Reviewerly/exHarmony huggingface dataset:
Stable entanglement and one-way steering via engineering of a single-atom reservoir

In this paper, we reexamine the quantum correlations in a four-state single-atom system in the weak coupling regime, aiming at the realization of stable entanglement and one-way steering via dissipation rather than coherent evolution process. Under the near-resonant conditions, we find out that a single atom can act as a reservoir and behave like a two-level system with a single dissipation channel, through which the composite Bogoliubov mode will evolve into a vacuum state, resulting in the appearance of stationary entanglement between two original modes. In addition, the one-way steering is generated when the symmetry is broken by choosing asymmetrical coupling constants. The present scheme may provide convenience for experimental implement and find applications in quantum information processing.
Denoised Autoencoder using DCNN Transfer Learning Approach

Many image denoising models have been designed for enhancing visibility of noisy images. In computer vision denoising of images is an important problem. A lot of emphasis is given to Machine Learning and Deep Learning approach in this regard. This work proposes the study of Deep Convolutional Neural network-based model VGG16 with the custom dataset of bad weather outdoor images via Transfer Learning. The sequential model 1 and model 2 is evaluated to have smooth image. The model architecture using Deep CNN is designed, and training of model is obtained using transfer learning. A model using autoencoder is designed. To decrease training time and perform better rectified Linear Unit (RELU) is used. Number of epochs identified to have increased performance in CNN. Further various optimizers are compared to have better accuracy. The estimation of performance such as RMSE and PSNR values are evaluated. The model is applied to single as well as 25 own outdoor images.
Face Recognition Application using Offloading Computation over Google Cloud

In recent days, face recognition is considered as one of most intriguing innovations. The proposed research provides a high level security by utilizing face recognition because the pixel values of each individual&#x2019;s face change. The proposed work uses Google Cloud to make a help record, and the proposed research work utilizes Open-Source Computer Vision Library (OpenCV), oauth2client, face reorganization, and gspread. These are the most commonly utilized modules. In the proposed work, first a VIRTUALENV named "face" has been developed. After the production of VIRTUALENV, the proposed work has actuated the environment, and the proposed work has utilized SPYDER IDE to execute the undertaking. The proposed work contains various modules named emailing.py, recognization.py, main.py, enroll.py, spreadsheets.py. These are the five modules that are utilized and further a .Json key has also been utilized. The proposed work has created another undertaking in the Google cloud, and after that, it made a help account after the formation of the administration account. It empowered the Google Drive API, and Google Sheets then downloaded the .Json document from the proposed work administration account and the email obtained from the administration account is utilized in Google Sheets to further share the information. The proposed work composes a code for an individual to enlist and, in the wake of selecting the individual, they need to enter their Gmail ID. Subsequent to entering the Gmail ID, it will consequently produce an OTP for the given Gmail, and it will get refreshed in Google sheets. Likewise, once the individual is enlisted, it can just run day by day to observe whether the individual is going to the course or not. Moreover, this research study has developed a mobile application to make it more convenient.
Efficient Player Prediction and Suggestion using Machine Learning for IPL Tournament

In the era of ML, the machine learning algorithms and mechanisms can be implemented in all fields. It has a wide range of applications, including image identification, speech recognition, and traffic prediction. It&#x2019;s used to solve problems like prediction and categorization. Cricket is one of those never-ending wacky issues in everyday life. And one kind of cricket that Indians are particularly fond of is the IPL, or Indian Premier League, in which players compete for one of eight franchises named after Indian cities. The individual franchise owners will auction off players from all countries. This selection is made by various methods. We created a module in this project to analyze player performance in various matches and assign them to the eight IPL teams. For bowling and batting skills, the player&#x2019;s performance was examined independently. The Random Forest Classification technique was used to select the teams. This algorithm is used to tackle problems involving regression and classification.
RobustScaler: QoS-Aware Autoscaling for Complex Workloads

Autoscaling is a critical component for efficient resource utilization with satisfactory quality of service (QoS) in cloud computing. This paper investigates proactive autoscaling for widely-used scaling-per-query applications where scaling is required for each query, such as container registry and function-as-a-service (FaaS). In these scenarios, the workload often exhibits high uncertainty with complex temporal patterns like periodicity, noises and outliers. Conservative strategies that scale out unnecessarily many instances lead to high resource costs whereas aggressive strategies may result in poor QoS. We present RobustScaler to achieve superior trade-off between cost and QoS. Specifically, we design a novel autoscaling framework based on non-homogeneous Poisson processes (NHPP) modeling and stochastically constrained optimization. Furthermore, we develop a specialized alternating direction method of multipliers (ADMM) to efficiently train the NHPP model, and rigorously prove the QoS guarantees delivered by our optimization-based proactive strategies. Extensive experiments show that RobustScaler out-performs common baseline autoscaling strategies in various real-world traces, with large margins for complex workload patterns.
Disease Detection Using RASA Chatbot

Chatbots, or conversational AI(Artificial Intelligence) Interfaces, provide individuals a new way to interact with computer systems. Chatbots allow users to have conversations with the system by asking questions in the way that they would with another human beings. The current adoption rate of chatbots on computer chat platforms is very high. Such robots use artificial intelligence to understand human input and respond accordingly. The core technology for the rise of chatbots is “Natural language processing” (NLP). The recent advancements in NLP have allowed chatbots to be more receptive than ever. Today, humans can interact with the chatbot systems anytime, anywhere. Chatbots can perform predictive tasks (especially in the medical field), which is now possible with advances in artificial intelligence and data mining technology. Healthcare, agriculture and education are important areas that need the most attention. In today’s world, with the change in lifestyle and the current pandemic, illnesses have increased in the general population. As a result, the need for hospitals and doctors have increased substantially. Patients have to spend their time waiting to be taken care of by the doctors. Also, doctors have an immense amount of workload with the amount of visits they have. Thus, the future of healthcare depends on the ability of care providers to perform accurate remote diagnosis. This can be done by collecting data remotely, and by using artificial intelligence to analyse data to improve business and health outcomes. In this paper we analysed datasets to accurately detect diseases with classification techniques such as SVM Classifier and Naïve Bayes Classifier.The tools used are RASA, machine learning classification algorithms, data extraction etc.
Joint State and Parameter Estimation for Hypersonic Glide Vehicles Based on Moving Horizon Estimation via Carleman Linearization

Aimed at joint state and parameter estimation problems in hypersonic glide vehicle defense, a novel moving horizon estimation algorithm via Carleman linearization is developed in this paper. First, the maneuver characteristic parameters that reflect the target maneuver law are extended into the state vector, and a dynamic tracking model applicable to various hypersonic glide vehicles is constructed. To improve the estimation accuracy, constraints such as path and parameter change amplitude constraints in flight are taken into account, and the estimation problem is transformed into a nonlinear constrained optimal estimation problem. Then, to solve the problem of high time cost for solving a nonlinear constrained optimal estimation problem, in the framework of moving horizon estimation, nonlinear constrained optimization problems are transformed into bilinear constrained optimization problems by linearizing the nonlinear system via Carleman linearization. For ensuring the consistency of the linearized system with the original nonlinear system, the linearized model is continuously updated as the window slides forward. Moreover, a CKF-based arrival cost update algorithm is also provided to improve the estimation accuracy. Simulation results demonstrate that the proposed joint state and parameter estimation algorithm greatly improves the estimation accuracy while reducing the time cost significantly.
Virtual reality in training artificial intelligence-based systems: a case study of fall detection

Abstract Artificial Intelligent (AI) systems generally require training data of sufficient quantity and appropriate quality to perform efficiently. However, in many areas, such training data is simply not available or incredibly difficult to acquire. The recent developments in Virtual Reality (VR) have opened a new door for addressing this issue. This paper demonstrates the use of VR for generating training data for AI systems through a case study of human fall detection. Fall detection is a challenging problem in the public healthcare domain. Despite significant efforts devoted to introducing reliable and effective fall detection algorithms and enormous devices developed in the literature, minimal success has been achieved. The lack of recorded fall data and the data quality have been identified as major obstacles. To address this issue, this paper proposes an innovative approach to remove the afformentioned obstacle using VR technology. In this approach, a framework is, first, proposed to generate human fall data in virtual environments. The generated fall data is then tested with state-of-the-art visual-based fall detection algorithms to gauge its effectiveness. The results have indicated that the virtual human fall data generated using the proposed framework have sufficient quality to improve fall detection algorithms. Although the approach is proposed and verified in the context of human fall detection, it is applicable to other computer vision problems in different contexts, including human motion detection/recognition and self-driving vehicles.
1D-DCGAN for Oversampling Minority Mitotic Patterns in HEp-2 Cell Images

This paper proposes a framework for oversampling the minority mitotic patterns of the HEp-2 cell images. The classification of mitotic vs. non-mitotic (interphase) cell patterns is important for validating the Indirect Immunofluorescence on Human Epithelial Type-2 cell-substrate (IIF HEp-2) protocol, which is the "gold standard" test for diagnosing autoimmune diseases. Typically, the mitotic cells appear in the HEp-2 specimen image in a significantly less number than the interphases. This causes difficulty in adopting deep learning approaches to classify mitotic vs. interphase patterns with such high imbalanced data. This work suggests using One-Dimensional Deep Convolutional Generative Adversarial Networks (1D-DCGAN) for oversampling the minority mitotic patterns in the feature space of the Deep Cross Residual Network (DCRNet) to cope with the data skewness problem. The results demonstrated that the proposed approach improves the classification performance over the UQ-SNP I3A Task-3 mitotic cell dataset with the advantage of using an end-to-end CNN classifier.
Multi-camera and multi-person indoor activity recognition for continuous health monitoring using long short term memory

Human activities play a vital role in many health-related fields of research. For example, changes in activities of daily living can predict neurodegenerative diseases like Alzheimer’s disease or the risk of suffering from a fall. Therefore, automatically recognizing activities in videos has become of scientific interest. In contrast to wearable devices, video cameras have the advantage of being truly unobtrusive without any physical contact, continuous as they do not require charging, and cannot be forgotten to wear. This work proposes a novel approach for multi-camera and multi-person human activity recognition (HAR) in videos. The aim is to classify each person in each frame of a video in one of the five classes: “Lying”, “Sitting”, “Standing”, “Walking” or “Falling”. We use a combination of YOLOv4 (person detection), DeepSort (tracking), a convolutional neural network (CNN, features extraction), and an attention-based multi-layer long short-term memory network (classification) to track actions of multiple subjects. Our entire dataset comprises of four publicly-available datasets (ETRI - Activity 3D, MSR Daily Activity 3D, HAR-UP Fall Dataset, High-Quality Fall Simulation Data) with a total file size of 300 gigabytes. In our experiments, we pick random subsets of 1% and 0.25% of data for training and testing respectively. We achieve a classification accuracy of 95%. In the presence of a single subject in the room, predictions from multiple cameras are combined using soft voting, which further improves the accuracy. In summary, HAR in videos is feasible using the proposed combination of machine learning techniques.
FPGA Based Implementation of Neural Network

The objective of this paper is to implement a set of Neural Networks (NN) for the detection and recognition of handwritten digit characters. This paper is based on the Modified National Institutes of Standards and Technology (MNIST) dataset which has been used for the testing and training of the NN model. For this application, both software and hardware platforms have been used to obtain efficient outcomes and identify a comparative analysis between the software and hardware performance on the basis of various parameters. These parameters include accuracy, resource utilisation and operating frequency. This implementation of NN has been performed over the software platform using python programming libraries like Tensorflow and Zynet. But studies referring to software-based implementations conclude various limitations in terms of the execution of Convolutional Neural Networks (CNN) as well as NN in computation-intensive, memory intensive, and resource-intensive characteristics of largescale, possessing various challenges. Hence similar techniques have been used to implement the hardware-based results over the Field Programmable Gate Array (FPGA) and to utilize its proficient properties such as parallelism and pipelining for efficient execution. This hardware implementation is achieved through Vivado High Level Synthesis (HLS) software using Verilog programming.
Unsupervised Learning Using Expectation Propagation Inference of Inverted Beta-Liouville Mixture Models for Pattern Recognition Applications

Learning statistical models successfully is both an essential and a challenging task for various pattern recognition and knowledge discovery applications. In particular, generative models such as finite and infinite mixture models have demonstrated to be efficient in terms of overall performance. In this paper, a robust framework based on an expectation propagation (EP) inference is developed to learn inverted Beta-Liouville (IBL) mixture models which is proper choice for positive data classification. Within the proposed EP learning method, the full posterior distribution is estimated accurately, the model complexity and all related parameters are evaluated simultaneously in a single optimization scheme. Extensive experiments using challenging real-world applications including recognition of facial expression, automatic human action categorization, and hand gesture recognition show the merit of our approach in terms of achieving better results than comparable techniques.
Machine learning driven course recommendation system

This paper presents a machine learning-driven course recommendation system based on similarities between courses. The proposed system employs various data mining techniques to mentioned similarities between courses. Based on the experimental phase of this paper, Cosine metrics proved the best to calculate these parameters. The method proposed in this paper relies on rankings based on areas of study. These techniques allowed us to create an algorithm that, based on input, returns courses that satisfy various conditions. The results satisfy the demands of finding similar courses presented through cross-platform application to the students who will use it to improve their education.
PSU-CNN: Prediction of student understanding in the classroom through student facial images using convolutional neural network

Facial expressions are a set of symbols of great importance for human-to-human communication. This communication can be assisted/optimized through use of artificial intelligence. Student-teacher interaction is important human-to-human communication. Spontaneous in nature, diverse and personal, facial expressions demand real-time, complex, robust and adaptable student’s facial expression recognition systems to facilitate the student-teacher interaction. This paper investigates the idea of performing automated analysis of a student’s understanding of the class participating in active face-to-face classroom teaching. Every student can understand the lecture which is crucial in classroom teaching. In the current study the facial images of the student were collected during the lecture. The facial images were tagged with help of student according to their understanding of lecture at that particular moment. This problem pursued by analyzing the faces of students attending the class and classifying the facial image as “Understanding” or “Not Understanding” in the lecture. Different machine learning (ML) methods were used for development of the model for the classification and the proposed deep learning framework (convolutional neural network (CNN)) outperformed the other methods i.e. Support Vector Machine, Naive Bayes classifier, achieving the test accuracy of 92%, Current work concludes that deep learning like CNN can be the better method for the classification of facial images as compared to other ML methods such as SVM and Naive Bayes for student understanding. Further high accuracy of the methods justifies its importance in optimization of classroom teaching.
Learning time-aware features for action quality assessment

• We propose to use the TA attention module to capture the relationship of different video clips. • We introduce an adversarial loss to ensure the stability and effectiveness to learn the model. • Our method achieves a competitive action evaluation result on the MTL-AQA dataset. Action quality assessment (AQA) is a task to assess the performance of a human action, which can be widely used in many real-world scenarios such as sport events. Current AQA methods generally extract features from the video and perform regression analysis to obtain the action quality score. In this process, aggregated video features may not reflect different stages of an action, which are important to evaluate an action is good or not. To address this issue, we propose to divide the video into different clips and learn the relationship between them, which may capture the action changes for accurate assessment. Time-aware (TA) attention mechanism is used to evaluate this relationship. In the experiment, our proposed method achieves promising results on the MTL-AQA dataset compared with existing AQA methods.
Personnel status detection model suitable for vertical federated learning structure

With the improvement of the medical system, the universal access of wearable devices and people's greater concern about personal health, personnel health detection has received greater attention. However, existing personnel status detection faces the problem that existing models cannot get more accurate results by multiple types of data because the data is difficult to obtain across domains due to privacy issue. In this paper, we propose a vertical federated learning model for person status detection using multimodal data. Considering the varying compute capability of training devices and the different inference speed caused by modality complexity, a lightweight feature extraction sub-model is used, while ensuring data privacy and tolerating accuracy degradation. Also, we design a Fast and Secure (FS) module that effectively reduces the amount of data onto the network transmission process, the model's dependence on the network and the risk of data being intercepted by the third party. Experiment results demonstrate the feasibility of multimodal vertical federated learning and the accuracy is increased by 5.6 percentage points compared with unimodal. The FS module reduces the amount of network transmission information by 50% and 48.1% during model use and model training, respectively
Mixed Reality Applications for Teaching Structural Design

Teaching structural engineering is a core part of education in every undergraduate civil engineering program. The content in these kinds of lectures is demanding advanced analytical thinking and abstract perception. Previous research showed the little level of using modern technology in structural engineering lectures to promote independent student learning, while the use of digital tools with spatial representation can enhance motivation. In this paper, the authors present the conception, workflow, and deployment of two mixed reality (MR) applications for verification of typical, yet geometrically complex, structural members: a reinforced concrete corbel and a steel frame. This paper reports the development, implementation, and evaluation of such MR applications in the context of structural engineering lectures. Participants were able to test the MR applications on their devices or watch pre-recorded videos. Their feedback indicated a great interest and potential in using such MR applications in teaching structural engineering.