Reviewerly/exHarmony huggingface dataset:
Whale optimization algorithm based on nonlinear convergence factor and chaotic inertial weight

Summary The whale optimization algorithm (WOA) is a new biological meta‐heuristic algorithm based on the social hunting behaviors of humpback whales. However, it can easily fall into a local optimum when solving complex problems and exhibits slow convergence speed and poor exploration. This study proposed three improved versions of the WOA based on the concepts of chaos initialization, nonlinear convergence factor, and chaotic inertial weight to enhance its exploration abilities. These properties were employed to improve the population diversity and maintain the balance between exploration and exploitation. The performance of the best version was compared with those of moth‐flame optimization, firefly algorithm, particle swarm optimization, gray wolf optimizer, flower pollination algorithm, original WOA, and two recently proposed hybrid WOA through 19 benchmark functions. Experimental results indicated that the proposed algorithms exhibit better performance in terms of complexity and convergence speed.
A Novel Multi-Stage Training Approach for Human Activity Recognition From Multimodal Wearable Sensor Data Using Deep Neural Network

Deep neural network is an effective choice to automatically recognize human actions utilizing data from various wearable sensors. These networks automate the process of feature extraction relying completely on data. However, various noises in time series data with complex inter-modal relationships among sensors make this process more complicated. In this paper, we have proposed a novel multi-stage training approach that increases diversity in this feature extraction process to make accurate recognition of actions by combining varieties of features extracted from diverse perspectives. Initially, instead of using single type of transformation, numerous transformations are employed on time series data to obtain variegated representations of the features encoded in raw data. An efficient deep CNN architecture is proposed that can be individually trained to extract features from different transformed spaces. Later, these CNN feature extractors are merged into an optimal architecture finely tuned for optimizing diversified extracted features through a combined training stage or multiple sequential training stages. This approach offers the opportunity to explore the encoded features in raw sensor data utilizing multifarious observation windows with immense scope for efficient selection of features for final convergence. Extensive experimentations have been carried out in three publicly available datasets that provide outstanding performance consistently with average five-fold cross-validation accuracy of 99.29% on UCI HAR database, 99.02% on USC HAR database, and 97.21% on SKODA database outperforming other state-of-the-art approaches.
Analysis of Bitcoin prices using market and sentiment variables

Abstract This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&amp;P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&amp;P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.
Learning With Virtual Reality in Nursing Education: Qualitative Interview Study Among Nursing Students Using the Unified Theory of Acceptance and Use of Technology Model

Digital games-based learning is a method of using digital games to impart knowledge. Virtual reality (VR) programs are a practical application of this method. Due to demographic changes, the nursing profession will become increasingly important. These VR applications can be of use in training nurses for future professional challenges they may encounter. The continuous development of VR applications enables trainees to encounter simulated real life effectively and to experience increasingly concrete situations. This can be of great importance in nursing education, since 3-dimensionality enables a better visualization of many fields of activity and can prevent potential future errors. In addition to this learning effect, VR applications also bring an element of fun to learning.The aim of this qualitative research effort is to observe the degree of acceptance of VR applications by nursing students in Germany. Various factors, including social influences, performance expectations, and effort expectations, are taken into consideration.With a qualitative cohort study, the acceptance of nursing students towards VR applications in anatomy teaching was determined. The 12 participants were first asked to fill out a quantitative questionnaire on their sociodemographic characteristics and the extent to which they valued and liked using technology. The participants were then allowed to test the VR application themselves and were finally asked about their experience in a qualitative interview. For the collection of data and the analysis of results, the unified theory of acceptance and use of technology was used in this study.Overall, the study shows that the interviewed persons rated the VR application quite positively. The greatest influence in this was the personal attitude towards technology; the higher this affinity is, the more useful the VR application appears. Social influences can also increase the participant's own acceptance if peers have a positive attitude towards such applications. The study shows that the trainees' motivation to learn was increased by using VR. We believe this is because each trainee could learn individually and the VR application was perceived as an enjoyable activity. Nevertheless, the cost factor of implementing VR applications in nursing training is currently still an obstacle, as not every institution has such financial capacities.The extent to which the use of VR applications in the training of nursing staff is justified depends on the degree of personal acceptance. The collected results give good practice-oriented insight into the attitude of trainees towards VR. Many of the interviewed persons saw benefits in the use of VR technologies. As VR applications are constantly developing, it is necessary to conduct further studies on VR applications in nursing education and to include other possible disciplines in which these applications can be helpful.
Mini-batch sample selection strategies for deep learning based speech recognition

With the use of deep learning technologies, speech recognition systems gained more success and human–computer interactions became more prevalent. Deep learning based speech recognition systems are getting more attention and are having tremendous success in all areas of speech recognition, such as voice search, mobile communication, and personal digital assistance. However, speech recognition is still challenging due to hardness of adapting new languages, difficulty in handling variations in speech datasets, and overcoming distorting factors. Deep learning systems have the ability to overcome these challenges using high-level abstractions in the datasets by using a deep graph with multiple processing layers using training algorithms, such as gradient descent optimization. In this study, a variant of gradient descent optimization, mini-batch gradient descent is used. We proposed four strategies for selecting mini-batch samples to represent variations of each feature in the dataset for speech recognition tasks to increase model performance of deep learning based speech recognition. For this purpose, gender and accent adjusted strategies are proposed for selecting mini-batch samples. The experiments show that proposed strategies perform better in comparison with standard mini-batch sample selection strategy.
Methodological Synthesis of Cluster Analysis in Second Language Research

Abstract We present a review of second language researchers’ use of cluster analysis, an advanced statistical method still uncommon but increasingly used to identify groups or patterns in a dataset and to examine group differences. After describing key methodological considerations in conducting cluster analysis, we present a methodological synthesis of 65 studies published between 1989 and 2018 that employed cluster analysis. We specifically review the use of cluster analysis for themes of usage and reporting practices. Our findings indicate that hierarchical cluster analysis and K‐means cluster analysis were the most commonly used cluster methods, but the widespread use of these two methods tended not to be accompanied by sound reporting practices, particularly when justifying cluster solutions. In our analysis, we highlight concerns related to reporting and evaluation. For future use and to inform methodological practices in second language research, we briefly report on a sample study of cluster analysis that uses open data.
Learning complex scattering media for optical encryption

Optical encryption has provided a new insight for securing information; however, it is always desirable that high security can be achieved to withstand the attacks. In this Letter, we propose a new method via learning complex scattering media for optical encryption. After the recordings through complex scattering media, a designed learning model is trained. The proposed method uses an optical setup with complex scattering media to experimentally record the ciphertexts and uses a learning model to generate security keys. During the decryption, the trained learning model with its parameters is applied as security keys. In addition, various parameters, e.g., virtual phase-only masks, can be flexibly applied to further enlarge key space. It is experimentally demonstrated that the proposed learning-based encryption approach possesses high security. The proposed method could open up a new research perspective for optical encryption.
A new robust fuzzy clustering validity index for imbalanced data sets

Determining the number of clusters of a data set, which is usually evaluated by a clustering validity index (CVI), is a significant issue in clustering analysis. While several CVIs have been proposed, the imperfect clustering results of the fuzzy c-means (FCM) clustering algorithm on imbalanced data sets may affect their decisions. To address this problem, the impact of imperfect clustering results on the traditional CVI is first analyzed, and it is found that the distance between two imbalanced clusters becomes closer, which will subsequently impact the separation metric. Inspired by this, a new fuzzy CVI called the imbalanced index (IMI) is proposed in this paper. IMI is the ratio of the fuzzy compactness and separation metrics. The main characteristic of IMI is the new definition of the separation metric, in which the imbalance ratio of two clusters is used to enlarge the distance between their centers. IMI is then employed to evaluate the clustering results of FCM on a variety of data sets, and is compared with several well-known CVIs. The experimental results demonstrate that IMI is robust to the imperfect clustering results of FCM caused by imbalanced data distributions and achieves superior performance as compared to other CVIs.
Leveraging Virtual and Real Person for Unsupervised Person Re-Identification

Person re-identification (re-ID) is a challenging instance retrieval problem, especially when identity annotations are not available for training. Although modern deep re-ID approaches have achieved great improvement, it is still difficult to optimize the deep re-ID model and learn discriminative person representation without annotations in training data. To address this challenge, this study considers the problem of unsupervised person re-ID and introduces a novel approach to solve this problem by leveraging virtual and real data. Our approach includes two components: virtual person generation and training of the deep re-ID model. For virtual person generation, we learn a person generation model and a camera style transfer model using unlabeled real data to generate virtual persons with different poses and camera styles. The virtual data is formed as labeled training data, enabling subsequent training deep re-ID model in supervision. For training of the deep re-ID model, we divide it into three steps: 1) pre-training a coarse re-ID model by using virtual data; 2) collaborative filtering based positive pair mining from the real data; and 3) fine-tuning of the coarse re-ID model by leveraging the mined positive pairs and virtual data. The final re-ID model is achieved by iterating between step 2 and step 3 until convergence. Extensive experiments demonstrate the effectiveness of our method. Experimental results on two large-scale datasets, Market-1501 and DukeMTMC-reID, show the advantages of our method over state-of-the-art approaches in unsupervised person re-ID. Our code is now available online <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Explainable AI in Manufacturing: A Predictive Maintenance Case Study

This paper describes an example of an explainable AI (Artificial Intelligence) (XAI) in a form of Predictive Maintenance (PdM) scenario for manufacturing. Predictive maintenance has the potential of saving a lot of money by reducing and predicting machine breakdown. In this case study we work with generalized data to show how this scenario could look like with real production data. For this purpose, we created and evaluated a machine learning model based on a highly efficient gradient boosting decision tree in order to predict machine errors or tool failures. Although the case study is strictly experimental, we can conclude that explainable AI in form of focused analytic and reliable prediction model can reasonably contribute to prediction of maintenance tasks.
Chaotic Weights: A Novel Approach to Protect Intellectual Property of Deep Neural Networks

Despite the high accuracy achieved by the deep neural network (DNN) technique, there is still a lack of satisfying methodologies to protect the intellectual property (IP) of DNNs, which involves extensive valuable training data, abundant hardware training resources, and fine-tuning skills of experienced experts. Existing solutions based on watermarking cannot prevent malicious/unauthorized users from using well-trained DNNs. This paper proposes chaotic weights (ChaoWs), a novel framework based on the Chaotic Map theory, to protect the IP of DNN providers with very low overhead. Specifically, in order to alleviate the storage overhead and abridge the decryption time, our method makes convolutional or fully connected kernels chaotic by exchanging the weight positions to obtain a satisfying encryption effect, instead of using the conventional idea of encrypting the weight values. Comprehensive experimental evaluations on image classification, semantic segmentation, and name generation demonstrate that ChaoW can effectively protect the IP of DNNs without damaging the inference accuracy, and the impact on the inference speed is negligible.
Using immersive virtual reality to support designing skills in vocational education

Abstract Immersive virtual reality (IVR) offers possibilities of creating a learner‐centric environment that can provide more presence and engagement for students leading to an enhanced learning experience compared to conventional classroom practices. However, the potential of IVR in vocational education and training (VET) has not yet been explored in‐depth, and it is an open question of whether it can effectively support learner creation in a designing task. In this paper, we present an IVR application developed to support gardener apprentices in designing gardens. Using this application, we conducted an experimental study with gardener apprentices to investigate the effect of the IVR interface compared to paper sketching and learner behavior on the proportion, composition, and creativity of the design outcome. Additionally, we investigated how it can be combined with a paper sketching activity to improve its effectiveness. Our analysis shows that the IVR interface can be more effective for the proportion aspect, but this may be limited to students that are able to use it after working with paper. In terms of the combination order, the effectiveness of IVR on the design quality was improved when it was carried out after the paper sketching and this ordering produced a more effective outcome for the proportion and composition aspects. Finally, our results show that IVR design quality is related to learner behaviors such as the time spent on designing and the number of simulations used. This study demonstrates the effectiveness of IVR applications in supporting designing skills and how effectiveness can be improved by combining it with a conventional method of practice. Practitioner Notes What is already known about this topic The effectiveness of IVR has been reported in a variety of educational settings. IVR enhances learning through situated experience, multiple perspectives, and simulations that transfer to the real world. Most IVR applications for VET have focused on passive learning or basic procedural skills. What this paper adds Design and implementation of an IVR application in VET to support design‐related learning. The use of IVR may have a significant impact on the quality of design outcomes. The use of IVR after paper sketching may increase the quality of designs during IVR use. Implications for practice and/or policy IVR can enhance the design outcome in terms of the proportion aspect, but this may be limited to students that are able to use it after working with paper. The design quality can be affected by the combination order between IVR and paper sketching where the effectiveness of IVR can be improved when it is carried out after paper sketching. The behavior of learners while using IVR can be used as indicators of the design quality.
Risk identification and modeling for blockchain-enabled container shipping

Purpose Although being considered for adoption by stakeholders in container shipping, application of blockchain is hindered by different factors. This paper investigates the potential operational risks of blockchain-integrated container shipping systems as one of such barriers. Design/methodology/approach Literature review is employed as the method of risk identification. Scientific articles, special institutional reports and publications of blockchain solution providers were included in an inclusive qualitative analysis. A directed acyclic graph (DAG) was constructed and analyzed based on network topological metrics. Findings Twenty-eight potential risks and 47 connections were identified in three groups of initiative, transitional and sequel. The DAG analysis results reflect a relatively well-connected network of identified hazardous events (HEs), suggesting the pervasiveness of information risks and various multiple-event risk scenarios. The criticality of the connected systems' security and information accuracy are also indicated. Originality/value This paper indicates the changes of container shipping operational risk in the process of blockchain integration by using updated data. It creates awareness of the emerging risks, provides their insights and establishes the basis for further research.
Fault Matters: Sensor data fusion for detection of faults using Dempster–Shafer theory of evidence in IoT-based applications

Fault detection in sensor nodes is a pertinent issue that has been an important area of research for a very long time. But it is not explored much as yet in the context of Internet of Things. Internet of Things work with a massive amount of data so the responsibility for guaranteeing the accuracy of the data also lies with it. Moreover, a lot of important and critical decisions are made based on these data, so ensuring its correctness and accuracy is also very important. Also, the detection needs to be as precise as possible to avoid negative alerts. For this purpose, this work has adopted Dempster–Shafer theory of evidence to collate the information from sensors to come up with a decision regarding the faulty status of a sensor node from a data-centric perspective. To verify the validity of the proposed method, simulations have been performed on a benchmark data set and data collected through a test bed in a laboratory set-up. For the different types of faults, the proposed method shows very high accuracy for both the benchmark (99.8%) and laboratory data sets (99.9%) when compared to the other state-of-the-art machine learning techniques.
Ensemble machine learning approaches for webshell detection in Internet of things environments

Abstract The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially the webshell. To develop a safe IoT system, in this article, we apply essential machine learning models to detect webshell to build secure solutions for IoT network. Future, ensemble methods including random forest (RF), extremely randomized trees (ET), and Voting are used to improve the performances of these machine learning models. We also discuss webshell detection in lightweight and heavyweight computing scenarios for different IoT environments. Extensive experiments have been conducted on these models to verify the validity of webshell intrusion. Simulation results show that RF and ET are suitable for lightweight IoT scenarios, and Voting method is effective for heavyweight IoT scenarios.
Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models

The usage of black-box classification models within the healthcare field is highly dependent on being interpretable by the receiver. Local Interpretable Model-Agnostic Explanation (LIME) provides a patient-specific explanation for a given classification, thus enhancing the possibility for any complex classifier to serve as a safety aid within a clinical setting. However, research on if the explanation provided by LIME is relevant to clinicians is limited and there is no current framework for how an evaluation of LIME is to be performed. To evaluate the clinical relevance of the explanations provided by LIME, this study has investigated how physician's independent explanations for classified observations compare with explanations provided by LIME. Additionally, the clinical relevance and the experienced reliance on the explanations provided by LIME have been evaluated by field experts. The results indicate that the explanation provided by LIME is clinically relevant and has a very high concordance with the explanations provided by physicians. Furthermore, trust and reliance on LIME are fairly high amongst clinicians. The study proposes a framework for further research within the area.