Reviewerly/exHarmony huggingface dataset:
Data Augmentation for Automated Essay Scoring using Transformer Models

Transfer learning is proving quite useful in Natural Language Processing. One of the most important problem in Natural Language Processing is Automated essay scoring, which remains partially unsolved especially when we are dealing with single language model capable to evaluate essays of multiple topics. In this work we examine the effectiveness of transformer models like BERT, RoBERTa, etc using data augmentation technique to build a model capable of evaluating essays of multiple topics having little data.
Anuvadak: Language System using Machine Learning Techniques

The study of machine translation has been underway for decades. The elimination of the language barrier is the primary goal of machine translation. Earlier studies in this area began by directly substituting target language for source language word for word. Later, data-driven models like statistical and neural machine translation techniques became more prevalent as computer and communication technology advanced. Language Translation system plays a significant role in many areas. In this paper, an efficient and an amazingly simple method is used to convert languages. In the proposed method, we used ML along with python language is used for processing word text. The input is taken and converted into another language.
Modeling a Hybrid Stemmer for Kokborok

Kokborok is a resource scarce and vulnerable language and is spoken only by around a million people in the north-east Indian state of Tripura. Lots of unstructured textual data in Kokborok is now available however efficient data mining for such a resource is not applicable. Reasons are the absence of an efficient pre-processing step that can help in solving the information retrieval or natural language processing. In this work, we present a model of hybrid stemming algorithm for the vulnerable and resource-scarce Kokborok language and assess its performance using various methods. Evaluation of the hybrid stemmer was performed on a corpus of 52,482 words that resulted in achieving an accuracy of 89.28%. Recall and Precision of 89.28% and 89.61 %, respectively, with F1-measure of 89.44 has been found for the stemmer.
Analysis and Optimization of Clustering-based Privacy Preservation using Machine Learning

Over the years, there has been an increase in the influx of data collected. With the advancements in Machine Learning, Deep Learning and Data Visualization techniques, leveraging these to perform predictive analysis to make data-driven decisions has risen to importance. In order to utilize the collected data to its maximum potential, it needs to be published and made available to a wider audience. This may pose a breach of privacy for the subjects of the data. In order to curb such a breach, it is necessary to anonymize the dataset.This work uses the SAC Algorithm [1] to anonymize the dataset acquired. A Predictive Analysis has been carried out by choosing the model most appropriate for the given input data. Further, a Comparative Analysis between the results obtained from Private and Anonymized data is done to study how anonymization may affect overall data analysis. This work also puts forth a technique leveraging Machine Learning that can perform optimal grouping of records to minimize the loss of data quality during anonymization.
System Control using Hand Gesture

As computer technology continues to change, today, it’s becoming more imperative to find Interactions with computer systems that are innovative and relevant. Every day, society becomes more dependent upon it. Touch screens are used on every device today, but the technology isn’t cheap for every application. Interactive modules such as Object Tracking, and Gestures will allow us to use a virtual mouse instead of a physical mouse for example. An Object Tracking application will be created to interface with the system. The proposed system employs a computer vision engine to move the mouse pointer based on hand motions captured by a camera. Also, we can control the volume and brightness of the system using simple hand gestures. We have used Python 3.7, OpenCV, MediaPipe, PyAutoGUI to implement this project.
Efficient Semantic Image Editing with Feature Shared Spatially-Adaptive Normalization

Recent advancements in GAN (Generative Adversarial Network) based image synthesis and image editing gave rise to powerful tools. One such tool for semantic image editing is SESAME (Semantic Editing of Scenes by Adding, Manipulating or Erasing objects). SESAME leverages SPADE (Spatially Adaptive Normalization) normalization and a novel two stream discriminator to set a new state-of-the-art. But the extensive use of computationally intensive SPADE layers makes it difficult to use in an interactive environment. We propose optimizations to reduce the redundancy in the network and improve the inference time. With our approach, we reduce the operations (in terms of MACs (Multiply-Accumulate)) of SESAME on Cityscapes dataset by about 5% and on ADE20k dataset by about 7%. Inference time is decreased by about 13% without much difference in the quality of the images produced.
Image De-hazing techniques for Vision based applications - A survey

Haze is defined as a poor condition described by an iridescent atmospheric appearance that reduces clarity and visibility. The main reason for this is lot of toxic elements like dust particles, smoke in the atmosphere scattering and absorbing sun light. This poor intelligibility causes various computer vision applications to fail, including intelligent transportation, video surveillance, element recognition, and in a method to perform operations on image to get better image. There is a problem in domain of image processing wherein image recovery by various degradations is a challenge. Pictures and videos taken in outdoor environments usually suffer from reduced contrast, faded colors and with reduced visibility due to airborne particles, which directly affect image quality. This can lead to problems recognizing objects captured in blurry or still images. Several images clean up techniques have been developed to solve this problem, each with their own strengths and weaknesses, but effective image recovery is daunting task. Recently, many learning-based methods (predictive analytics and natural language processing) have tried to overcome the shortcomings of mechanical representation of properties and alleviated the challenge of efficiently reconstructing images by spending with reduce cost and comparatively reduced time. This overview delves into latest techniques for imaging with no-fog. In addition, hardware execution of many real time dehaze methods have been methodically outlined by this paper. The study done in this paper paves a way for researches in image dehazing domain as-well-as will direct them for doing further enhancement on the basis of achievements done currently.
Bullet Hole Detection in a Military Domain Using Mask R-CNN and ResNet-50

Small arms shooting practices and competitions are routine activities in the military domain. The shooting group or bullet group analysis serves as a metric for the precision of a weapon, the shooter’s accuracy, and consistency, and as a method for improving or refining one’s shooting abilities. This analysis mechanism, however, is either manual or semi-automatic, employing image processing-based algorithms such as template matching, histogram equalization, white balancing, median and gaussian altering, peak detection, and image subtraction in an indoor setting, which is incapable of adapting to environmental conditions such as humidity, temperature, ambient light, wind speed, and rain, among others. Recent advancements in artificial intelligence or deep learning techniques explored ways to facilitate automation in various sectors. In this paper, we have used such deep learning approaches to automize the shooting system in real-time within a military domain and achieved success in resolving the traditional image processing drawbacks. Our proposed methodology has two phases. The first phase uses Mask R-CNN a conceptually simple, flexible, and general framework for object instance segmentation to extract the target region from the environment, and in the second phase, we fed the output segmented target of the first phase to ResNet-50 a convolutional neural network architecture to detect the bullet holes. Several experiments have been conducted on real-time datasets and the results show 0.87 of average precision using mask R-CNN to segment the target and ResNet-50 give 0.80 to detect bullet holes.
Utilizing Mixture Methods for Classifier in NLP: An Essential Consideration

The primary purpose of natural language program is to use it for desktop programming tool. This is very effective, and it can transform common biological conversation into a device which can be easily usable form. The robot may receive inputs in the form of data, voice, or visual. Currently, the machine earning is growing and finding usage in a variety of fields. The machine learning tool is developed by leading companies are having very big effect on the mobile and communication industry. Previous generations' daily tasks will be made easier by chat bots that are educated for all kind of issues which need to be discussed and or taken guidance. This well-known platform was mostly constructed using machine learning algorithms (ML) methods. In order to obtain the best outcomes and also the accuracy rate, the study intends to offer a principle component analysis gives the answer to a text categorization issue.
An Enhanced Student Attendance Monitoring System at Low Resolution and Various Angled Face Positions based on LBPH and CLAHE

This study offers an efficient real-time face recognition system with low image resolution variations and diverse angular positions of face. We have generated our own dataset for training and classification. The facial photos are first taken using an HD 1080p camera, and after preprocessing using CLAHE, a noise reduction is done where median filtering is used. Gaussian filtering also been used while capturing face in recognition process. This technology also effectively recognizes faces in varying lightning conditions. According to the study, accuracy rises as pixel resolution rises and declines when face deviation angle rises. For feature extraction, we have utilized the LBPH method here. If attendance is collected by hand, teachers may find it difficult to keep track of their students. Further system is improvised to collect attendance of students. The system that is being used creates an excel sheet in addition to recording attendance. This approach will be a successful technique for keeping track of students’ attendance.
Blockchain Powered Skill Verification System

A skill verification system is the process of checking and verifying the skill legitimacy of a graduate student or an experienced employee. To maintain an objective, effective, transparent, and secure environment, these methods are necessary. Blockchain, a distributed digital ledger-based technology, can make it easier to implement these criteria successfully [1]. One of the most fundamental yet crucial tasks for every company is the verification of the candidate’s qualifications and experience before hiring. In order to understand the present status of information technology usage in the field of human resource management and how Blockchain might help achieve smart, affordable, efficient, transparent, and secure factory management, a thorough literature research was undertaken. With an increasing number of applying candidates especially in larger firms, verifying candidate qualifications and experience is becoming very time-consuming, and as a result is overlooked many times. This problem of candidates falsifying their information is an increasing concern for the HR department and while conventional skill verification systems are useful, they are still far from perfect to handle this system [3].
Recognizing Face Features using Convolutional Neural Networks

One of the challenging focuses of Computer Vision in modern times has been detecting and analyzing facial aspects of humans. Recognizing the features of a person usually takes a considerable process involving a lot of processing, filtering, and developing algorithms to identify points of interest on a face. Several approaches in this regard are employed and facial landmarks and skin tones are extracted. In this research article, we detected various features and dynamic aspects of the face (facial expressions). Our research essentially is the interface where the input is the user’s image, and the output is the user’s facial shape, skin tone, and expressions. These simple techniques can be used in various other applications where one has to classify multiple features of a person from an image. Facial Features of a person are their facial characteristics such as eyes, ears, nose, and mouth. Apart from these characters, facial features are identified by considering the size, placement, and orientation of these characters. For example, dimensions can be mapped using the following characteristics: eye size, eye spacing, nose width, nose length, pupil size, eyebrow slant, eye eccentricity, mouth width, mouth openness, mouth curvature, and head eccentricity. Using Convolutional Neural Networks (CNN), we proposed a model which can more accurately recognize these features and minimize the error ratio.
Image Enhancement Technique for Underwater Image using Wavelet Transform

Ones underwater degrade differently than images above water. When we proceed to capture images, from the air to the water, the complete image is changed. Some absorption, reflection, and scattering effects are introduced during the capture process and manifest as noise, contrast, and hazy or blurry images. As a result, one shading tends to dominate the image. Clear underwater images are necessary for many underwater applications. Here, we used methods for underwater images that are incredibly clear. These methods mainly use wavelet transforms and Contrast-Limited Adaptive Histogram Equalization (CLAHE). As a result, we suggested a three-step technique in this research. Finally, a combination of these approaches is used to improve the results. In these paper we performed image enhancement technique in two domain: both transform-domain and spatial-domain image enhancement.
Smart Video Retrieval and Question Answering System

More and more video content is being produced on daily basis and utilizing it to its fullest has never been easier. In this paper we propose a new solution for this problem as a smart video retrieval system, leveraging the progress made in extractive question-answering models like RoBERTa and DistilBERT. The system is designed mainly for educational videos in English, with major use cases in schools and colleges. The problem is divided mainly into two parts: 1. Video retrieval and 2. Timestamps retrieval within the video. The paper discusses different variations of the proposed architecture involving slide-text extraction, TFIDF and semantics-based indexing while also speaking about the implementation. We also introduce two new datasets based on educational YouTube videos and Cloud-computing course videos in the paper which are used to generate results and evaluate performance across all the variations implemented. We are able to achieve the best of 89.35% and 84.83% accuracy in video retrieval, and just 1% and 1.7% error in timestamp retrieval considering outliers across the two datasets respectively.
An investigation on the role of artificial intelligence in scalable visual data analytics

Due to the huge amounts of data being generated from various sources, including online communities, the internet of things, and interactive applications, big data has emerged as an important field of research. Numerous prediction and decision-making processes have benefited greatly from the use of big data. Business planning, medical, display ads marketing, physicians, mobility, fraud detection, and tourism marketing are a few examples. In the research and industry, a number of big datasets, including as Hadoop technique quickly developed. These tools enable it to distribute, communicate, and analyze enormous amounts of data. Big dataset processing tools and methods allow for the effective analysis of massive amounts of data. However, choosing the best information solutions focused on bulk and streaming information extraction and analytical methodologies for creation of a big collection system seems to be difficult because of the challenges in processing and utilizing enormous data. Researchers and developers who are developing big data systems do not fully comprehend the existing technologies and specifications again for big internet traffic platform. Therefore, it is necessary to discuss the advantages and disadvantages of big data technology as well as practical answers to big data problems. Through use of big digital information methodologies and big info insights techniques in different fields, such as care and wellness treatment, virtual communities and the online, administrative structure and the civil service, resource management, and the trading and investment sector, is investigated in this study that used a systematic literature review. The goals of this article are to: (1) recognize recent developments in big data and current frameworks for big data innovation; (2) use or investigate of big data tools based on pack and flow calculation as well as big info science methodologies; and (3) support and propose educators and students with devices people really have to orientate latest research acts in this field.
Interpretable Lung Cancer Detection using Explainable AI Methods

Lung cancer is the second most frequently diagnosed cancer worldwide. It is one of the major causes of death among both men and women. In order to reduce the death rate, early detection of lung cancer is the only approach. In this paper, we have proposed an interpretable lung cancer diagnostic system using different ML models like Decision Tree, Logistic Regression, Random Forest, and Naive Bayes classifier. A Kaggle dataset named ‘Lung Cancer Detection’ is used to perform our research work. Among different ML models, Logistic regression and Random forest classifiers scored the highest accuracy of 97% in predicting lung cancer. We have also used the two most popular XAI models, SHAP and LIME to show the interpretability of our used models.
Exploiting the Joint Potential of Instance Segmentation and Semantic Segmentation in Autonomous Driving

The history of transportation spanning from 4000 BC to the present, is a mesmerizing subject that can deepen our understanding of travel from horses and camels, fixed-wheeled carts, riverboats, chariots, cycles, trains, airplanes, cars, and the development of the road locomotive. The future of transport is all about driverless cars or autonomous vehicles. The most crucial requirement for autonomous vehicles for navigation is to detect objects on the road. Autonomous vehicles provide potential answers for traffic congestion, road safety issues, and passenger comfort. The proposed model can detect and classify objects for assisting autonomous driving vehicles with the use of deep learning and neural network-based learning approaches. It aims to segment objects like people and automobiles through the Xception model, which carries out semantic segmentation, and Mask RCNN approaches, for instance, segmentation. Both these methods exhibit improvements in detection and are effective and relatively accurate with the results compared to Deep neural networks (DNN) in Contrast to Mask R-CNN and Xception model, respectively.
Implementation of Smartphone Based Activity Recognition

Hobby reputation is a detectable technique of recognizing distinct sports finished by way of an man or woman in exclusive conditions. Such sports consist of walking, jogging, strolling, going up stairs, taking place stairs, mendacity down. Our main objective is to access various acts taken by individual. Although there are a variety of approaches for detecting various activities, we prefer to employ Smartphone accelerometers due to their widespread availability and ease of use. There are a diffusion of open-supply sensor-based completely time-series datasets that are used to check rule overall performance, that is finally applied to locate human pastime. We have balanced dataset with information and labels are essential for testing the performance of a set of rules. Size, labelling, accuracy, a wide range of activities carried out by each user, formed sensor information contained, and a variety of other factors all influence the size, labelling, and accuracy of such datasets. In this study, we evaluate the popular datasets. We have performed different algorithm i.e. Decision Tree, Logistic Regression, SVC, Gradient Boosted Decision Tree, Random Forest alongside this we have also predicted precision, recall, and F1 score. On the basis of the above parameter the accuracy rate achieved by Logistic Regression in UCI-HAR is 96.19% and in WISDM is 100%
Convolutional Acceleration Algorithm Combining Loop Optimization and Automatic Scheduling

Convolutional neural networks are widely used in deep learning, and convolutional computation accounts for the largest proportion of time in convolutional neural networks, which basically determines the performance of the whole network, so it is important to perform acceleration optimization for the convolutional layer, which can improve the operation speed of the whole neural network to a certain extent and thus accelerate the forward inference of the model. In this paper, we propose an effective convolutional acceleration method that combines traditional compilation techniques and deep learning compilation techniques, starting from the data layout and computational order of the input matrix for convolutional operations. First, we combine the data layout of the input matrix in the MEC algorithm with the loop unrolling, which enhances the parallelism of data transformation. Next, we integrate the intermediate state matrix multiplication operation with the traditional compilation optimization technique and the Ansor [22] in deep learning compiler TVM, which effectively improves the speed of the matrix multiplication operation. In addition, our experiments show that our method effectively speeds up the convolution operation and reduces the running time.
Improved Food Traceability for Restaurant customers using Blockchain Technology

Traceability has become prominent in multi-tiered production systems. It provides assurance to consumers about the transparency and quality of the product. Blockchain food traceability has reasonable attention from the agri-food area. To boost the credibility, efficiency, and safety of the food products, the supply chain should quickly trace the entire lifecycle of food items from their starting point to each phase of the networks. Restaurant customers can trace their food from “farm to fork” with the help of QR codes and smart contracts. This study focuses on restaurant traceability as an example, in which the relationship between supply-chain partners and their networks is shown using a smart contract. The proposed system can build trust among entities that participate in the supply chain network, where distributed ledgers are often used to record and authenticate transactions.