Reviewerly/exHarmony huggingface dataset:
Using Padlet as a Vocabulary Learning Platform on Chinese Lessons

Padlet is an interactive media platform that allows collaboration between teachers and students. In learning Mandarin, especially grammar, the use of the Padlet media platform can have a positive impact on the progress of students' language skills. This research uses a qualitative method to explain the difference between mastery of grammar and new words to compose sentences before using Padlet and after using Padlet. The object of the research is 20 students, who consist of those who didn't use padlet and who have studied two-month Mandarin Grammar using Padlet 5 times. The test material is according to the topic that students have learned, and it's 40% to 60% of the learning material. The type of exam is divided into two kinds: completing the conversation and making sentences. Based on the results of student exams, The percentage change in the increase in student errors in students using padlet shows a significant difference. In the questions completing the conversation, the correct answers to questions increased by −10 to +45%. In contrast, in the questions making sentences, there is a decrease in the accuracy of student answers from −5 % to −15%. It was found that students using Padlet directly impact the memory of the new word but need to increase the ability to master Mandarin grammar, at first for the grammar usage section.
GIS-based Analysis of Solar Power Plant Suitability in Thailand: Minimizing Natural Disaster Risks

Thailand, a member of the UN Framework on Climate Change, aims to be carbon-neutral by 2050 and have zero greenhouse gas emissions by 2065. To reach this goal, Thailand is promoting renewable energy, particularly solar power plants. The location for installing solar power plants is need considered due to avoid natural disasters and flooding. The study attempted to evaluate the suitability of various regions in Thailand for developing solar power plants. To minimize the risk of natural disasters such as floods and landslides, the analysis considered these variables by processing actual flood data, landslide susceptibility data, and land use/land cover images through ArcGIS Pro. There were 3 cases for determining the level of disasters. The results showed that if the rainfall is less than 1829 mm, there is a suitable area of 182,169.33 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , representing 35.35% of the entire country's area. When all cases are considered, the Northeastern region has the biggest remaining area, while the Northern region has the least. In every region of Thailand, there is still a huge potential for establishing solar power plants. For solar power generation of 7,500 kWp, the installation area was used around 60 Km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , less than 1% of the remaining area for all cases. Utilizing clean energy, free energy source and reducing greenhouse gas emissions are all additional advantages of the solar power plant.
Reduction of Noise in Medical Imaging Quality

When it comes to diagnosing patients’ illnesses, digital image modalities like X-ray, Ultrasound (US), Computer Tomography (CT), Magnetic resonance imaging (MRI), etc. play an essential part. Noise is a common problem in the pictures produced by these modalities, reducing image quality. An important factor in making correct diagnosis of illness is the quality of the medical pictures used. Poisson noise is a prevalent problem in X-ray pictures. Hairline fractures inside bones, chest coughs, and other similar conditions become more difficult to diagnose when this noise is present. These sounds need to be eliminated from the X-ray picture before it may be improved. In this study, we aimed to establish a method for effectively denoising X-ray pictures, hence reducing the amount of Poisson noise present in them. The suggested filter makes use of the Absolute Difference and Mean Filter (ADMF) to replace the processed pixel with the mean of its nearest neighbors within a 5x5 frame when the absolute difference between them is minimal. Using 75 X-rays of teeth from the Digital Dental X-ray Database, the proposed technique is compared to the state-of-the-art Region Classification and Response Median Filtering (RCRMF) method. Filter performance is measured by Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) scores; the suggested approach improves PSNR by 5.41 percentage points and reduces MSE by 33.44 percentage points.
A Bangla Text Search Engine Using Pointwise Approach of Learn to Rank(LtR) Algorithm

A search engine is the prime source of information in this modern time. Where a good number of monolingual and bilingual search engines are emerging, Bangla language falls behind in the area of information retrieval (IR) in the native language. Currently, Bangladesh has no active search engine as the first ever Bangla search engine Pipilika is not providing service for a long time. To fill the absence of such a platform we aim to initiate the development of a Bangla search engine through this work. We have developed a framework for the Bangla search engine that provides search results by performing syntax analysis of a given query. The information retrieval process of Pipilika was carried out using the score generated by a ranking function called BM25. But modern IR systems are acing with the aid of machine learning. Learn to Rank (LtR) is a class of techniques in machine learning to deal with the ranking problem in IR. The search engines employ any of the three approaches of LtR (pointwise, pairwise, listwise) to show the result on Search Engine Result Page (SERP) and we opted for the pointwise approach. In pointwise approach of LtR a numerical score (bid) is assigned to each keyword-document pair in the training set. Then our ranking problem can be treated as a regression problem. We achieved our pointwise approach by employing Random Forest Regressor which yields an accuracy of 70.14%. The dataset was prepared with the aid of feature extraction following the bid-based ranking approach of Google where we treat the BM25 score as one of the features. In the bid-based approach not only the BM25 score acts as one of the ranking factors but also other ranking factors such as TF-IDF score, keyword density, total words in a document contribute to generating the result. Our approach is capable of providing search results based on the ranking score which is generated by combining all the features.
Color Descriptors based Correlation Coefficients and Adaptive Threshold for detection of Wipe transitions

Shots are the basic elements which are used for video summarization and analysis. On the other hand special lightening effects, rapid object-camera motions and scene blending have posed significant challenges in content based video retrieval. A robust transition detection system may sometime be fooled easily owing to the consistency established between scenes and special effects during video editing. Wipes are gradual transitions which includes two simultaneous scenes spatially separated within the transition frames. We propose a simple and efficient approach to detect wipe transitions in videos subjected to uneven illuminations, poor backgrounds, object movements and camera motions. The scheme uses global feature obtained from sum of absolute histogram difference (SAHD) between two successive frames for each color component. The normalized value of mean of SAHD is then used to obtain an adaptive threshold. A two stage normalization correlation coefficient and the adaptive threshold with Expectation Maximization (EM) curve are then used to detect wipe transitions. The result over 27 videos shows that our approach was successful in detection of wipes in 26 videos.
Smart-Hire Personality Prediction Using ML

Through technological changes, data science and artificial intelligence are altering the world. One of the most significant uses of machine learning is the classification of people based on their personality features. We can see many applications of machine learning in our daily lives. Each individual on the world has a distinct personality type. Targeting particular demographics has made it possible to make marketing campaigns more effective. This is made possible by the availability of high-dimensional data. Such personality-based promotions are quite effective at raising brand awareness and enhancing the appeal of goods and services. Using the Big Five personality traits, we created a system for predicting personality. Every day, a large number of students take competitive exams with a strong personality component. These tests' primary goal is to evaluate the student's talents and personality. Writing the personality test and assessing the subject's personality are made easier by this initiative. The person can view their personality type and make improvements to their personality depending on the results of the personality classification. In our paper, we tried to combine phrase frequency algorithm to determine a person's talent and personality prediction utilizing ML algorithms like KNN, CNN, and Logistic regression to predict a person's personality. From this model or system, users can quickly determine his personality and level of technical proficiency.
A study on Security of IOT based Blockchain System using Artificial Intelligence

Internet of Things (IoT) networks have cleared their direction into business and home applications, which incorporate computerization, observing, control and investigation. These applications are under steady danger of assaults from substances which are available inside to the organization, just as outer ones. To get the IoT network from these assaults, methods like high intricacy encryption, hashing, security conservation, trust displaying, and so on are utilized. These strategies target making the organization strong against a gathering of assaults, however don’t give an exhaustive security burden the framework. Because of this computational burden, by and large nature of administration for the framework lessens, accordingly diminishing materialness of framework for ongoing and superior execution application. To defeat this disadvantage, this text presents an AI calculation for age of side chains. These side chains have high security execution like an ordinary blockchain, yet are generally less computationally intricate. Because of which they can be utilized for continuous secure IoT applications. Machine learning is utilized for sidechain creation and the board which advances number of sidechains being made. Countless sidechains would increment computational intricacy during the board, while few sidechain would prompt bigger chains, accordingly expanding mining delay. This lacuna is taken care of by the AI model, which is trailed by plan of an exchange learning instrument for sidechain truncation. Blockchain based arrangements consolidate encryption, hashing, permanence, believability, straightforwardness, trust ability and different other safety efforts,because of which they can be utilized for getting IoT networks against a huge number of assaults. However, joining blockchain requires complex computational tasks like hash creation, hash confirmation, block mining, and so on which includes extra computational arrangement. For example, trust-based directing models help with diminishing refusal of administration, man-in-the center, and other hub-based assaults, however are not tough towards hub capturing, worm-opening and other
Review Based on Blockchain and Financial Transactions Related to Cryptocurrencies Across Globe

Blockchain is one of the most interesting and exciting field of technology today it provides a decentralized network for anyone who is willing to store data on it as blockchain is relatively new there is still experiments going on it to reveal its true potential. One of the applications of blockchain which has gone incredibly popular is cryptocurrency and also blockchain payment system. In today’s blockchain payment system there are few components which are required for a successful transaction such as nodes, ledger, wallet, nonce and hash. Payment gateways are one of the first blockchain application that provide a decentralized way to do transactions we will also discuss some of the ledger applications of blockchain. We carefully selected the most recent research papers in this review paper. We will be reviewing various research paper and they take on blockchain technology.
Analysis of DenseNet -MobileNet-CNN Models on Image Classification using Bird Species Data

Image classification using a convolution neural network (CNN) has been one of the emerging topics in recent accomplishments. A good number of classification models are designed based on the CNN mechanism. Out of which, three deep neural network models are discussed in this article on image classification using bird species data. Bird species dataset is collected from public repository and deep neural network models are applied on it to classify the type of bird as part of implementation. The analysis of outcome produced by the model’s evidence that the Mobile Net model is given better result compared to Dense Net and traditional CNN models in terms of accuracy. This project will be looking at various entities which we discussed and will be likely experimenting with how good the model is in terms of recognition. There are many species that are endangered and there should be taken special care for their sustainment in the ecosystem. More than 10,000 species of these birds differ in their color, size, breed, and habitat part of the ecosystem. Mainly, this model focuses on image processing of the bird which is provided as an input to the model. CNN, Mobil Net, and Dense Net are such models that are very much challenging research areas where if there is even a slight change in a particular image, it will probably result in an entirely new image that a completely different species of bird might get generated. So, we will be thoroughly comparing the above three neural networks and provide the right amount of accuracy.
Enriching Trustworthy trade in Decentralized system using Blockchain

A growth in consumers of current Web Applications may be attributed in large part to the improved affordability of the internet platform, which has led to a surge in users across a wide range of internet networks in current history. This data can be put to many good uses, but it really shines when applied to machine learning and artificial intelligence systems. Machine learning algorithms are additional essential and basic technique for gaining in-depth understanding of a topic or event of interest. Machine-learning algorithms perform best with massive amounts of data. Multiple solutions for providing safe ways to transfer data have indeed been investigated in this research. These methods have indeed been instrumental in assisting us in developing a safe, reliable, and productive system for managing information flows and accessibility. Therefore, there is a need for an effective and useful mechanisms that can allow for the effective sharing of the data as well as incorporate reliability and accountability to the entire system. This is achieved through the use of the reward and penalty scheme that is enabled through the use of a judgement module that is integrated with the distributed blockchain framework. The approach has been subjected to extensive tests that have been crucial in the realization of the superiority of the approach in achieving effective and accountable data sharing through blockchain.
Hand Gesture Controlled Smart Wheelchair with GPS Tracking and Deep Learning Based Object Detection for Collision Avoidance

As per the World Health Organization, 15% of the total world’s population faces some form of physical impairment. Mobility impairments are one of the most common physical handicaps we have, and wheelchairs are the most commonly used assistive devices for improving different levels of mobility impairments. However, wheelchairs vary in their functionality. Therefore, specific cohorts of users such as individuals with muscular dystrophy and partial paralysis may find it difficult or impossible to operate wheelchairs with manual or powered functionalities, which tend to be the most common. A smart wheelchair is can provide an accessible solution for this cohort of users where it can adapt to the needs of the user using IoT. In this paper, we present a hand gesture-controlled smart wheelchair with gyroscopic stabilization. Furthermore, we introduce collision avoidance functionality for the wheelchair with the help of Deep Learning based object detection methods and an ultrasonic sensor. Apart from these, the proposed wheelchair also provides GPS tracking features.
Thermal Imaging Dataset for Human Presence Detection

In this paper, a created elevated thermal dataset that is designed for training deep learning models for human presence detection will be presented. The dataset consists of 7,101 elevated thermal images of humans captured in different scenarios while walking, running, and clustering. The recordings are captured using Seek Thermal Compact Pro camera under different environments and light conditions, different body positions, and distances from the camera and were preprocessed for the algorithm. The dataset was evaluated using YOLOv7-Tiny regarding performance in mAP scores and loss charts generated when training and testing the captured images. The robustness of the dataset was tested using cross-validation and reducing the number of datasets. The dataset was also combined with other publicly available datasets to compare the difference between the training of the created dataset alone. Combining the private dataset with other public datasets upon training significantly increased the mAP score in detecting the images belonging to the testing datasets that contained publicly available images.
Real-Time Sign Language Identification using KNN: A Machine Learning Approach

A disability may actually constitute incapacity. Speech impairment is a handicap that affects a person’s capacity to communicate verbally and auditorily. People with this handicap use signing as their primary form of communication. A real-time sign detector could be a big improvement in how the hearing impaired and the general public communicate. We have created a solid model that reliably categorizes signing in the vast majority of situations involving several languages. Additionally, this approach is very helpful to those learning sign language in the interim. Throughout the study, various human-computer interaction approaches for gesture recognition were investigated and evaluated. By identifying a person’s hand signs and translating them into legible text, our initiative attempts to communicate with non-sign language users. We have accomplished this using the aid of OpenCV and machine learning, particularly KNN. By using an internet camera or phone camera to take a photo as input, we hope to create a model that can predict the sign and produce the output in form of text/ audio. The system is prepared to have the highest level of accuracy ever seen in a model of this kind.
Statistical Estimation of Rooftop Solar PV System Using Satellite Image: A Dhaka City Perspective

In this study, rooftop solar PV arrays in Sector6, Uttara, Dhaka, Bangladesh, have been estimated and statistically analyzed using Google Earth satellite pictures. The condition of solar PV panels in 2030 is inferred using manual annotation and a regression model because high-resolution photos of the chosen area are not yet available. The outcome demonstrates that the rooftop panel area has grown throughout the course of the ten-year period between 2011 and 2021. Rooftops comprise about 29.6% of the sector’s total area. However, solar PV has not yet been installed on 45% of rooftop space which can be utilized. In eight years, in 2030, the regression model projects that the area of solar panels will have doubled from that of 2021.
Phishing Website Detection using Machine Learning Techniques

Phishing attempts seek to take advantage of vulnerabilities in human-made systems’ security. Since the majority of cyberattacks are spread through techniques that take advantage of end user weaknesses, people are the weakest link in the security chain. Numerous strategies are typically used to protect against different types of assaults because of the complexity of the phishing problem and the lack of a viable single solution. In order to fully understand the many types of phishing mitigation tactics, including detection, offensive defence, rectification, and prevention, it is important to provide a high-level overview.
A novel blockchain architecture with mutable block and immutable transactions for enhanced scalability

Blockchain is a distributed ledger based technology that stores and processes data without a central point of control. The growing number of blockchain-based transactions has increased the demands for scalability and storage solutions. To address these challenges, we introduce a new modified blockchain approach called mutable block with immutable transaction (MBIT), which quantifies unspent coins by storing user-specific transactions in a block using a trapdoor cryptographic hash function. New transactions are added to the user-specific block without altering the existing transactions, which guarantees transaction immutability, consistency, and security. Experimental results demonstrate that compared with current models, the MBIT blockchain model reduces both verification and confirmation times. Additionally, we analyzed the storage overhead of a node and wallet import time of the proposed model.
Redesign of the myLPU Mobile Application of Lyceum of the Philippines University

With the online world becoming more prominent, it changes how the educational landscape is seen. Since the Corona Virus Disease in 2019 (COVID-19) struck the world, learning must continue, and universities have adapted to how they can continue to educate their students. The Lyceum of the Philippines University (LPU) Manila launched myLPU, which is based on an open-source learning management system from Moodle. Focusing on the mobile application, the researchers had challenging experiences using myLPU. Bugs, being unable to locate or access certain topics or resources, an uninteresting user interface, and down servers are just a few examples. Utilizing a mixed-methods approach, the researchers conducted a survey, and the results were neutral, yet some respondents have suggestions on how the myLPU mobile application can improve. At the end of the study, the researchers conducted a redesign of the myLPU mobile application with regards to the notions of the 367 student participants and research findings.