• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.289-293
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• 2022

"Tolerance" is considered as an important professional quality of a modern specialist, which is manifested in an active moral position and readiness for constructive interaction with other participants in the pedagogical process; characterized by the manifestation of humanity, tolerance, friendliness, focus on resolving conflict situations in the professional sphere on a non-violent basis. The article considers scientific approaches to understanding the phenomenon of "tolerance". There are a number of factors that significantly affect the formation of tolerance in students. The way to the formation of tolerance is the rejection of social prejudices, negative social stereotypes, the development of an objective attitude to man regardless of his individual characteristics, the formation of skills of tolerant interpersonal interaction, the use of lectures, discussions, games and training in educational work. The purpose of this article is to highlight communicative tolerance as a necessary component of pedagogical practice of future professionals. It was emphasized that tolerance is the basis of religious tolerance and peace, prevention of all kinds of extremism, which are of particular importance for a multinational and multi-religious Ukraine.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.119-130
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• 2022

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.201-212
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• 2022

Prior to the emergence of Covid-19, Saudi Arabia (SA) had never faced the challenge of dealing with a global pandemic. Significantly, the current crisis has impacted all industries and sectors in the country, including the healthcare system, and has led to an emphasis on human life being more precious and valuable than economic profit. This study focuses on the impact of Covid-19 on the health of nurses, including their quality of life, during 2020. Understanding the position of the nursing profession during the pandemic, including the most effective methods of preventing work-related stress is important. Information was acquired through an online survey method (i.e. self-completion), known as the Expanded Nursing Stress Scale (ENSS), which was distributed to nurses in all regions of SA. It was found that the main aspects impacting nurses' work-related stress include gender, employment type, training, and dealing with infected patients. In addition, they highlight that such stress plays a substantial role in patient safety and nurses' satisfaction at work, as well as the future survival of organizations. The emergence of Covid-19 as a novel infectious disease has increased nurses' uncertainty and work-related stress. The results of this research will provide insights into the views of both nurses and their managers, in order to identify the main indicators of stress.

• Smart Structures and Systems
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• v.29 no.1
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• pp.221-235
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• 2022

Manual inspection of steel box girders on long span bridges is time-consuming and labor-intensive. The quality of inspection relies on the subjective judgements of the inspectors. This study proposes an automated approach to detect and segment cracks in high-resolution images. An end-to-end cascaded framework is proposed to first detect the existence of cracks using a deep convolutional neural network (CNN) and then segment the crack using a modified U-Net encoder-decoder architecture. A Naïve Bayes data fusion scheme is proposed to reduce the false positives and false negatives effectively. To generate the binary crack mask, first, the original images are divided into 448 × 448 overlapping image patches where these image patches are classified as cracks versus non-cracks using a deep CNN. Next, a modified U-Net is trained from scratch using only the crack patches for segmentation. A customized loss function that consists of binary cross entropy loss and the Dice loss is introduced to enhance the segmentation performance. Additionally, a Naïve Bayes fusion strategy is employed to integrate the crack score maps from different overlapping crack patches and to decide whether a pixel is crack or not. Comprehensive experiments have demonstrated that the proposed approach achieves an 81.71% mean intersection over union (mIoU) score across 5 different training/test splits, which is 7.29% higher than the baseline reference implemented with the original U-Net.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.650-664
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• 2022

Mobile applications can be easily downloaded and installed via markets. However, malware and malicious applications containing unwanted advertisements exist in these application markets. Therefore, smartphone users install applications with reference to the application review to avoid such malicious applications. An application review typically comprises contents for evaluation; however, a false review with a specific purpose can be included. Such false reviews are known as fake reviews, and they can be generated using artificial intelligence (AI)-based text-generating models. Recently, AI-based text-generating models have been developed rapidly and demonstrate high-quality generated texts. Herein, we analyze the features of fake reviews generated from Generative Pre-Training-2 (GPT-2), an AI-based text-generating model and create a model to detect those fake reviews. First, we collect a real human-written application review from Kaggle. Subsequently, we identify features of the fake review using natural language processing and statistical analysis. Next, we generate fake review detection models using five types of machine-learning models trained using identified features. In terms of the performances of the fake review detection models, we achieved average F1-scores of 0.738, 0.723, and 0.730 for the fake review, real review, and overall classifications, respectively.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.300-312
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• 2021

Compressed sensing (CS) has been investigated in magnetic resonance (MR) parametric mapping to reduce scan time. However, the relatively long reconstruction time restricts its widespread applications in the clinic. Recently, deep learning-based methods have shown great potential in accelerating reconstruction time and improving imaging quality in fast MR imaging, although their adaptation to parametric mapping is still in an early stage. In this paper, we proposed a novel deep learning-based framework DEMO for fast and robust MR parametric mapping. Different from current deep learning-based methods, DEMO trains the network in an unsupervised way, which is more practical given that it is difficult to acquire large fully sampled training data of parametric-weighted images. Specifically, a CS-based loss function is used in DEMO to avoid the necessity of using fully sampled k-space data as the label, thus making it an unsupervised learning approach. DEMO reconstructs parametric weighted images and generates a parametric map simultaneously by unrolling an interaction approach in conventional fast MR parametric mapping, which enables multi-tasking learning. Experimental results showed promising performance of the proposed DEMO framework in quantitative MR T1ρ mapping.

• Journal of Hospice and Palliative Care
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• v.25 no.3
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• pp.99-109
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• 2022

Purpose: More than 60% of patients with advanced cancer experience pain, and uncontrolled pain reduces the quality of life. Nurses are the closest healthcare providers to the patient and are suitable for managing cancer pain using pharmacological and non-pharmacological interventions. This study aimed to identify factors affecting the performance of cancer pain management among nurses. Methods: This study was conducted among 155 participating nurses working at a tertiary hospital who had experience with cancer pain management. Data collection was performed between October 18, 2021 and October 25, 2021. Data analysis was conducted using descriptive statistics, the independent-sample t-test, one-way analysis of variance, and hierarchical regression analysis. Results: There were 110 subjects (71.0%) who had no experience of cancer pain management education. The results of regression analysis indicated that barriers included medical staff, patients, and the hospital system for cancer pain management (𝛽=0.28, P<0.001). The performance of cancer pain management was also affected by experience of cancer pain management training (𝛽=0.22, P=0.007), and cancer pain management knowledge (𝛽=0.21, P=0.006). The explanatory power of the variable was 16.6%. Conclusion: It is crucial to assess system-related obstacles, as well as patients and medical staff, in order to improve nurses' cancer pain management performance. A systematic approach incorporating multidisciplinary interventions from interprofessional teams is required for effective pain management. Furthermore, pain management education is required both for cancer ward nurses and nurses in other wards.

• Journal of Advanced Marine Engineering and Technology
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• v.17 no.5
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• pp.18-32
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• 1993

A marine engine system (MES) should be evaluated by not only reliability (R) but also maintainability (M) and manning index (MI), because this system can be recognised as a typical man-machine system. In 1977 at the first ICMES Conference in Paris one of the authors presented a total evaluation of the MES with the three indices, R, M, MI, proving the human ability of detecting faults and defects in this system. This paper describes how the MES has developed from the point of view of the above three indices during the past 30 years in Japan and its problems in the future, and arrives at the following conclusions : the reliability of the MES has developed due to quality control (QC) ; the maintainability of the MES has improved due to education and training ; the manning index of the MES has improved due to Rand M ; the availability of the MES has kept constant due to the decreasing complement onboard, at the rate of one person per year approximately ; and two esimations having the three indices were shown by the SRIC 1990 Data Base in Japan, for the two kinds of subsystems in the developed MES.

• Journal of Information Processing Systems
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• v.19 no.1
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• pp.109-117
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• 2023

Defect detection is one of the key factors in fabric quality control. To improve the speed and accuracy of denim fabric defect detection, this paper proposes a defect detection algorithm based on cascading feature extraction architecture. Firstly, this paper extracts these weight parameters of the pre-trained VGG16 model on the large dataset ImageNet and uses its portability to train the defect detection classifier and the defect recognition classifier respectively. Secondly, retraining and adjusting partial weight parameters of the convolution layer were retrained and adjusted from of these two training models on the high-definition fabric defect dataset. The last step is merging these two models to get the defect detection algorithm based on cascading architecture. Then there are two comparative experiments between this improved defect detection algorithm and other feature extraction methods, such as VGG16, ResNet-50, and Xception. The results of experiments show that the defect detection accuracy of this defect detection algorithm can reach 94.3% and the speed is also increased by 1-3 percentage points.

• ETRI Journal
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• v.44 no.6
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.