Reviewerly/exHarmony huggingface dataset:
The moderating role of creativity and the effect of virtual reality on stress and cognitive demand during preservice teacher learning

Virtual reality (VR) has received considerable attention related to its use in teacher development over the last decade. Despite this attention, there is insufficient understanding of how specific underlying cognitive system responses and autonomic nervous system responses moderate the use of VR and the associated learning outcomes in the development of preservice teachers. This work intends to explore and evaluate preservice service teachers’ (PSTs) experiences using VR and the effects of creativity, mental flexibility (MF), acute stress, and cognitive demand (CD). Forty-eight undergraduate college students in year two of their teacher preparation program were recruited for the study. Each of the preservice teachers was assigned randomly to one of two conditions, microteaching (n ​= ​24) or VR (n ​= ​24). The use of these two conditions allowed the researchers to compare the effects of creativity and MF [measured using the Torrance Test of Creative Thinking] on cognitive demand and stress responses [measured using heart rate variability and electrodermal activity]. Results from analysis of the hemodynamic data and stress response data illustrate that the protective factors of creativity and MF may moderate success in VR and the reduction of cognitive demand and stress when VR is used to develop skills related to teaching.
Long-Horizon Informative Path Planning with Obstacles and Time Constraints

We apply non-myopic informative path planning in a simulated river plume case study with several constraints on our agent. A cost valley philosophy is proposed to guide the agent through the field. The purpose of this path planner is to reveal the river plume front with the long-horizon while safely returning home in time. Among others, we employ RRT*, a variant of RRT (rapidly-exploring random trees), as the path planner to determine the least-cost path between locations. The distance budget from start to end destination, the obstacle constraint, and directional change are penalties, whereas the reduced variance of the field and an excursion set are the two rewards. The cost valley is then computed by superimposing those five fields. The simulation results demonstrate the efficiency of such a strategy. They show that the suggested approach balances exploitation and exploration while bearing in mind the go-home constraint.
Estimating Determinants of Stage at Diagnosis of Breast Cancer Prevalence in Western Nigeria Using Bayesian Logistic Regression

Breast cancer is the most common cancer-affecting women globally, and the stage at diagnosis remains a key factor to the final outcome. Late stage at diagnosis means a significant challenge and this is common in low middle income countries with Nigeria included. Patients with early-stage breast cancers are expected to have good survival rates. Hence, it is important to identify risk factors that predict diagnosis of early-stage breast cancers among Nigeria women. Although many studies have been carried out on the risk factors associated with breast cancer, but to the best of our knowledge little has been studied on the stage at diagnosis in the context of Nigeria. The use of advanced statistical techniques coupled with hospital-based data can enhance proper estimation of determinants of stage at diagnosis of breast cancer prevalence as well as giving appropriate explanations on the role of each determinants factors on the stage at diagnosis. In this chapter we estimated the prevalence and investigated determinants of stage at diagnosis by constructing Bayesian logistic regression model from a generalized linear modeling using socio-economic, demographic, and medical factors. It was established that age, higher educational level, being a westerner as well as choosing nursing as a career were the major factors that motivate early stage at breast cancer diagnosis in this part of Nigeria. Our findings affirmed that delays in diagnosis reflected a lack of education. This chapter suggests that further education as well as awareness of breast cancer diagnosis is required in order to increase early stage diagnosis for patients.
Algorithmic Decision Making and Model Explainability Preferences in the Insurance Industry: A Delphi Study

There is growing attention to the transparency, fairness, and explainability of algorithmic decision-making systems as they permeate many aspects of our lives. Despite the awareness of the need for algorithmic transparency and the right-to-meaningful-explanation provided by GDPR, little is known regarding what makes such explanations meaningful and useful. This issue becomes especially challenging in certain situations in which high levels of transparency may conflict with the best interest of organizations. The insurance industry poses an interesting case as the business model of insurance providers depends on the discrimination of customer groups. In this paper, we present the results of a Delphi study with experts from the Dutch insurance industry informed by an initial survey. Our results include the preferred explanation elements towards customers, from the perspective of the insurer, for five commonly used algorithmic decision-making systems. They also show that there is not a one-size-fits-all explanation approach, and that it depends on the system itself and the context in which it is used. Finally, the results highlight risks and concerns of the insurance experts regarding the disclosure of sensitive information in the form of explanations.
Remote Sensing Object Detection Based on Improved YOLOv3

Considering typical remote sensing object detection problems, this paper proposes an improved remote sensing object detection model based on the YOLOv3 algorithm. This model introduces the SE attention module to get richer features and uses K-means to extract the more suitable anchor for remote sensing images. The experiments are carried out under the DOTA data set, and the results show that, under the premise of unchanged detection speed, the accuracy of detection is improved, and the improved algorithm has a better effect on detecting the small and dense objects.
A Deep Learning Technique as a Sensor Fusion for Enhancing the Position in a Virtual Reality Micro-Environment

Most virtual reality (VR) applications use a commercial controller for interaction. However, a typical virtual reality controller (VRC) lacks positional precision and accu-racy in millimeter-scale scenarios. This lack of precision and accuracy is caused by built-in sensors drift. Therefore, the tracking performance of a VRC needs to be enhanced for millimeter-scale scenarios. Herein, we introduce a novel way of enhancing the tracking performance of a commercial VRC in a millimeter-scale environment using a deep learning (DL) al-gorithm. Specifically, we use a long short-term memory (LSTM) model trained with data collected from a linear motor, an IMU sensor, and a VRC. We integrate the virtual environment developed in Unity software with the LSTM model running in Python. We designed three experimental conditions: the VRC, Kalman filter (KF), and LSTM modes. Furthermore, we evaluate tracking performances in the three conditions and two other experimental scenarios, namely stationary and dynamic. In the stationary experimental scenario, the system is left motionless for 10 s. By contrast, in the dynamic experimental scenarios, the linear stage moves the system by 12 mm along the X, Y, and Z axes. The experimental results indicate that the deep learning model outperforms the standard controllers positional performance by 85.69 % and 92.14 % in static and dynamic situations, respectively.
Augmented Virtuality Training for Special Education Teachers

The last decade has seen a significant surge in the popularity of virtual reality in countless fields and it keeps on spreading into more areas of application. While Virtual Reality (VR) is already being used in various educational settings, such as medicine and teaching, the technology is constantly being enhanced and augmented into Mixed Reality (MR) approaches. Especially in the training of certain skills, mixed reality provides numerous new training perspectives. The specific domain of special education is no exception to that fact and potential applications of VR are increasingly being explored. The aim of this work is to develop a VR trainer tor the training of special educators, in order for them to acquire new skills in specific teaching techniques. All aspects of this work are conducted in collaboration with Reykjavik University, where previous work on the topic was conducted in 2017. The goal is to improve on the existing trainer by adding new interaction modalities, such as voice input and virtual touch. In addition to that, the state of the art is pushed even further by introducing Augmented Virtuality (AV) through physical objects in a second version of this trainer. The goal of implementing a new simulator for training special education teachers is successfully achieved, as witnessed during two user tests with students and teachers from the field of special education. In the future, this VR trainer will continue to be used at Reykjavik University, superseding the previous one. The goal of introducing AV through physical objects is achieved through a viable technical solution that still needs to be simplified in its deployment before extensive user testing.
An Efficient Multibiometric-based Continuous Authentication Scheme

Mobile devices are vulnerable to different security and privacy threats and attacks from attackers. In order to protect user information from attacks by opponents, it is increasingly important to perform continuous identity authentication on intelligent users' devices. Different from other static authentication technologies, continuous authentication technology improves the security and reliability of the system through regular authentication of intelligent devices. By fusing the sensor and the front camera, we propose an efficient multibiometric-based continuous authentication scheme. On the one hand, use the built-in sensors of smart phones such as accelerometers, gyroscopes and magnetometers to continuously monitor the user's behavior patterns. On the other hand, the smartphone's front-facing camera is used to capture faces to continue continuous authentication when the sensor data does not change. We extract features from the collected data, and then use two-class SVM to train the classifier to complete user authentication. The experimental results show that the proposed method can achieve the authentication accuracy of 98.3%.
Comic assisted by augmented reality: Its use to empower students’ critical thinking skill

The pandemic is ongoing, and teachers must continue to transfer knowledge to their students. Students’ frustration during the pandemic caused their thinking ability and performance to decline. The purpose of our research is to empower students’ Critical Thinking Skills (CTS) using comic media assisted by augmented learning (AR). Research has used a one-group pre-test – post-test design. This research was conducted on nineteen students of class XI science in a public high school senior during online learning. Thirty multiple-choice items measured students’ CTS. Data were analyzed by determining the average, n-gain, and significant changes in CTS using paired t-test. The results of the study revealed that the average CTS of students was 80.95, n-gain=0.74 and P=0.00<0.05. This research found that AR-assisted comic learning media can have a significant impact on the CTS of students. The increase in student CTS is in the high category with outstanding CTS achievements. Comic and AR media are combined as a form of innovation to strengthen students’ CTS. This study concludes that comics and AR media can empower students’ CTS. The combination of comics and AR becomes a medium to empower students’ CTS and can be used by teachers to meet the needs of learning resources during the pandemic era.
A Benchmark Gurmukhi Handwritten Character Dataset: Acquisition, Compilation, and Recognition

Gurmukhi script is used to write the official 'Punjabi' language of the people of the western part of Indian Punjab. The script is having approximately 160 million native speakers. Recognition of handwritten characters in the Gurmukhi script is still in its embryonic stage due to intricate character shapes and the scarcity of standard datasets. This paper introduces a new large-scale benchmark dataset "Gurmukhi_HWdb1.0" which is an important development in the handwritten character recognition of this script. This dataset has a total of 137,700 handwritten samples of 41 basic Gurmukhi characters and 10 numeral classes. Out of these, 110,160 images are used for training,13,770 images are set aside for validation, and 13,770 images are used for testing. Here, 265 individuals have contributed to the development of the dataset. Recognition of the script is carried out using a CNN architecture based on transfer learning on the VGG16 network. We fine-tuned the model and added our own fully connected layers needed for Gurmukhi characters. The proposed model is executed on this collected "Gurmukhi_HWdb1.0" dataset for evaluation. A detailed comparison with different batch sizes is performed to understand the functionality of the model. Experimental results show that the proposed model can be benchmarked against the concerned dataset with a test accuracy of 98.42% for Gurmukhi characters and 97.51% for Gurmukhi numerals.
Transfer Anomaly Detection for Maximizing the Partial AUC

The partial area under the receiver operating characteristic curve (pAUC) is a useful performance measurement for binary classification that summarizes true positive rates (TPRs) within a specific range of false positive rates (FPRs). Obtaining anomaly detectors that achieve high pAUCs is important in practice. Although several methods have been proposed for maximizing the pAUC, existing methods require both anomalous and normal instances for training. However, anomalous instances are difficult to collect due to their rarity. In this paper, we propose a method to maximize the pAUC on target tasks, in which only normal instances are available. To alleviate the absence of anomalies, the proposed method transfers useful knowledge of both anomalous and normal instances in multiple source tasks to the target tasks. Our model first embeds each instance into a task-specific latent space created from normal instances in a task using neural networks. Then, the anomaly score for each instance is obtained by a FPR range-specific linear projection, which is modeled by a neural network that tasks a FPR range as input. By this modeling, we can achieve high pAUCs within any FPR range given target normal instances without re-training. Our model is trained by maximizing the expected test pAUC within any FPR range given normal training instances using anomalous and normal instances in source tasks. We experimentally demonstrate that the proposed method outperforms various existing methods.
Securing data over cloud – enhanced hybrid data encryption algorithm

Cloud computing became popular due to its elasticity, unparalleled resource availability on demand and almost zero downtime. Looking into the advantages of cloud computing, industries are moving their IT infrastructure from on-premises to cloud infrastructure irrespective of their size, business area, and customer orientation. Major challenge for cloud service providers is to provide guaranteed data security without compromising the performance of data encryption/decryption, both at rest and during intransit. Many symmetric and asymmetric algorithms already exist with their own pros and cons. The robustness of the algorithm depends upon the key size and the complexity of the algorithm used in encryption and decryption process. In this paper we derived a hybrid algorithm to make the encryption process robust without degrading performance. This proposed algorithm makes the encryption and decryption process simple but harden the sub-keys generation process by taking larger dynamic key length and complex processing steps. We refer AES for robustness during sub-key generation process and refers blowfish for encryption and decryption process to make it simpler and fast. As per the experimental result, the proposed hybrid algorithm performs better than AES in terms of encryption/ decryption time and more robust than Blowfish and AES.
Drugs clustering based on their compositions using Word2Vec and K-means clustering

With the rapid growth in medicine, it is essential to determine a method of cluster drug composition data to make it easy for industries to define medicine composition. K-means clustering is one way to cluster the composition of drugs. In this paper, we use the Word2Vec model and convert the composition of the drug into a vector. We cluster it using K-means, also visualize the data results of the clustering. In Word2Vec, we use two methods, namely CBOW and SG. Meanwhile, in K-means, we determine the number of centroids using the Elbow Criterion and Silhouette Coefficient method. Datasets consist of more than 250 product names of drug from Farmaku and K24. The experiment results show that the Silhouette Coefficient value using the CBOW and SG methods are 0.901 and 0.877. Both CBOW and SG method generating the best value of the number of clusters is three.
Obstacle Avoidance Algorithm of Handling Robot in Cultural Complex

In order to solve the problem of rapid switching of space equipment in the cultural complex, this paper combines Moth-Flame optimization Algorithm (MFO) and Dynamic Window Approach (DWA) to realize the hybrid path planning of handling robots in dynamic environments. Compared with the mainstream algorithms, the Moth-Flame optimization algorithm has the best performance. Different paths were classified and judged, and the global path planning based on MFO was proposed. The intersection of the dynamic obstacle and the global path were detected, and the dynamic window approach was used for local path planning in the road section where the collision occurs. Finally the combination of the global path planning and the local dynamic obstacle avoidance was realized. The effect of the hybrid path planning algorithm in the complex environment of the cultural complex had been verified.
Influence of jabbalabab.com Site Media as a German Online Learning Support on Learning Motivation of XI Grade Students at SMA Negeri 7 Malang

Knowing the influence of the uses of Jabbalab.com site media as a German online learning support on learning motivation of XI grade students of SMA Negeri 7 Malang is this researchs aim. T Quasi-experimental method used to identify the impact between each variables, which the result will answer whether Ha or H0 accepted or not. In this research, datas obtained from the results of pre-questionnaire and post-questionnaire. Finally, the result of this research is Ha was accepted, which sounds that there is an influence of the uses of Jabbalab.com site media as a German online learning support on learning motivation of XI grade students of SMA Negeri 7 Malang.
Conclusion and Future Works

In this chapter, we revisit the key concepts derived from each chapter in the book. Then, we discuss the future research directions and open issues to solve toward deploying Federated Learning at scale.
A Capacity-Based Semantics for Inconsistency-Tolerant Inferences

Classical logic can deal with uncertainty caused by incomplete information and, beside the standard interpretation-based semantics, also has a semantics in terms of modalities expressing “known to be true”, “known to be false”, where binary-valued possibility theory and necessity measures are instrumental. When uncertainty is caused by inconsistency, the standard semantics of classical logic collapses, but the other one survives provided that we replace necessity measures by more general set functions. This is the topic of this paper where we show that various inconsistency-tolerant inferences for propositional bases, some of which defined at the syntactic level only, have a semantics in terms of binary-valued capacities.
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks

Deep neural networks (DNNs) often rely on massive labelled data for training, which is inaccessible in many applications. Data augmentation (DA) tackles data scarcity by creating new labelled data from available ones. Different DA methods have different mechanisms and therefore using their generated labelled data for DNN training may help improving DNN's generalisation to different degrees. Combining multiple DA methods, namely multi-DA, for DNN training, provides a way to further boost generalisation. Among existing multi-DA based DNN training methods, those relying on knowledge distillation (KD) have received great attention. They leverage knowledge transfer to utilise the labelled data sets created by multiple DA methods instead of directly combining them for training DNNs. However, existing KD-based methods can only utilise certain types of DA methods, incapable of making full use of the advantages of arbitrary DA methods. In this work, we propose a general multi-DA based DNN training framework capable to use arbitrary DA methods. To train a DNN, our framework replicates a certain portion in the latter part of the DNN into multiple copies, leading to multiple DNNs with shared blocks in their former parts and independent blocks in their latter parts. Each of these DNNs is associated with a unique DA and a newly devised loss that allows comprehensively learning from the data generated by all DA methods and the outputs from all DNNs in an online and adaptive way. The overall loss, i.e., the sum of each DNN's loss, is used for training the DNN. Eventually, one of the DNNs with the best validation performance is chosen for inference. We implement the proposed framework by using three distinct DA methods and apply it for training representative DNNs. Experimental results on the popular benchmarks of image classification demonstrate the superiority of our method to several existing single-DA and multi-DA based training methods.
Recommender System for Coupon Discount of E-commerce Applications

Recommender systems have recently been integrated into many fields to introduce numerous appealing products and help with consumer decisions in item selections. Applying recommender systems to coupon discount applications could improve consumers accessibility to coupons of their interest, leading to increased product sales related to coupons. The research aims to create a recommender system model for coupon suggestions based on customer data interactions. The proposed recommender model is a hybrid model that combines matrix factorization and association rule mining. While matrix factorization utilises customer-item interactions with customer transactions history and information as the implicit feedback, the association rule uses the Apriori algorithm to find frequent itemsets from customer discount transactions to create the weight of each item. As a result, the proposed hybrid recommender model provides overall performance higher than the baseline models and other benchmark models. In addition, the proposed model has a precision rank increase of nearly 20% in the same dataset compared to that of the baseline models.