Reviewerly/exHarmony huggingface dataset:
HyperMean: Effective Multidimensional Mean Estimation with Local Differential Privacy

Multidimensional mean estimation with local differential privacy (LDP) extracts the numerical features from groups while protecting users’ personal information without relying on a trusted server. However, the increase of dimensionality would lead to a deficiency in the allocable privacy budget, resulting in excessive accuracy loss. To solve this problem, we propose HyperMean, an effective privacy-preserving mean estimation mechanism for multidimensional data whose accuracy is at least no worse (and better in most cases) than existing solutions. We first design a multidimensional staircase function to obfuscate users’ data, significantly reducing the output variance. Second, an adaptive dimensionality reduction is performed on users’ data to allocate the privacy budget to some focused dimensions. Finally, the server averages all users’ obfuscated outputs to obtain an unbiased estimate of the mean results. Theoretical analysis reveals that HyperMean effectively reduces the worst-case variance of multidimensional mean estimation under LDP while maintaining low computational complexity. Experiments on both simulated and real-world datasets show that HyperMean outperforms existing multidimensional mean estimation mechanisms in terms of aggregated error.
Detecting anomalies, contradictions, and contextual analysis through NLP in text

This work aims to find contradictions between various sentence pairs, practical application of the model involves various disciplines such as law enforcements for comparison of various witness testimonies or to understand historical manuscripts from different point of views, using NLP based model training such as neural networks, KNN and random forest. We would try to find out the model providing us with the most accurate figures suitable.
Evaluation of Subjective Answers Using Machine Learning

Negative methods are currently used to evaluate subjective writing. The evaluation of the subjective responses is an essential responsibility. When a human analyses anything, the evaluation's quality can change depending on the person's emotions. All outcomes in machine learning are solely dependent upon the user's input data. To address this issue, our suggested method combines machine learning (ML) and natural language processing (NLP). To analyse the subjective response, our algorithm performs tasks including tokenizing words and phrases, classifying parts of speech, chunking and chinking, lemmatizing words, and word netting. Our suggested approach also offers the context's semantic meaning. There are two modules in our system. Extracting data from scanned photos is the initial step. Then arranging it properly, and the second is using ML and NLP to analyse the text obtained in the previous phase and assigning grades to it.
Identification of Fraud Transactions using Lightgbm Technique

In today's economic necessities, credit card theft is an emerging concern. The rate of cheat/fraudulent has been increased rapidly now-a-days, which causes significant monetary losses for many organizations, businesses, and branches of government. The number of these fraudulent are highly expected to increase in the future in a low time, which is why many investigators in the field have focused on early detection of fraudulent actions using inimical methods. Even so Due to two factors, identifying fraudsters is a difficult process. 1. different natural fraudulent actions for each minority sample overtake (fraud cases). When an oracle model receives input from a particularly unstable class distribution, the model favors mature observations. The data-level methodology, in which different re - sampling methods like down sampling, overfitting, and aggregation techniques are managed by an algorithmic approach, in which the same aggregation models as bagging and boosting are applied to a broad-slope dataset containing 28,807 transactions, is used to resolve this issue. Exactly 92 of these bids were found to be malicious. For predicting if a transaction is malicious or real, Oracle techniques such as logistic model, RF, and Light GBM have been integrated with various Machine Learning approaches. The recovery, accuracy, f1 score, precision recovery arc, and receiver basic characteristic curve were used to evaluate the precision. The findings suggest that the RF paired with the Synthetic Minority Oversampling Technology composite interpolation approach and Tomek association elimination outperforms other methods.
Generative Adversarial Networks, Their Various Types, A Comparative Analysis, and Applications in Different Areas

GANs(Generative Adversarial Networks) are two neural networks that compete with each other to develop new data which hasn't been there originally. If we train the GAN on images, the resultant will be imaged with realistic characteristics. A GAN consists of two sub-models in which is called a generator model and the other model called the discriminator model. The Generator generates the data from the input data. The Discriminator checks or distinguishes whether the data is a sample from the real world or a generated one. This model was given for the unsupervised learning method, but it can also be applied to semi-supervised models. In this paper, we present a survey on GANs. First, we explain what simply generative adversarial networks(GANs) are, what are the different types of GANs, then what are the applications of generative adversarial networks(GANs) and then we explain the different areas in which GANs are being used and lastly what future GANs hold in different areas or fields or how can we use GANs in technology and research as a future evolving aspect.
Deep Learning Based Text to SQL Conversion on WikiSQL Dataset: Comparative Analysis

Due to tremendous expansion in the banking, business, and IT sectors, data is growing at an exponential rate these days, and the majority of it is stored in relational databases. In order to access and alter information, users must be proficient with languages such as SQL (Structured Query Language). However, the customers are unfamiliar with SQL. As a result, a number of different models have been presented to date to convert English queries into SQL for extracting data from databases to achieve human performance on the dataset. It is an approach toward developing an end-to-end solution that may provide an intuitive text interface for many data sources within the corporate ecosystem, allowing them to obtain real-time data visualization or insights. Some of the models developed to date have already surpassed the 90 % test accuracy on the WikiSQL dataset. However, more research is needed to develop a largely scalable model with higher accuracy on custom and unknown databases. The purpose of this paper is to shed light on different strategies involved in this domain and to understand the contrast between them. We mainly focus on those factors and aspects that impact the performance and accuracy of the task significantly. This paper presents comprehensive research on the most prominent algorithms and analyses them based on different factors.
A Literature Review on Automatic Comment Generation using Deep Learning Techniques

During the development of software, thousands of lines of codes are written and without proper comments it gets difficult for the developers to read the code. Automatic Comment Generation overcomes this problem by generating comments for the source code. Many researchers are already working on automatic comment generation in the source code and some have already published their results but still there is lot of scope of improvement in the field of automatic generation of comments in the source code. In this paper, we will be explaining and reviewing deep learning techniques that areused in comment generation to make the code easily readable. Generating comments automatically become crucial in the source code for any developer to understand it easily without spending much time. Hence, it's very important to understand how deep learning techniques like CNN, RNN, LSTM and others are making it possible to generate automatic comments in source code and also why researchers have now shifted on deep learning techniques to do automatic comment generation.
Mime Recognition for Indian Sign Language

Signs are part of non-verbal conveyance. Deaf and mute people primarily use sign language to interact with one another. Communicating with them has always been a significant challenge for the general public, as gestures are difficult to understand. Therefore, the important idea is to assist the com- munication absence among the public and the hearing impaired. Various sign language systems have been developed, but they are neither flexible nor cost-effective. Therefore, this project proposes an effective and user-friendly sign language recognition interface that makes it easy for hearing-impaired people to communicate with the general public using Tensorflow, Keras, and CNN (convolutional neural networks) for gesture recognition.
Architecture and Types of Intelligent Agent and Uses of Various Technologies

“INTELLIGENT AGENT” or “AI AGENT” is a term got more popularized in recent years but a term got introduced many years back. The main aim is to have a system that can interact with a human. HCI (Human-Computer interaction) formulates the basis of an intelligent agent. In this paper, various technologies used in an agent along with its architecture are being presented. An Intelligent agent is software that can interact with humans using Artificial intelligence techniques. It is intractable for machines to understand natural language and incite its meaning and produce the response in the same language. A machine must be able to perceive the content and context of the data conducive to producing the correct result.
Comparative Analysis of Various Machine Learning Techniques to Predict Breast Cancer

Medical sciences have developed a lot in previous years which has significantly boosted the average lifespan of Humans. Researchers have developed medications and therapies to cure many deadly diseases including Cancer. This has contributed to the healthcare sector as many ‘common’ deadly diseases like breast cancer have got effective treatment procedures. Advanced Technologies like Artificial Intelligence assist practitioners in the treatment of the same. Machine Learning and Neural Networks serve the purpose of predicting possible stages, symptoms, and patient-specific treatment of breast cancer. This manuscript suggests methods (algorithms) which can predict the type of breast cancer and categorize cases as Malignant or Benign. Also, it presents the comparison of the accuracy of different Algorithms over different Train-Test Ratios. Also, the accuracy of previously done research is compared with self-experimental results and reviewed for better accuracy.
State-of-the-Art Methods for Fine-Grained Emotion Detection from Malayalam Text using Deep Learning: A Survey

Social media has changed the way people express their opinions, views, and feedback about anything in their minds. This large volume of sentiment-rich data needs to be examined properly in order to comprehend the mood, attitude, or sentiment underlying those words. Computers can process, interpret, and analyze vast volumes of natural data using natural language processing (NLP), which helps them to comprehend human languages. Now all organizations are looking for more fine-grained sentiment analysis to plan their business strategy. This paper investigates state-of-the-art textual data analysis using NLP techniques. The increased use of Indian languages in communicating platforms is a challenging task for opinion mining. In this paper, we survey the state-of-the-art methods based on deep learning and fuzzy rules for sentiment analysis.
Role Based Access Control Framework for Healthcare Records Using Hyperledger Fabric

Blockchain has a significant influence on the health-care sector with respect to data sharing. Electronic Health Records (EHR), a highly sensitive method, are currently used in healthcare to store and share medical records. Due to a lack of trustworthy and dependable health data sharing systems, the majority of sharing of Electronic Health Record data is still done by mail. This leads to considerable delays in the patient's treatment. Smart contracts enabling data access, security, and cryptographic guarantees of data integrity are available on the decentralized blockchain platform. By putting the patient at the center of the system, enhancing healthcare data privacy, and enhancing interoperability, the storage of electronic medical records on a centralized or private blockchain has the potential to improve healthcare. Using the Hyperledger fabric technology, a permissioned blockchain system has been suggested to store and share medical records. By using grant and revoke access procedures, patients have full control over their medical information and give clinicians permission to see the patient's medical records. Three different roles have been considered and hence, the access permissions have been given to the roles accordingly, hence ensuring integrity and security. The proposed solution shows a 42.71% in average latency for granting and revoking access to EHRs and also shows a 46% decrease in execution time as compared to previously proposed solutions.
Handwritten Mathematical Equation Recognition and Solver

Our contribution to the field of Handwritten Math- ematical Equation Recognition is the development of an end-to- end pipeline that combines character recognition and equation solving. Both areas have been extensively worked on individually, hence we aim to combine both pieces to form a complete user application. Recognition will be performed by a pipeline consisting of Image Cleaning, Segmentation, and Recognition. A shallow Convolutional Neural Network performs recognition and the SymPy math engine solves the recognized equation. We have also included a feedback mechanism to correct anyfalsely classified symbols. The proposed system is tested on the CROHME dataset, and the model accuracy is tested along with user interface testing. To demonstrate the final system, we have also created a Graphical User Interface (GUI) that provides the user with options to handwrite equations, upload images of equations, interact with graphs and provide feedback on incorrect equations.
MeshFormer: High‐resolution Mesh Segmentation with Graph Transformer

Abstract Graph transformer has achieved remarkable success in graph‐based segmentation tasks. Inspired by this success, we propose a novel method named MeshFormer for applying the graph transformer to the semantic segmentation of high‐resolution meshes. The main challenges are the large data size, the massive model size, and the insufficient extraction of high‐resolution semantic meanings. The large data or model size necessitates unacceptably extensive computational resources, and the insufficient semantic meanings lead to inaccurate segmentation results. MeshFormer addresses these three challenges with three components. First, a boundary‐preserving simplification is introduced to reduce the data size while maintaining the critical high‐resolution information in segmentation boundaries. Second, a Ricci flow‐based clustering algorithm is presented for constructing hierarchical structures of meshes, replacing many convolutions layers for global support with only a few convolutions in hierarchy structures. In this way, the model size can be reduced to an acceptable range. Third, we design a graph transformer with cross‐resolution convolutions, which extracts richer high‐resolution semantic meanings and improves segmentation results over previous methods. Experiments show that MeshFormer achieves gains from 1.0% to 5.8% on artificial and real‐world datasets.
ISEKAI: A Virtual Real Estate Web Application Utilizing Constructive Simulation for Improving User Experience in Online Home Tours

This study explores the untapped potential of real estate technology and its effectiveness in user experience in online home tours by implementing panoramic techniques that help users improve their experience in home touring using a web application. The purpose of the application, ISEKAI, is to be a web app for an online virtual home tour. The study aims to measure the effectiveness of the application as well as its usability. This study uses a quantitative method, specifically an experimental-research design, to retrieve valuable information from the study using SUS and UEQ. The results of the findings were positive for SUS; the ISEKAI application received an excellent rating, and overall, the application is a proper-performing application by the users' standards, and its usability and performance were able to enrich the user experience of the users. The same could be said for UEQ as, overall, the user experience of the ISEKAI web application has an excellent rating for all six scales. The ISEKAI application demonstrated that virtual environments positively affect the experience of real estate virtual touring applications and achieve the desired result of the research objectives. Therefore, it is a functional application that has satisfied the needs of the researchers. More participants, partnered professionals in the real estate field, and features can improve the study. Future researchers can enhance the application by improving or implementing other virtual reality techniques.
Analysis of Regulation Policies in May 2021 on Cryptocurrency Market

In May 2021, Chinese government issued a series of policies to strengthen the supervision of cryptocurrency market. Based on the principle of event study and combing of Chinese and American cryptocurrency policies, this paper selects May 18 as the event announcement day and constant mean return models are constructed to measure the abnormal returns of top ten market value coins. It is found that the strengthening of regulatory policies had an evidently negative influence on the market. Combined with the analysis of the features of typical cryptocurrencies, this paper figures out that on the whole there is a strong correlation between the abnormal returns of currencies in the cryptocurrency market. At the same time, by analyzing the performance of daily abnormal returns of Bitcoin and Ether in the event window, this study draws a conclusion that investors in that market have patent speculativeness. In addition, complimentary survey on stable coin is also done to verify the overall policy impact, which indicates that the market value fluctuation of stable coin is consistent with the abnormal return changes of the whole market. On this basis, through information query and literature reference, this paper also forecasts the evolution trend of regulatory policies in various countries and the development of the overall cryptocurrency market by period.
Learning Multi‐Scale Deep Image Prior for High‐Quality Unsupervised Image Denoising

Abstract Recent methods on image denoising have achieved remarkable progress, benefiting mostly from supervised learning on massive noisy/clean image pairs and unsupervised learning on external noisy images. However, due to the domain gap between the training and testing images, these methods typically have limited applicability on unseen images. Although several attempts have been made to avoid the domain gap issue by learning denoising from singe noisy image itself, they are less effective in handling real‐world noise because of assuming the noise corruptions are independent and zero mean. In this paper, we go step further beyond prior work by presenting a novel unsupervised image denoising framework trained from single noisy image without making any explicit assumptions on the noise statistics. Our approach is built upon the deep image prior (DIP), which enables diverse image restoration tasks. However, as is, the denoising performance of DIP will significantly deteriorate on nonzero‐mean noise and is sensitive to the number of iterations. To overcome this problem, we propose to utilize multi‐scale deep image prior by imposing DIP across different image scales under the constraint of a scale consistency. Experiments on synthetic and real datasets demonstrate that our method performs favorably against the state‐of‐the‐art methods for image denoising.
Ref‐ZSSR: Zero‐Shot Single Image Superresolution with Reference Image

Abstract Single image superresolution (SISR) has achieved substantial progress based on deep learning. Many SISR methods acquire pairs of low‐resolution (LR) images from their corresponding high‐resolution (HR) counterparts. Being unsupervised, this kind of method also demands large‐scale training data. However, these paired images and a large amount of training data are difficult to obtain. Recently, several internal, learning‐based methods have been introduced to address this issue. Although requiring a large quantity of training data pairs is solved, the ability to improve the image resolution is limited if only the information of the LR image itself is applied. Therefore, we further expand this kind of approach by using similar HR reference images as prior knowledge to assist the single input image. In this paper, we proposed zero‐shot single image superresolution with a reference image (Ref‐ZSSR). First, we use an unconditional generative model to learn the internal distribution of the HR reference image. Second, a dual‐path architecture that contains a downsampler and an upsampler is introduced to learn the mapping between the input image and its downscaled image. Finally, we combine the reference image learning module and dual‐path architecture module to train a new generative model that can generate a superresolution (SR) image with the details of the HR reference image. Such a design encourages a simple and accurate way to transfer relevant textures from the reference high‐definition (HD) image to LR image. Compared with using only the image itself, the HD feature of the reference image improves the SR performance. In the experiment, we show that the proposed method outperforms previous image‐specific network and internal learning‐based methods.
EL‐GAN: Edge‐Enhanced Generative Adversarial Network for Layout‐to‐Image Generation

Abstract Although some progress has been made in the layout‐to‐image generation of complex scenes with multiple objects, object‐level generation still suffers from distortion and poor recognizability. We argue that this is caused by the lack of feature encodings for edge information during image generation. In order to solve these limitations, we propose a novel edge‐enhanced Generative Adversarial Network for layout‐to‐image generation (termed EL‐GAN). The feature encodings of edge information are learned from the multi‐level features output by the generator and iteratively optimized along the generator's pipeline. Two new components are included at each generator level to enable multi‐scale learning. Specifically, one is the edge generation module (EGM), which is responsible for converting the output of the multi‐level features by the generator into images of different scales and extracting their edge maps. The other is the edge fusion module (EFM), which integrates the feature encodings refined from the edge maps into the subsequent image generation process by modulating the parameters in the normalization layers. Meanwhile, the discriminator is fed with frequency‐sensitive image features, which greatly enhances the generation quality of the image's high‐frequency edge contours and low‐frequency regions. Extensive experiments show that EL‐GAN outperforms the state‐of‐the‐art methods on the COCO‐Stuff and Visual Genome datasets. Our source code is available at https://github.com/Azure616/EL-GAN .