Reviewerly/exHarmony huggingface dataset:
Entangled Quantum Neural Network

Quantum entanglement (QE) is the phenomenon that when several particles interact, the properties of each particle will be integrated into the properties of the overall system, and the properties of each particle cannot be described independently from others. QE can be proved by violating Bell Inequality, that is, it can describe strong statistical correlation (i.e., quantum correlation). By introducing QE into machine learning, the adjusted framework would have advantages such as faster execution time of the learning algorithms and stronger capacity. Therefore, here we introduce our novel framework called Entangled Quantum Neural Network, EQNN. Using quantum entanglement to development on neuron networks can be described in three different ways: By replacing the hidden layer nodes of the Multi-Layer Perception (MLP) with a measurement process of entangled states (QECA, QCCA); by replacing the output layer of MLP with a quantum measurement operation (ECA); or by reconstructing the neurons in NN using regularizer to constrain state vectors to entangled states (QNN). With extensive experiments on the three most frequently used machine learning datasets from UCI, Abalone, Wine Quality (Red), and Wine Quality (White), we demonstrate that all QCCA, QECA, ECA, and QNN outperform the baseline algorithms. Under the same parameter settings, which are: learning rate is 0.001, mini-batch is 1, training epochs is 500 and initial weight is 0.01, the performance among themselves in descending order are QNN > ECA > QECA > QCCA.
An Example Praxis for Teaching German as a Second Foreign Language with Augmented Reality Technology at Secondary Education Level

The developments in technology bring to mind the questions of how the acquisitions and contents of the curriculum in the foreign language teaching process can be transferred to the students in a more effective and permanent way and which methods and technological opportunities can be used in the education process, revealing that different perspectives and different methods should be used. In this context, the aim of this study is to create an exemplary digital instructional design to be used in teaching German as a second foreign language with augmented reality technology at secondary school level and to evaluate the effectiveness of this design through pre-test, post-test and in line with the opinions of the students participating in the application. In order to achieve this aim, one group pre-test-post-test design, which is one of the experimental designs, was preferred. Dependent groups t-test was used in the analysis of the data obtained in the single-group pre-test post-test design. The research group of the study consists of 20 9th grade students at Turgut Reis Anatolian High School in Muğla. In the study, semi-structured interview questions were prepared in order to determine the opinions of the students participating in the application on the use of technology in German lessons at secondary education level. Descriptive analysis was used to analyze the data obtained by the interview technique. As a result of the study, it was seen that there was a significant difference between the pre-test and post-test scores of the students. It has been concluded that the students in the course where digital content is used through VR glasses show more progress and are successful.
Soft-Trust Based Architecture for NextG IIoT/IoET Security, Authentication and Authorization

One of the biggest challenges in scalable deployments of massive machine type communication (mMTC) is the efficient, scalable and low-delay security and authentication solutions. MMTC involves Industrial Internet of Things (IIoT) and Internet of Energy Things (IoET) in 5G/6G and Next Generation (NextG) Power Systems. Criticality of this challenge is amplified when considering characteristic limitations of typical IIoT & IoET devices and inherent lack of robust built-in security features. Scalable solutions for IIoT dynamic authentication and authorization could address many of the concerns. In this work, we review and propose an architecture for trust management of IIoT/IoET devices in NextG Systems using suitable trust tools and frameworks such as Blockchain. The proposed architecture adopts a two-tier hierarchical Blockchain-based consensus. To support scalability, we examine the benefits of utilizing a directed acyclic graph (DAG) data structure on the Blockchain.
Action Recognition with Non-Uniform Key Frame Selector

Current approaches for spatiotemporal action recognition have achieved impressive progress, especially in temporal information processing. Meanwhile, the power of spatial information may be underestimated. Thus, a non-uniform key frame selector is proposed to pick the most representative frames according to the relationship between frames along the temporal dimension. Specifically, the reweight high-level frame features are used to generate an importance score sequence, while the key frames, in each temporal section, are selected based on the above scores. Such selected frames have richer semantic information, which has positive impact on the network training. The proposed model is evaluated on two action recognition, namely datasets HMDB51 and UCF101, and promising accuracy improvement is achieved.
Human Motion Prediction based on IMUs and MetaFormer

Human motion prediction forecasts future human poses from the histories, which is necessary for all tasks that need human-robot interactions. Currently, almost existing approaches make predictions based on visual observations, while vision-based motion capture (Mocap) systems have a significant limitation, e.g. occlusions. The vision-based Mocap systems will inevitably suffer from the occlusions. The first reason is the deep ambiguity of mapping the single-view observations to the 3D human pose; and then considering the complex environments in the wild, other objects will lead to the missing observations of the subject. Considering these factors, some researchers utilize non-visual systems as alternatives. We propose to utilize inertial measurement units (IMUs) to capture human poses and make predictions. To bump up the accuracy, we propose a novel model based on MetaFormer with spatial MLP and Temporal pooling (SMTPFormer) to learn the structural and temporal relationships. With extensive experiments on both TotalCapture and DIP-IMU, the proposed SMTPFormer has achieved superior accuracy compared with the existing baselines.
A View Direction-Driven Approach for Automatic Room Mapping in Mixed Reality

Virtual Reality and Augmented Reality technologies have greatly improved recently, and developers are trying to make the experience as realistic as possible and close the gap between the physical world and the virtual world. In this paper, we propose an efficient and intuitive method to create an immersive Mixed Reality environment by automatically mapping your room. Our method is view direction driven, which allows the users to simply “look at” any indoor space to create a 3-dimensional model of the area the user is located in. This approach is easier and more intuitive for the users to use and reduces the time and effort compared to other MR environment generating methods. We use the Meta Quest 2’s cameras and gyroscope sensor and the Unity engine for the ray casting and the passthrough API. We will present the mathematical details of our method and show that the proposed method achieves better results than previous methods through the user study results.
Advanced Security Systems for Home Surveillance

In modern times, Security and surveillance in households have become somewhat of an especially important necessity. Advancement in technology has made it possible for everyone to access or break into different houses easily. The main purpose of our project is to build an advanced surveillance system that can be used to detect the different faces or any movement that may occur while in the view of the surveillance camera. This system is also supported by an application that has unique features to make it more user friendly for the users. Not only is the user notified when an unauthorized entity is detected, the user is also allowed to add different faces or objects that will be ignored during the process of theft detection. This has been achieved using unsupervised machine learning where a given set of data is compared with the actual live feed from the surveillance camera to check for any anomalies in its surroundings. The Dataset or the data used in the proposed system are a few images in the format of. JPEG and. JPG which can be stored in the given location manually by the user or through the application itself. The proposed model recognizes the images in any of the available formats. The modules used in this system are powered by a strong python module named Open CV. This module supports various face recognition algorithms such as Haar Cascade, Eigen Faces, Fischer Faces, Local Binary Pattern Histogram (LBPH), etc and this module is responsible for all the image recognition, classification, and identification. The images extracted from the dataset are real time Image frames obtained from the user webcam, both are compared using the Face Recognition module in python which uses the Regions for - Convolutional Neural Network Algorithm (R-CNN) and Unsupervised learning approach to detect and differentiate between objects in Real Time. This system also includes a message transmitting feature which works with the help of the Simple Message Transfer Protocol (SMTP) module in python. Whenever an unknown user is identified by the system an email is sent to the admin or the user using the SMTP message transfer module which registers the mail address of the user when the initial setup of the system takes place. Hence a robust, secure and user-friendly device is developed that can always keep your house theft free.
Image Quality Enhancement by format-specific Digital Object Tracking

In general, selecting an image format is crucial for deciding what kind of results to display in picture searches. Among the most significant discoveries about RAW and JPEG formats, this is. Even yet, most experts in the field are able to tell the difference between the two. Images in JPEG format are widely used because of their quality and convenience. Images are often saved in JPG format before being printed or uploaded online. There are now worries regarding JPEGs' visual quality. This is the most used picture compression format, thus it's required by the great majority of digital cameras. However, this alters the photos irreversibly, rendering them unrecognisable. Selecting an appropriate picture format is the primary challenge here. This research proposed a smart format improved object tracking approach to boost digital image quality. By default, the camera is set to reduce the size of the captured image files by discarding unnecessary pixels. Depending on your preferences, the compression might be higher or lower. Cameras will reject the bare minimum of data if the file size is set too high. As the number of potential viewers increases, the resolution drops. The suggested method is effective in improving the compression ratio of items.
Survey on Data Security with Intersection of AI and Blockchain

The two major trends of today's tech world are artificial intelligence (AI) and blockchain. These include the advancement in machine translation, computer vision, processing and generation of natural language and handling of massive datasets. The integrated structure of AI & blockchain provide users with innovative data models and results in improved authenticity on data. This technology is largely playing a disruptive role in every industry - banking, manufacturing, real estate, education, healthcare, governance and entertainment. This paper gives a detail on the data security issues, defensive mechanisms, tools and simulation environments with intersection of AI and Blockchain.
Video Compression using Deep Neural Networks

Advanced video compression is required due to the rise of online video content. A strong compression method can help convey video data effectively over a constrained bandwidth. We observed how more internet usage for video conferences, online gaming, and education led to decreased video quality from Netflix, YouTube, and other streaming services in Europe and other regions, particularly during the COVID-19 epidemic. They are represented in standard video compression algorithms as a succession of reference frames after residual frames, and these approaches are limited in their application. Deep learning's introduction and current advancements have the potential to overcome such problems. This study provides a deep learning-based video compression model that meets or exceeds current H.264 standards.
Storage Virtualization Mechanism for Securing Electronic Health Records in Cloud

In the present situation, storing digital health records in the cloud for the immediate usage of patients and treatment providers is the most convenient and economical way for patients. Cloud based Electronic Health Records contain information about the patients and also provide updates to the treatment providers. From the treatment providers' perspective, it is easy for them to see the previous health records of their patients. As a result, the duplication of health records is eliminated. However, the major issue in this system is storing health records and protecting the privacy of patient's details in the cloud. Currently, there are many research scholars who are working constantly to maintain and update the existing electronic health records in the cloud. The aim of this paper is to create virtual storage to secure electronic health records and to provide privacy and backups to customers.
Aadhaar Secure: An Authentication System for Aadhaar Base Citizen Services using Blockchain

In the past decades, people had to carry a bunch of documents for acquiring citizen beneficiary services such as opening a bank account, issue of a new sim card, new gas connection, Pradhan Mantri Jan Dhan Yojana etc. In this digital era, Aadhaar is acting as the driving force to gain all citizen services. Unfortunately, lot of security breaches have been occurred on compromising the Aadhaar details which is stored in the database. Providing security to Aadhaar data and mitigate the misuse of other's Aadhaar data to gain citizen services by unauthenticated users is a main concern. Hence, a secure and authenticated system is required to store and use the Aadhaar details securely by authenticated user. In this paper, we are proposing a secure authenticated system which uses IPFS to store finger prints of the customers and blockchain is used to store the Aadhaar details of the registered citizens. In this system, the biometric is integrated with blockchain to secure Aadhaar details and authenticate the customers before providing any citizen services. The system also generates the Aadhaar card after passing redundancy check for customers who do not have Aadhaar card to access the citizen services. The system is tested and verified for multiple citizen services.
Sign Language Recognition Using Keypoints Through DTW

Hearing-impaired people utilize hand signals, which is an organized collection of hand gestures with distinct meanings. Despite the fact that automated Sign Language Recognition(SLR) is essential for these people, the focus of the research is still challenging and mostly unexplored. This paper is focused on an implemented system for recognizing sign language using SLR system. For numerous computer vision that help to increase communications among hearing impaired, deaf and dumb the sign language identification is a exciting and crucial research area. This research, offers a model with Dynamic Time Wrapping(DTW) approach for recognizing signs in video clips. This innovative technique uses body and hand skeletal features that are derived from RGB movies to capture highly discriminative skeletal data for gesture identification. Experiments on a sizable sign language videos shows that this methodology is better than other cutting-edge methods that just use RGB features. The holistic features extracted as connected key points to identify sign language of the image and accuracy of the results are analyzed.
Analysis On Word2Vec For User Recommendations

It is crucial to have the best person or group of people for a task or job. As a result, having an effective individual or team is crucial to the success of a business. Choosing the best candidate depends on how productive he is at work and how well-liked he is by the team. The history of a person's ratings from the team leader, higher authority, or employer will aid in creating the ideal team or choosing the best candidate for a position. We are using a deep learning model to classify a group of people or an individual who is most well-suited for a job by using these ratings as a vector. There has been no study on team formation with respect to peer rating.
Identification of Breast Cancer Using RESNET152

Breast cancer may be a malignant sickness that may be life threatening as a result of cancer cells begin to grow out of management and becomes untreatable if not diagnosed at early stage. The planned analysis focuses on up accuracy by designation the tumor at earlier stages with improved prediction rate. The application of the Resnet152 deep learning model is presented in this study. within the detection of carcinoma exploitation diagnostic procedure information on Wisconsin Dataset that consists of around five 100000 pictures. This analysis work leads to improved detection of tumor with associate accuracy of 98.5% compared to previous models like VGGNet19 with take a look at accuracy of 96.24%, MobileNetV2 77.84%. The pretrained model Resnet152 is employed for easier implementation, achieving higher accuracy than the previous strategies. This paper uses transfer learning to use theResnet152 on to custom trained model with a binary classifier that offers the result as malignant or benign. The model takes roentgenogram pictures as its input. complexness is that the issue with diagnostic procedure pictures. To urge price out of those we have a tendency to use image process and extract options to help radiologists in tumor detection and additionally minimizing the dependence of medical specialist.
A Fuzzy Approach for Opinion Summarization of Product Reviews

In the past few years, there has been tremendous increase in the amount of opinions posted by reviewers on various social networking sites. This explosion on Web has led to the need of opinion mining so that mined information from these unstructured reviews can be provided to the users for effective decision making. Generation of summary from the available reviews on various e-commerce sites like amazon, flipkart, e-bay etc. is a challenging task. This paper proposes an ontology-based approach for product's feature identification and then identified features are scored using fuzzy logic technique to provide a pictorial feature-based summary to the buyers. Further every review is classified as low, medium or high according to the range of computed review score. To evaluate the proposed work, review text of 11 products is extracted from the available review data of amazon site. The performance metrics such as accuracy, precision, recall and f-measure proves that the proposed system is efficient.
IVF Success Rates Prediction Using Hybrid ANN-GA based Machine Learning Model

Machine learning techniques have been studied with the aim of improving the predictions of In Vitro fertilization (IVF) Live Birth occurrence rates by assessing the extrinsic and intrinsic parameters that principally influence IVF process. Predictive performance of the machine learning techniques is directly related to the quality of the training database and also on the set of hyperparameters screened in the prediction process. Obtaining the best hyperparameters is not a trivial task, but can be achieved by implementing bioinspired algorithms such as Artificial neural network (ANN) and Genetic Algorithms (GA). ANN-GA hybrid design works based on the natural selection theory and evolve the solutions that produce good hyperparameters for Machine learning techniques to register higher accuracy predations Predictions. The IVF/ANN-GA has the aim to improve the performance of hybrid machine learning design with the addition of IVF-Inspired mechanisms that better exploit the information of individuals. With this aim, the present study explores the combination of an ANN with GA to search for the best set of hyperparameters to predict the success rates of the process. The results supported with high accuracy, precision, and recall. Performance values of the model such as F1-measure precision 0.85, recall values 0.76, F1_score 0.80 and accuracy measure 0.89 were noted. The measured values indicate that the model applied exhibits the true positive detection rate of 85%. Models detecting with false positives chance is measured to be only 15%. Study concludes that, present investigation rely both on precision and recall and which were successfully considered in the study metrics. F1 score of the employed design explains the arithmetic ratio of both precision and recall with 89% value. The present studied ANN-GA hybrid model achieved the overall accuracy rates of 90% in predicting the IVF Live Birth rates measures.
Compression of High – Resolution Medical and Space Color Video using Butterworth Filter

Due to improvements in camera sensors, high-resolution videos are easily available nowadays and are widely used in many applications. The main advantage of using high-resolution videos is that due to the presence of large information, they are suitable for analytical applications. But due to the presence of large information, proper storage of high-resolution videos is a challenging task. For optimization of storage space, the video must be compressed for further processing. However, compression of video claims loss of information which may not be acceptable in some applications. In this paper, we propose a novel idea of video compression with minimum loss of information. Primarily the selected video is split into multiple frames. Butterworth filtering with a suitable cut-off value is applied in each frame. To check the quality of the output frames, Peak Signal to Noise Ratio (PSNR), and correlation coefficients are used. The entire research work is performed in Python. Result proves the effectiveness of our proposed method.
Sign Language Recognition Using Deep Learning

The ability to converse with hearing and deaf persons has always been difficult for those who are tongue-tied. In this paper, we can see different methods which are introduced to help them to communicate effectively. There are many human interpreters or assistant tools to help them communicate, but each person cannot afford that aid. The only mode of communication for them is sign language. Therefore, the project's primary goal is to assist those individuals by providing a system that will recognize the signs, translate them into text, and enable them to lead a normal social life. Previously, a method including hand detection had been developed as a learning tool for novices in sign language. The system was developed using a method based on skin color modeling known as explicit skin-color space thresholding. The specified range of skin tones will distinguish between pixels, or the hand, and non-pixels, or the background. The photos were given as input to a model called the CNN a deep learning algorithm. We will be implementing this project using Keras to train the images. This document provides information on a variety of projects/research on sign language detection in the domains of machine learning, deep learning, and image depth data. This study considers a number of the numerous problems that must be overcome in order to overcome this problem, as well as the future scope.