Reviewerly/exHarmony huggingface dataset:
ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection

Abstract Oversampling is a promising preprocessing technique for imbalanced datasets which generates new minority instances to balance the dataset. However, improper generated minority instances, i.e., noise instances, may interfere the learning of the classifier and impact it negatively. Given this, in this paper, we propose a simple and effective oversampling approach known as ASN-SMOTE based on the k -nearest neighbors and the synthetic minority oversampling technology (SMOTE). ASN-SMOTE first filters noise in the minority class by determining whether the nearest neighbor of each minority instance belongs to the minority or majority class. After that, ASN-SMOTE uses the nearest majority instance of each minority instance to effectively perceive the decision boundary, inside which the qualified minority instances are selected adaptively for each minority instance by the proposed adaptive neighbor selection scheme to synthesize new minority instance. To substantiate the effectiveness, ASN-SMOTE has been applied to three different classifiers and comprehensive experiments have been conducted on 24 imbalanced benchmark datasets. ASN-SMOTE is also extensively compared with nine notable oversampling algorithms. The results show that ASN-SMOTE achieves the best results in the majority of datasets. The ASN-SMOTE implementation is available at: https://www.github.com/yixinkai123/ASN-SMOTE/ .
Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.
The Potential of Blockchain Technology in Higher Education as Perceived by Students in Serbia, Romania, and Portugal

Lifelong learning approaches that include digital, transversal, and practical skills (i.e., critical thinking, communication, collaboration, information literacy, analytical, metacognitive, reflection, and other research skills) are required in order to be equitable and inclusive and stimulate personal development. Realtime interaction between teachers and students and the ability for students to choose courses from curricula are guaranteed by decentralized online learning. Moreover, through blockchain, it is possible to acquire skills regarding the structure and content while also implementing learning tools. Additionally, documentation validation should be equally crucial to speeding up the process and reducing costs and paperwork. Finally, blockchains are open and inclusive processes that include people and cultures from all walks of life. Learning in Higher Education Institutions (HEI) is facilitated by new technologies, connecting blockchain to sustainability, which helps understand the relationship between technologies and sustainability. Besides serving as a secure transaction system, blockchain technology can help decentralize, provide security and integrity, and offer anonymity and encryption, therefore, promoting a transaction rate increase. This study investigates an alternative in which HEI include a blockchain network to provide the best sustainable education system. Students’ opinions were analyzed, and they considered that blockchain technology had a very positive influence on learning performance.
Assessing acceptance of augmented reality in nursing education

The Covid-19 pandemic has negatively affected every aspect of human life. In these challenging times nursing students, facing academic and psychological issues, are advised to use augmented reality applications in the field of health sciences for increasing their motivations and academic performances. The main motive of the study was to examine the acceptance status of nursing students in implementing augmented reality technology in their education and training. The study is a quantitative research study, and it uses the causal-comparative screening method. The data used in the study was collected online from 419 nursing students. The hybrid method was preferred. First, the hypotheses based on the linear relationships were defined between the variables which were then tested by the method of structural equation modeling. Second, the method of artificial neural networks was used to determine the non-linear relationships between the variables. The results show that the nursing students have a high intention of using augmented reality technology as a way of self-learning. It was also found that the most emphasized motive behind this intention is the expectation that using augmented reality technology will increase their academic performance. They also think that AR technology has many potential benefits to offer in the future. It was observed that a considerable number of students already use augmented reality technology for its usefulness and with a hedonic motivation. In conclusion, nursing students have a high acceptance of using augmented reality technology during their education and training process. Since we live in a world where e-learning and self-learning education/training have become widespread, it is estimated that students will demand augmented reality applications as a part of holistic education, and as an alternative to traditional textbooks.
Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles

The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route's accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time.
Faster CNN-based vehicle detection and counting strategy for fixed camera scenes

Abstract Automatic detection and counting of vehicles in a video is a challenging task and has become a key application area of traffic monitoring and management. In this paper, an efficient real-time approach for the detection and counting of moving vehicles is presented based on YOLOv2 and features point motion analysis. The work is based on synchronous vehicle features detection and tracking to achieve accurate counting results. The proposed strategy works in two phases; the first one is vehicle detection and the second is the counting of moving vehicles. Different convolutional neural networks including pixel by pixel classification networks and regression networks are investigated to improve the detection and counting decisions. For initial object detection, we have utilized state-of-the-art faster deep learning object detection algorithm YOLOv2 before refining them using K-means clustering and KLT tracker. Then an efficient approach is introduced using temporal information of the detection and tracking feature points between the framesets to assign each vehicle label with their corresponding trajectories and truly counted it. Experimental results on twelve challenging videos have shown that the proposed scheme generally outperforms state-of-the-art strategies. Moreover, the proposed approach using YOLOv2 increases the average time performance for the twelve tested sequences by 93.4% and 98.9% from 1.24 frames per second achieved using Faster Region-based Convolutional Neural Network (F R-CNN ) and 0.19 frames per second achieved using the background subtraction based CNN approach (BS-CNN ), respectively to 18.7 frames per second.
Effective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis

The state-of-the-art model for structured sentiment analysis casts the task as a dependency parsing problem, which has some limitations:(1) The label proportions for span prediction and span relation prediction are imbalanced.(2) The span lengths of sentiment tuple components may be very large in this task, which will further exacerbates the imbalance problem.(3) Two nodes in a dependency graph cannot have multiple arcs, therefore some overlapped sentiment tuples cannot be recognized.In this work, we propose nichetargeting solutions for these issues.First, we introduce a novel labeling strategy, which contains two sets of token pair labels, namely essential label set and whole label set.The essential label set consists of the basic labels for this task, which are relatively balanced and applied in the prediction layer.The whole label set includes rich labels to help our model capture various token relations, which are applied in the hidden layer to softly influence our model.Moreover, we also propose an effective model to well collaborate with our labeling strategy, which is equipped with the graph attention networks to iteratively refine token representations, and the adaptive multi-label classifier to dynamically predict multiple relations between token pairs.We perform extensive experiments on 5 benchmark datasets in four languages.Experimental results show that our model outperforms previous SOTA models by a large margin.
Experiences and perceptions of final‐year nursing students of using a chatbot in a simulated emergency situation: A qualitative study

The aim of this study is to explore the experiences and perceptions of final-year nursing students on the acceptability and feasibility of using a chatbot for clinical decision-making and patient safety.The effective and inclusive use of new technologies such as conversational agents or chatbots could support nurses in increasing evidence-based care and decreasing low-quality services.A descriptive qualitative study was used through focus group interviews. The data analysis was conducted using a thematic analysis.This study included 114 participants. After our data analysis, two main themes emerged: (i) experiences in the use of a chatbot service for clinical decision-making and and (ii) integrating conversational agents into the organizational safety culture.The findings of our study provide preliminary support for the acceptability and feasibility of adopting SafeBot, a chatbot for clinical decision-making and patient safety. Our results revealed substantial recommendations for refining navigation, layout and content, as well as useful insights to support its acceptance in real nursing practice.Leaders and managers may well see artificial intelligence-based conversational agents like SafeBot as a potential solution in modern nursing practice for effective problem-solving resolution, innovative staffing and nursing care delivery models at the bedside and criteria for measuring and ensure quality and patient safety.
Seismic precursors to the Whakaari 2019 phreatic eruption are transferable to other eruptions and volcanoes

Abstract Volcanic eruptions that occur without warning can be deadly in touristic and populated areas. Even with real-time geophysical monitoring, forecasting sudden eruptions is difficult, because their precursors are hard to recognize and can vary between volcanoes. Here, we describe a general seismic precursor signal for gas-driven eruptions, identified through correlation analysis of 18 well-recorded eruptions in New Zealand, Alaska, and Kamchatka. The precursor manifests in the displacement seismic amplitude ratio between medium (4.5–8 Hz) and high (8–16 Hz) frequency tremor bands, exhibiting a characteristic rise in the days prior to eruptions. We interpret this as formation of a hydrothermal seal that enables rapid pressurization of shallow groundwater. Applying this model to the 2019 eruption at Whakaari (New Zealand), we describe pressurization of the system in the week before the eruption, and cascading seal failure in the 16 h prior to the explosion. Real-time monitoring for this precursor may improve short-term eruption warning systems at certain volcanoes.
A reinforcement learning based artificial bee colony algorithm with application in robot path planning

Artificial bee colony (ABC) algorithm is a popular optimization algorithm with excellent exploration ability and various applications. Nevertheless, its effectiveness is limited by the one-dimensional search strategy. Therefore, in order to improve its performance, a reinforcement learning (RL) based ABC algorithm is proposed (named ABC_RL). In ABC_RL, the number of dimensions to be updated in search equation of the employed bee phase is varied and adjusted intelligently via RL. Moreover, two improved search strategies are adopted to maintain a nice balance between diversification and intensification. The performance of ABC_RL is evaluated through a series of comparisons conducted on CEC 2017 benchmark problems. The results indicate that ABC_RL outperforms the compared ABC variants considering the solution accuracy. In addition, a robot path planning problem is concerned to further test the effectiveness of ABC_RL. And the comparison results show the advantages of ABC_RL in terms of path length and running time.
Out-of-the-classroom learning of English vocabulary by EFL learners: investigating the effectiveness of mobile assisted learning with digital flashcards

Abstract The importance of vocabulary knowledge as one of the most challenging aspects of foreign language learning has been widely acknowledged. In order to facilitate learning vocabulary items in English by language learners, mobile assisted vocabulary learning attracted considerable attention in recent years. Nevertheless, the application of mobile devices for learning the most frequently used vocabulary items in core vocabulary lists received far less attention. To address this gap, the current study investigated the contribution of a digital flashcard application (i.e. NGSL builder) designed for smartphone devices in out-of-the-classroom learning of essential vocabulary in English among EFL learners. The participants were 86 university students in experimental and control groups. In addition to formal language education during a semester, the experimental group used the mobile application for studying vocabulary items outside the classroom, while the control group used traditional materials (i.e. word list) to study the same content. The participants’ vocabulary knowledge was measured in three times (pre, post, and delayed posttest), and the results revealed that the use of mobile applications contributed significantly to vocabulary knowledge development, and those in the experimental group outperformed their counterparts in the control group. The findings also provided empirical evidence for the long-term impacts of mobile-assisted vocabulary learning. The study highlights the significance of mobile technologies in helping L2 learners to acquire essential vocabulary in English and discusses the pedagogical implications of these findings.
A survey of fitness landscape analysis for optimization

Over past few decades, as a powerful analytical tool to characterize the fitness landscape of a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all kinds of optimization areas. Since its introduction by Sewell Wright in 1932, FLA has attracted more and more attention of researchers for its research significance and application value such as an intuitive understanding features of complex optimization problems, explaining evolutionary algorithm behavior, assessing performances of algorithms, and guiding selections and/or configures of algorithms. This paper attempts to give an overview of fitness landscape analysis and its typical application for optimization so far. We hope that this survey can help to understand features of complex optimization problems in depth and thus to improve the certain algorithm performance of for a given optimization problem.
Content Based Automated File Organization Using Machine Learning燗pproaches

In the world of big data, it's quite a task to organize different files based on their similarities. Dealing with heterogeneous data and keeping a record of every single file stored in any folder is one of the biggest problems encountered by almost every computer user. Much of file management related tasks will be solved if the files on any operating system are somehow categorized according to their similarities. Then, the browsing process can be performed quickly and easily. This research aims to design a system to automatically organize files based on their similarities in terms of content. The proposed methodology is based on a novel strategy that employs the charactaristics of both supervised and unsupervised machine learning approaches for learning categories of digital files stored on any computer system. The results demonstrate that the proposed architecture can effectively and efficiently address the file organization challenges using real-world user files. The results suggest that the proposed system has great potential to automatically categorize almost all of the user files based on their content. The proposed system is completely automated and does not require any human effort in managing the files and the task of file organization become more efficient as the number of files grows.
Detecting Human Actions in Drone Images Using YoloV5 and Stochastic Gradient Boosting

Human action recognition and detection from unmanned aerial vehicles (UAVs), or drones, has emerged as a popular technical challenge in recent years, since it is related to many use case scenarios from environmental monitoring to search and rescue. It faces a number of difficulties mainly due to image acquisition and contents, and processing constraints. Since drones' flying conditions constrain image acquisition, human subjects may appear in images at variable scales, orientations, and occlusion, which makes action recognition more difficult. We explore low-resource methods for ML (machine learning)-based action recognition using a previously collected real-world dataset (the "Okutama-Action" dataset). This dataset contains representative situations for action recognition, yet is controlled for image acquisition parameters such as camera angle or flight altitude. We investigate a combination of object recognition and classifier techniques to support single-image action identification. Our architecture integrates YoloV5 with a gradient boosting classifier; the rationale is to use a scalable and efficient object recognition system coupled with a classifier that is able to incorporate samples of variable difficulty. In an ablation study, we test different architectures of YoloV5 and evaluate the performance of our method on Okutama-Action dataset. Our approach outperformed previous architectures applied to the Okutama dataset, which differed by their object identification and classification pipeline: we hypothesize that this is a consequence of both YoloV5 performance and the overall adequacy of our pipeline to the specificities of the Okutama dataset in terms of bias-variance tradeoff.
Performance analysis of a private blockchain network built on Hyperledger Fabric for healthcare

The healthcare industry suffers from poor interoperability due to its fragmented communication systems. Each medical institution has a communication system that is not necessarily compatible with the other systems on the same network. Poor communication has serious implications for patients, resources, and costs. The literature proposes blockchain technology as a governance data management system that optimizes the flow of information between multiple organizations by providing a trust component. However, much of the literature is conceptual and addresses blockchain in healthcare in a very general way. This paper contributes to the literature by presenting the use of Hyperledger Fabric in healthcare to improve information flow and solve the fragmentation problem between two medical institutions. In addition, two rate controllers on Hyperledger Caliper are used to evaluate the performance of our network: fixed and linear. We believe that our work will be useful for those working in value chains in healthcare and academia.
DWT-PCA based Video Watermarking

Progressed watermarking video may be a methodology for embedding additional data another to video salute. Embedded data is utilized for proprietor copyright or recognizable affirmation. It added up to approach for watermarking is shown in this System, by utilizing Discrete Wavelet Alter (DWT) and Crucial Component Examination (PCA). There are a number of watermarking strategies like DCT, DWT, and DWT-SVD, but there's a downside inside the watermarking to stand up to attacks. In this way the cutting edge progressed picture watermarking calculation is proposed which provide solid watermarking with insignificant whole of bending in case of ambushes. DWT offers flexibility and PCA makes a distinction in diminishing relationship among the wavelet coefficients.