Reviewerly/exHarmony huggingface dataset:
Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection

The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more effcient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversampling technique than in the undersampling technique. However, the Random Forest performed better than the other algorithms in the baseline approach. After comparing our results with some existing state-of-the-art works, we achieved an improved performance using real-world datasets.
A Novel Multipurpose Watermarking Scheme Capable of Protecting and Authenticating Images With Tamper Detection and Localisation Abilities

Technologies that fall under the umbrella of Industry 4.0 can be classified into one of its four significant components: cyber-physical systems, the internet of things (IoT), on-demand availability of computer system resources, and cognitive computing. The success of this industrial revolution lies in how well these components can communicate with each other, and work together in finding the most optimised solution for an assigned task. It is achieved by sharing data collected from a network of sensors. This data is communicated via images, videos, and a variety of other signals, attracting unwanted attention of hackers. The protection of such data is therefore pivotal, as is maintaining its integrity. To this end, this paper proposes a novel image watermarking scheme with potential applications in Industry 4.0. The strategy presented is multipurpose; one such purpose is authenticating the transmitted image, another is curtailing the illegal distribution of the image by providing copyright protection. To this end, two new watermarking methods are introduced, one of which is for embedding the robust watermark, and the other is related to the fragile watermark. The robust watermark’s embedding is achieved in the frequency domain, wherein the frequency coefficients are selected using a novel mean-based coefficient selection procedure. Subsequently, the selected coefficients are manipulated in equal proportion to embed the robust watermark. The fragile watermark’s embedding is achieved in the spatial domain, wherein self-generated fragile watermark(s) is embedded by directly altering the pixel bits of the host image. The effective combination of two domains results in a hybrid scheme and attains the vital balance between the watermarking requirements of imperceptibility, security and capacity. Moreover, in the case of tampering, the proposed scheme not only authenticates and provides copyright protection to images but can also detect tampering and localise the tampered regions. An extensive evaluation of the proposed scheme on typical images has proven its superiority over existing state-of-the-art methods.
Failure Detection in Quadcopter UAVs Using K-Means Clustering

We propose an unmanned aerial vehicle (UAV) failure detection system as the first step of a three-step autonomous emergency landing safety framework for UAVs. We showed the effectiveness and feasibility of using vibration data with the k-means clustering algorithm in detecting mid-flight UAV failures for that purpose. Specifically, we measured vibration signals for different faulty propeller cases during several test flights, utilizing a custom-made hardware system. After we made the vibration graphs and extracted the data, we investigated to determine the combination of acceleration and gyroscope parameters that results in the best accuracy of failure detection in quadcopter UAVs. Our investigations show that considering the gyroscope parameter in the vertical direction (gZ) along with the accelerometer parameter in the same direction (aZ) results in the highest accuracy of failure detection for the purpose of emergency landing of faulty UAVs, while ensuring a quick detection and timely engagement of the safety framework. Based on the parameter set (gZ-aZ), we then created scatter plots and confusion matrices, and applied the k-means clustering algorithm to the vibration dataset to classify the data into three health state clusters-normal, faulty, and failure. We confirm the effectiveness of the proposed system with flight experiments, in which we were able to detect faults and failures utilizing the aforementioned clusters in real time.
Optimization of a Simulated Annealing Algorithm for S-Boxes Generating

Cryptographic algorithms are used to ensure confidentiality, integrity and authenticity of data in information systems. One of the important areas of modern cryptography is that of symmetric key ciphers. They convert the input plaintext into ciphertext, representing it as a random sequence of characters. S-boxes are designed to complicate the input–output relationship of the cipher. In other words, S-boxes introduce nonlinearity into the encryption process, complicating the use of different methods of cryptanalysis (linear, differential, statistical, correlation, etc.). In addition, S-boxes must be random. This property means that nonlinear substitution cannot be represented as simple algebraic constructions. Random S-boxes are designed to protect against algebraic methods of cryptanalysis. Thus, generation of random S-boxes is an important area of research directly related to the design of modern cryptographically strong symmetric ciphers. This problem has been solved in many related works, including some using the simulated annealing (SA) algorithm. Some works managed to generate 8-bit bijective S-boxes with a nonlinearity index of 104. However, this required enormous computational resources. This paper presents the results of our optimization of SA via various parameters. We were able to significantly reduce the computational complexity of substitution generation with SA. In addition, we also significantly increased the probability of generating the target S-boxes with a nonlinearity score of 104.
UAV-Supported Route Planning for UGVs in Semi-Deterministic Agricultural Environments

Automated agricultural operations must be planned and organized to reduce risk and failure potential while optimizing productivity and efficiency. However, the diversity of natural outdoor environments and the varied data types and volumes required to represent an agricultural setting comprise critical challenges for the deployment of fully automated agricultural operations. In this regard, this study develops an integrated system for enabling an unmanned aerial vehicle (UAV) supported route planning system for unmanned ground vehicles (UGVs) in the semi-structured environment of orchards. The research focus is on the underpinning planning system components (i.e., world representation or map generation or perception and path planning). In particular, the system comprises a digital platform that receives as input a geotagged depiction of an orchard, which is obtained by a UAV. The pre-processed data define the agri-field’s tracks that are transformed into a grid-based map capturing accessible areas. The grid map is then used to generate a topological path planning solution. Subsequently, the solution is translated into a sequence of coordinates that define the calculated optimal path for the UGV to traverse. The applicability of the developed system was validated in routing scenarios in a walnuts’ orchard using a UGV. The contribution of the proposed system entails noise reduction techniques for the accurate representation of a semi-deterministic agricultural environment for enabling accuracy in the route planning of utilized automated machinery.
Does innovation moderate the relationship between digital facilitators, digital transformation strategies and overall performance of SMEs of UAE

This study tests how innovation influences the empirical relationship among digital facilitators, digital transformation strategies and overall performance of manufacturing SMEs in UAE. First, the study found the positive relationship between digital facilitators and overall performance of SMEs. Second, there is a positive link between digital transformation strategies and overall performance of SMEs. Third, the innovation significantly moderates the relationship between digital facilitators and overall performance of SMEs, and digital transformation strategies and overall performance of SMEs. Innovations are pre-requisite of digitalisation as digitalised products are continuing to develop through the reconsolidation of digital objects through the process of innovation.
Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.
Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLAHE), is applied to all datasets to enhance the compressed regions and smooth the distribution of the pixels. Additionally, two image augmentation methods, namely rotation and flipping, are used to increase the amount of training data and to prevent overfitting. The proposed model has been evaluated on two publicly available datasets, specifically INbreast and the curated breast imaging subset of digital database for screening mammography (CBIS-DDSM). The proposed model has also been evaluated using a private dataset obtained from Cheng Hsin General Hospital in Taiwan. The experimental results show that the proposed Connected-SegNets model outperforms the state-of-the-art methods in terms of Dice score and IoU score. The proposed Connected-SegNets produces a maximum Dice score of 96.34% on the INbreast dataset, 92.86% on the CBIS-DDSM dataset, and 92.25% on the private dataset. Furthermore, the experimental results show that the proposed model achieves the highest IoU score of 91.21%, 87.34%, and 83.71% on INbreast, CBIS-DDSM, and the private dataset, respectively.
Performance Evaluation of Data-driven Intelligent Algorithms for Big data Ecosystem

Artificial intelligence, specifically machine learning, has been applied in a variety of methods by the research group to transform several data sources into valuable facts and understanding, allowing for superior pattern identification skills. Machine learning algorithms on huge and complicated data sets, computationally expensive on the other hand, processing requires hardware and logical resources, such as space, CPU, and memory. As the amount of data created daily reaches quintillion bytes, A complex big data infrastructure becomes more and more relevant. Apache Spark Machine learning library (ML-lib) is a famous platform used for big data analysis, it includes several useful features for machine learning applications, involving regression, classification, and dimension reduction, as well as clustering and features extraction. In this contribution, we consider Apache Spark ML-lib as a computationally independent machine learning library, which is open-source, distributed, scalable, and platform. We have evaluated and compared several ML algorithms to analyze the platform's qualities, compared Apache Spark ML-lib against Rapid Miner and Sklearn, which are two additional Big data and machine learning processing platforms. Logistic Classifier (LC), Decision Tree Classifier (DTc), Random Forest Classifier (RFC), and Gradient Boosted Tree Classifier (GBTC) are four machine learning algorithms that are compared across platforms. In addition, we have tested general regression methods such as Linear Regressor (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Gradient Boosted Tree Regressor (GBTR) on SUSY and Higgs datasets. Moreover, We have evaluated the unsupervised learning methods like K-means and Gaussian Mixer Models on the data set SUSY and Hepmass to determine the robustness of PySpark, in comparison with the classification and regression models. We used "SUSY," "HIGGS," "BANK," and "HEPMASS" dataset from the UCI data repository. We also talk about recent developments in the research into Big Data machines and provide future research directions.
LTE4G

Existing Graph Neural Networks (GNNs) usually assume a balanced situation where both the class distribution and the node degree distribution are balanced. However, in real-world situations, we often encounter cases where a few classes (i.e., head class) dominate other classes (i.e., tail class) as well as in the node degree perspective, and thus naively applying existing GNNs eventually fall short of generalizing to the tail cases. Although recent studies proposed methods to handle long-tail situations on graphs, they only focus on either the class long-tailedness or the degree long-tailedness. In this paper, we propose a novel framework for training GNNs, called Long-Tail Experts for Graphs (LTE4G), which jointly considers the class long-tailedness, and the degree long-tailedness for node classification. The core idea is to assign an expert GNN model to each subset of nodes that are split in a balanced manner considering both the class and degree long-tailedness. After having trained an expert for each balanced subset, we adopt knowledge distillation to obtain two class-wise students, i.e., Head class student and Tail class student, each of which is responsible for classifying nodes in the head classes and tail classes, respectively. We demonstrate that LTE4G outperforms a wide range of state-of-the-art methods in node classification evaluated on both manual and natural imbalanced graphs. The source code of LTE4G can be found at https://github.com/SukwonYun/LTE4G.
Reservoir Computing with Spin Waves in Skyrmion Crystal

Magnetic skyrmions are nanometric spin textures characterized by a quantized topological invariant in magnets and often emerge in a crystallized form called skyrmion crystal in an external magnetic field. We propose that magnets hosting a skyrmion crystal possess high potential for application to reservoir computing, which is one of the most successful information processing techniques inspired by functions of human brains. Our skyrmion-based reservoir exploits precession dynamics of magnetizations, i.e., spin waves, propagating in the skyrmion crystal. Because of complex interferences and slow relaxations of the spin-wave dynamics, the skyrmion spin-wave reservoir attains several important characteristics required for reservoir computing, e.g., the generalization ability, the nonlinearity, and the short-term memory. We investigate these properties by imposing three standard tasks to test the performances of reservoir, i.e., the duration-estimation task, the short-term memory task, and the parity-check task. Through these investigations, we demonstrate that magnetic skyrmion crystals are promising materials for spintronics reservoir devices. Because magnetic skyrmions emerge spontaneously in magnets via self-organization process under application of a static magnetic field, the proposed skyrmion reservoir requires neither advanced nanofabrication nor complicated manufacturing for production in contrast to other previously proposed magnetic reservoirs constructed with fabricated spintronics devices. Our proposal is expected to realize a breakthrough in the research of spintronics reservoirs of high performance.
A Low Coupling and Lightweight Algorithm for Ship Detection in Optical Remote Sensing Images

In recent years, many ship detection algorithms based on convolutional neural networks (CNNs) have been proposed to improve the performance of ship detection. However, with the increase in model complexity and size, it is challenging to deploy these models to resource-constrained edge platforms. In this letter, a low coupling algorithm that belongs to anchor-free methods is proposed for ship detection to reduce the model complexity and still obtain a competitive performance. The proposed low coupling network (LCNet) is easy to deploy and contributes to speeding up the inference and improving memory utilization. In addition, we propose a model compression process consisting of the quantization-aware training (QAT) method and a structural pruning method based on Taylor expansion, which can effectively reduce the model size according to hardware resource constraints. Comparative experimental results demonstrate that LCNet outperforms the state-of-the-art ship detection and natural object detection algorithms, with a 95.27&#x0025; mAP and 88.91&#x0025; F1 score on the HRSC2016 dataset. Our proposed model compression method also achieves a compression ratio of at least 80&#x0025; with a negligible loss of performance.
Super-resolution of sea surface temperature with convolutional neural network- and generative adversarial network-based methods

Abstract In this paper, we perform the super-resolution of sea surface temperature data with the enhanced super-resolution generative adversarial network (ESRGAN), which is a deep neural network-based single-image super-resolution (SISR) method that uses a generative adversarial network (GAN). We generate high-quality super-resolution data with ESRGAN and with the super-resolution convolutional neural network (SRCNN) and residual-in-residual dense block network (RRDBNet) methods, which are based on convolutional neural networks (CNNs). The images generated with these methods are compared with high-resolution optimum interpolation sea surface temperature (OISST) data using root mean square error (RMSE), learned perceptual image patch similarity (LPIPS), and perceptual index (PI) evaluation methods. RRDBNet has a better RMSE than SRCNN and ESRGAN. However, CNN-based SISR methods do not provide a faithful representation of the ocean currents of OISST. ESRGAN has a better LPIPS and PI than CNN-based methods and can represent the complex distribution of ocean currents.
Agile methodology in higher education quality assurance system for SDGs 4, 8 and 9 achievement: national experience

This paper is focused on considering agile methodology as an instrument to use in education quality assurance. We propose the Scrum method applicable for education quality assurance based on adapted Manifesto for Agile Education Quality Assurance and twelve principles behind it. The Scrum procedure is described and roles are distributed for two real-life cases of external and internal educational program quality evaluation. We illustrate that proposed Scrum procedure perfectly fits existing practices and can be used to enhance both external and internal quality assurance processes in higher education. We consider achievement of SDG 4 targets through proposed methodology as the necessary step to take in achieving SDGs 8 and 9. It is concluded that stakeholders feedback about their satisfaction by economic and innovative factors should be included in each sprint review procedure in proposed Scrum methodology. We discuss SDG 4 achieving within multilayered DIKW+DM hierarchy as a framework for education quality assurance that allow to join information processing, knowledge acquisition and corresponding decision-making algorithm.
Railway Obstacle Intrusion Detection Based on Convolution Neural Network Multitask Learning

The detection of train obstacle intrusion is very important for the safe running of trains. In this paper, we design a multitask intrusion detection model to warn of the intrusion of detected target obstacles in railway scenes. In addition, we design a multiobjective optimization algorithm that performs with different task complexity. Through the shared structure reparameterized backbone network, our multitask learning model utilizes resources effectively. Our work achieves competitive results on both object detection and line detection, and achieves excellent inference time performance (50 FPS). Our work is the first to introduce a multitask approach to realize the assisted-driving function in a railway scene.