Reviewerly/exHarmony huggingface dataset:
Avoiding the Hook: Influential Factors of Phishing Awareness Training on Click-Rates and a Data-Driven Approach to Predict Email Difficulty Perception

Phishing attacks are still seen as a significant threat to cyber security, and large parts of the industry rely on anti-phishing simulations to minimize the risk imposed by such attacks.This study conducted a largescale anti-phishing training with more than 31000 participants and 144 different simulated phishing attacks to develop a data-driven model to classify how users would perceive a phishing simulation.Furthermore, we analyze the results of our large-scale anti-phishing training and give novel insights into users' click behavior.Analyzing our anti-phishing training data, we find out that 66% of users do not fall victim to credential-based phishing attacks even after being exposed to twelve weeks of phishing simulations.To further enhance the phishing awareness-training effectiveness, we developed a novel manifold learningpowered machine learning model that can predict how many people would fall for a phishing simulation using the several structural and state-of-the-art NLP features extracted from the emails.In this way, we present a systematic approach for the training implementers to estimate the average "convincing power" of the emails prior to rolling out.Moreover, we revealed the top-most vital factors in the classification.In addition, our model presents significant benefits over traditional rule-based approaches in classifying the difficulty of phishing simulations.Our results clearly show that anti-phishing training should focus on the training of individual users rather than on large user groups.Additionally, we present a promising generic machine learning model for predicting phishing susceptibility.
Quantum Optimization with Lagrangian Decomposition for Multiple-process Scheduling in Steel Manufacturing

Steel manufacturing involves multiple processes, and optimizing the production schedule is essential for improving the quality, cost, and delivery of products. However, the corresponding optimization problems are usually NP-hard and generally intractable even for modern high-performance computers. Recent advances in quantum computers, such as those developed by D-Wave Systems, have opened new possibilities for handling this type of optimization problem. Nevertheless, a major obstacle to the application of currently available quantum computers is the relatively small number of quantum bits, which limits the feasible problem size. To overcome this obstacle, we propose an algorithm for solving optimization problems based on Lagrangian decomposition and coordination. D-Wave quantum computers can explore diverse solutions simultaneously, and we leverage this unique characteristic to obtain the upper bound for Lagrangian decomposition and coordination. We apply the proposed algorithm to a simplified problem of two-process production scheduling. By decomposing the problem into two stages for the processes, the number of steel products that can be handled increases from five to eight (60% increase). The optimal solutions are reached even for the extended case of the eight products. The proposed algorithm is a promising technique for enabling the application of quantum computers to real-world problems by thoroughly exploiting quantum bits.
SETMIL: Spatial Encoding Transformer-Based Multiple Instance Learning for Pathological Image Analysis

Considering the huge size of the gigapixel whole slide image (WSI), multiple instance learning (MIL) is normally employed to address pathological image analysis tasks, where learning an informative and effective representation of each WSI plays a central role but remains challenging due to the weakly supervised nature of MIL. To this end, we present a novel Spatial Encoding Transformer-based MIL method, SETMIL, which has the following advantages. (1) It is a typical embedded-space MIL method and therefore has the advantage of generating the bag embedding by comprehensively encoding all instances with a fully trainable transformer-based aggregating module. (2) SETMIL leverages spatial-encoding-transformer layers to update the representation of an instance by aggregating both neighbouring instances and globally-correlated instances simultaneously. (3) The joint absolute-relative position encoding design in the aggregating module further improves the context-information-encoding ability of SETMIL. (4) SETMIL designs a transformer-based pyramid multi-scale fusion module to comprehensively encode the information with different granularity using multi-scale receptive fields and make the obtained representation enriched with multi-scale context information. Extensive experiments demonstrated the superior performance of SETMIL in challenging pathological image analysis tasks such as gene mutation and lymph node metastasis prediction.
The challenge for the digital age: making learning a part of life

Purpose The main argument behind this paper is learning in the digital age should not be restricted to creating digital infrastructures for supporting current forms of learning nor taking schools in their current form as God-given, natural entities, but changing current forms of education by developing new frameworks and socio-technical environments for making learning an integral part of life. The authors provide a framework for this argumentation as well as a call-to-action for research on the co-evolution of learning, media, and learning organizations. Design/methodology/approach This paper theoretically and argumentatively explores the core assumption that the digitalization of society results in challenges and opportunities for learning and education based on fundamental transformations (Collins and Halverson, 2009; Fischer et al. , 2020). Findings The digital age greatly enhances the opportunities and supports the necessity for “making learning a part of life”. But while the growth of technology is certain, the inevitability of any particular future is not. The impact of schooling goes beyond that new information about computers, the Internet, and social media are integrated into the schools of today. The transformation of schools needs to be informed by an understanding of the impact of mindset formation that will determine people's approach to learning for the rest of their lives. The authors’ framework is focused on moving “beyond gift-wrapping” by not only fixing and existing systems but to change them and not only reforming but transforming them. Originality/value It is the authors’ hope that this article will be of interest to many stakeholders (including learners, teachers, curriculum designers, technology experts, parents, and politicians) and provide a foundation for an ongoing debate and informed actions for “Making Learning a Part of Life” in the digital age.
TitleGen-FL: Quality prediction-based filter for automated issue title generation

To automatically generate the issue title, researchers formulated the problem into a one-sentence summarization problem and then proposed an effective method iTAPE. However, after analyzing the quality of the titles generated by the method iTAPE, which is measured by ROUGE-L, we find the ROUGE-L scores of only 42.7% of titles can exceed 0.3. This means the quality of the generated titles is not satisfactory and can limit the practicability of the method iTAPE. Therefore, we propose a quality prediction-based filter TitleGen-FL, which can predict whether the method iTAPE can generate a high-quality title after analyzing the issue body. To achieve this goal, TitleGen-FL includes the deep learning-based (DL) module and the information retrieval-based (IR) module. If either of these two modules predict that the high-quality title cannot be generated by iTAPE, TitleGen-FL can automatically filter this issue and return a warning message. To evaluate the effectiveness of our proposed filter, we select the benchmark dataset gathered from real-world open-source projects as our experimental subject. Both automatic evaluation and human study show that our proposed filter TitleGen-FL can effectively filter the issues, which cannot generate high-quality titles by iTAPE.
Recent text-based research and applications in railways: A critical review and future trends

In the railway industry, a significant amount of data is stored in the textual format. The advanced development of natural language processing and text mining techniques enable automatic knowledge extraction and discovery from such documents. This paper presents a systematic review with quantitative and qualitative analyses to understand the current state of text-based research in the context of railway transport. The paper collects 107 relevant publications in the past decade and identifies different channels for researchers to obtain text data in railways and the corresponding text analysis application use-cases. Moreover, a comprehensive analysis is performed on the state-of-the-art machine learning and natural language processing methods. Four key research directions, namely multilingual NLP, digital maintenance, external data integration, and railway-centred solution pipeline, are identified from Siemens Mobility's perspective to highlight the most prominent challenges faced in the railway industry.
A Comprehensive Survey on Cloud Computing

Cloud computing is one of the most spectacular technological achievements of the 21st century, allowing users to access various on-demand services (e.g., storage, servers, networks, apps, and services) from anywhere. All advanced technologies such as internet of things (IoT), smart cities, smart grid, system automation, 5G, and logistics systems require services offered by cloud computing. This paper provides a complete architectural tutorial on cloud computing, focusing on various service and deployment models, architectures, inter-clouds concepts, and multiple merits of cloud computing in artificial intelligence, fog computing, edge computing, and IoT. It also discusses different quality-of-service (QoS) parameters that are helpful for the consumers to decide upon the service quality offered by any cloud provider. A comparative study of the hypervisor and container-based virtualization technologies has been discussed. Finally, simulation tools and various open research issues of cloud computing are presented, appealing for the researchers to decide on future research directions.
4D-OR: Semantic Scene Graphs for OR Domain Modeling

Surgical procedures are conducted in highly complex operating rooms (OR), comprising different actors, devices, and interactions. To date, only medically trained human experts are capable of understanding all the links and interactions in such a demanding environment. This paper aims to bring the community one step closer to automated, holistic and semantic understanding and modeling of OR domain. Towards this goal, for the first time, we propose using semantic scene graphs (SSG) to describe and summarize the surgical scene. The nodes of the scene graphs represent different actors and objects in the room, such as medical staff, patients, and medical equipment, whereas edges are the relationships between them. To validate the possibilities of the proposed representation, we create the first publicly available 4D surgical SSG dataset, 4D-OR, containing ten simulated total knee replacement surgeries recorded with six RGB-D sensors in a realistic OR simulation center. 4D-OR includes 6734 frames and is richly annotated with SSGs, human and object poses, and clinical roles. We propose an end-to-end neural network-based SSG generation pipeline, with a rate of success of 0.75 macro F1, indeed being able to infer semantic reasoning in the OR. We further demonstrate the representation power of our scene graphs by using it for the problem of clinical role prediction, where we achieve 0.85 macro F1. The code and dataset are publicly available at github.com/egeozsoy/4D-OR .
Variational quantum solutions to the advection–diffusion equation for applications in fluid dynamics

Constraints in power consumption and computational power limit the skill of operational numerical weather prediction by classical computing methods. Quantum computing could potentially address both of these challenges. Herein, we present one method to perform fluid dynamics calculations that takes advantage of quantum computing. This hybrid quantum–classical method, which combines several algorithms, scales logarithmically with the dimension of the vector space and quadratically with the number of nonzero terms in the linear combination of unitary operators that specifies the linear operator describing the system of interest. As a demonstration, we apply our method to solve the advection–diffusion equation for a small system using IBM quantum computers. We find that reliable solutions of the equation can be obtained on even the noisy quantum computers available today. This and other methods that exploit quantum computers could replace some of our traditional methods in numerical weather prediction as quantum hardware continues to improve.
Discriminating Earthquakes From Quarry Blasts Using Capsule Neural Network

Discrimination between earthquakes and quarry blasts is crucial for precise seismic analysis, e.g., seismic hazard mitigation, earthquake cataloging, etc. However, the discrimination process is challenging due to the similarity of waveforms between the local earthquakes and quarry blasts. We propose to use the scalogram and the capsule neural network to distinguish between earthquakes and quarry blasts. First, we obtain the scalogram for 60s 3-channel waveforms, where we extract 10s before and 50s after the first arrival time of the seismic event. Secondly, we utilize the capsule neural network to extract the important information from the input scalogram which leads to robust classification performance. The proposed capsule neural network consists of the convolutional layer, primary capsule layer, and digit caps layer. The convolutional layer extracts the important information from the input data, and the primary capsule layer extracts the spatial relationship between different feature maps. Thirdly, we use the dynamic routing process to connect the primary capsule to the digit caps layer. We train and test the proposed capsule network using a small and unbalanced dataset which is recorded by the Egyptian Seismic Network (ENSN) in the Red Sea and the surrounded area in Egypt. Accordingly, the proposed method achieves a test accuracy of 96.08%. The proposed method is compared to the benchmark methods, i.e., convolutional neural network (CNN), AlexNet, VGG, and ResNet networks, and demonstrated to outperform all of the competing methods. Finally, we apply the proposed method to classify real-time seismic events and obtain promising results.
Asymmetric U-shaped network with hybrid attention mechanism for kidney ultrasound images segmentation

Kidney ultrasound (KUS) images segmentation is one of the key steps in computer-aided diagnosis. The perturbation of heterogeneous structure, similar intensity distribution, and kidney morphology pose challenges for the segmentation of KUS images. In this paper, we proposed an asymmetric U-shaped network based on the U-net core architecture to segment KUS images accurately and reliably. Specifically, the architecture mainly consists of a dense residual connection encoder, a multi-step up-sampling decoder with the hybrid attention module, and a side-out deep supervision module. The design of the dense residual connection encoder can capture sufficient kidney feature information to improve the representation ability of the network. The development of the hybrid attention module can further guide the network to pay more attention to the representation of the kidney. In addition, the introduction of the side-out deep supervision model can help the network obtain segmentation results that are closer to the ground-truth masks. Moreover, to reduce network parameters, we proposed a multi-step up-sampling optimization strategy to simplify the design of the network. We compare with several state-of-the-art medical image segmentation methods on the same KUS dataset using seven quantitative metrics. The results of our method on Jaccard, Dice, Accuracy, Recall, Precision, ASSD and AUC are 89.95%, 94.59%, 98.65%, 94.47%, 95.07%, 0.3006 and 0.9703, respectively. Experimental results demonstrate that the proposed method achieves the most competitive segmentation performance on KUS images.
FEA-Swin: Foreground Enhancement Attention Swin Transformer Network for Accurate UAV-Based Dense Object Detection

UAV-based object detection has recently attracted a lot of attention due to its diverse applications. Most of the existing convolution neural network based object detection models can perform well in common object detection cases. However, due to the fact that objects in UAV images are spatially distributed in a very dense manner, these methods have limited performance for UAV-based object detection. In this paper, we propose a novel transformer-based object detection model to improve the accuracy of object detection in UAV images. To detect dense objects competently, an advanced foreground enhancement attention Swin Transformer (FEA-Swin) framework is designed by integrating context information into the original backbone of a Swin Transformer. Moreover, to avoid the loss of information of small objects, an improved weighted bidirectional feature pyramid network (BiFPN) is presented by designing the skip connection operation. The proposed method aggregates feature maps from four stages and keeps abundant information of small objects. Specifically, to balance the detection accuracy and efficiency, we introduce an efficient neck of the BiFPN network by removing a redundant network layer. Experimental results on both public datasets and a self-made dataset demonstrate the performance of our method compared to the state-of-the-art methods in terms of detection accuracy.
A Deep Learning Model Applied to Optical Image Target Detection and Recognition for the Identification of Underwater Biostructures

Objective: We propose a deep-learning-based underwater target detection system that can effectively solve the problem of underwater optical image target detection and recognition. Methods: In this paper, based on the depth of the underwater optical image target detection and recognition and using a learning model, we put forward corresponding solutions using the concept of style migration solutions, such as training samples. A lack of variability and poor generalization of practical applications presents a challenge for underwater object identification. The UW_YOLOv3 lightweight model was proposed to solve the problems of calculating energy consumption and storage resource limitations in underwater application scenarios. The detection and recognition module, based on deep learning, can deal with the degradation process of underwater imaging by embedding an image enhancement module into the detection and recognition module for the joint tuning and transferring of knowledge. Results: The detection accuracy of the UW_YOLOv3 model designed in this paper outperformed the lightweight algorithm YOLOV3-TINY by 7.9% at the same image scale input. Compared with other large algorithms, the detection accuracy was lower, but the detection speed was much higher. Compared with the SSD algorithm, the detection accuracy was only 4.7 lower; the speed was 40.9 FPS higher; and the rate was nearly 16 times higher than Faster R-CNN. When the input scale was 224, although part of the accuracy was lost, the detection speed doubled, reaching 156.9 FPS. Conclusion: Based on our framework, the problem of underwater optical image target detection and recognition can be effectively solved. Relevant studies have not only enriched the theory of target detection and glory, but have also provided optical glasses with a clear vision for appropriate underwater application systems.
Intelligent Multi-Strategy Hybrid Fuzzy K-Nearest Neighbor Using Improved Hybrid Sine Cosine Algorithm

The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully studies the main parameters affecting the performance of the sine and cosine algorithm, integrates the reverse learning algorithm, adds an elite opposition solution and forms the hybrid sine and cosine algorithm (hybrid SCA). Combined with the fuzzy k-nearest neighbor method and the hybrid SCA, this paper numerically simulates two-class datasets and multi-class datasets, obtains a large number of numerical results and analyzes the results. The hybrid SCA FKNN proposed in this paper has achieved good accuracy in classification and prediction results under 10 different types of data sets. Compared with SCA FKNN, LSCA FKNN, BA FKNN, PSO FKNN and SSA FKNN, the prediction accuracy is significantly improved. In the Wilcoxon signed rank test with SCA FKNN and LSCA FKNN, the zero hypothesis (significance level 0.05) is rejected and the two classifiers have a significantly different accuracy.
Multi-scale deformable transformer for multi-contrast knee MRI super-resolution

Magnetic resonance imaging can present the precise anatomic structure in clinical applications. Nevertheless, due to the limited scanning equipment cost, scanning time and so on, high-resolution knee MR images are difficult to obtain. So the super-resolution technique is developed to improve the image quality. Unfortunately, conventional CNN-based methods cannot explicitly learn the long-range dependencies in images and simply integrate the auxiliary contrast without considering the characteristics of medical images. To tackle this issue, our approach aims to adaptively capture and fuse the significant auxiliary information of the multi-contrast images to improve the knee magnetic resonance image quality. We propose a multi-scale deformable transformer network (MSDT) for multi-contrast knee magnetic resonance imaging super-resolution. First, we aggregate multi-scale patch embedding from the multi-contrast knee MR images to effectively preserve the local contextual details and global structure information. Then, the deformable transformer architecture is designed to learn the data-dependent sparse attention of the knee MR image, which can adaptively obtain the high-frequency foreground details according to the image content. The proposed method is evaluated on the fastMRI dataset under 2× and 4× enlargements. Our MSDT achieves higher PSNR of 31.98 and SSIM of 0.713 at 2× upsampling factor and PSNR of 30.38 and SSIM of 0.615 at 4× upsampling factor. Moreover, our method can generate clear tissue structures and fine details. The experimental results show superior performance in comparison to the state-of-the-art super-resolution methods. This indicates that the MSDT can effectively reconstruct the high-quality knee MR images.