Reviewerly/exHarmony huggingface dataset:
Character Recognition of Vedic Sanskrit using Deep Learning Algorithms

One of the applications of deep learning is deciphering the unscripted text over the walls and pillars of historical monuments is the major source of information extraction. This information gives us an idea about the art and the culture of that respective era. In this paper we proposed a model for digital recognition of textual characters written in Sanskrit language using the 4-fold Convolutional Neural Network (CNN) architecture. At the first stage we remove the noise and subsequently segmentation of characters from the input image is performed, later we convert the image into binary image format by the help of image processing techniques. The research is performed over the Devanagari script which was developed during 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> to the 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> century CE and is common to many languages from the Indian subcontinent.
Using CoordConv for Tabular Data Detection and Structure Recognition

This paper explores the usage of CoordConv, the novel upgrade to general convolutional layers in the problem of Tabular Data Detection and Cell-Based Structure Recognition. CoordConv has been shown to provide considerably better results in the domain of Object Detection than its counterpart. The authors integrate it within the established Anchor optimization approach which leverages guided anchors to accomplish the task of recognizing rows and columns present in tabular data. In contrast to the majority of techniques implemented for Table Structure Recognition, the authors attempt to recognize the cells present in the tabular images instead of the rows and columns. They evaluate this method on the coveted ICDAR-19 dataset (International Conference on Document Analysis and Recognition - 2019) which comprises of scanned document images containing tabular regions and achieve results surpassing those of many popular techniques. They also apply this approach for the task of Table Detection and achieve results comparable to other established techniques.
Attention-based Residual Network for Single Image Remote Sensing Super-resolution

Single image super-resolution (SISR) reconstruction is crucial in meeting the growing demand for remote sensing imaging applications that need high spatial resolution. With the advancement of deep convolutional neural networks (CNNs), remote sensing SR has received considerable attention and has shown promising performance in the recent years. An effective feature extraction approach of CNN-based SR methods determines the quality of reconstructed images as well as increase the representation ability of CNN by more accurately extracting feature abstraction. In order to enhance the representational ability of CNN, a deep residual spatial and channel squeeze-and-excitation (RSCSE) SR network is proposed for remote sensing images (RSIs) to extract the deeper features in terms of channel-wise and spatially. Furthermore, a local feature fusion approach is incorporated in RSCSE module to preserve local features adaptively. Experiments conducted on two remote sensing datasets demonstrate that the proposed RSCSE network performs better than state-of-the-art methods both visually and quantitatively.
A Novel object Tracking Algorithm Based on mean shift algorithm and SURF

Mean shift algorithm (MSA) is a powerful object tracking technique due to its simplicity and robustness. However, it causes easily the inaccurate tracking when the scene is complex, for example, the object is seriously occluded, the color of the object is similar to that of the background, the background is dynamic, and the camera shakes or moves. Aiming at the above problems, a novel object tracking algorithm based on MSA and SURF (Speeded Up Robust Features) is proposed. Firstly, the object area in the current frame is determined by MSA, and secondly, SURF algorithm is used to match the feature points in the object area of initial frame with those in the object area of current frame, and finally, the coordinates of the center point in the object area of current frame are adjusted according to the matching results. The experimental results on three videos in complex scenes show that the proposed algorithm can track the object more accurately than MSA, and realize the real-time and accurate tracking of video objects in complex scenes.
LSTM based Receiver Design for Baseband Signal Demodulation

This paper summarizes an intelligent correlation receiver design for baseband demodulation with Long Short Term Memory based Deep Learning technique. We explain the generation of training data, bipolar signaling, consideration of channel noise. i.e, Additive White Gaussian Noise, training of model, fine tuning of dense layers to properly fit the communication application. Better Bit Error Rate performance over conventional correlation receiver is observed by DL model subjected to a AWGN noise channel with a noise in Signal to Noise Range of −50 dB to 20 dB. We validate this by applying the customized model to reconstruct successfully a Pulse Code Modulated 255 $\mu$-law companded audio signal immersed in AWGN noise channel.
Theoretical Development based Nature-Inspired Optimization Algorithms: Applications and Challenges

The process to achieve optimal solutions is becoming complex as the data generation is being proliferated. The intelligent metaheuristics algorithms are being recognized to obtain the optimal solution for these complex optimization problems, especially when there are various constraints to solve these problems. Many novel algorithms are being developed to find efficient approaches for handling these optimization problems. These algorithms are either exploring their application in a different context or are being enhanced to handle problems in different areas efficiently. There is rapid development in this field which results in unawareness regarding their application in different domains. This paper identifies the popular nature-inspired optimization algorithms by discussing their developments, principles and domain of application to address the gap of unawareness. Specifically, we have analyzed and explored the subsequent algorithms grasshopper, binary bat, chicken swarm, and cuttlefish algorithm. This review can also act as a guide to choose appropriate algorithms for future studies. Owing to the absence of significant literature, we have identified various nature-inspired algorithms having tremendous scope in theory and application.
Research on interactive learning model assisted by multimedia computer technology

Since the beginning of the 21st century, with the popularization of network, information and computer multimedia education to education, the teaching reform of various disciplines has been greatly promoted, which is of great significance to English classroom teaching. Since 2018, the scientific implementation of network and computer-aided teaching in English teaching has created a positive educational environment and a good educational atmosphere from the aspects of interests, knowledge integration, image coexistence, practice expansion and self-study. The integration of multimedia computer technology and interactive learning mode has greatly promoted English teaching to a high quality and high level.
An Online Classroom Question Answering Evaluation System Based on Voiceprint and Behavior Recognition

Faculty of Xiangyu proposed and designed an online class question answering and judgment system based on convolutional neural network behavior recognition, voice pattern recognition and dialect recognition for the wide development of online teaching. This design collects students' voice patterns and behaviors when answering questions through microphone, camera and other equipment, identifies and analyzes students' voice patterns and behaviors through computer algorithm, and judge's students' mastery of knowledge points.
Content-Based Image Retrieval: Feature Extraction Techniques and Similarity Metrics

The method which is used here for the purpose of feature extraction keep visible qualities of the picture in mind. In this paper, various feature extraction techniques based on color, shape, texture etc. are discussed. In addition to the color bar chart and its correlogram, CCV, CCM and tamura, wavelet transform, DWT, GLCM, DCD, SCD, CSD etc. comes under feature extraction methods. Firstly, feature extraction procedure is discussed in the paper then various techniques for feature extraction is discussed. After that an algorithm is proposed to retrieve images depending on various features. Then results of different similarity metrics are shown.
AI Based Deepfake Detection

Advances in machine learning, especially following the 2014 release of Generative Adversarial Networks, have allowed techniques and methods to be used for nefarious ends. Generative Adversarial Networks can even create fake images and videos which appears to be real to human eyes. Generative Adversarial Networks can swap the faces of two different people. For film producers or graphic designers, this tool is quite useful. Face swapping has been done in movies to replace the real person's face with that of the actor. A computer-generated versions of actors Carrie Fisher and Peter Cushing have been featured in a movie named “Star Wars: The Rise of Skywalker” from the “Star wars” film series just like they appeared in the 1977 original, while other Marvel films have “de-aged” actors such as Michael Douglas and Robert Downey Jr. However, this has the potential to be misused. By producing ultra-realistic Deep Fake videos using various trailblazing machine learning techniques, felon are trying to harass, blackmail the innocents. It can also be used to induce political instability by disseminating erroneous information, which can cause communal, diplomatic, and violent outbreak with disastrous consequences. This gives rise to a significant menaces to security of the person as well as national defence, necessitating the development of automated methods for detecting deep fake videos. The eye blinking pattern in deepfaked videos is not formed as naturally as it should be due to the incapability of Generative Adversarial Networks. This will come in handy when constructing a deepfake detecting algorithm. The project uses an object's eye blinking pattern to determine whether or not a video is deepfaked.
An Enhanced Copy-deterrence scheme for Secure Image Outsourcing in Cloud

In this paper, we propose a novel watermarking-based copy deterrence scheme for identifying data leaks through authorized query users in secure image outsourcing systems. The scheme generates watermarks unique to each query user, which are embedded in the retrieved encrypted images. During unauthorized distribution, the watermark embedded in the image is extracted to determine the untrustworthy query user. Experimental results show that the proposed scheme achieves minimal information loss, faster embedding and better resistance to JPEG compression attacks compared with the state-of-the-art schemes.
Application of Particle Swarm Optimization Algorithm in Solving Mathematical Modeling Optimal Problems

In today's society with the rapid development of science and technology, the status of mathematics education has been significantly improved at home and abroad. With the gradual introduction of mathematical modeling into the classrooms of Chinese universities, it has become one of the indispensable qualities of students. However, due to the lack of application of mathematics education in our country, the form of mathematical modeling teaching is still not optimistic. This paper aims to solve and analyze the optimal problem of mathematical modeling. Among different mathematical modeling problems, many problems can be solved by establishing optimization models. to solve the analysis and get the optimal solution in the optimization model. Based on the research on the optimization mechanism of particle optimization algorithm, this paper discusses the solution methods of mathematical modeling optimal problems and their functional characteristics. Although there is no guarantee that the optimal solution can be obtained, when the scale of the problem is relatively large and the solving task is relatively complex, A satisfactory solution to the problem can often be found within a certain period of time. The cultivation of practical ability of mathematical modeling is one of the core qualities that college students should master. It can not only improve students' ability to express objective things through mathematical models, but also cultivate students' innovative ability and teamwork ability. The ratio of mathematical modeling problems to the optimal solution path under the optimization algorithm has reached more than 90%. With the increase of the number of solutions, the ratio of the optimal solution path changes in the opposite direction. The ratio of optimal solution is 99.23%.
The complexity attachment in modernization journey

Often referred to as heritage systems, Legacy systems have proven their efficacy and durability over the decades but struggling to prove their viability in the digital market. Legacy systems are being modernized and migrated with a complexity that increases the cost and risk in those journeys. Their underlying characteristics require an in-depth assessment of the factors contributing to their complexity. Therefore, this paper proposes a systematic review to trace and analyze the emergence of characteristics leading to complexity attachment and associated factors and illustrate different techniques to assess factors.
Modern Art Design Knowledge Teaching System Based on Cloud Computing Environment

A large number of resources are integrated in the cloud computing environment for users to query and utilize in real time. Apply cloud computing technology to the field of education, and use advanced technology to make the teaching process conform to the modern education concept. This paper uses the. NET development framework to build a modern art (MA) design knowledge network system based on cloud environment, and then designs a teaching system with multiple functional modules. This paper realizes the management of system login rights and ensures login security by verifying the login function. The teaching system was applied to the teaching of art and design majors, and the satisfaction of using the system was investigated. The results showed that the satisfaction of students reached 68.5%, the satisfaction of teachers reached 65.6%, and the students and teachers were dissatisfied with the teaching system, accounting for 14% and 14% respectively. 12.6%, the reason for dissatisfaction is that the system needs to further develop the functions of examination evaluation and performance evaluation management. It is hoped that the teaching system designed in this paper can help students to learn and understand modern art design knowledge more deeply.
Ant Colony System with Sparse Pheromone

Ant colony optimization algorithm is a typical meta-heuristic algorithm, which is widely used in various combinatorial optimization problems, but its high space complexity, which has become one of the main constraints affecting the application of ACO algorithm. The pheromone matrix is one of the major storage overheads. This paper based on the analysis of typical ant colony optimization algorithms, it is observed that the pheromone matrix of ACS has very strong sparseness. Therefore, SACS is proposed, whose pheromone matrix uses triplet sparse storage. In order to solve the problem of how to deal with the number of items of the initial allocated pheromone triplet and the new non-default pheromone update, this paper proposed the method based on the small fixed storage space with different replacement policies, those are, SACS-Max, SACS-Min, SACS-Rand. Many experiments show that this method basically eliminating the storage bottleneck of the pheromone matrix.
Deep-Learning and Spatio-Temporal Incremental Clustering based Human Detection and Tracking

Human detection and tracking play important roles in the computer vision. Effective tracking technique helps in various fields such as robotics, entertainment, surveillance, sports and assistive living, etc. In this paper, we perform human detection using the deep learning technique- YOLOv5. Then, we propose a spatio-temporal incremental clustering based framework to track the detected person. We perform our experiments on publically available video dataset which consists of a large number of videos covering various weather conditions, day timings, locations and other environmental conditions. The experimental results show that the proposed detection and tracking method gives accurate tracks of single as well as multiple people detected in each video even in complex conditions.
Fuzzy Logic in Suggesting The Appropriate University Major for Students According to Their Preferences

Currently, professional specialization is requested by employers to run high-level business quality. Moreover, it is important for employees for career stability and self-improvement. As a result, universities provide a lot of specialized programs that make it hard for new students to select the most appropriate study program that fits well their qualifications. This work is related to suggesting the most suitable university program for students taking into account a set of criteria such as academic achievement, interests, standard of living, and income. Fuzzy logic is used to build a recommendation system to match university academic programs and the set of students' criteria in order to generate the most suitable programs for students. The experimental test that measures the amount of accuracy of the work is conducted at the end of the work reflects promising results were it emerges a good percentage in the correct selection of majors with respect to the participants.
Japanese Translation Teaching Reform System in Internet Era on Account of Big Data Algorithm

China and Japan are promoting economic and cultural exchanges in many fields year by year. Due to the need of reality, the society strengthens the bilingual language foundation of Chinese and Japanese with solid basic skills and cultivates talents with language application ability suitable for the business needs of China and Japan. This paper adopts big data algorithm to improve the reform of Japanese translation teaching in the Internet era. At present, most of the undergraduate universities are weak in Japanese translation ability, and their teaching content, teaching form, teaching personnel and other aspects are facing very severe challenges of The Times. Applied talents need to quickly cooperate with professional colleges and universities, so as to enhance the applicable conditions for enterprise development. This paper studies the systematic research of Japanese translation teaching reform on account of big data algorithm in the Internet era, and explains the working principle and mechanism of Japanese translation teaching reform in the Internet era. The data analysis shows that the systematic research of Japanese translation teaching reform on account of big data algorithm in the Internet era has excellent performance in the reform of Japanese translation teaching.
Smooth PRM Implementation for Autonomous Ground Vehicle

Generating continuous and smooth paths with collision avoidance that avoid sharp turns is a significant challenge for autonomous mobile robot navigation. Sampling-based motion planners do widely use in robotics due to their computing efficiency, flexibility, and simplicity. One of the sampling-based planners, Probabilistic Roadmap(PRM), starts with a random sampling of the points in the free space. Although this sampling-based planner is generally very efficient, it can occasionally become computationally expensive when it runs dangerously close to an obstacle. In addition, the computed path can contain sharp turns challenging for the differential drive robot to navigate. Also, the path is not optimal and can be longer than necessary. The idea presented in this paper is to demonstrate how to use the gradient descent approach to find an optimal (smoother) path even though PRM provides a longer path with abrupt turns. PRM and Smoothened PRM were both run on the given operational environment and compared the performance in simulation and hardware. The simulation result shows that the algorithm can shorten the length of the searched path. The smoothness of the path has significantly improved even if the PRM offers a path with abrupt turns. Moreover, the proposed algorithm runs well on Turtlebot3 waffle pi, performing real-time obstacle avoidance.
Breast Cancer Prediction Using Long Short-Term Memory Algorithm

Breast cancer (BC) isconsidered the second leading cause of death in both developed and developing countries, with 8% of women being diagnosed with the disease at some time in their life. So, it's more crucial to identify BC and the damaged breast region. In today's world, Machine Learning (ML) algorithms are frequently employed in the classification of breast cancer datasets. These algorithms have quite a significant level of classification accuracy and diagnostic capability. Because a specific classifier may or may not perform well enough for such datasets, a comparison examination of classifiers is necessary in order to get maximum performance in such significant breast cancer predictions. Deep learning is the branch of machine learning with architecture and functions inspired by the human brain. It's especially effective for classifying enormous data sets because the findings are fast and accurate. In this paper, we have used five different machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and long short-term memory (LSTM) on BC dataset. The outcomes produced by KNN, SVM, RF, and DT classifier will all be compared to the LSTM classifier on the basis of confusion matrix, precision, F1 score, Recall, and accuracy. This study's main aim is to diagnose the best machine-learning algorithm for breast cancer prediction. It is observed that the LSTM algorithmoutperforms all other discussed algorithms with 96% accuracy.
Continuous Cloud User Authentication By Efficient Facial Recognition

Designing any cloud-based application typically includes a vital step for securing user identification and access control. A potential approach to increase security against mali-cious and unauthorized access to protected applications is multi-factor authentication based on biometrics. A versatile and reliable method of biometric identification is facial recognition since it can be implemented with less specialized hardware. Facial recognition-based continuous authentication is a promising strat-egy for assuring security. Because Convolutional Neural Networks are capable of comprehending the high level characteristics required to recognize human faces, they are known to be the most accurate and reliable approach for used identification. Their broad use is restricted by the computational requirements that results from utilizing this method for facial recognition. In order to minimize the computational load and boost the functional speed of authentication, this research suggests creating a modular facial recognition system that integrates techniques like frame differencing and face detection and face recognition process. A system of modular processing phases is designed to make the continuous authentication process efficient, as respective modules are activated when triggered by changes in the input data.