*Article* **Preprocessing Pipelines Including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks**

**Marek Pawlicki 1,\* and Ryszard S. Chora´s 2**

**Abstract:** Artificial neural networks have become the go-to solution for computer vision tasks, including problems of the security domain. One such example comes in the form of reidentification, where deep learning can be part of the surveillance pipeline. The use case necessitates considering an adversarial setting—and neural networks have been shown to be vulnerable to a range of attacks. In this paper, the preprocessing defences against adversarial attacks are evaluated, including blockmatching convolutional neural network for image denoising used as an adversarial defence. The benefit of using preprocessing defences comes from the fact that it does not require the effort of retraining the classifier, which, in computer vision problems, is a computationally heavy task. The defences are tested in a real-life-like scenario of using a pre-trained, widely available neural network architecture adapted to a specific task with the use of transfer learning. Multiple preprocessing pipelines are tested and the results are promising.

**Keywords:** deep learning; computer vision; adversarial attacks; adversarial defences

## **1. Introduction**

Artificial neural networks offer a collection of benefits which have proved useful in image processing, especially in tasks including artificial-intelligence-based computer aided detection [1]. The progress of the last decade allowed to push the results obtained by artificial neural networks to levels surpassing human performance, in select tasks [2]. In computer vision, deep neural networks became the go-to solution for a wide variety of problems [3], capable of producing an impressive result in a sensible time frame [4]. Recently, artificial neural networks found success in person reidentification [5–7].

In general, reidentification (reID) refers to the process of re-attaching publicly available data to an anonymised record in order to discover the identity of an individual. In the context of computer vision, the phrase refers to the ability of an image recognition system to spot an individual across different cameras, and different angles [7]. ReID is a challenging task which stirred up a significant amount of research recently, particularly due to the significant benefits it could bring for public safety [7]. The use in the context of intelligent surveillance systems forces the consideration of adversarial behaviour against the artificial intelligence (AI) technologies used for reID. In a real-world scenario, impressive detection metrics are not the only thing that matters [8]. The current trend in reID involves the use of deep neural networks, which have been proven to be susceptible to a novel kind of attacks [9–12].

Deep neural networks, particularly convolutional neural networks (CNN), are widely used for the CV tasks [13]; some of the best-performing ImageNet contest architectures were based on the premise of utilising convolutional layers. The network architectures tend to be very deep: Inception features over 6 million trainable parameters [14,15], ResNet18

**Citation:** Pawlicki, M.; Chora´s, R.S. Preprocessing Pipelines Including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks. *Entropy* **2021**, *23*, 1304. https://doi.org/10.3390/ e23101304

Academic Editor: Ernestina Menasalvas

Received: 17 August 2021 Accepted: 30 September 2021 Published: 3 October 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(Residual neural network) over 11 million [16], AlexNet over 60 million [17], VGG16 (Visual Geometry Group) over 138 million [18], etc. Therefore, training a top-tier deep neural network is a huge computational endeavour [19]. In order not to repeat this effort for each task, transfer learning can be employed [20]. Transfer learning leverages pre-trained networks, essentially using them as feature extractors with frozen weights, feeding samples to the network and only training the added dense layers at the output end of the topology. However, the use of openly-available, pre-trained networks poses a security problem in an adversarial setting, as it raises the capability of the attacker [21,22].

The idea of attacking deep neural networks has focused the attention of the deep learning community over the last few years [23–25]. A range of adversarial attacks effective against AI were discovered, uncovering the vulnerabilities of data driven technologies [25]. In this work, the attacks performed at test-time are considered, which are known as evasion attacks [26,27].

The goal of an Evasion attack is to force the AI-based system to misclassify a particular sample. This is achieved by adding a specifically crafted noise to the tested sample. This added noise, in case of images, is imperceptible to humans, but leverages the 'intriguing properties of neural networks' to fool the AI algorithm [28]. The issue of defending against those attacks is a fierce arms race and the satisfactory defence has not yet emerged [29].

The algorithms and technologies presented in this paper were used to form a submission to the reidentification defences track of the H2020 SPARTA SAFAIR contest. The task was formulated around the CelebA face recognition dataset [30,31]. The dataset, as used in the task, featured 5304 classes, with 85,612 samples in the training subset and 28,523 samples in the testing set. The objective of the defensive track was to propose ways of preventing adversarial samples from lowering the accuracy of the face recognition model. The following sections describe the specific technologies used for defining the submission of the contest, the rationale behind those choices, the formulated defences, and provide the results of the experiments.

As such, the research and, thus, the paper is conducted and formulated to answer the following research question:

Thus, the innovative contribution of this paper comes in the formulation and evaluation of a plug-and-play preprocessing pipeline for robustification of already-existing or pre-trained CV classifiers, easily deployable in a real-world situation and saving on the cost of re-training the classifier

The paper is structured as follows: In Section 2, the related works are introduced and the most important categories of defences are described. Section 3 lists the setup of the used reidentification pipeline, showcases the effects of the adversarial attacks and introduces the specific defences, including the block-matching convolutional neural network (BMCNN) for image denoising, which, to the best of our knowledge, has never before been used to counter adversarial attacks. Section 4 contains the experimental setups and the results obtained by specific pipelines. Section 5 encompasses the conclusions along with the impact the defensive pipeline has over a clean dataset.

#### **2. Related Works**

The advent of adversarial perturbations revealed the vulnerabilities of contemporary AI-based technologies. There is a considerable body of research into both the attacks and the defences. However, as noted by [32], the construction of a theoretical model of crafting adversarial perturbations is problematic, as it is a sophisticated optimisation procedure for most machine learning models. This absence of a theoretical baseline makes it troublesome to verify whether administering a certain defence can proof a system against a certain set of attacks. This situation finds its expression in the fact that whenever a new defence is proposed, a new attack capable of breaking through that defence appears [33–38].

Against this canvas, the authors of [38] propose a set of guidelines for research into the defensive mechanisms against adversarial attacks, listing common pitfalls and a range of best practices. There is a substantial body of work gathering both the available attacks and possible defences geared towards machine and deep learning [27,29,32,39–42] and even specifically deep learning in computer vision [23,43,44].

A thorough analysis of the sources allows one to roughly divide the adversarial defences into these categories:

According to [29], the category of gradient masking encompasses defences which fit either intentionally or unintentionally. This category of defences relies on making the gradient unfit for the operation of the attack algorithms. Some defences do not aim at gradient masking specifically, but achieve it as a by-product of defensive procedures. One of the most popular approaches, adversarial training, frequently has a gradient masking effect, even though it is not the goal of the process.

Adversarial (re)training is considered as the brute-force approach [32]. The procedure relies on crafting adversarial samples and including them in the training set. The problem with retraining the whole classifier is the computational cost of such course of conduct. This problem will be touched upon later in this paper.

The defences in the input reconstruction category perform various forms of input pre-processing. Although it might be possible to circumvent those methods in a scenario where the attacker has full knowledge of the system, in a real-world setting the defences from this category can be very effective, and computationally much cheaper in use than retraining. The detection approaches are effective as long as the adversary is not aware of the existence of the detector. For an attacker of sufficient capability it is possible to build an adversarial sample which, at the same time, circumvents the detector and fools the classifier, as proven by [33].

#### **3. Materials and Methods**

*3.1. Classifier Setup*

In this work, the VGG-face network was used [45] with the pre-trained 'resnet-50' [46] architecture. VGGFace is trained on a dataset containing 2.6 million face images of over 2.6k people. The resnet50 network is a CNN assembled of 50 layers. The detailed hyperparameter setup of the entire network can be found in [45]. The final layer of the pre-trained network is AveragePooling2D with the shape of (None, 1, 1, 2048). To perform transfer learning, a dense layer of 2048 neurons is added to the the pre-trained network, followed by a dropout layer, and wrapped up by the softmax layer set with the number of neurons equal to the number of classes. The added dense layer uses the rectified linear unit (ReLU) activation function. The weights between the AveragePooling layer and the dense layer along with the weights between the dense layer and the output layer constitute the part of the network that is trained on the CelebA dataset, with the weights of the remainder of the network frozen. The batch size used for training was set to 1, while early stopping was used to find the optimal number of epochs, which capped at 32. Multiple different hyperparameters setup were tested, and learning rate scheduling was also tested. For the reduced dataset used in the experiments the default learning rate of straight 0.01 proved optimal.

The trainable part of the model contains 15,064,248 parameters when it is prepared to recognise all the 5304 identities found in the CelebA dataset. To allow fast prototyping, a toy model was built on fourteen most populated classes in the CelebA dataset. The most populated classes were chosen to avoid having to deal with the data imbalance problem, allowing the research to focus on adversarial defences. Changing just the number of classes allowed to reduce the number of trainable parameters to just over 4 million; a reduction

of over 70%. The prior probability of the occurrence of each of the classes is displayed in Table 1

**Table 1.** The priors of the classes

Multi-task cascaded convolutional neural networks (MTCNN) is a technique capable of spotting faces and extracting them for later processing by other networks. A state-of-theart face recognition processing pipeline consists of MTCNN for face detection and landmark placement, and a CNN used for placing the extracted face in adequate categories [47–49]. In this work, MTCNN is used for preprocessing the CelebA images for both training and testing. The CelebA subset selected for the formulation of the model was further split into the training set and the testing set. The classifier performance on the test set containing the 14 most populated classes is presented in Table 2.

For better evaluation of the effects of adversarial perturbations and adversarial defences, the misclassified samples were removed from the set, manually pushing the performance to 100% accuracy. That way, any adversarial perturbations are registered as drops in performance, avoiding a situation where an attack pushes the misclassified sample to the correct class. Furthermore, the way the defences affect the classifier performance is more clearly readable.

#### *3.2. Adversarial Attacks*

The testing set was then subjected to the procedure of creating the adversarial samples. To produce the adversarial attacks, the projected gradient descent (PGD) method was used, considering PGD as the universal first-order adversary, following [50]. The maximum number of iterations was set to 100, the epsilon step to 0.1. The value of epsilon determines the maximum size of perturbation allowed for the attack. Along with the number of

iterations, multiple values of epsilon were tested to simulate different strengths of attack. The effect different strengths of the attacks have on the image can be seen in Figure 1. The pictures are reformatted to fit the vgg-face input shape. The effects of PGD eps = 4 on the performance of the classifier can be seen in Table 3.

 The effects of PGD eps=4 on the performance of the classifier.

#### *3.3. Defences*

There have been a number of defences proposed by the research community [51]. The task is to design robust AI tools that are resilient to adversarial attacks. Some methods rely on retraining the entire classifier using attacks generated with the known attack methods [52]. This method, called adversarial training, not only impacts the effectiveness of the classifier, but also requires an immense computational effort. The proposition contained in this section utilises the idea of using pre-processing methods to robustify existing AI-based classifiers, so as the users do not need to re-train their models. The proposed methods are accompanied by an assessment of how the defensive measures affect the classifier performance, which helps optimise the resiliency of AI against the loss of performance some defences introduce.

#### 3.3.1. JPEG Compression

The Joint Photographic Experts Group (JPEG) compression used as adversarial defence relies on the fact that JPEG-compressed images are very prevalent in contemporary usage. Following the authors of [53], who noted that JPEG compression often has the ability to reverse the effects of small adversarial perturbations, the technique is evaluated here for the use as a purely pre-processing defence against adversarial attacks. The compression has the effect of removing additive artefacts in square blocks of an image, effectively working as a filter removing adversarial perturbations [54]. The effect of different magnitudes of

compression (20, 40, 80) can be seen in Figure 2. The results of the classifier using JPEG compression with quality set to 20 on PGD attacks with epsilon = 4 can be found in Table 4

**Figure 2.** JPEG compression.

**Table 4.** The results of the classifier using JPEG compression with quality set to 20 on PGD attacks with epsilon = 4.

3.3.2. Gaussian Data Augmentation

Gaussian data augmentation [55] is a process of adding Gaussian noise to a sample. This method is proven not to produce adversarial samples and can reverse the effects of known adversarial attacks. Image samples with different sigma settings can be seen in Figure 3. The value of sigma expresses the variance.

**Figure 3.** Gaussian augmentation—sigma 255.0/5, 255.0/17, 255.0/3.

#### 3.3.3. Local Spatial Smoothing

Following the research of [56], spatial smoothing can be used to reduce the effects of added adversarial noise. The algorithm uses local blurring filters to remove the effects of adversarial noise. The approach is one of the feature squeezing methods and can be effectively applied as a pre-processor-based defence. The image before and after spatial smoothing can be seen in Figure 4.

**Figure 4.** The image before and after spatial smoothing.

3.3.4. Total Variance Minimisation

Total variance minimisation is a model-agnostic preprocessor approach. In the original paper [57], the defence is used for retraining the model and then the inputs are also preprocessed at test time. The method reassembles the image by rebuilding a randomly chosen set of pixels with the plainest depiction of these pixels. The image before and after total variance minimisation can be seen in Figure 5.

**Figure 5.** The image before and after total variance minimisation.

3.3.5. Block-Matching Convolutional Neural Network (BMCNN) for Image Denoising as an Adversarial Defence

Following the work in image denoising presented in [58], and extending the idea of applying autoencoders as adversarial defences [59], the BMCNN is proposed for the a method of robustifying the image recognition system against adversarial attacks. BMCNN is an attempt to merge two leading approaches to image denoising: non-local self-similarity prior based methods [60] and feed-forward denoising with the use of convolutional neural networks [61]. The method is applied as a pre-processor to remove adversarial noise before the sample is fed to the classifier. The results of the BMCNN with sigma set to 20 used on adversarial samples created with PGD with epsilon set to four can be seen in Table 5. The value of sigma has been chosen experimentally.

**Table 5.** The results of the classifier using BMCNN with sigma set to 20 used on adversarial samples created with PGD with epsilon set to four.

#### **4. Results**

The low computational cost of the preprocessors in comparison with re-training the classifier allows to mix and match the defences. The experiments show that some pipelines are more effective than others. An example of a defensive pipeline which utilises all the researched defences is displayed in Figure 6.

**Figure 6.** A defensive pipeline which utilises all the researched defences.

The pipeline makes intuitive sense, as blurring the image should remove some of the artefacts added by PGD, same for JPEG compression, then adding Gaussian noise and removing it with BMCNN denoising has the potential of removing both the Gaussian and the adversarial noise at the same time. The results of this particular pipeline are shown in Table 6.

**Table 6.** The results of the classifier using spatial smoothing with JPEG compression, Gaussian augmentation, total variance minimisation and BMCNN with sigma set to 20 on PGD images with epsilon set to four.

As showcased by the results of the experiment in Table 6, the mix of defences improved the detection metrics as compared to the undefended model; however it did not perform as well as, for example, BMCNN denoising alone (Table 5). For the next experiment, the total variance minimisation preprocessor was removed, as it has a similar filtering effect as localised spatial smoothing. The pipeline is shown in Figure 7. The results of the experiment are contained in Table 7.

**Figure 7.** A defensive pipeline which utilises all the researched defences, except total variance minimisation.

**Table 7.** The results of the classifier using spatial smoothing with JPEG compression, Gaussian augmentation, and BMCNN with sigma set to 20 on PGD images with epsilon set to four, without total variance minimisation.

To find the optimal mix of preprocessors that would minimise or eliminate the effect of adversarial perturbations without significantly deteriorating the classifier results, a range of experiments was performed. The results of some of those tests are contained in Tables 8 and 9. To assess the results of the preprocessing defences, the best performing preprocessing pipeline was tested on a clean, unperturbed set. The results of this experiment can be found in Table 10. The best performing pipeline is illustrated in Figure 8.

**Table 8.** The results of the classifier using spatial smoothing with JPEG compression on PGD images with epsilon set to four.

**Figure 8.** A defensive pipeline with JPEG compression, Gaussian augmentation, and BMCNN.

**Table 9.** The results of the classifier using JPEG compression, Gaussian augmentation, and BMCNN on PGD images with epsilon set to four.

**Table 10.** Results of classification with preprocessing defences on a clean dataset.

#### **5. Conclusions**

The classifier performance indicates that using preprocessing defences causes a drop in the measured metrics; at the same time, the achieved robustness is considerable. The results of the experiments prove that input transformations are an effective weapon against adversarial attacks, though the robustness comes at a cost. The utility of the proposed preprocessing pipeline solution comes in the fact that it can be used as a plug-and-play quick-fix, granting a measure of robustness against adversarial attacks without having to incur the costs of re-training the classifier. This answers RQ1 affirmatively, using preprocessing defensive methods is feasible for robustification of ANN-based classifiers against adversarial evasion attacks in computer vision tasks. The results of the experiments also point out that passing the images through a series of filters can have adverse effects on the accuracy of protected classifier. Joining all the researched preprocessing methods in one pipeline did alleviate some of the effects of the adversarial attacks. However, the accumulative distortion introduced by those methods hindered the effectiveness of the classifier to a considerable extent. Extensive experimentation made it possible to answer RQ2—some combinations are more effective than others and more effective than using all the preprocessors together.

Additionally, data augmentation is a booming area of research [62], and mixing preprocessing adversarial defences with novel approaches to data augmentation could potentially offset the performance loss of the researched defensive techniques, an approach which is part of future research.

**Author Contributions:** The authors contributed equally. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded under the SPARTA project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 830892.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The dataset used in this study is the Large-Scale CebelFaces Attributes (CelebA) Dataset, available here: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html (accessed on 30 September 2021).

**Acknowledgments:** This work is funded under the SPARTA project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 830892.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**


APN as a Regulator of Salt Sensitivity

Renal APN regulates mechanisms that facilitate renal sodium excretion after increased saline intake, producing a coordinated decrease in Na-K-ATPase abundance on the basal side of the tubule (by endocytosis) and a reduction in sodium transporters (by internalization) [66–68].

APN abundance is higher in Dahl salt-resistant rats. In these animals, APN may reduce basolateral Na+-K+-ATPase as a protective mechanism in response to the increased saline intake [64]. Reduced tissue and plasma APN levels have also been reported in the L-NAME model but not in the controls [68]. In conclusion, renal tubule levels of APN can regulate sodium excretion and, therefore, salt sensitivity and BP.


*2.5. Statistical Analysis*

Statistical analyses were performed with SPSS v.19 (SPSS Inc., Chicago, IL, USA). Descriptive data for continuous variables are presented as mean + SD and as frequencies or percentages for categorical variables. The Chi-square test or Fisher exact test were used to compare frequencies, whereas differences in continuous variables were evaluated with either the Student *t* test or Mann–Whitney *U* test. *p* < 0.05 was considered to be significant.


*2.7.3 Postoperative*

Following surgery, the patient may get benefit from oral analgesics to manage acute pain. Opioid analgesics may be considered for a short course in appropriate patients. A great concern about the use of opioids in any patient is the potential for OUD. Risk stratification tools exist that can help to determine which patients may be at elevated risk for opioid misuse and abuse [50] (see **Table 1**). Opioid overdose may result in potentially life-threatening respiratory depression; naloxone is a rapid-acting rescue drug. Patients taking opioids following CIED implantation may benefit from a prescription for naloxone and the family or caregivers should be trained in how to administer it in an emergency.

In a single-center retrospective study from Croatia (*n* = 372), 31% of patients being implanted with an ICD received pain medication following surgery; the highest intensity pain recorded in this study was 8 on a 0 to 10 scale [4]. The most frequently prescribed medications in this study were fixed-dose combination oral tramadol and acetaminophen 37.5/325 mg (29%), diazepam 5 mg (17%), tramadol 5 mg monotherapy (16%), and acetaminophen monotherapy

#### **Table 1.**

*While there is no consensus as to the best opioid-screening tool, a variety of validated instruments exist [51–53]. In place of an assessment tool, a clinical interview with the patient may be conducted to assess past drug experiences, familial history of substance use disorders, and attitudes about pain control. Note that these tools are often used in the setting of long-term opioid therapy, rather than short-term postoperative use.*

500 mg (12%) [4]. It should be noted that in this study 69% of patients received no postoperative analgesic medications at all [4]. This strongly suggests that many CIED patients have poorly controlled pain after surgery. Of course, postoperative pain control may be inadequate for many types of surgery. In USA survey of surgical inpatients, who had a variety of procedures, about 80% reported they suffered pain following surgery with 86% of them ranking this pain as "moderate" to "severe" [42]. Perhaps most important is that pain was reported to occur more frequently after discharge than before [42]. Patients may not always know what to expect and some may accept moderate to severe postoperative pain following surgery, not knowing that postoperative pain can often be safely and effectively managed.

An important analgesic strategy involves a combination of multimodal analgesia. Multimodal analgesia is based on the use of two or more analgesics with different mechanisms of action to offer a synergistic benefit to patients. Some fixed-dose combination products offer oral acetaminophen plus, a small amount of opioid, such as oxycodone, in a single oral dose. Adjuvant agents may also be helpful such as gabapentin or pregabalin to help with a neuropathic component to postsurgical pain.

A challenge in pain management following implant is the fact that most device patients do not spend prolonged periods of time in the hospital. Most CIED patients are discharged home shortly after surgery, whether they are outpatients or spend the night in the hospital. Thus, most device patients must manage the longest duration of their postsurgical pain at home. For this reason, patients and their families or caregivers must be educated about the pain medications they are to take, the appropriate doses and timing, and the risks as well as the benefits of these medications. Following transvenous device implant, patients should be educated about arm movements to prevent capsulitis ("frozen shoulder") [54].


**1. Introduction**

Societies are gradually becoming a world of digitally rich environments, which includes classroom practice, home equipment and private pocket devices. Digital technologies are deeply transforming what it means to be literate, and digital competence is considered a vital aspect of education that organizations should systematically improve [1]. Educational technologies have brought about many changes in the teaching and learning environment in our schools. While application of appropriate technological processes and the use of ICT facilitates learning, there is an ongoing debate about the usefulness of technology. This chapter attempts to illuminate some of the tensions in this debate.

The global situation within higher education changed drastically in 2020. A public survey in Europe on the impact of the Covid-19 crisis found these results [2]:

The respondents also said that online learning resources and content need to be more relevant, interactive and easy to use. Preparing teacher students to use ICT is an enduring focus in teacher education, an issue that has been accelerated by the school closures during the Covid-19 crisis and the transfer to remote teaching. The new European Digital Education Action Plan (2021–2027) [2] outlines the European Commission's vision for high-quality, inclusive and accessible digital education in Europe. It is a call for action towards stronger cooperation at the European level to learn from the Covid-19 pandemic, during which technology is being used on an unprecedented scale in education and training, and make education and training systems fit for the digital age. The new action plan has two strategic priorities: (a) fostering the development of a high-performing digital education ecosystem and (b) enhancing digital skills and competences for the digital transformation.

In this situation, it is of great importance to understand academics' perceptions and professional usage of digital technologies in higher education. The digital revolution may be technologically inevitable; however, research shows that access to digital tools are less important for students' learning than how teachers use them across subjects [3, 4]. Krumsvik [5] even argued that one are still in the infancy of understanding how digital technology might contribute to the field of education, and Elstad [6] asserted that educational technology so far has raised several false expectations. Moving to the next phase of implementing education technology (EdTec), the question becomes: How can we improve on what we have learned?

Research has shown that the integration of ICT into academic pedagogic practice is a complex process [7, 8]. Teaching and learning are in themselves complex processes, and ICT integration should not only focus on academics' knowledge of technology, pedagogy and curriculum, but also consider academics' attitudes. Against the multitude of important issues to be considered within EdTec, the research identifies academics' *attitudes* as the most important element [9, 10]. Hence, it is not merely the nature of the technology itself, or the access to technology, that promotes or prevents good use of technology, but academics' beliefs and attitudes are essential and at the heart of the process.

The purpose of this study is to explore how teacher educators make the pedagogical shift in their use of ICT. Teacher education is of special interest because it plays a double role concerning technology. It is a learning organization, and at the same time, the object of study and research is learning itself. Teacher educators are, to a large extent, role models reflecting the practice of EdTec, using technology by design, collaboration with peers, scaffolding authentic experiences and continuous feedback [11, 12]. A teacher educator who uses digital tools for the enhancement of learning also prepares pre-service teachers for how digital tools can be used in their future work [13, 14]. Creating good-quality teacher education in digital arenas embraces the needs of children, schools, technology, and the curriculum.

This chapter describes the situation from countries with quite different educational cultures and presents some recommendations that may contribute to an interactive development of integrating digital technologies within a learning organization.

#### **2. Conceptual framework**

The studies in this chapter are based on the 'theory of action' by Argyris and Schön [15, 16], which is widely used in organizational theory to describe the relationship of people in learning organizations [17]. The descriptive framework serves as a methodological instrument for the systematic analysis of learning organizations at the meso level (between macro and micro level). This approach begins

**5**

digital tools in teaching.

*Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe*

espoused theory and the theory in use may be challenging.

Briefly, the three terms can be described like this:

by defining a concept of humans as action makers, and the theory explains the mechanisms by which we connect our thoughts to our actions. Human beings can take action for a stimulus if they have the attitude and the competencies they need. The theoretical framework offers an analytical distinction between espoused theory (attitudes towards digital technology), and theory in use (digital competence). Espoused theory is the theory of action, which is used to explain or justify a particular pattern of activity. In other words, espoused theory can be understood as the attitude of an individual or an organization towards practices. The theory in used is defined as the theory of action implicated in carrying out this pattern of activity, in other words, the practical action of competence. As described by Argyris and Schön [16], the applied organizational theory can be tacit rather than explicit. The tacit theories in use do not necessarily match the organization's espoused theory. The formal documents of an organization, such as such as policy statements or job descriptions, often contain espoused theories of action that are not compatible with the actual pattern of activity of the organization [16]. The mechanisms can occur both consciously and subconsciously; determining the discrepancy between the

The study assesses the following three different constructs: the teacher educators' level of professional digital competence (PDC), attitudes towards digital technology in education and the application of digital technology in educational contexts. In the appendix of this chapter, details of the questionnaire are presented.

**Professional digital competence (PDC).** The concept of PDC is a central element in discourses about teachers' proficiency in using ICT [18–20]. PDC refers to aspects of teachers' work related to the teaching profession that extend beyond subject knowledge. PDC is not limited to classroom teaching, for example school-home communication, online feedback and assessment, classroom management in technologyrich classrooms and how a teacher approaches her/his own continuous professional development in the use of ICT. Subject-related digital competence deals with the particulars of every subject and how each can be taught with and through technology. This may include the use of modeling and simulations. The term is a moving target in the sense that it evolves rapidly in line with the emergence of new technologies.

**Espoused theory.** Items were prepared based on the OECD report 'Connected Minds: Technology and Today's Learners' [21] and its description of the field's existing attitudes towards technology to understand teacher educators' professional attitudes (their espoused theories). In this chapter, the field is characterized by a continuum from being technology averse to being technology positive. Statements were created to determine respondents' own motivations for using digital tools, their attitudes towards the position of digital tools in the public domain and their attitudes towards the use of digital tools in the classroom. Were prepared to identify the respondents' own motivations for using digital tools, their attitudes towards digital tools' position in the public arena and their attitudes towards the use of

**Professional application of tools.** This shows the magnitude to which the participants used digital tools and work procedures in their teaching for the past year (i.e., the *digital performance* within the organization). The construct consisted of 16 single items on digital tools/work methods applied in teaching during the past year.

A central and comprehensive topic in Argyris and Schön's learning theory is the connection between learning, change and resistance to change. It defines two models, namely single-loop learning processes (SLL, often called Model I) and

**2.1 Single-loop learning (SLL) and double-loop learning (DLL)**

*DOI: http://dx.doi.org/10.5772/intechopen.96206*

#### *Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe DOI: http://dx.doi.org/10.5772/intechopen.96206*

by defining a concept of humans as action makers, and the theory explains the mechanisms by which we connect our thoughts to our actions. Human beings can take action for a stimulus if they have the attitude and the competencies they need. The theoretical framework offers an analytical distinction between espoused theory (attitudes towards digital technology), and theory in use (digital competence). Espoused theory is the theory of action, which is used to explain or justify a particular pattern of activity. In other words, espoused theory can be understood as the attitude of an individual or an organization towards practices. The theory in used is defined as the theory of action implicated in carrying out this pattern of activity, in other words, the practical action of competence. As described by Argyris and Schön [16], the applied organizational theory can be tacit rather than explicit. The tacit theories in use do not necessarily match the organization's espoused theory. The formal documents of an organization, such as such as policy statements or job descriptions, often contain espoused theories of action that are not compatible with the actual pattern of activity of the organization [16]. The mechanisms can occur both consciously and subconsciously; determining the discrepancy between the espoused theory and the theory in use may be challenging.

The study assesses the following three different constructs: the teacher educators' level of professional digital competence (PDC), attitudes towards digital technology in education and the application of digital technology in educational contexts. In the appendix of this chapter, details of the questionnaire are presented. Briefly, the three terms can be described like this:

**Professional digital competence (PDC).** The concept of PDC is a central element in discourses about teachers' proficiency in using ICT [18–20]. PDC refers to aspects of teachers' work related to the teaching profession that extend beyond subject knowledge. PDC is not limited to classroom teaching, for example school-home communication, online feedback and assessment, classroom management in technologyrich classrooms and how a teacher approaches her/his own continuous professional development in the use of ICT. Subject-related digital competence deals with the particulars of every subject and how each can be taught with and through technology. This may include the use of modeling and simulations. The term is a moving target in the sense that it evolves rapidly in line with the emergence of new technologies.

**Espoused theory.** Items were prepared based on the OECD report 'Connected Minds: Technology and Today's Learners' [21] and its description of the field's existing attitudes towards technology to understand teacher educators' professional attitudes (their espoused theories). In this chapter, the field is characterized by a continuum from being technology averse to being technology positive. Statements were created to determine respondents' own motivations for using digital tools, their attitudes towards the position of digital tools in the public domain and their attitudes towards the use of digital tools in the classroom. Were prepared to identify the respondents' own motivations for using digital tools, their attitudes towards digital tools' position in the public arena and their attitudes towards the use of digital tools in teaching.

**Professional application of tools.** This shows the magnitude to which the participants used digital tools and work procedures in their teaching for the past year (i.e., the *digital performance* within the organization). The construct consisted of 16 single items on digital tools/work methods applied in teaching during the past year.

#### **2.1 Single-loop learning (SLL) and double-loop learning (DLL)**

A central and comprehensive topic in Argyris and Schön's learning theory is the connection between learning, change and resistance to change. It defines two models, namely single-loop learning processes (SLL, often called Model I) and

*Teacher Education in the 21st Century - Emerging Skills for a Changing World*

The respondents also said that online learning resources and content need to be more relevant, interactive and easy to use. Preparing teacher students to use ICT is an enduring focus in teacher education, an issue that has been accelerated by the school closures during the Covid-19 crisis and the transfer to remote teaching. The new European Digital Education Action Plan (2021–2027) [2] outlines the European Commission's vision for high-quality, inclusive and accessible digital education in Europe. It is a call for action towards stronger cooperation at the European level to learn from the Covid-19 pandemic, during which technology is being used on an unprecedented scale in education and training, and make education and training systems fit for the digital age. The new action plan has two strategic priorities: (a) fostering the development of a high-performing digital education ecosystem and (b) enhancing digital skills and competences for the digital transformation. In this situation, it is of great importance to understand academics' perceptions

and professional usage of digital technologies in higher education. The digital revolution may be technologically inevitable; however, research shows that access to digital tools are less important for students' learning than how teachers use them across subjects [3, 4]. Krumsvik [5] even argued that one are still in the infancy of understanding how digital technology might contribute to the field of education, and Elstad [6] asserted that educational technology so far has raised several false expectations. Moving to the next phase of implementing education technology (EdTec), the question becomes: How can we improve on what we have learned? Research has shown that the integration of ICT into academic pedagogic practice is a complex process [7, 8]. Teaching and learning are in themselves complex processes, and ICT integration should not only focus on academics' knowledge of technology, pedagogy and curriculum, but also consider academics' attitudes. Against the multitude of important issues to be considered within EdTec, the research identifies academics' *attitudes* as the most important element [9, 10]. Hence, it is not merely the nature of the technology itself, or the access to technology, that promotes or prevents good use of technology, but academics' beliefs and

The purpose of this study is to explore how teacher educators make the pedagogical shift in their use of ICT. Teacher education is of special interest because it plays a double role concerning technology. It is a learning organization, and at the same time, the object of study and research is learning itself. Teacher educators are, to a large extent, role models reflecting the practice of EdTec, using technology by design, collaboration with peers, scaffolding authentic experiences and continuous feedback [11, 12]. A teacher educator who uses digital tools for the enhancement of learning also prepares pre-service teachers for how digital tools can be used in their future work [13, 14]. Creating good-quality teacher education in digital arenas

embraces the needs of children, schools, technology, and the curriculum.

This chapter describes the situation from countries with quite different educational cultures and presents some recommendations that may contribute to an interactive development of integrating digital technologies within a learning

The studies in this chapter are based on the 'theory of action' by Argyris and Schön [15, 16], which is widely used in organizational theory to describe the relationship of people in learning organizations [17]. The descriptive framework serves as a methodological instrument for the systematic analysis of learning organizations at the meso level (between macro and micro level). This approach begins

attitudes are essential and at the heart of the process.

**4**

organization.

**2. Conceptual framework**

**Figure 1.**

*Single-loop learning (SLL) and double-loop learning (DLL) processes. Adapted from [15]. Organizations that only stress SLL operate within a so-called SLL trap.*

double-loop learning processes (DLL or Model II), to highlight organizational learning potential. The models are illustrated in **Figure 1**.

SLL processes involve following the routines and some sort of pre-set plan. This is less risky for the individual and the organization as well as affords greater control. It may also be characterized as a technical way of thinking. SLL seems to be present when aims, values, frameworks and strategies are taken for granted, with only minor updates. The emphasis is on techniques being made more efficient. Any reflection has the same goal. This chapter links SLL with PDC.

DLL processes, by contrast, are more creative and reflexive, as they involve the consideration of notions about what is good. Reflection here is more fundamental. First, the basic assumptions behind ideas or policies are confronted and challenged. Second, hypotheses are publicly tested. Third, the processes are challenging, not self-seeking and have organizational goals. The governing aim includes valid information and internal commitment. DLL involves questioning the role of the framing and learning systems that underlie the actual goals and strategies [15, 16]. Here, DLL is linked with professional attitudes.

The study applies Argyris and Schön's [16] definition of a learning organization to be the 'ability to see things in new ways, gain new understandings, and produce new patterns of behaviours—all on a continuing basis and in a way that engages the organisation as a whole'. Learning within teacher education is a dynamic process, not a prescriptive checklist of best practices [22]. Argyris describes what he calles 'SLL traps' as patterns of values, behaviors and outcomes that 'make it difficult to produce the learning that is required to generate fundamental change' [22]. To be a learning organization means having a culture cantered on DLL processes and staying resilient against the SLL traps that may emerge in the organization [22, 23]. When SLL traps are formed in a DLL organization, there is a growing dissonance. Organizations that focus on innovation learning are more likely to develop a learning culture in SLL and DLL. The motivation in DLL has to be developed in line with the required pedagogy of the organization.

#### **2.2 The study objectives**

This chapter seeks to increase the understanding of how the technological revolution and its impact on education can be understood from a practitioner's point of view. It elaborates further the complexity behind the observed mismatch between policies and the use of digital technology in teacher education. Our study addressed the following three research questions:

**7**

**Table 1.**

*Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe*

sional attitudes and professional use of tools in teaching?

1.How do teacher educators and teacher students perceive their PDC, profes-

2.Is it possible to predict the extent of digital technologies used in teaching

3.What is the relationship of SLL and DLL with digital performance in the orga-

We hypothesized that there is no significant positive relationship between SLL and DLL and organizational learning performance among the respondents. The examination of causal relations is based on Argyris and Schön's theory of action.

The materials in this chapter are based on information from both quantitative and qualitative studies. Previous quantitative studies [24, 25] examined different strategies to implement digital technology in teacher education based on Argyris and Schön's theory of action. In these studies, the researcher applied samples from academic staff in three countries: Norway (N = 67, response rate 83.8%), New Zealand (N = 47, response rate = 73.4%) and Jordan (N = 107, response rate = 31%). The study from Norway was carried out at the University of Tromsø at the Department of Education, the study from New Zealand from the University of Waikato Department of Education and the Jordanian study was carried out at Al-Hussein Bin Talal University in Ma'an, a poor-environment area of Southern Governorate with lectures from the Faculty of Educational Sciences. All participants answered the questionnaire (Appendix 1) based on Argyris and Schön's theory of action. The items were collapsed into three multi-item constructs, as described in Section 2. All three constructs showed acceptable reliability values, with Cronbach's alpha coefficients between 0.71 and 0.88. The construct describing 'use of tools' had to be modified in Jordan, according to the common types of software available for academics in the university's

What is the level of ICT use, PDC and attitudes in teaching and learning among lecturers in the university under study? **Table 1** shows the mean and standard deviation of the multi-item concepts in teaching during the past year. The highest score on PDC was in Norway, while New Zealand scored highest in 'use' (2.99 = occasional use when teaching) and on 'professional attitudes'. The samples from New Zealand and Norway are representative data and may be tested for statistical differences. Both the difference in PCD and in 'use' proves to be statistically

Jordan 3.25 (.90) 3.55 (1.20) — New Zealand 3.37 (.62) 3.71 (.69) 2.99 (.53) Norway 3.00 (.73) 3.91 (.76) 2.59(.54)

*Self-perceived results from Jordan, New Zealand and Norway. The variable describing 'use of tools' had to be*

**PDC Mean (SD)**

**Use Mean (SD)**

significant with p-value = .045 and p-value<0.001, respectively.

**Attitudes Mean (SD)**

*modified in Jordan, and the results cannot be compared directly with the other countries.*

*DOI: http://dx.doi.org/10.5772/intechopen.96206*

nization's learning processes?

**3. Materials and results**

computer centre.

through the degree of PDC and attitudes?

*Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe DOI: http://dx.doi.org/10.5772/intechopen.96206*

We hypothesized that there is no significant positive relationship between SLL and DLL and organizational learning performance among the respondents. The examination of causal relations is based on Argyris and Schön's theory of action.

## **3. Materials and results**

*Teacher Education in the 21st Century - Emerging Skills for a Changing World*

double-loop learning processes (DLL or Model II), to highlight organizational

SLL processes involve following the routines and some sort of pre-set plan. This is less risky for the individual and the organization as well as affords greater control. It may also be characterized as a technical way of thinking. SLL seems to be present when aims, values, frameworks and strategies are taken for granted, with only minor updates. The emphasis is on techniques being made more efficient. Any

*Single-loop learning (SLL) and double-loop learning (DLL) processes. Adapted from [15]. Organizations that*

DLL processes, by contrast, are more creative and reflexive, as they involve the consideration of notions about what is good. Reflection here is more fundamental. First, the basic assumptions behind ideas or policies are confronted and challenged. Second, hypotheses are publicly tested. Third, the processes are challenging, not self-seeking and have organizational goals. The governing aim includes valid information and internal commitment. DLL involves questioning the role of the framing and learning systems that underlie the actual goals and strategies [15, 16].

The study applies Argyris and Schön's [16] definition of a learning organization to be the 'ability to see things in new ways, gain new understandings, and produce new patterns of behaviours—all on a continuing basis and in a way that engages the organisation as a whole'. Learning within teacher education is a dynamic process, not a prescriptive checklist of best practices [22]. Argyris describes what he calles 'SLL traps' as patterns of values, behaviors and outcomes that 'make it difficult to produce the learning that is required to generate fundamental change' [22]. To be a learning organization means having a culture cantered on DLL processes and staying resilient against the SLL traps that may emerge in the organization [22, 23]. When SLL traps are formed in a DLL organization, there is a growing dissonance. Organizations that focus on innovation learning are more likely to develop a learning culture in SLL and DLL. The motivation in DLL has to be developed in line with

This chapter seeks to increase the understanding of how the technological revolution and its impact on education can be understood from a practitioner's point of view. It elaborates further the complexity behind the observed mismatch between policies and the use of digital technology in teacher education. Our study addressed

learning potential. The models are illustrated in **Figure 1**.

*only stress SLL operate within a so-called SLL trap.*

reflection has the same goal. This chapter links SLL with PDC.

Here, DLL is linked with professional attitudes.

the required pedagogy of the organization.

the following three research questions:

**2.2 The study objectives**

**6**

**Figure 1.**

The materials in this chapter are based on information from both quantitative and qualitative studies. Previous quantitative studies [24, 25] examined different strategies to implement digital technology in teacher education based on Argyris and Schön's theory of action. In these studies, the researcher applied samples from academic staff in three countries: Norway (N = 67, response rate 83.8%), New Zealand (N = 47, response rate = 73.4%) and Jordan (N = 107, response rate = 31%). The study from Norway was carried out at the University of Tromsø at the Department of Education, the study from New Zealand from the University of Waikato Department of Education and the Jordanian study was carried out at Al-Hussein Bin Talal University in Ma'an, a poor-environment area of Southern Governorate with lectures from the Faculty of Educational Sciences. All participants answered the questionnaire (Appendix 1) based on Argyris and Schön's theory of action. The items were collapsed into three multi-item constructs, as described in Section 2. All three constructs showed acceptable reliability values, with Cronbach's alpha coefficients between 0.71 and 0.88. The construct describing 'use of tools' had to be modified in Jordan, according to the common types of software available for academics in the university's computer centre.

What is the level of ICT use, PDC and attitudes in teaching and learning among lecturers in the university under study? **Table 1** shows the mean and standard deviation of the multi-item concepts in teaching during the past year. The highest score on PDC was in Norway, while New Zealand scored highest in 'use' (2.99 = occasional use when teaching) and on 'professional attitudes'. The samples from New Zealand and Norway are representative data and may be tested for statistical differences. Both the difference in PCD and in 'use' proves to be statistically significant with p-value = .045 and p-value<0.001, respectively.

#### **Table 1.**

*Self-perceived results from Jordan, New Zealand and Norway. The variable describing 'use of tools' had to be modified in Jordan, and the results cannot be compared directly with the other countries.*

The regression analysis conducted for each country (**Table 2**) reveals interesting differences and showed that the degree of using digital technology tools can be predicted statistically. In Norway, the best predictor was 'professional attitude' (Beta = .282, p-value = .003), while the best predictor in New Zealand was 'PDC' (Beta = .363, p-value = .002). It appears from this analysis that the influence and contribution of digital practice is carried out quite differently in the two countries. In Norway, the professional use or application of digital tools is dominated by professional and autonomous attitudes, while in New Zealand it is dominated by PDC. At the same time, PDC is somewhat lower in New Zealand than in Norway, but the professional application of digital tools is significantly higher.

The results in **Tables 1** and **2** provide answers to our first two research questions. To address the third research, the study had to go one step deeper in a qualitative analysis of interviews with the academic staff in New Zealand and Norway [26, 27], to explore closer how curriculum and motivation are affected by educational policy and strategies.

One difference between Norway and New Zealand is what educational traditions their curricula for schools are based on, as shown in **Table 3**. In 2006, Norway was the first country in Europe with a curriculum based on digital skills [28]. Norwegian schools operated with a set of five basic skills seen as fundamental to all learning at all levels through school: oral skills, reading, writing, numeracy and digital skills [29]. Such skills are often referred to as the 3Rs – reading, writing and arithmetic, and traditionally, they have been considered the foundations of learning. Nowadays, the 3Rs alone are not enough to provide students with the skills needed to function in the 21st century [30]. The New Zealand curriculum had to a greater extent, integrated the notion of the 4Cs as central to 21st century skills: critical thinking, communication, collaboration and creativity [31]. This is just a small excerpt of the two curricula, and they both include the 3Rs and the 4Cs, but they are not equally in focus when comparing the two sets of formal documents.

Both the Norwegian and New Zealand teacher educators expressed a concern regarding the political pressure they are experiencing. The New Zealand teacher educators fear a potential political movement towards a more skill-based curriculum and

*\*\*Significant at the 0.01 level (2-tailed).*

*§ The sample from Jordan has a low response rate and is not reliable for statistical testing.*

#### **Table 2.**

*Linear regression coefficients to predict use of ICT technology in three countries.*

**9**

*Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe*

assess the skill-based perspective as outdated. On the other hand, all of the Norwegian teacher educators expressed critical positions regarding their own skill-based definition of learning, and expressed that a change of definition of what is regarded as fundamental for learning would be a change for the better. When asked to take a stance regarding the skill-based and competency-based perspective on learning, both the Norwegian and New Zealand teacher educators' attitudes towards this difference were surprisingly coherent despite the different affiliations and national curricula. The Norwegian curriculum consisted of three formal documents and covered both the 3Rs and the 4Cs in different sections. It could therefore be legitimately claimed that merely comparing the two excerpts could paint an unjust picture of the differences between the two educational cultures. To correct for this possibility, the Norwegian participants were also asked about their use of the parts expressing the 4Cs. One of the participants expressed having a somewhat vague knowledge of these frameworks, and the remaining teacher educators claimed to 'know of it'. Of all the participants, not one expressed a close and reflective attitude towards the framework, and only one replied that she/he had used the document explicitly in her/his own teaching. The remainder either did not use it at all or explained that the use is implicit or that it merely exists as a backdrop to their teaching. So, when espousing their views, both the Norwegian and the New Zealand teacher educators were generally critical towards a narrower definition of skills, but when the Norwegian educators were asked about their theory in use, there were discrepancies between their espoused theory and their theory in use [26]. This insight made us question what motivated their practices, and ask whether different motivational

Motivational theory is central because although Norwegian schools have had a widespread ongoing policy regarding the use of digital technology, national surveys reveal a gap between the established policy and actual practice in Norwegian education. An often-used formula for work performance is: performance = abilities × motivation [33–35] Norwegian and New Zealand teacher educators' abilities (digital competence) and performance (professional application of digital tools) was measured. Our research indicates Norwegian teacher educators have better abilities to teach using digital technology, but are teaching less using digital technology compared to their New Zealand counterparts. Based on Maier's [35] formula, motivation seems to be a key aspect, and Herzberg's [36] two-factor theory was used to categorize the responses. Herzberg's findings suggest that the factors involved in producing motivation are separate and distinct from those that lead to job dissatisfaction. The opposite of job satisfaction is no job satisfaction, and the opposite of job dissatisfaction is no job dissatisfaction [36]. Two different needs are involved—one stems from the built-in drive to avoid pain, and the other from the ability to achieve and experience psychological growth. The stimulus for growth is the job content, and the stimulus inducing pain avoidance behavior is the job environment [36]. He called the two sets of factors: motivation factors (growth)

When clarifying their motivation for using digital technology, the Norwegian teacher educators explained their digital practices with nine motivation factors and nine hygiene factors spread across ten informants. The hygiene factors were explained as mandatory curricula and work conditions when teaching online. The New Zealand teacher educators explained their use of digital technology with 14 motivation factors and only four hygiene factors. The explained motivation among the New Zealand teacher educators was generally intrinsic, and its presence created job satisfaction. While the Norwegian teacher educators explained their pedagogical practices with equal occurrences of extrinsic factors, that further was perceived

*DOI: http://dx.doi.org/10.5772/intechopen.96206*

factors could explain why such discrepancies occurred.

and hygiene factors (dissatisfaction avoidance).

in a way creating dissatisfaction.

#### **Table 3.**

*The New Zealand key competences [32] and the Norwegian basic skills [29].*

#### *Decoding the Digital Gap in Teacher Education: Three Perspectives across the Globe DOI: http://dx.doi.org/10.5772/intechopen.96206*

assess the skill-based perspective as outdated. On the other hand, all of the Norwegian teacher educators expressed critical positions regarding their own skill-based definition of learning, and expressed that a change of definition of what is regarded as fundamental for learning would be a change for the better. When asked to take a stance regarding the skill-based and competency-based perspective on learning, both the Norwegian and New Zealand teacher educators' attitudes towards this difference were surprisingly coherent despite the different affiliations and national curricula.

The Norwegian curriculum consisted of three formal documents and covered both the 3Rs and the 4Cs in different sections. It could therefore be legitimately claimed that merely comparing the two excerpts could paint an unjust picture of the differences between the two educational cultures. To correct for this possibility, the Norwegian participants were also asked about their use of the parts expressing the 4Cs. One of the participants expressed having a somewhat vague knowledge of these frameworks, and the remaining teacher educators claimed to 'know of it'. Of all the participants, not one expressed a close and reflective attitude towards the framework, and only one replied that she/he had used the document explicitly in her/his own teaching. The remainder either did not use it at all or explained that the use is implicit or that it merely exists as a backdrop to their teaching. So, when espousing their views, both the Norwegian and the New Zealand teacher educators were generally critical towards a narrower definition of skills, but when the Norwegian educators were asked about their theory in use, there were discrepancies between their espoused theory and their theory in use [26]. This insight made us question what motivated their practices, and ask whether different motivational factors could explain why such discrepancies occurred.

Motivational theory is central because although Norwegian schools have had a widespread ongoing policy regarding the use of digital technology, national surveys reveal a gap between the established policy and actual practice in Norwegian education. An often-used formula for work performance is: performance = abilities × motivation [33–35] Norwegian and New Zealand teacher educators' abilities (digital competence) and performance (professional application of digital tools) was measured. Our research indicates Norwegian teacher educators have better abilities to teach using digital technology, but are teaching less using digital technology compared to their New Zealand counterparts. Based on Maier's [35] formula, motivation seems to be a key aspect, and Herzberg's [36] two-factor theory was used to categorize the responses. Herzberg's findings suggest that the factors involved in producing motivation are separate and distinct from those that lead to job dissatisfaction. The opposite of job satisfaction is no job satisfaction, and the opposite of job dissatisfaction is no job dissatisfaction [36]. Two different needs are involved—one stems from the built-in drive to avoid pain, and the other from the ability to achieve and experience psychological growth. The stimulus for growth is the job content, and the stimulus inducing pain avoidance behavior is the job environment [36]. He called the two sets of factors: motivation factors (growth) and hygiene factors (dissatisfaction avoidance).

When clarifying their motivation for using digital technology, the Norwegian teacher educators explained their digital practices with nine motivation factors and nine hygiene factors spread across ten informants. The hygiene factors were explained as mandatory curricula and work conditions when teaching online. The New Zealand teacher educators explained their use of digital technology with 14 motivation factors and only four hygiene factors. The explained motivation among the New Zealand teacher educators was generally intrinsic, and its presence created job satisfaction. While the Norwegian teacher educators explained their pedagogical practices with equal occurrences of extrinsic factors, that further was perceived in a way creating dissatisfaction.

*Teacher Education in the 21st Century - Emerging Skills for a Changing World*

professional application of digital tools is significantly higher.

and strategies.

*\*\*Significant at the 0.01 level (2-tailed).*

The regression analysis conducted for each country (**Table 2**) reveals interesting differences and showed that the degree of using digital technology tools can be predicted statistically. In Norway, the best predictor was 'professional attitude' (Beta = .282, p-value = .003), while the best predictor in New Zealand was 'PDC' (Beta = .363, p-value = .002). It appears from this analysis that the influence and contribution of digital practice is carried out quite differently in the two countries. In Norway, the professional use or application of digital tools is dominated by professional and autonomous attitudes, while in New Zealand it is dominated by PDC. At the same time, PDC is somewhat lower in New Zealand than in Norway, but the

The results in **Tables 1** and **2** provide answers to our first two research questions. To address the third research, the study had to go one step deeper in a qualitative analysis of interviews with the academic staff in New Zealand and Norway [26, 27], to explore closer how curriculum and motivation are affected by educational policy

One difference between Norway and New Zealand is what educational traditions their curricula for schools are based on, as shown in **Table 3**. In 2006, Norway was the first country in Europe with a curriculum based on digital skills [28]. Norwegian schools operated with a set of five basic skills seen as fundamental to all learning at all levels through school: oral skills, reading, writing, numeracy and digital skills [29]. Such skills are often referred to as the 3Rs – reading, writing and arithmetic, and traditionally, they have been considered the foundations of learning. Nowadays, the 3Rs alone are not enough to provide students with the skills needed to function in the 21st century [30]. The New Zealand curriculum had to a greater extent, integrated the notion of the 4Cs as central to 21st century skills: critical thinking, communication, collaboration and creativity [31]. This is just a small excerpt of the two curricula, and they both include the 3Rs and the 4Cs, but they are

not equally in focus when comparing the two sets of formal documents.

*The sample from Jordan has a low response rate and is not reliable for statistical testing.*

**New Zealand key competencies Norwegian basic skills**

*Linear regression coefficients to predict use of ICT technology in three countries.*

Thinking Oral skills Using language, symbols, and texts Reading Managing self Writing Relating to others Digital skills Participating and contributing Numeracy

*The New Zealand key competences [32] and the Norwegian basic skills [29].*

Both the Norwegian and New Zealand teacher educators expressed a concern regarding the political pressure they are experiencing. The New Zealand teacher educators fear a potential political movement towards a more skill-based curriculum and

**Jordan** Use = 0.55§ 0.27§ **New Zealand** Use = 0.15 0.36\*\* **Norway** Use = 0.28\*\* 0.12

**Attitudes PDC**

**8**

**Table 3.**

*§*

**Table 2.**


Preface

The proposed book contains a lot of recent research devoted to computational and numeri‐ cal simulations. It presents both new theories and their applications, showing bridge be‐ tween theoretical investigations and possibility to apply them by engineers of different branches of science.

Chapter 1 by Awrejcewicz et al. describes possibility of application of the Lyapunov expo‐ nents and wavelets in studies of plates and shells. It focuses on characterization of their reg‐ ular and chaotic dynamics, using different methods including Finite Element Method, Bubnov-Galerkin Method, Poincarè maps, power spectra or phase portraits.

In chapter 2, Kartushinsky et al. present effect of the pipe diameter at a constant Reynolds number in the particulate turbulent flow by focusing on the external effect of the flow con‐ figuration instead of internal effect with variation of the parameters of the flow.

In chapter 3, Kartushinsky et al. extend their investigation of Reynolds stress turbulence model (RSTM) by numerical simulations of the channel particulate flow with rough walls and initial level of turbulence. RSTM model was applied and investigated for different cases, for both horizontal and vertical flows, with channel cross-sections of rectangular and square, also for channels with smooth and rough walls and with obstacle in form of the vertical grid of different mesh size.

Validation of the most frequently used plasma cutting torch models is presented by Manci‐ nelli et al. in chapter 4. They describe plasma torch model with physical details, model as‐ sumption, governing equations, and the boundary conditions are verified by confrontation with temperature and velocity values.

In chapter 5, wall pressure fluctuations m oscillations over the hemisphere-cylinder and un‐ steady flow characteristics of over blunt bodies at high speed are analyzed. As an example Mehta used the bulbous payload shroud of a typical satellite launch vehicle and the conical spoke attached forward to the facing blunt body. Determined was also the influence of dif‐ ferent technical parameters on the overall characteristics of the flow.

Ryabicheva and Usatyuk in chapter 6 approach the problem of quality improvement of au‐ tomotive parts applying theoretical analysis in form of computer modeling to the stressstrain state, temperature field and density distribution during radial-direct extrusion of porous powder billets.

In chapter 7, Kopecki presents results of experiments concerning post-critical behavior of thin-walled load-bearing structures. It is focused on number of variants of thin-walled cylin‐ drical shell structures utilized commonly in aircraft design. Changes in post-buckling defor‐ mation of fuselage skin segments in aircraft structures were observed.

Martins-Filho et al. (chapter 8) discuss possibility of using co-simulation approaches, such as Processor–In-The-Loop and Hardware-In-The-Loop, for development of applications for embedded systems and for designing of controllers to be performed by dedicate processors. The chapter is focused on example of a module function for the attitude control to be per‐ formed by an embedded digital processor, but finds also application on design of artificial satellite embedded systems.

currents flowing in the grounding system of a high voltage. It has been validated through

Chapter 19 by Uda et al. is devoted to predictions of development of sand spits and cuspate forelands with rhythmic shapes and their deformation by means of the numerical modeling. Investigated is the influence of the construction of costal structures effect on the shape of

Sporcic and Landekic present in chapter 20 the results of the investigation of efficiency of forest management in Croatia. They constructed nonparametric model for business perform‐

I would like to thank all book contributors for their patience and improvement of their chap‐ ters. In addition, it is my great pleasure to thank Ms. Iva Lipović for her professional sup‐

Department of Automation, Biomechanics and Mechatronics,

Department of Vehicles, Warsaw University of Technology

Lodz University of Technology, Lodz, Poland

**Jan Awrejcewicz**

Preface XI

Warsaw, Poland

numerical simulations and experimental results from literature.

ance evaluation and compared its results with real-life data.

port during the book preparation.

sand spits.

In chapter 9 Hyde et al. deal with approach of modeling creep crack growth using finite element modeling. Obtained theoretical results are compared with corresponding experi‐ mental data. Multiple specimen geometries are tested for validation, in terms of crack growth and final crack length.

Lu in chapter 10 describes summary of recent efforts in research on improvement of sub‐ grid-scale parameterization, that aim at the increase of large-edgy simulation reliability in studies of turbulent flow and of atmospheric boundary layer.

Munteanu and Autran investigate in chapter 11 the possibility of application of 3-D numeri‐ cal simulation for determination of sensitivity to heavy-ion irradiation of junctionless dou‐ ble-gate in metal-oxide-semiconductor field effect transistor (MOSFET). Obtained results are confirmed by already published experimental and simulation data.

In chapter 12, relativistic Vlasov-Maxwell Code is applied by Shoucri and Afeyan for stimu‐ lated Raman scattering. They report results concerning Raman forward scatter in plasmas and their ability to excite kinetic electrostatic electron nonlinear waves .

Wu et al. in chapter 13 describe application of synchronous computations of recurrent multi‐ layer perceptrons for tracking mean field dynamics. By means of numerical studies, it is proved that proposed approach can be successfully applied for translation of mean field equations of solving the graph bisection problem.

Abramov et al. describe in chapter 14 methods for blind estimation if speckle variance in SAR images. They present results of simulations of noise characteristics, then compare them with real-life data. They consider applicability of the blind estimation of noise characteristics method and compare it with other mapping methods.

In chapter 15, Munoz-Luna et al. discuss development of the frequency domain, concentrat‐ ing mostly on algorithm which obtains the first two formants of a vowel segment. Those two formants correspond to mouth opening and tongue position and are crucial for non-native speakers oral training as they provide necessary information for proficient pronunciation.

Surianu presents in chapter 16 experimental and numerical simulations of the validation of the mathematical models of the effects of electromagnetic interferences into overheated power lines with double circuit. This research can become useful tool for the professionals in electric power system and for increasing safety of high voltage maintenance workers.

Chapter 17 by Autran and Munteanu is devoted to the development of a numerical simula‐ tion code that can be applied for computation of the soft-error of floating-gate flash memo‐ ries. Computational modeling and Monte Carlo simulations are validated by comparison with experimental data.

Li et al. present in chapter 18 application of Fourier transform for development of mathe‐ matical model for the lightening response of a grounding system buried in multilayered Earth. This novel method can be applied for high accuracy computation of the lightening currents flowing in the grounding system of a high voltage. It has been validated through numerical simulations and experimental results from literature.

Martins-Filho et al. (chapter 8) discuss possibility of using co-simulation approaches, such as Processor–In-The-Loop and Hardware-In-The-Loop, for development of applications for embedded systems and for designing of controllers to be performed by dedicate processors. The chapter is focused on example of a module function for the attitude control to be per‐ formed by an embedded digital processor, but finds also application on design of artificial

In chapter 9 Hyde et al. deal with approach of modeling creep crack growth using finite element modeling. Obtained theoretical results are compared with corresponding experi‐ mental data. Multiple specimen geometries are tested for validation, in terms of crack

Lu in chapter 10 describes summary of recent efforts in research on improvement of sub‐ grid-scale parameterization, that aim at the increase of large-edgy simulation reliability in

Munteanu and Autran investigate in chapter 11 the possibility of application of 3-D numeri‐ cal simulation for determination of sensitivity to heavy-ion irradiation of junctionless dou‐ ble-gate in metal-oxide-semiconductor field effect transistor (MOSFET). Obtained results are

In chapter 12, relativistic Vlasov-Maxwell Code is applied by Shoucri and Afeyan for stimu‐ lated Raman scattering. They report results concerning Raman forward scatter in plasmas

Wu et al. in chapter 13 describe application of synchronous computations of recurrent multi‐ layer perceptrons for tracking mean field dynamics. By means of numerical studies, it is proved that proposed approach can be successfully applied for translation of mean field

Abramov et al. describe in chapter 14 methods for blind estimation if speckle variance in SAR images. They present results of simulations of noise characteristics, then compare them with real-life data. They consider applicability of the blind estimation of noise characteristics

In chapter 15, Munoz-Luna et al. discuss development of the frequency domain, concentrat‐ ing mostly on algorithm which obtains the first two formants of a vowel segment. Those two formants correspond to mouth opening and tongue position and are crucial for non-native speakers oral training as they provide necessary information for proficient pronunciation. Surianu presents in chapter 16 experimental and numerical simulations of the validation of the mathematical models of the effects of electromagnetic interferences into overheated power lines with double circuit. This research can become useful tool for the professionals in electric power system and for increasing safety of high voltage maintenance workers.

Chapter 17 by Autran and Munteanu is devoted to the development of a numerical simula‐ tion code that can be applied for computation of the soft-error of floating-gate flash memo‐ ries. Computational modeling and Monte Carlo simulations are validated by comparison

Li et al. present in chapter 18 application of Fourier transform for development of mathe‐ matical model for the lightening response of a grounding system buried in multilayered Earth. This novel method can be applied for high accuracy computation of the lightening

studies of turbulent flow and of atmospheric boundary layer.

equations of solving the graph bisection problem.

method and compare it with other mapping methods.

confirmed by already published experimental and simulation data.

and their ability to excite kinetic electrostatic electron nonlinear waves .

satellite embedded systems.

X Preface

growth and final crack length.

with experimental data.

Chapter 19 by Uda et al. is devoted to predictions of development of sand spits and cuspate forelands with rhythmic shapes and their deformation by means of the numerical modeling. Investigated is the influence of the construction of costal structures effect on the shape of sand spits.

Sporcic and Landekic present in chapter 20 the results of the investigation of efficiency of forest management in Croatia. They constructed nonparametric model for business perform‐ ance evaluation and compared its results with real-life data.

I would like to thank all book contributors for their patience and improvement of their chap‐ ters. In addition, it is my great pleasure to thank Ms. Iva Lipović for her professional sup‐ port during the book preparation.

> **Jan Awrejcewicz** Department of Automation, Biomechanics and Mechatronics, Lodz University of Technology, Lodz, Poland Department of Vehicles, Warsaw University of Technology

Warsaw, Poland

**Chapter 1**

**Application of the Lyapunov Exponents and Wavelets to**

We study regular and chaotic vibrations of continuous mechanical systems using the following structural members: plates, flexible shells and cylindrical panels. They are often used in various measurement devices, and numerous ship and planes constructions. Owing to a high devel‐ opment of technology and due to industrial requirements, in many cases the mentioned structural members are subjected to action of high intensity loads being spatially and time dependent. The structural member exhibit either regular or chaotic dynamics, and hence an important question arises: How to predict safe and dangerous regimes of behavior of the

In order to solve the mentioned problems, the investigations are carried out using the ach‐ ievements of the qualitative analysis of differential equations and non-linear dynamics. Namely, we analyze time histories (signals), phase and modal portraits, Poincaré sections, autocorrelation functions, Lyapunov exponents, Fourier and wavelet spectra. Charts of vibration regimes versus the load excitation amplitude and frequency are constructed, which

Usually the data provided by numerical experiments are presented in time domain. In other words, we take time as an independent co-ordinate, and amplitude as a dependent co-ordinate, and the studied signal as analyzed through its amplitude-time representation. However, in order to understand deeply non-linear continuous systems subjected to various types of load actions and in order to fully understand the occurring dynamics, we have to apply the

> © 2014 Awrejcewicz et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Study and Control of Plates and Shells**

Т.V. Yakovleva, N.A. Zagniboroda, М.V. Zhigalov,

J. Awrejcewicz, V.А. Krysko, I.V. Papkova,

A.V. Krysko, V. Dobriyan, E.Yu. Krylovа and

Additional information is available at the end of the chapter

allow to control the vibration character of plates and shells.

S.A. Mitskevich

**1. Introduction**

http://dx.doi.org/10.5772/57452

studied mechanical objects?


P < 0.05 vs 4 months-old SHR;

0

50

100

150

200

Nitrotyrosyne (%)

250

300

350

 Wistar SHR

400

0,0

0,2

0,4

0,6

TBARS (nmol/mg protein)

0,8

1,0

 SHR Wistar

1,2

Normotensive and Spontaneously Hypertensive Rats: Effect of Ageing 145

Fig. 1. TBARS content in nmol/mg protein, expressed in nmol/mg protein in hearts from SHR and Wistar rats at 4, and 19 months-old . \* P < 0.05 in SHR vs Wistar ; # P < 0.05 vs 4

4m 19m

Fig. 2. Nitrotyrosine content, expressed as percentage with respect to 4-month-old Wistar rats in hearts from SHR and Wistar rats at 4 and 19 months-old. \* P < 0.05 in SHR vs Wistar ;

4m 19m

P < 0.05 vs 4-month-old Wistar rats.

#\*


**3. Conclusions**

The world of science has changed, and there is no question about it. The new model is for the data to be captured by instruments or generated by simulations before being processed by software and for the resulting information or knowledge to be stored in computers. The continued improvement of ML methods in chemistry, which compete with standard computational approaches and expertise are continuously developing the modern computational medicinal chemistry. Machine learning potentials are capable of carrying out high-throughput calculations in millisecond time scales with DFT accuracy and help to avoid false positives and false negatives. *De novo* molecular design are giving accurate predictions of lead compounds to target for simulation, effectively narrowing the search space for highthroughput screening applications. The advancement of AI along with its remarkable tools is continuously aims to reduce challenges faced by drug development process along with the overall lifecycle of the product as healthcare sector is facing several complex challenges, such as the increased cost of drugs and therapies, and society needs specific significant changes in this area. Though there are specific challenges remain with regards to the implementation of this technology, it is likely that AI will become an invaluable tool in the pharmaceutical industry in the near future. The vast knowledge of physics needs to be utilized to improve these advance techniques and tools while also not sacrificing speed and accuracy.

*Transformation of Drug Discovery towards Artificial Intelligence: An* in Silico *Approach DOI: http://dx.doi.org/10.5772/intechopen.99018*


*Article* **A Robust Algorithm Based on Phase Congruency for Optical and SAR Image Registration in Suburban Areas**

#### **Lina Wang 1,2, Mingchao Sun 1, Jinghong Liu 1,2,\*, Lihua Cao 1,2 and Guoqing Ma 3**

Received: 3 September 2020; Accepted: 10 October 2020; Published: 13 October 2020

**Abstract:** Automatic registration of optical and synthetic aperture radar (SAR) images is a challenging task due to the influence of SAR speckle noise and nonlinear radiometric differences. This study proposes a robust algorithm based on phase congruency to register optical and SAR images (ROS-PC). It consists of a uniform Harris feature detection method based on multi-moment of the phase congruency map (UMPC-Harris) and a local feature descriptor based on the histogram of phase congruency orientation on multi-scale max amplitude index maps (HOSMI). The UMPC-Harris detects corners and edge points based on a voting strategy, the multi-moment of phase congruency maps, and an overlapping block strategy, which is used to detect stable and uniformly distributed keypoints. Subsequently, HOSMI is derived for a keypoint by utilizing the histogram of phase congruency orientation on multi-scale max amplitude index maps, which effectively increases the discriminability and robustness of the final descriptor. Finally, experimental results obtained using simulated images show that the UMPC-Harris detector has a superior repeatability rate. The image registration results obtained on test images show that the ROS-PC is robust against SAR speckle noise and nonlinear radiometric differences. The ROS-PC can tolerate some rotational and scale changes.

**Keywords:** optical and synthetic aperture radar (SAR); image registration; phase congruency (PC); radiometric difference


**3. Function of CL in Mitochondrial Morphology**

Structural abnormalities of mitochondria, including fragmentation, disruption of mitochondrial membranes and loss of the electron-dense matrix have been observed in animal models of heart failure and in patients suffering from hypertrophic cardiomyopathy [24]. In a dog model of chronic heart failure, the mitochondrial ultrastructure changes from tightly packed to disorganized cristae [25]. Mitochondria possess two highly specialized membranes—the outer and the inner mitochondrial membrane. Invaginations in the inner membrane form the cristae structures, which are areas of intensive membrane curvature. CL plays a considerable role in shaping the morphology of mitochondria and prominently locates in the bended regions of the inner membrane [26]. CL adopts a cone-shaped structure due to the high content of unsaturated fatty acids, which is causative for the membrane bending at sites of high CL concentration. In addition, mitochondrial morphology is also shaped by a constant remodeling through the fission and fusion of individual mitochondria [27]. The opposing processes of fusion (merging of two mitochondria) and fission (segregation of two mitochondria) are shown to form a highly dynamic network of mitochondria in the cell. The continuing remodeling by fission and fusion is essential for normal mitochondrial function, downregulation of mitochondrial fusion promotes apoptosis and cardiomyocyte loss [28]. Mitochondrial fragmentation was elucidated to influence heart failure notably [29]. By its role in regulating mitochondrial fission and fusion, CL actively participates in shaping the mitochondrial network. Fission and fusion are regulated by a set of dynamin-related GTPases, whereby CL serves as a modulator of the activity of the fission protein Drp1 and the fusion protein OPA1 [30,31]. Morphological alterations of mitochondria are evident in human patients with defects in CL biosynthesis and remodeling as well as in animal models (Table 1).

The formation of cristae structures critically depends on the mitochondrial contact site and cristae organizing system (MICOS). These protein complexes locate at the cristae junctions and seal individual cristae to maintain individual membrane potentials [8]. The MICOS complex is a central component of a large interaction network which includes several complexes in the inner membrane and outer membrane [41]. MIC27 (APOOL) as a MICOS complex constituent directly interacts with CL and this interaction was found to be essential for MIC27 assembly in the MICOS complex [42,43] (Figure 2). MIC27 and the structurally related MIC26 also modulate the cardiolipin remodeling enzyme Tafazzin (see below) [44]. In fibroblasts from Barth syndrome patients, a compensatory increase in MICOS subunits was observed [38,45]. Interestingly, a slightly lower molecular mass was detected for MICOS subunits in Barth syndrome (BTHS) fibroblasts, indicative of structural changes due to CL deficiency. Application of the CL-interacting molecule SS31/Elamipretide normalized deregulated structural proteins, such as Drp1, Mfn2, Opa1 and Mic60 in human heart failure patients [46].

**Figure 2.** The role of CL in mitochondrial morphology: The mitochondrial contact site and cristae organizing system (MICOS) complex requires CL for optimal structural integrity. MICOS is a protein complex located in the inner mitochondrial membrane, playing an essential role in cristae junction formation. The resulting membrane invaginations harbor the respiratory chain complexes. By interactions with other proteins in the outer mitochondrial membrane such as the Translocase of the Outer membrane (TOM) and the sorting and Assembly Machinery (SAM), the mitochondrial intermembrane space (IMS) bridging complex (MIB) is formed. The MICOS subunits MIC27 and MIC26 influence the regulation of CL levels, while CL is remodeled by Tafazzin [47]. Not only the integration of MIC27 in MICOS, but also the function of other mitochondrial membrane anchored proteins such as the fission and fusion proteins Drp1 and OPA1 is affected by CL [48]. OM, outer membrane; IMS, intermembrane space; IM, inner membrane.