Reviewerly/exHarmony huggingface dataset:
Understanding Banding—Perceptual Modeling and Machine Learning Approaches for Banding Detection

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Banding is an annoying visual artifact that frequently appears at various stages along the chain of video acquisition, production, distribution, and display. With the thriving popularity of ultrahigh definition (UHD), high-dynamic range (HDR), wide-color-gamut (WCG) content, and the increasing user expectations that follow, the banding effect has been attracting increased attention for its strong negative impact on viewer experience in visual content that would otherwise have nearly perfect quality. Here, we present two different types of frameworks to detect the banding artifact. The first is knowledge-driven and is built upon computational models that account for the characteristics of the human visual system (HVS), the content acquisition, production, distribution, and display processes, and the interplay between them. The second is data-driven and is based on machine learning methods, by training deep neural networks (DNNs) with large-scale datasets</i> .
Exploring Realtime Conversational Virtual Characters

<i>Advancements in artificial intelligence (AI) such as speech to text (STT), language understanding models, language generation models, and text to speech (TTS) enable various types of applications, one of which is realtime conversational virtual characters. Building an end-to-end framework with the right AI technology components enables relatable and multidimensional virtual characters, who can naturally converse in creatively controlled domains, while consistently maintaining their state and personality in predetermined narratives. In this work, we designed such a conversational framework with interchangeable and loosely coupled components to support granular creative details in character performance, efficiency in mass creation of virtual characters, and flexibility to embrace future improvements of each component in the fields. We then evaluated the robustness and modularity of the framework by creating Melodie, a virtual character who is fond of music and is a fan and promoter of the Eurovision Song Contest. With Melodie, we went through the full cycle from processing a speaker&#x2019;s audio signals, to generating a proper response using a natural language generation model, to synthesizing the response in a character&#x2019;s voice font, to finally synchronizing the synthesized response with corresponding body and facial movements to produce a coherent and believable character performance. Testing and analyzing the implementation of Melodie brought forth areas of improvement and ethical considerations that are, and continue to be, essential to the design of our future applications involving virtual characters</i>.
Ensemble Learning using Vision Transformer and Convolutional Networks for Person Re-ID

Person Re-Identification is the process of recognizing a targeted individual across multiple views at different times, in different and challenging real-life diverse settings. It remains a conundrum due to the significant amount of intra-class variation present in same individual caught across different cameras. Most of the existing models require a large amount of data for training, as a result of which they do not generalize well on small datasets and hence decreases the robustness of the identification process. To reduce this variance, this paper introduces an end-to-end triple stream ensemble model making minimal changes in the Vision Transformer, Resnet50 and Densenet121 architectures respectively. Our model performs well on the Market1501 dataset achieving an accuracy of 90.05% and 80.45% on the Duke MTMC ReID dataset.
Performance Improvement of Optimization Algorithm for Digital Image Watermarking in Hybrid DWT-DCT Transform

Security and authentication of the people information is effectively provided through various watermarking algorithms. Most of the conventional watermarking algorithms have their own drawbacks in maintaining the quality and security of the image against various attacks. In order to overcome the above stated issues, a watermarking technique based on optimization is required for preserving image quality. In this paper, singular value decomposition (SVD) based digital image watermarking (DIW) scheme is proposed in hybrid discrete cosine transform (DCT) and discrete wavelet transform (DWT) using optimization algorithms like particle swarm optimization (PSO) and Jaya algorithm. The watermark image (WI) is embedded into low frequency DWT sub-band of DCT of cover image. PSO and Jaya algorithms are applied to improve the security threats and quality by assessing the fitness function. The work involves in utilizing the feature points in embedding of watermarks and analyzing the impact of various types of attacks with respect to image quality. The performance parameters like peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation coefficient of fitness function given by PSO and Jaya algorithm.
Analysis of Spatial Domain Image Steganography Technique for high-capacity

At present, data security over public communication channel is a major issue. Image steganography technique hide the original data from the unintended user while transferring it over the public channel. Several techniques are present to facilitate secure communication. In this article, sudoku matrix, genetic algorithm, and particle swarm optimization algorithm based steganography technique considered for detail study based on various qualitative parameters. The parameters are maximum embedding capacity (EC),highest embedding rate (ER), and peak-signal-noise ratio (PSNR). Using qualitative matrices, the analysis of the capacity, security, and robustness carried out. The EC of 1,608,070 bits at embedding rate of 4.0 bpp is achieved. PSNR 57.17 dB implies that distortion in original image is nearly indistinguishable by human vision system (HVS). So, experimental results confirm robustness of the technique against HVS.
Deep Fakes Image Animation Using Generative Adversarial Networks

The idea of picture activity is for the most part moving the pictures at a specific speed so the unaided eye can't detect the distinction. We intend to do the investigation so that for certain adjustments to the current structure that is the deepfake that does the examination without earlier information on the movement target. To do this, We will be training a dataset on a bunch of pictures and recordings for objects of a similar class (e.g., face, body, road view). As of late, a few uses of neural organizations (CNNs) have been applied to the genuine human head. The informational index can be prepared on many pictures and recordings to make practical talking heads. You can energize the first picture of an individual into an objective individual posture (driving video) while safeguarding the individual's appearance and body. In the mean time, in any case, frameworks are being fostered that can recognize recordings and activities produced by Deep-Fakes. Since this is a significant security issue. We energized pictures to create talking heads and tried different things with picture age Using the Deep-Fakes age's contingent generative threatening organization, the outcomes were reasonable. Likewise executed Deep-Fake Detector XceptionNet (a Deep Learning Algorithm that Detects Face Swaps in Videos) with slight adjustments to arrive at 95° exactness when identifying Deep-Fake. At last, you can without much of a stretch idiot Deepfake identifiers by executing an as of late acquainted method with quit making Deep-Fakes. XceptionNet had the option to accomplish a precision of under 30 in recognizing the Deep-Fake age when maddened.
Sentimental Analysis on reviews of Protein Supplement using RNN-LSTM

In the most recent period, people are building up a habit of checking reviews before investing their time, money, and emotions in almost every aspect of their lives. To adapt to this newly made custom, a rise in the use of deep learning models for sentiment analysis is noticed from the reviews. Sentiment analysis is now widely used in various social media monitoring, product analysis, feedback analysis, target specific customers, and many other sectors. This paper introduces a deep learning model that will analyze the sentiments from people's reviews and predict whether a person's review is positive, negative, or neutral. This is done with the help of LSTM (an artificial recurrent neural network (RNN) as this model is more convenient to use to train the reviews and the outcomes are significantly better than any other model. For the LSTM model, our approach has achieved tremendous success, with 82.8% accuracy. It is strongly hoped and believed that this model will impact the customer's decision-making.
Chaotic Enhanced Genetic Algorithm for Solving the Nonlinear System of Equations

Many engineering and scientific models are based on the nonlinear system of equations (NSEs), and their effective solution is critical for development in these domains. NSEs can be modeled as an optimization problem. So, the goal of this paper is to propose an optimization method, to solve the NSEs, which is called a chaotic enhanced genetic algorithm (CEGA). CEGA is a chaotic noise-based genetic algorithm (GA) that improves performance. CEGA will be configured so that it uses a new definition which is chaotic noise to overcome the drawbacks of optimization methods such as lack of diversity of solutions, the imbalance between exploitation and exploration, and slow convergence of the best solution. The goal of chaotic noise is to reduce the number of repeated solutions and iterations to speed up the convergence rate. In the chaotic noise, the chaotic logistic map is utilized since it has been used by numerous researchers and has proven its efficiency in increasing the quality of solutions and providing the best performance. CEGA is tested using many well-known NSEs. The suggested algorithm's results are compared to the original GA to prove the importance of the modifications introduced in CEGA. Promising results were obtained, where CEGA's average percentage of improvement was about 75.99, indicating that it is quite effective in solving NSEs. Finally, comparing CEGA's results with previous studies, statistical analysis by Friedman and Wilcoxon's tests demonstrated its superiority and ability to solve this kind of problem.
A Novel Conceptual Chatbot Architecture for the Sinhala Language – A Case Study on Food Ordering Scenario

Instant messaging applications (apps) are one of the most popular methods that people use for daily communication. The usage of such apps has increased rapidly in the past couple of years. Merging Natural Language Processing (NLP) with those messaging apps has become a trending area in software application development. Localizing Chatbot conversations into different languages is an active research field. The Sinhala language is one of the most used and common languages in Sri Lanka. In our study, we introduce a novel architecture for the Sinhala language-based Chatbot development, which possesses the Natural Language Understanding (NLU) for intent identification and entity extraction. When considering the intent identification, we utilized existing tools to identify the Sinhala language-based utterances by training their machine learning-based services. The proposed architecture was tested using an in-house dataset related to the restaurant food ordering scenario. Our results show an accuracy of 89.16% for intent identification. We strongly believe that the novel conceptual architecture for the Sinhala chatbot development will provide a gateway for companies to adopt it for their customized contexts. Further, the proposed architecture will pave a way for researchers to enhance the architecture to the next level.
A Recommendation Method for Social Media Users based on a Sentiment Analysis Model

Sentiment analysis for social media users analyzes text and images that the users submitted to social media. However, it has been difficult for conventional sentiment analysis methods to properly analyze the sentiment of social media users in the cases that there is no relation between text and images that the user submitted, or there is the contradiction of the user's sentiment extracted from the text and images even if there is correlation between them. In this study, in the context of content recommendation according to user's sentiment, we propose a method for calculating user's sentiment score for text and images based on the correlation between the text and images and the difference of sentiment scores calculated for the text and images, respectively. In the experiment, we verify the feasibility of our proposed method by the evaluation using content analysis models and sentiment analysis models trained with a text dataset and an image data set.
Retracted: Intelligent Analysis Framework of Art Measurement Based on Multi-Angle Image Switching Technology

This article introduces the related theories of image stitching technology, focusing on the classic scale-invariant feature transform (SIFT) algorithm-based feature detection and description problems, and elaborates its basic theory and feature extraction process; for SIFT's existence during registration Problems such as large amount of calculation and many mismatches. A consistent point shift (CPD) image registration algorithm based on SIFT features is proposed. The general principles of art measurement and the image of template matching algorithm based on art measurement are discussed. Splicing technology and strategies to improve algorithm efficiency.
Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning Models

The COVID-19 (SARS-CoV-2 virus) pandemic has led to a substantial loss of human life worldwide by providing an unparalleled challenge to the public health system. The economic, psychological, and social disarray generated by the COVID-19 pandemic is devastating. Public health experts and epidemiologists worldwide are struggling to formulate policies on how to control this pandemic as there is no effective vaccine or treatment available which provide long-term immunity against different variants of COVID-19 and to eradicate this virus completely. As the new cases and fatalities are recorded daily or weekly, the responses are likely to be repeated or longitudinally correlated. Thus, studying the impact of available covariates and new cases on deaths from COVID-19 repeatedly would provide significant insights into this pandemic's dynamics. For a better understanding of the dynamics of spread, in this paper, we study the impact of various risk factors on the new cases and deaths over time. To do that, we propose a marginal-conditional based joint modelling approach to predict trajectories, which is crucial to the health policy planners for taking necessary measures. The conditional model is a natural choice to study the underlying property of dependence in consecutive new cases and deaths. Using this model, one can examine the relationship between outcomes and predictors, and it is possible to calculate risks of the sequence of events repeatedly. The advantage of repeated measures is that one can see how individual responses change over time. The predictive accuracy of the proposed model is also compared with various machine learning techniques. The machine learning algorithms used in this paper are extended to accommodate repeated responses. The performance of the proposed model is illustrated using COVID-19 data collected from the Texas Health and Human Services.
Blockchain based Application: Decentralized Financial Technologies for Exchanging Crypto Currency

In this paper, the distributed decentralized network with the nodes at any place can access the data behaviour intention performance is analysed and explained in detail. The bitcoin blockchain is elementary of several crypto-currencies. The blockchain technology holds principal assurance for a difference of enterprises, including financial services, cryptocurrency exchange, laundering of anti-money tracking system, unique identity security, healthcare, real estate, supply chain, and logistics monitoring. Results showed that blockchain is considerably new technology and the nodes are sharing their data in the network without any trust in other nodes. Furthermore, key aspects of using this technology, the users in the crypto world buy, sell, or exchange the crypto-coins in the blockchain network. Many of the protocols are there to exchange cryptocurrencies. The main contributions of this work liesbased on the protocol named uniswap protocol, which helps the users to exchange the crypto-coins. Uniswap protocol is defined to be the automated liquidity protocol provision on the blockchain Ethereum platform. With the uniswap, a user can buy or sell the ERC20 tokens in the decentralized distributed network by using an Ethereum smart contract. This paper will help the users in the blockchain network to useuniswap protocol as a trustworthy and efficient way of exchanging the crypto-coins or the tokens in the blockchain network.
Artificial Intelligence in Recording the Number of Times the Ball Has Crossed the Net in a Badminton Game

In today’s intelligent age, the research and development of intelligent equipment are now an important direction, and the combination of artificial intelligence and sports is also a very popular research nowadays. This article uses the current popular artificial intelligence technology to record the number of times the ball crosses the net in a badminton game and provide real-time feedback to ensure the fairness and justice of the game. In this paper, artificial intelligence technology is the center. Experiments were conducted to record the number of times badminton crossed the net in official competitions, and the very popular artificial intelligence technology was analyzed. The algorithm we designed was first applied to amateur competitions. Recording real-time data lays a solid foundation for subsequent research and will be used in professional competitions later. The experimental results of this article have developed a function based on artificial intelligence to record the number of times the ball has crossed the net in a badminton game. We have studied traditional games and found that badminton has an average of more than 3000 times over the net in a game, and there are also some in amateur competitions. Nearly 2,000 times, and more than 50% of people like this sport in our daily lives, so our research is very meaningful and can be implemented in concrete terms.
An Industrial Application of Multi Target Detection in Thermal Images from Different Cameras with DeepLearning

In this study, the main aim is to automatically perform the manual target detection process used in the camera field of view testing of mass-produced thermal cameras. A data set is prepared by taking images using different mass production cameras and different test systems. With this prepared data set multi target detection architecture is proposed. This proposed hybrid architecture consist of ResNet50 block, which is used for feature extraction, and YOLOv3 block. The accuracy of this proposed architecture to detect targets whose number and position changes in each image, compared with Minimum Output Sum of Squared Error(MOSSE), Single Shut Detection(SSD), Aggregate Channel Features(ACF), Recurrent Convolutional Neural Network(RCNN), FAST-RCNN, FASTER-RCNN, and YOLO versions target detection architectures. As a result of this comparison, it is seen that the proposed hybrid architecture has higher accuracy than other architectures. The use of proposed hybrid architecture in the camera field of view test of each camera produced with mass production will reduce the workload and increase the accuracy of the camera field of view calculation.
Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

The food industry is undergoing a phase of very good improvement, where business actors are experiencing very rapid growth. Creative ideas are many and creative on several social media. When an online business is growing rapidly, many managers in the food sector market their products through online media. So it is quite easy for customers to place orders via mobile. Especially during the COVID-19 pandemic, where a ban on gatherings has become a government recommendation for many food business actors to sell online. Since then, almost all food industry players have made their sales online. There are many advantages of doing business online. The food served is in the form of pictures that attract market visitors so that it can create its own charm. Food is just a click away to order, and the order comes. No need to queue and everything has been delivered to the ordered goods. After the ordered goods arrive, the customer reviews the food or drink. Because customer reviews are the result of customer ratings. The result of the review is one of the sentiment analyses, which in this study is in the form of a review of the images available on the display marketplace. The method used is Convolutional Neural Network. The dataset will be extracted features and classifications. The research will do a comparison using VGG19, ResNet50, and Inception-V3. Where the accuracy of VGG19 = 96.86; Resnet50 : 97.29; Inception_v3 : 97.57.
Incomplete clustering analysis via multiple imputation

Clustering analysis is a prevalent statistical method which divides populations into several subgroups of similar units. However, most existing clustering methods require complete data. One general method that addresses incomplete data is multiple imputation (MI) which avoids many limitations found in other single imputation-based methods and complete case analyses. Nevertheless, adopting MI framework to clustering analysis can be challenging since each imputed data might consist of a different number of clusters and there is not a unique parameter for clustering analysis. In response to this problem, we have developed MICA: Multiply Imputed Cluster Analysis. MICA is a framework for clustering incomplete data consisting of two clustering stages. We assess the properties of MICA and its superiority over other existing incomplete clustering strategies based on a simulation study under various data structures. In addition, we demonstrate the usage of MICA by applying it to the Youth Risk Behavior Surveillance System (YRBSS) 2019 data.
Comparison Analysis and Data Retrieval to identify the associated people in social media by Image Processing

Data retrieving, Data analyzing, and face recognition has become most famous and very often using technologies in computer applications today. So that, in recent years, there has been a lot of interest in researching the topics of Data retrieving, Data analyzing and facial recognition. When concerning Social Media platforms such as Instagram, Facebook, twitter, they have become more fastest growing social networks in the last three years. It let the users to share their status by uploading images with a descriptive text, a location, and certain hashtags that do not necessarily represent the substance of the pictures. So now the social media platforms have become most popular photo-sharing websites. But unfortunately, some people misuse these social media platforms for unethical activities such as sharing false propaganda and fake news, terrorist activities, unethical religious activities, illicit drug distributions etc. Therefore, this work is to recognize the suitable technologies that can be used to retrieve and analyze image data from social media platforms such as Demographic analysis, Text analysis, Image analysis, Snowball Technology and some of the face recognition technologies used in iPhone photos, face recognition technologies such as Eigenfaces technology, Neural Networks, Graph Matching, Line Edge Mapping for a system to retrieve and analyze image data from social media platforms and to identify the most associated people of a certain social media user.