Reviewerly/exHarmony huggingface dataset:
An effective knowledge transfer method based on semi-supervised learning for evolutionary optimization

Effective knowledge transfer has been proven to achieve superior performance in evolutionary optimization. Evolutionary multitasking optimization (EMT), which can solve several optimization tasks simultaneously using evolutionary algorithms, continues to be a young research field but is growing rapidly. Due to the parallelism of population-based search, the performance of component tasks can be improved through effective knowledge transfer between different tasks. The main challenge in the EMT field is addressing the negative transfer. Aiming to overcome this challenge, this paper proposes an EMT algorithm that transfers effective knowledge through semi-supervised learning. In addition, a semi-supervised classification method is designed based on the cluster assumption, which is part of the geometric basis of semi-supervised learning. By using both labeled and unlabeled samples generated in the optimization process, the proposed method can identify individuals that contain valuable knowledge and select them to transfer the knowledge between tasks. In this way, the performance of the EMT algorithm can be significantly improved. The effectiveness of the proposed method is verified by empirical tests and comparison with two benchmarks. Further, a case study is conducted. The results indicate that the proposed algorithm can achieve highly competitive performance compared with the state-of-the-art EMT algorithms.
A pediatric seizure management virtual reality simulator for nursing students: A quasi-experimental design

Seizures are a common neurologic disorder observed in children. A virtual reality (VR) simulator trains nursing students to understand and respond to pediatric seizures.The aim of this study was to examine knowledge acquisition and acceptance of a pediatric seizure management VR simulator.A quasi-experimental design was used to study the effectiveness of VR in nursing education.Two out of nine possible third-year Pediatric Nursing classes were assigned by the office of academic affairs. A total of 105 students participated. The two classes were randomly allocated into the intervention (n = 53) and control (n = 52) groups.The intervention group was taught using a pediatric seizure management simulator; the control group was taught by in-person lecture. The Seizure Management Knowledge Test was administered to all participants before each group underwent their VR simulator and lecture respectively. The Pediatric Seizure Management Virtual Reality Acceptance Questionnaire and the Virtual Reality Sickness Questionnaire were given to participants in the intervention group. Independent t-tests and chi-square tests were used to test differences in knowledge acquisition between the two groups.The posttest knowledge score in the intervention group was significantly higher than that in the control group (t = 5.05, p < .001). The intervention group had a mean cybersickness score of 18.17 of 100. The average score of the acceptance questionnaire for perceived usefulness was 3.26 of 4; ease of use was 3.09 of 4; attitude toward use was 3.26 of 4; and willingness to use was 3.32 of 4. Over 90 % of participants expressed willingness to use the VR simulator.The newly developed pediatric seizure management VR simulator is acceptable and worthwhile for training nursing students to develop their skills and professionalism. Follow-up research is needed to evaluate the long-term effect of VR education in nursing practice.
Self-supervised endoscopic image key-points matching

Feature matching and finding correspondences between endoscopic images is a key step in many clinical applications such as patient follow-up and generation of panoramic image from clinical sequences for fast anomalies localization. Nonetheless, due to the high texture variability present in endoscopic images, the development of robust and accurate feature matching becomes a challenging task. Recently, deep learning techniques which deliver learned features extracted via convolutional neural networks (CNNs) have gained traction in a wide range of computer vision tasks. However, they all follow a supervised learning scheme where a large amount of annotated data is required to reach good performances, which is generally not always available for medical data databases. To overcome this limitation related to labeled data scarcity, the self-supervised learning paradigm has recently shown great success in a number of applications. This paper proposes a novel self-supervised approach for endoscopic image matching based on deep learning techniques. When compared to standard hand-crafted local feature descriptors, our method outperformed them in terms of precision and recall. Furthermore, our self-supervised descriptor provides a competitive performance in comparison to a selection of state-of-the-art deep learning based supervised methods in terms of precision and matching score.
(Retracted) Modeling of human action recognition using hyperparameter tuned deep learning model

Recently, human action recognition (HAR) has become an important focus of computer science research because of its applications in surveillance, robotics, sentiment analysis, and other areas. Human activity classification is a time-consuming operation, especially when photos are cluttered, and the background is unclear. In addition, conventional machine learning models fail to achieve robust performance due to the increasing number of activities. Comparatively, deep learning (DL) approaches help to automatically learn and describe the necessary features of the input data with low manual work and robust discriminant abilities. In addition, a preprocessing stage is included in this study’s HAR utilizing hyperparameter tuned DL (HAR-HPTDL) model that removes undesired background and improves the quality of the input. The model also implements a bidirectional long short-term memory model as a feature extractor, the sparrow search algorithm to tune the hyperparameters, and a SoftMax layer for the effective classification of human actions. In addition, the curse of dimensionality can be overcome via entropy-based feature reduction and Chi square-based feature selection. Based on a variety of measures, the HAR-HPTDL methodology has been put through its paces with other published techniques. The results indicate that the HAR-HPTDL technique outperforms current state-of-the-art techniques in simulations. The outcome of this work demonstrates that an HAR-HPTDL model may achieve comparable or even superior recognition accuracy of 0.949 than the prior best deep classifier(s) on all databases with proper parameter optimization.
mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar

Millimeter Ware (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWare Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors.
Economic Feasibility of Conventional and Building-Integrated Photovoltaics Implementation in Brazil

Economic feasibility analysis is essential in the decision-making process regarding investment in photovoltaic projects. Project profitability must be measured based not only on the costs and revenues, but also on the climatic particularities of the different locations. Therefore, performing simulations of technical and economic performance of photovoltaic models is fundamental. Thus, the objective of this study was to analyze deterministic and stochastic models of investments in two types of photovoltaic systems, one incorporated into the enterprise’s architecture (a BIPV system) and the other, a conventional one, in different Brazilian locations, covering the predominant climatic factors in the country. The methodological proposal consisted of choosing a city in Brazil with each predominant climate type and compiling its data on irradiation, monthly sunshine hours, and tariffs of the electric power concessionaire, to simulate the electrical generation performance of the proposed photovoltaic systems and their profitability. For the economic analysis, the cumulative probability of positive Net Present Value (NPV) returns was obtained through deterministic simulations in all municipalities. Only the municipality of Pau dos Ferros-RN was chosen to perform 10,000 stochastic simulations, and its cumulative probabilities of positive NPV returns were obtained. In both models of photovoltaic technology analyzed and simulation logics, 100% of the NPVs were positive, indicating profitable cash flows in all scenarios. However, some municipalities obtained better results than others when the climate types favored sunny weather. Moreover, although all cases returned positive NPVs, the conventional model proved to be more economically attractive than BIPV system.
Utilizing Non-fungible Tokens for an Event Ticketing System

Non-fungible tokens (NFTs) are unique and indivisible blockchain-based tokens introduced in late 2017. While fungible tokens have enabled new use cases such as Initial Coin Offerings (ICOs), the potentials of NFTs as a valuable component have not been fully exploited yet. Our work addresses this gap in theoretical and practical knowledge and demonstrates the efficacy of NFTs in the domain of event ticketing. We follow a design science research approach of designing, building, and thoroughly evaluating a prototype of an event ticketing system based on NFTs. Thereby, we demonstrate the usefulness of NFTs to tokenize digital goods, prevent fraud, and improve control over secondary market transactions. Further, we contribute generalizable knowledge of the benefits and challenges of NFTs and derive implications for both researchers and practitioners. Finally, we propose managerial recommendations for building applications utilizing NFTs and enable other researchers to draw on our findings and design principles.
Relexi — A scalable open source reinforcement learning framework for high-performance computing

Relexi is an open source reinforcement learning (RL) framework written in Python and based on TensorFlow’s RL library TF-Agents. Relexi allows to employ RL for environments that require computationally intensive simulations like applications in computational fluid dynamics. For this, Relexi couples legacy simulation codes with the RL library TF-Agents at scale on modern high-performance computing (HPC) hardware using the SmartSim library. Relexi thus provides an easy way to explore the potential of RL for HPC applications.
Predicting climate factors based on big data analytics based agricultural disaster management

Aggressive, unexpected, and catastrophic changes in the environment-induced or impacted by the cultivation of land, crops, and cattle are known as agricultural disasters. In agriculture, the volume of data unpredictability, processing, and data management standards for interoperability are significant concerns. While natural catastrophes are still a considerable problem, the enormous amount of data available has opened up new avenues for coping. Accordingly, big data analytics has profoundly changed the way people respond to disasters in the agriculture sector. In this paper, the Data handling model using big data analytics (DHM-BDA) explores the role of big data in managing agricultural disasters and highlights the technical status of delivering practical and efficient disaster management solutions. DHM-BDA is used to address the essential sources of big data that include climatic causes and associated successes and developing technological problems in different disaster management phases. In addition, it aids in the monitoring, mitigation, alleviation, and acceptance of agricultural catastrophes and the process of recovery and rebuilding. The simulation findings have been executed, and the suggested model enhances the prediction ratio of 98.9%, decision-making level of 97.8%, data management of 96.5%, production ratio of 95.6%, and risk reduction ratio of 97.1% compared to other existing approaches. • Aggressive, unexpected, and catastrophic changes in the environment-induced. • Data unpredictability, processing, and data management standards for interoperability. • Data handling model using big data analytics (DHM-BDA)explores. • The simulation findings have been executed, and the suggested model enhances.
Multi-round QAOA and advanced mixers on a trapped-ion quantum computer

Abstract Combinatorial optimization problems on graphs have broad applications in science and engineering. The quantum approximate optimization algorithm (QAOA) is a method to solve these problems on a quantum computer by applying multiple rounds of variational circuits. However, there exist several challenges limiting the application of QAOA to real-world problems. In this paper, we demonstrate on a trapped-ion quantum computer that QAOA results improve with the number of rounds for multiple problems on several arbitrary graphs. We also demonstrate an advanced mixing Hamiltonian that allows sampling of all optimal solutions with predetermined weights. Our results are a step toward applying quantum algorithms to real-world problems.
Federated Learning With Nesterov Accelerated Gradient

Federated learning (FL) is a fast-developing technique that allows multiple workers to train a global model based on a distributed dataset. Conventional FL (FedAvg) employs gradient descent algorithm, which may not be efficient enough. Momentum is able to improve the situation by adding an additional momentum step to accelerate the convergence and has demonstrated its benefits in both centralized and FL environments. It is well-known that Nesterov Accelerated Gradient (NAG) is a more advantageous form of momentum, but it is not clear how to quantify the benefits of NAG in FL so far. This motives us to propose FedNAG, which employs NAG in each worker as well as NAG momentum and model aggregation in the aggregator. We provide a detailed convergence analysis of FedNAG and compare it with FedAvg. Extensive experiments based on real-world datasets and trace-driven simulation are conducted, demonstrating that FedNAG increases the learning accuracy by 3-24% and decreases the total training time by 11-70% compared with the benchmarks under a wide range of settings.
Applications of Augmented Reality in Neurology: Architectural Model and Guidelines

Neurology is considered as one of the most challenging fields of medical science due to the complexity of the nervous system in human body.The treatment processes of the patients suffering from neurological disorders are greatly influenced by the advancements in technology.In this regard, the infusion of augmented reality (AR) has immensely improved patients' and healthcare workers' experiences in the field of neurology.Several efforts are being made by the community to introduce various applications of AR for patients as well as for healthcare workers in neurology.This study systematically examines the applications of AR in neurology and synthesizes their uses and impacts in the treatment and assistance of the patients suffering from neurological disorders as well as in the assistance of healthcare workers.The results reveal that most of the AR applications are used for motor disorders while lesser systems are designed for patients with degenerative diseases, most of them being task assistance systems.Moreover, the use of AR applications leaves both long-term and short-term impacts in improving the quality of patient's life.Furthermore, there is a need of introducing more interactive AR systems for patients with degenerative diseases as well as exploring the possibility of using such systems for mental rehabilitation.It has also been identified that there is a potential to develop AR applications for healthcare workers, to train and assist them through simulations.In addition to reviewing the AR applications, this work proposes a taxonomy of AR applications in neurology with different dimensions including scope of AR, type of neurological disorder, and impact of AR assistance on patient's life.Furthermore, this article also highlights challenges the patients with neurological disorders face while using AR applications.Based on the identified challenges, a model has been proposed for application designers and developers to help them develop more useful AR systems.An AR applications architecture has also been suggested for designing the AR systems for patients suffering specifically from degenerative or movement neuro-disorders.Lastly, this article presents some directions for future work in terms of AR usage in neuroscience.
Weather-degraded image semantic segmentation with multi-task knowledge distillation

The semantic segmentation of degraded image in adverse weather is of great importance for the navigation system of autonomous driving. However, weather-degraded images increase the difficulty of semantic segmentation as well as decrease the accuracy. It is natural to integrate image enhancement into degraded image semantic segmentation to improve the accuracy, which is computation intensive and time consuming. To meet the challenge, we propose a fast degraded image semantic segmentation with Multi-Task Knowledge Distillation called MTKD. The proposed MTKD method encourages image enhancement and semantic segmentation networks to learn from each other to make full use of the correlation between two tasks. Additionally, we propose shift operator to realize a lightweight model design. Extensive experiments demonstrate that the proposed MTKD outperforms state-of-the-art methods not only with better semantic segmentation performance but also with higher speed in weather-degraded images, which achieves 0.038 s in semantic segmentation for a 2048 × 1024 image. • We propose a multi-task knowledge distillation method. • Create a system that combines feature distillation and structure distillation. • We propose a shift operator to realize lightweight model. • We build a new dataset called Snowy-Cityscapes.
Knowledge “hiding and seeking” during the pandemic: who really wins in the new normal?

Purpose The recent COVID-19 pandemic caused a severe economic downturn. Employees working in these organisations face employment uncertainty. The pandemic disrupted their daily routines, and it added a layer of complexity to the already resource-constrained environment. During these times, employees would conserve their resources to maintain competitiveness, one of which is knowledge hiding. While economic activities are resuming, the appearance of new variants could mean the transition towards endemicity could be put on hold. Hence, there is a need to rethink the behaviour of employees as they would have elevated levels of anxiety towards resuming daily work activities. Therefore, this study aims to address the question of understanding employees’ perspectives toward knowledge sharing and knowledge hiding. Design/methodology/approach Drawing on the conservation of resources theory, social learning theory and the social exchange theory (SET), a conceptual framework involving ethical leadership was developed to examine if knowledge hiding or knowledge sharing behaviour is a resource for employees during these times. The partial least squares method of structural equation modelling was used to analyse results from 271 white-collar employees from Singapore. Findings The results show that ethical leadership encourages knowledge sharing but does not reduce knowledge hiding. At the same time, knowledge hiding, not knowledge sharing, improves one’s perception of work performance. Additionally, psychological safety is the key construct that reduces knowledge hiding and encourages sharing behaviour. Originality/value Overall, this study extends the theories, demonstrating that, first and foremost, knowledge hiding is a form of resource that provides employees with an added advantage in work performance during the endemic. At the same time, we provide a new perspective that ethical leaders’ demonstration of integrity, honesty and altruism alone is insufficient to encourage knowledge sharing or reduce knowledge hiding. It must lead to a psychologically safe environment.
Design and Application of Artificial Intelligence Technology-Driven Education and Teaching System in Universities

In recent years, many colleges and universities have been experimenting and exploring the evaluation of education and teaching system and have achieved certain results. In order to understand the quality of education and teaching system in colleges and universities, to improve the school conditions, and to promote the reform of teaching management, methods and means of evaluating the quality of education and teaching system in general higher education institutions are needed. Modern university education and teaching system should realize the combination of classroom teaching and practice teaching, and education and teaching system adopts the mode of the combination of on-campus practice and off-campus practice, so the design of teaching system is the key to the quality of teaching. Aiming at the current problem that talents cultivated by colleges and universities can hardly meet social demands in terms of engineering practice ability, innovation ability, and international competitiveness, this paper proposes the evaluation and adjustment of college education and teaching system driven by algorithms based on artificial intelligence (AI). By designing the teaching system of talent cultivation, and then establishing a quantitative and controllable quality assurance system for practical teaching, a new mechanism for the design of university education system is further explored. Specifically, the framework of the instructional system is built with the aid of an actor-critic algorithm in reinforcement learning, which assists in the design of the university education system, allowing students to truly understand, master and flex their knowledge, and strengthening the correct understanding of the students' internal learning mechanisms. The practical teaching effect shows that the AI-driven instructional designs are more popular with contemporary students and have higher evaluation scores. The numerical experiment results also show the stability of the instructional design, overcoming the drawbacks of traditional manual subjectivity in the design. AI-driven college education and teaching system is conducive to cultivating students' solid technical theoretical foundation. Therefore, through the AI-driven teaching system to strengthen the training of practical ability, so as to comprehensively improve students' comprehensive quality and innovation ability.