordinates. By placing several iBeacons at certain coordinates, it can be used to determine the estimated position of the receiver to the iBeacon.
Early Buddy-A Smart Auto Alarm

EARLYBUDDY is a smart alarm clock app for Android devices. It primarily uses traffic data to help the user wake up on time when there is an estimated delay. Users can choose a timeframe within which they must awaken rather than a single wake-up time. The app will then select a time inside that window based on the projected delay based on frequent traffic data updates. If it detects that the user will be late to their destination, it will change the existing alarm to wake them up. Users can also specify their morning habits, which are factored into the total delay estimate. These routines are optional, but they are encouraged because they help the user to organize their morning and provide more data for the application to use when calculating delays. All humans are not made equal when it comes to sleep. EARLYBUDDY features a provision that prevents users from turning off the phone to silence the alarm if they find it difficult to get up from bed in the morning. Users must tap the stop alarm or snooze button, which only appears in the app itself at the center of the screen, to turn off the alarm. If you press anyplace else on the screen or push the snooze button incorrectly, the alarm will ring again. The basic goal here is to actively wake up the body and mind before hitting the snooze button. Users can also place their phones on the mattress nextto them, and EARLYBUDDY will analyze their sleeping habits using the device’s sensors. This may be used to assess the quality of the user’s night’s sleep and set the alarm at the most natural time.
Not Just for Libraries: The Value of the MLIS

The LIS job market is devastating, and the MLIS is no longer a guarantee of landing a library job. So, what’s the point? The MLIS is valuable because of the skills it teaches. Whether we work in a library or not, having an MLIS means that we know how to be leaders and advocates. We know how people look for information, and we can use that knowledge to help people find and evaluate information. We know how to organize information for most efficient retrieval. We know how to write grants and how to maintain relationships with stakeholders. Information is everywhere and always changing, and librarians are the ones who ensure we can keep up.
A Fraudulent Transaction Prediction in Credit Card by Using Novel LGBA over LR Algorithms

The Research is to predict the accuracy of credit card fraudulent transactions using the Light Gradient Booster algorithm.Novel Light Gradient Booster with sample size =10 then Logistic Regression of sample size = 10 were executed for estimating accuracy rate of credit card fraudulent transactions. The sigmoid functions used in Light Gradient maps the values between 0 and 1. Light Gradient Booster Algorithm has a Maximum accuracy (91.6%) while comparing to the performance of Logistic Regression (81.4%). There was a statistical significance difference between Light Gradient Booster and Logistic Regression with p = 0.0001 (p&lt;0.05) based on 2-tailed analysis. Light Gradient algorithm helps in predicting with better accuracy percentage of credit card fraudulent transaction than Logistic regression.
A Design Method to Achieve Decarbonisation in Airports with Battery Operation Algorithm Considering Uncertainties

Decarbonisation of social infrastructures such as airports have attracted wide attention. In commiting to the global decarbonisation target, introduction of renewable energy is important. However, few research has focused on the specifications of renewable energy facilities for airports. Thus, it is vital to enable airport operators to determine the necessary facility specifications for installing renewable energy systems. Moreover, the efficiency of battery operation influences the necessary facility specifications. Yet, many previous studies have proposed linear battery optimisation, even though forecasts for renewable energy supply include uncertainties derived from natural factors, and demand forecasts can also fluctuate due to future uncertainties. This study provides a framework for analysing the cost efficiency of photovoltaic systems, wind systems, and batteries to determine the optimal specifications for each hardware to achieve 100% renewable electricity. Further, it proposes a novel battery operation algorithm considering uncertainties of supply and demand forecasts. A case study on Chubu Centrair International Airport was conducted. The results show practical steps for the airport to achieve decarbonisation. In addition, battery operation based on the proposed algorithm outperformed the linear optimisation in the unpredicted rain weather scenario. It can be said that the proposed method performs well in considering uncertainties.
Mobile Educational Game of Animal Guess in Android Platform

The purpose of this research is to create educative digital learning media, easy to understand by children using the waterfall method, educational games guessing animal names with various categories of animals, which can display animal images, animal sounds and animal animations. All designs in the educational game guess the name of the animal
Improved Accuracy for Fake News in Social Media Using Logistic Regression Comparing Naive Bayes Classifier

The fundamental purpose of the work is to detect the Fake News in Social Media with the use of Machine Learning Algorithms.TRUE and FAKE dataset is used to detect false news. This dataset carries the record of data i.e. TRUE or FAKE news. Fake News detection is accomplished via Logistic Regression and Naive Bayes classifier. Naive-Bayes algorithm is a simple approach mainly used for classification. Sample size has been determined to be 20 for both the groups using G Power 80%. Logistic Regression algorithm provides mean accuracy of 97.5% when compared to Naive Bayes algorithm with mean accuracy of 89.43%. Statistical significance value is obtained as 0.002 (p&lt;0.05). Logistic Regression has extensively higher accuracy than Naive Bayes algorithm.
Feed Forward Neural Network and K-Nearest Neighbour Based Comparative Accurate Social Media Spam Detection

The aim of this work is to perform spam detection in social media using Feed Forward Neural Network (FFNN) algorithm and compare its accuracy with K-Nearest Neighbour (KNN) algorithm. The experiment was carried out and classification was performed using KNN algorithm (N=10) for spam detection in social media and the accuracy was compared with SVM algorithm (N=10). For this experiment, G power value was calculated as 80 % and alpha value was as 0.05 %. The value obtained in terms of accuracy was identified for KNN algorithm (95.2%) and for FFNN algorithm (98.2%) with significant value 0.276. It was conclude that the accuracy of detecting spam using the FFNN algorithm give the impression to be slightly better than the KNN algorithm.
Classification of Face Mask Detection Using Transfer Learning Model DenseNet169

COVID-19 has become a threat to the world because it has spread throughout the world. The fight against this pandemic is becoming an unavoidable reality for many countries. The government has set policies on various transmission prevention efforts. One of these efforts is for everyone to wear masks to break the transmission chain. With such conditions, the government must continue to monitor so that people can apply the appeal in their daily lives when participating in outdoor activities. The present time involves new problems in so many fields of information technology research, especially those related to artificial intelligence. The purpose of this study is to discuss the classification of face image detection in people who wear masks and do not wear masks. designed using the Convolutional Neural Network (CNN) model and built using the transfer learning method with the DenseNet169 model. The model used is also combined with the DenseNet169 transfer learning method and the fully connected layer model architecture, to optimize the performance test in the evaluation. These models were trained under similar conditions and evaluated on benchmarks with the same training and validation images. The result of this research is to get an accuracy value of 96% by combining the two datasets. This dataset is the same as previous research; the number of datasets is 8929 images
A Secure E-Governance System Using Blockchain Techniques

Secure data and property transmission between two parties is a difficult task in the real world. The existing system for e-governance operations is riddled with inefficiencies and dishonesty, resulting in records that are not safeguarded, and people are the ones who face the brunt of it. The proposed system is built upon utilizing blockchain principles to securely transfer E-governance processes from one party to another. It is an electronic record of data that necessitates the use of digital security. All of the data in the blockchain is immutable; it is almost impossible to modify the value of data once it has been placed into the blockchain. The proposed model is based on blockchain technology and implements a secure E-governance system in land registration. It aids in the upliftment of the disadvantaged and underprivileged sections of society by combating illicit land authorization. Because of this, the suggested system outperforms the current system.
Human Emotions Recognition Using Softmax Classifier and Predict the Error Level Using OpenCV Library

The goal of this research project is to compare the accuracy of the CNN method using the Softmax classifier and the OpenCV library by taking out new features and comparing them to the accuracy of the CNN method. To analyse the suggested system, CNN algorithms with softmax classifier and OpenCV library were used with a sample size of 20 to find facial expression recognition by comparing the accuracy and error rate between the techniques. In the proposed study, the dataset was used to collect data on the outcome and accuracy of the OpenCV error rate, and the OpenCV improvement was compared to the CNN softmax classifier technique. The average accuracy of OpenCV over techniques that use convolutional neural networks is 95.5%. The T-Test doesn’t show anything important (p 0.05). Face recognition systems were used to get information from a number of places for the study. Compared to the convolution neural network method, the OpenCV algorithm made the result more accurate and less likely to make mistakes.
OPTIMALISASI KOMPETENSI BERBAHASA INGGRIS MELALUI PROGRAM PEMBELAJARAN BUSINESS ENGLISH BUAT SEKOLAH VOKASIONAL-ALMAKIYAH

&lt;p&gt;&lt;em&gt;Vocational learning in Indonesia at the upper secondary level provides a large portion of improving English language skills. Besides being used as a compulsory subject/college in schools and universities, English lessons are studied in the context of vocational education to bridge the world of education with the needs of industry and the workforce on a national, multinational, and international scale. The high need for a competent workforce, which can be seen from the ability to speak English, presents a variety of specific subjects in line with this condition. The subject variant in question is business English or 'business English'. Referring to this condition and examining the need for vocational education, Community Service activities (ABDIMAS) are carried out with the theme 'Introduction to Business English'. ABDIMAS is held at the Almakiyah Vocational High School (SMK) in East Jakarta. ABDIMAS was carried out in response to the needs of partners who felt that the English learning materials did not meet the needs of students and graduates who were required to be absorbed in the world of work. ABDIMAS is carried out through three sessions of a hybrid online and offline enrichment program for students. Each session is taught about what aspects are included in business English and linguistic aspects commonly used in business and work contexts. This activity is oriented towards increasing the knowledge and insight of the participants regarding (i) business conversations, (ii) formal and informal expressions in business communication, (iii) basics of email writing. The activity ends with an evaluation that broadly shows that the activities are carried out in line with the work context so that they do not focus on the theoretical and implementation levels.&lt;/em&gt;&lt;/p&gt;
Text-Based Game — A Tool to Enhance Critical Reading and Critical Thinking Skills in English Classrooms

Game technology has recently attracted a lot of attention and provides a favourable environment for language learning students who want to improve their English communicative abilities. The purpose of this article is to explore how students utilize gaming technology to increase their critical reading and critical thinking skills while playing. This research is a pre-experimental study with a one-group pre-test and post-test design. Students at the tertiary level are the participants in this study. Choice of the Dragon was a fantasy text-based game used in the experiment. A storyline from the game was transcribed into scripts and distributed to students for the pre-test. The game was introduced with the goal of gaining points on the achievement chart. In the end, the game will display an achievement chart. It encourages the students to continue the gameplay and read intensively until they reach their goal. Google Form was used to collect data for the research. The collected data was analysed with the help of a statistical tool. Students’ critical reading and critical thinking skills have increased significantly due to the intervention of the fantasy text-based game.