Reviewerly/exHarmony huggingface dataset:
Enhanced Experience Prioritization: A Novel Upper Confidence Bound Approach

Value-based Reinforcement Learning algorithms achieve superior performance by utilizing experiences gathered in the past to update their so-called value-function. In most cases, it is accomplished by applying a sampling strategy to an experience buffer, in which state transitions are stored during the training process. However, the design of such methods is not so intuitive. General theoretic approaches tend to determine the expected learning progress from each experience, based on which the update of neural networks can be carried out efficiently. Proper choice of these methods can not only accelerate, but also stabilize the training significantly by increasing sampling efficiency, which indirectly leads to a reduction in time and computing capacity requirements. As one of the most critical aspects of using Machine Learning (ML) based techniques originates from the lack of decent computing power, thus endeavour to find optimal solutions has long been a researched topic in the field of Reinforcement Learning. Therefore the main focus of this research has been to develop an experience prioritization method acquiring competitive performance, besides having the overall cost of training considerably lowered. In this paper, we propose a novel priority value assignment concept for experience prioritization in Reinforcement Learning, based on the Upper Confidence Bound algorithm. Furthermore, we present empirical findings of our solution, that it outperforms current state-of-the-art in terms of sampling efficiency, while enabling faster and more cost-efficient training processes.
Are Nursing Faculty Future-Ready? The Effects of Emerging Technologies on Nursing Education

Preparing the next generation of nurses requires consideration for the integration of emerging technologies. This refers specifically to technologies that will shape and affect how care is delivered-not in today's health care facilities but in facilities 20 years from now and beyond.A cross-sectional survey examining how seven emerging technologies were integrated into nursing curricula was developed and distributed to nursing faculty. A multiple linear regression model was used to analyze the data for the level of integration. The model was adjusted for all demographic measures, and the independent variables of interest were developed from unified theory of acceptance and use of technology (UTAUT) constructs.Performance, attitude, social influence, and effort were insignificant. Education and training, costs, and complexity were noted as top barriers to adoption.A literature gap regarding integrating emerging technologies in nursing curricula exists. Further research is warranted to understand factors influencing integration. [J Nurs Educ. 2023;62(12):689-700.].
Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning

In today’s automation control systems, the PID controller, as a core technology, is widely used to maintain the system output near the set value. However, in some complex control environments, such as the application of ball screw-driven rotating motors, traditional PID parameter adjustment methods may not meet the requirements of high precision, high performance, and fast response time of the system, making it difficult to ensure the stability and production efficiency of the mechanical system. Therefore, this paper proposes a cuckoo search optimisation coupled with an improved grey wolf optimisation (CSO_IGWO) algorithm to tune PID controller parameters, aiming at resolving the problems of the traditional grey wolf optimisation (GWO) algorithm, such as slow optimisation speed, weak exploitation ability, and ease of falling into a locally optimal solution. First, the tent chaotic mapping method is used to initialise the population instead of using random initialization to enrich the diversity of individuals in the population. Second, the value of the control parameter is adjusted by the nonlinear decline method to balance the exploration and development capacity of the population. Finally, inspired by the cuckoo search optimisation (CSO) algorithm, the Levy flight strategy is introduced to update the position equation so that grey wolf individuals are enabled to make a big jump to expand the search area and not easily fall into local optimisation. To verify the effectiveness of the algorithm, this study first verifies the superiority of the improved algorithm with eight benchmark test functions. Then, comparing this method with the other two improved grey wolf algorithms, it can be seen that this method increases the average and standard deviation by an order of magnitude and effectively improves the global optimal search ability and convergence speed. Finally, in the experimental section, three parameter tuning methods were compared from four aspects: overshoot, steady-state time, rise time, and steady-state error, using the ball screw motor as the control object. In terms of overall dynamic performance, the method proposed in this article is superior to the other three parameter tuning methods.
RF Drone Detection System Based on a Distributed Sensor Grid With Remote Hardware-Accelerated Signal Processing

Unmanned Aerial Vehicles (UAVs), sometimes known as drones, evolved from military to civilian applications, opening up novel perspectives in a variety of everyday services. The rapidly growing consumer interest in amateur drones equipped with high-end cameras compromises the everyday safety and privacy of people. In the literature, a variety of sensing techniques based on different physical phenomena have been proposed for drone detection. Among acoustic, optical, or radar detection systems, passive radiofrequency sensing is the only one that can identify a drone even before it takes off and additionally indicate the operator’s location. A spectrogram-based method is developed and optimised in terms of computing location, resulting in the possibility of sensor grid deployment over a standard Ethernet network. The detection phase involves hardware-accelerated energy sensing to extract the data frames from the background noise. Drone presence is then identified using machine learning based solely on preamble pattern recognition, which reduces the computational effort. The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. The analytical energy detector stage reaches a margin of roughly -8.7 dB in the signal-to-noise (SNR) ratio. With 1.1 M parameters, the proposed neural network achieves 99.93% simulation accuracy in up to -9.5 dB SNR range. Even after quantization for embedded platform implementation, the device can be used as a stand-alone early intrusion detector or as part of a distributed sensor grid.
Fall Prediction of Elder Person Using CCTV Footage and Media Framework

The elderly population often requires assistance with day-to-day activities and healthcare. The development and use of efficient procedures and systems that would enable affordable clinic care and monitoring services, particularly for the senior population, are receiving more attention. The concept of “ageing in place” refers to the capability of older individuals to live in their own homes and communities comfortably, securely, and independently, without being restricted by their age, financial situation, or physical ability. Moving in with relatives, relocating to a medical facility, or entering an assisted living facility can all cause psychological distress for older adults. This stress can deteriorate their health and lower their quality of life. MediaPipe's Body Landmark model employs BlazePose to infer 33 3D body landmarks or 25 upper-body landmarks from RGB video frames to provide high-quality body posture monitoring. It can be configured to focus solely on the upper body, in which case it only estimates the first 25 landmarks, but typically, it identifies landmarks for every body pose. MediaPipe Hands is an effective method for monitoring hands and fingers. A single picture may be used to infer up to 21 3D hand landmarks. Mobile devices can enable a variety of modern life applications by simultaneously detecting human gestures, face landmarks, and hand movements in real-time, such as augmented reality try-on and effects, posture control, sign language recognition, fitness and sport analysis, and posture control.MediaPipe now offers immediate, exact, distinct, yet complementary solutions for these challenging jobs. Combining them all into a real-time, conceptually cohesive edge solution requires the simultaneous inference ofmany, dependent neural networks, which makes it a particularly difficult challenge. We are proposing a low cost and efficient system by using mediapipe body landmark model for extracting the features and PCA-LSTM algorithm to predict the fall in elderly people.
Sentiment Analysis and its Application in Analysing Consumer Behaviour

Sentiment analysis helps in identifying and extracting subjective information from text, such as feelings, sentiments, and attitudes. Its usage is increasing day by day in different fields like marketing, customer service, social media analysis, and, most importantly, analyzing customer behaviour. It assists in understanding customer needs and preferences and enhances the overall customer experience. Lexicon-based approaches, machine learning algorithms, and deep learning models are some of the approaches used in the sentiment analysis. This paper is an attempt to find how sentiment analysis can provide valuable insights into customer sentiment and help identify changing customer trends, patterns, and preferences. This will result in businesses making informed decisions and thus improving upon their customer service, product development, and marketing strategies, thus gaining a better understanding of their customers and improving their overall customer experience.
Learning feature alignment and dual correlation for few‐shot image classification

Abstract Few‐shot image classification is the task of classifying novel classes using extremely limited labelled samples. To perform classification using the limited samples, one solution is to learn the feature alignment (FA) information between the labelled and unlabelled sample features. Most FA methods use the feature mean as the class prototype and calculate the correlation between prototype and unlabelled features to learn an alignment strategy. However, mean prototypes tend to degenerate informative features because spatial features at the same position may not be equally important for the final classification, leading to inaccurate correlation calculations. Therefore, the authors propose an effective intraclass FA strategy that aggregates semantically similar spatial features from an adaptive reference prototype in low‐dimensional feature space to obtain an informative prototype feature map for precise correlation computation. Moreover, a dual correlation module to learn the hard and soft correlations was developed by the authors. This module combines the correlation information between the prototype and unlabelled features in both the original and learnable feature spaces, aiming to produce a comprehensive cross‐correlation between the prototypes and unlabelled features. Using both FA and cross‐attention modules, our model can maintain informative class features and capture important shared features for classification. Experimental results on three few‐shot classification benchmarks show that the proposed method outperformed related methods and resulted in a 3% performance boost in the 1‐shot setting by inserting the proposed module into the related methods.
Amortizing Samples in Physics-Based Inverse Rendering Using ReSTIR

Recently, great progress has been made in physics-based differentiable rendering. Existing differentiable rendering techniques typically focus on static scenes, but during inverse rendering---a key application for differentiable rendering---the scene is updated dynamically by each gradient step. In this paper, we take a first step to leverage temporal data in the context of inverse direct illumination. By adopting reservoir-based spatiotemporal resampled importance resampling (ReSTIR), we introduce new Monte Carlo estimators for both interior and boundary components of differential direct illumination integrals. We also integrate ReSTIR with antithetic sampling to further improve its effectiveness. At equal frame time, our methods produce gradient estimates with up to 100× lower relative error than baseline methods. Additionally, we propose an inverse-rendering pipeline that incorporates these estimators and provides reconstructions with up to 20× lower error.
Revolutionizing Learning: An Interactive VR Application for Solids of Revolution

The understanding and visualization of the solids of revolution can be challenging for students due to the abstract nature of the topic. This paper presents the development and evaluation of a novel interactive Virtual Reality (VR) application framework designed to enhance students' comprehension and spatial reasoning skills related to solids of revolution. The tool offers interactive 3D visualization of arbitrary functions of standard 2D graphs and solids of revolution. The methodology involves utilizing Unity to create the 3D objects and implementing algorithms for generating surfaces of revolution. At this stage, the tool is evaluated through a comparison with other related works through comprehensive comparisons and literature review. The findings indicate that the developed application offers improved interactivity by adeptly managing complexity. Moreover, employing Unity to create interactive learning tools for visualizing 3D objects can effectively enhance students' understanding of complex mathematical concepts.
Exploring the potential of blockchain technology for citizen engagement in smart governance

<ns3:p>This review article explores the potential of blockchain technology (BCT) as a key enabler for fostering transparency, trust, and citizen engagement in smart governance within smart cities. By examining the benefits of BCT in various aspects of smart city systems, such as data security and privacy, the paper highlights the advantages of implementing consortium blockchain architecture and the Proof of Authority (PoA) consensus algorithm and discusses the challenges of scalability and security concerns. Based on the literature showed in this article, we believe that the use of BCT in smart governance could significantly enhance citizen participation and help manage and deliver public services, paving the way for more effective decision-making processes and improved quality of life for citizens.</ns3:p>
Superposed Quantum Error Mitigation

Overcoming the influence of noise and imperfections is a major challenge in quantum computing. Here, we present an approach based on applying a desired unitary computation in superposition between the system of interest and some auxiliary states. We demonstrate, numerically and on the IBM Quantum Platform, that parallel applications of the same operation lead to significant noise mitigation when arbitrary noise processes are considered. We first design probabilistic implementations of our scheme that are plug and play, independent of the noise characteristic and require no postprocessing. We then enhance the success probability (up to deterministic) using adaptive corrections. We provide an analysis of our protocol performance and demonstrate that unit fidelity can be achieved asymptotically. Our approaches are suitable to both standard gate-based and measurement-based computational models.
Enhancing quantum computation via superposition of quantum gates

Overcoming the influence of noise and imperfections in quantum devices is one of the main challenges for viable quantum applications. In this article, we present different protocols, which we denote as ``superposed quantum error mitigation,'' that enhance the fidelity of single gates or entire computations by performing them in coherent superposition. Our results demonstrate that via our methods, significant noise suppression can be achieved for most kinds of decoherence and standard experimental parameter regimes. Our protocols can be either deterministic, such that the outcome is never postselected, or probabilistic, in which case the resulting state must be discarded unless a well-specified condition is met. By using sufficiently many resources and working under broad assumptions, our methods can yield the desired output state with unit fidelity. Finally, we analyze our approach for gate-based, measurement-based, and interferometric-based models, demonstrating the applicability in all cases and investigating the fundamental mechanisms they rely upon.
General quantum secure multiparty computation protocol for simultaneous summation and multiplication

Abstract Quantum secure multiparty computation occupies an important place in quantum cryptography. Based on access structure and linear secret sharing, we propose a new general quantum secure multiparty computation protocol for simultaneous summation and multiplication in a high-dimensional quantum system. In our protocol, each participant within any authorized sets only needs to perform local Pauli operation once on the generalized Bell state, then the summation and multiplication results can be output simultaneously, which improves the practicality of the protocol. Moreover, in the privacy computation phase, the decoy particle detection technique as well as the addition of random numbers are applied to blind the privacy information, making our protocol higher privacy protection. Security analysis shows that our protocol is resistant to a series of typical external attacks and dishonest internal participant attacks such as individual attack and collusion attack. Finally, compared with the existing protocols, our protocol not only has higher efficiency but also lower consumption.
Efficient Data Collaboration Using Multi-Party Privacy Preserving Machine Learning Framework

In a modern era where data-driven insights are the foundation of technological advancements preserving the privacy and security of sensitive information while harnessing the collective intelligence of multiple parties is imperative. This research presents a Secure Collaborative Learning Algorithm (SCLA) designed to facilitate efficient multi-party machine learning without compromising data privacy. Our research focus is on leveraging existing, secure databases without requiring an additional data collection process. SCLA seamlessly integrates homomorphic encryption and Federated Learning (FL) to enable secure data collaboration among various stakeholders. The proposed algorithm aggregates model updates in a privacy-preserving manner, demonstrating enhanced model accuracy, competitive convergence speed, and robust scalability. By carefully balancing privacy preservation and learning efficiency, the SCLA showcases a promising avenue for privacy-focused collaborative learning using existing data repositories.
Evaluating and Improving Domain Invariance in Contrastive Self-Supervised Learning by Extrapolating the Loss Function

Despite the remarkable progress of self-supervised learning (SSL), how self-supervised representations generalize to out-of-distribution data remains little understood. In this paper, we study the effects of distribution shifts on self-supervised representations. Our findings indicate that self-supervised representation learning is more robust than traditional supervised learning (52.8% versus 17.1% on the CMNIST dataset, 63.6% versus 60.6% on the Waterbirds dataset). However, self-supervised representations still suffer significantly from domain shifts, especially when spurious correlations are present. Motivated by this limitation, we propose a risk-extrapolated information NCE (ReinformNCE) to facilitate self-supervised learning algorithms to learn more stable representations. Our approach integrates the infoNCE loss function and a robust optimization approach that extrapolates the risks of training domains. Extensive experiments show that ReinformNCE helps to extract domain-invariant self-supervised representations and it substantially improves the robustness of the self-supervised representations (68.2% versus 52.8% on the CMNIST dataset, 77.9% versus 63.6% on the Waterbirds dataset). To the best of our knowledge, this is the first work demonstrating the feasibility of learning domain-invariant representations based on robust optimization theory and without supervised information.
G-bic: generating synthetic benchmarks for biclustering

Abstract Background Biclustering is increasingly used in biomedical data analysis, recommendation tasks, and text mining domains, with hundreds of biclustering algorithms proposed. When assessing the performance of these algorithms, more than real datasets are required as they do not offer a solid ground truth. Synthetic data surpass this limitation by producing reference solutions to be compared with the found patterns. However, generating synthetic datasets is challenging since the generated data must ensure reproducibility, pattern representativity, and real data resemblance. Results We propose G-Bic, a dataset generator conceived to produce synthetic benchmarks for the normative assessment of biclustering algorithms. Beyond expanding on aspects of pattern coherence, data quality, and positioning properties, it further handles specificities related to mixed-type datasets and time-series data.G-Bic has the flexibility to replicate real data regularities from diverse domains. We provide the default configurations to generate reproducible benchmarks to evaluate and compare diverse aspects of biclustering algorithms. Additionally, we discuss empirical strategies to simulate the properties of real data. Conclusion G-Bic is a parametrizable generator for biclustering analysis, offering a solid means to assess biclustering solutions according to internal and external metrics robustly.
Decision Tree

This chapter first introduces the basic concept of the decision tree, then introduces feature selection, tree-generation and tree-pruning through ID3 and C4.5 algorithms, and finally introduces the CARTClassification and Regression Tree (CART) algorithm.
EM Algorithm and Its Extensions

This chapter first describes the EM algorithm, then discusses the convergence of the EM algorithm, then introduces an application of the EM algorithm—the learning of the Gaussian mixture model, and finally describes the extension of the EM algorithm—the GEM algorithm.
Artificial Intelligence and Augmented Reality in Higher Education: a systematic review

Augmented reality is a technology that combines elements of the real and virtual world to enhance the user experience by providing additional information and enriching interaction. In education, AR has been used to enhance the teaching of complex concepts by providing interactive content and immersive experiences. This review examines various aspects related to the implementation of AR in higher education, including its educational benefits, impact on student motivation and engagement, and its effectiveness in achieving learning objectives. Associated challenges and limitations, such as device availability and effective experience design, are also explored. The results indicate that AR can improve content comprehension and retention, encourage active student participation, and enhance collaborative learning. However, significant challenges are identified, such as the initial investment in technology and the need for adequate teacher training. In addition, diversity in institutional infrastructure and resources may limit the widespread adoption of AR in higher education. In conclusion, augmented reality in higher education offers promising potential to enhance teaching and learning, but its successful implementation requires careful considerations of pedagogy, accessibility, and overcoming technological barriers. It highlights the need for further research to thoroughly understand its impact and maximize its benefits in academic training.