Reviewerly/exHarmony huggingface dataset:
Rock CT Image Super-Resolution Using Residual Dual-Channel Attention Generative Adversarial Network

Because of its benefits in terms of high speed, non-destructiveness, and three-dimensionality, as well as ease of integration with computer simulation, computed tomography (CT) technology is widely applied in reservoir geology research. However, rock imaging is restricted by the device used as there is not a win–win for both the image receptive field and corresponding resolution. Convolutional neural network-based super-resolution reconstruction has become a hot topic in improving the performance of CT images. With the help of a convolution kernel, it can effectively extract characteristics and ignore disturbance information. The dismal truth is that convolutional neural networks still have numerous issues, particularly unclear texture details. To address these challenges, a generative adversarial network (RDCA-SRGAN) was designed to improve rock CT image resolution using the combination of residual learning and a dual-channel attention mechanism. Specifically, our generator employs residual attention to extract additional features; similarly, the discriminator builds on dual-channel attention and residual learning to distinguish generated contextual information and decrease computational consumption. Quantitative and qualitative analyses demonstrate that the proposed model is superior to earlier advanced frameworks and is capable to constructure visually indistinguishable high-frequency details. The quantitative analysis shows our model contributes the highest value of structural similarity, enriching the more detailed texture information. From the qualitative analysis, in enlarged details of the reconstructed images, the edges of the images generated by the RDCA-SRGAN can be shown to be clearer and sharper. Our model not only performs well in subtle coal cracks but also enriches more dissolved carbonate and carbon minerals. The RDCA-SRGAN has substantially enhanced the reconstructed image resolution and our model has great potential to be used in geomorphological study and exploration.
Challenges in threat modelling of new space systems: A teleoperation use-case

A growing number of adversaries are targeting space missions, and as such, there have been increasing academic and industrial efforts in identifying threats and risks through modelling techniques. In parallel, the research communities are collaborating to lower the entry barriers for space activities to deliver more innovative and cost-effective space missions. This evolution has been termed as New Space. However, this transformation of the space ecosystem has led to changes in the threat landscape, introducing new threat vectors and threat actors intent on compromising space systems and missions. As a result, it is expected that cyber threats could increase against space systems. Furthermore, teleoperation, a significant use case for building extraterrestrial habitats, has already been shown vulnerable in other domains as well. For example, teleoperated robots developed for remote surgery have been shown to be vulnerable to threats, such as malicious control due to an elevation-of-privilege attack. Threat modelling is a systematic and structured method to determine associated system vulnerabilities, possible attack entry points and vectors, and potential impacts on the system. In this work, we examine the efficacy of the de facto threat modelling methods such as STRIDE/DREAD in capturing highly adaptive security requirements and threats from a system-centric perspective for the teleoperation mission scenario. Understanding and protecting these hardware-software assets and their interaction in the mission is of foremost importance since security breaches threaten human safety across the broader New Space ecosystem. This research presents the limitations of existing threat modelling approaches in capturing hardware-software interaction in space systems, which is an open area for scientific enquiry. Moreover, research challenges are raised to improve the safety and security of the teleoperation mission. The output of this work can then be used to develop more appropriate threat modelling approaches to support security requirement engineering for different New Space mission scenarios.
Tracing Sustainability Indicators in the Textile and Clothing Value Chain using Blockchain Technology

In the current days, the Textile & Clothing industry sector is one of the largest and one with the greatest environmental impact, not only due to the consumption of water and the use of toxic chemicals, but also due to the increasing levels of textile waste. In addition, this industry often resorts to countries with cheap labor where workers’ rights are typically not met. The solution may involve the final consumer who, while purchasing these products, supports the entire value chain. If the final consumer chooses to buy products that are more socially and environmentally sustainable, it inspires the industry to follow this trend. For this, the final consumer must know the social and environmental indicators of the entire value chain and must trust on the information received. In this paper, Design Science Research is being used to build a system architecture capable of tracing these environmental and social indicators using decentralized and distributed technologies, like blockchain and smart contracts, to build Decentralized Applications (DApp).
Credit Card Fraud Detection using Machine and Deep Learning Techniques

With the increase in smart services, it became very easy to do online credit card transactions via IoT environment. It has facilitated people to do transactions under a short span of time. Along with the electronic facilities there are many issues and challenges, which take place during online transactions. These challenges could cause a huge loss in data and billions of dollars. In the year of 2017, it was identified that financial institute of North America has faced a loss of 3 billion dollars due to credit card scams. These scams could happen frequently and result worst in the area of finance. There are many technologies like Phishing or Trojan, which could be used by hackers/criminals to steal credit card holder’s personal information. Therefore, an effective method is needed to deal with the credit card challenges to solve the problem of personal information leakage. This, article discusses various Machine Learning classification algorithms for identification of credit card scams. Python is used to carry out the work whereas accuracy and precision metrics are used to evaluate the performance of each algorithm. The results demonstrate that the KNN classifier performs best with an optimal accuracy and precision of 98.33% and 0.5504 respectively. The results demonstrate that K-Nearest Neighbor outperforms as compared to other classifiers taken for the identification of credit card fraud in the article.
Internet Financial Fraud Detection Based on Graph Learning

The rapid development of information technology such as the Internet of Things, Big Data, artificial intelligence, and blockchain has changed the transaction mode of the financial industry and greatly improved the convenience of financial transactions, but it has also brought about new hidden frauds, which have caused huge losses to the development of Internet and IoT finance. As the size of financial transaction data continues to grow, traditional machine-learning models are increasingly difficult to use for financial fraud detection. Some graph-learning methods have been widely used for Internet financial fraud detection, however, these methods ignore the stronger structural homogeneity and cannot aggregate features for two structurally similar but distant nodes. To address this problem, in this article, we propose a graph-learning algorithm TA-Struc2Vec for Internet financial fraud detection, which can learn topological features and transaction amount features in a financial transaction network graph and represent them as low-dimensional dense vectors, allowing intelligent and efficient classification and prediction by training classifier models. The proposed method can improve the efficiency of Internet financial fraud detection with better Precision, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score, and AUC.
Unsupervised Representation Learning for Tissue Segmentation in Histopathological Images: From Global to Local Contrast

Tissue segmentation is an essential task in computational pathology. However, relevant datasets for such a pixel-level classification task are hard to obtain due to the difficulty of annotation, bringing obstacles for training a deep learning-based segmentation model. Recently, contrastive learning has provided a feasible solution for mitigating the heavy reliance of deep learning models on annotation. Nevertheless, applying contrastive loss to the most abstract image representations, existing contrastive learning frameworks focus on global features, therefore, are less capable of encoding finer-grained features (e.g., pixel-level discrimination) for the tissue segmentation task. Enlightened by domain knowledge, we design three contrastive learning tasks with multi-granularity views (from global to local) for encoding necessary features into representations without accessing annotations. Specifically, we construct: (1) an image-level task to capture the difference between tissue components, i.e., encoding the component discrimination; (2) a superpixel-level task to learn discriminative representations of local regions with different tissue components, i.e., encoding the prototype discrimination; (3) a pixel-level task to encourage similar representations of different tissue components within a local region, i.e., encoding the spatial smoothness. Through our global-to-local pre-training strategy, the learned representations can reasonably capture the domain-specific and fine-grained patterns, making them easily transferable to various tissue segmentation tasks in histopathological images. We conduct extensive experiments on two tissue segmentation datasets, while considering two real-world scenarios with limited or sparse annotations. The experimental results demonstrate that our framework is superior to existing contrastive learning methods and can be easily combined with weakly supervised and semi-supervised segmentation methods.
Embodied Active Domain Adaptation for Semantic Segmentation via Informative Path Planning

This work presents an embodied agent that can adapt its semantic segmentation network to new indoor environments in a fully autonomous way. Because semantic segmentation networks fail to generalize well to unseen environments, the agent collects images of the new environment which are then used for self-supervised domain adaptation. We formulate this as an informative path planning problem, and present a novel information gain that leverages uncertainty extracted from the semantic model to safely collect relevant data. As domain adaptation progresses, these uncertainties change over time and the rapid learning feedback of our system drives the agent to collect different data. Experiments show that our method adapts to new environments faster and with higher final performance compared to an exploration objective, and can successfully be deployed to real-world environments on physical robots.
EFENet: Reference-Based Video Super-Resolution with Enhanced Flow Estimation

In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i.e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence. The existing approaches to RefVSR essentially attempt to align the reference and the input sequence, in the presence of resolution gap and long temporal range. However, they either ignore temporal structure within the input sequence, or suffer accumulative alignment errors. To address these issues, we propose EFENet to exploit simultaneously the visual cues contained in the HR reference and the temporal information contained in the LR sequence. EFENet first globally estimates cross-scale flow between the reference and each LR frame. Then our novel flow refinement module of EFENet refines the flow regarding the furthest frame using all the estimated flows, which leverages the global temporal information within the sequence and therefore effectively reduces the alignment errors. We provide comprehensive evaluations to validate the strengths of our approach, and to demonstrate that the proposed framework outperforms the state-of-the-art methods.
Machine Learning Based Prediction of Output PV Power in India and Malaysia with the Use of Statistical Regression

Climate change and pollution are serious issues that are driving people to adopt renewable energy instead of fossil fuels. Most renewable energy technologies rely on atmospheric conditions to generate power. Solar energy is a renewable energy source that causes the least environmental damage. Solar energy can be converted to electricity, which necessitates the use of a PV system. This study presents a design, which analyses the output power performance of PV, using machine learning technique in India and Malaysia; using this, we would get the predicted amount of solar power using different weather conditions for both India and Malaysia. This study is divided into two sections, such as the data collection section and the implementation system. Dataset was collected from a weather NASA website, which took various weather parameters, based on which the model will be evaluated. The proposed research work is developed using ANN and is an amalgamation of statistical regression and neural networks, which help the model to get high accuracy by helping the model learn more complex relationships between parameters, which is able to evaluate the output power performance of photovoltaic cells with different environmental condition parameters in India and Malaysia. The ANN models are found to successfully predict PV output power with root mean square error (RMSE) of 1.5565, which was used as a measure of our model’s accuracy. This ANN model also outperforms other models available in the literature. This will have a noteworthy contribution in scaling the PV deployment in countries such as India and Malaysia and will increase the share of PV power in their national power production, as it would give the industry and the two countries an idea as to how the predicted output PV power would vary based on weather conditions, such as temperature.
RETRACTED: Distilled and filtered deep neural networks for real-time object detection in edge computing

This article has been retracted: please see Elsevier Policy on Article Withdrawal ( http://www.elsevier.com/locate/withdrawalpolicy ). This article has been retracted at the request of the Editors-in-Chief. The article plagiarizes significant results, observations and findings that had already appeared in the following three papers; Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, and Sameer Singh. 2019. “Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems”. In Proceedings of the 2019 Workshop on Hot Topics in Video Analytics and Intelligent Edges (HotEdgeVideo'19). Association for Computing Machinery, New York, NY, USA, 21–26. https://doi.org/10.1145/3349614.3356022 Yoshitomo Matsubara and Marco Levorato. 2020. “Split Computing for Complex Object Detectors: Challenges and Preliminary Results: . In Proceedings of the 4th International Workshop on Embedded and Mobile Deep Learning (EMDL’20). Association for Computing Machinery, New York, NY, USA, 7–12. https://doi.org/10.1145/3410338.3412338 Yoshitomo Matsubara and Marco Levorato, “Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks,” 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 2272–2279, doi: 10.1109/ICPR48806.2021.9412388 , https://ieeexplore.ieee.org/abstract/document/9412388 One of the conditions of submission of a paper for publication is that authors declare explicitly that the paper has not been previously published and is not under consideration for publication elsewhere. Re-use of any data should be appropriately cited. As such this article represents severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission process.
Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network

Liver plays an important role in metabolic processes, therefore fast diagnosis and potential surgical planning is essential in case of any disease. The automatic liver segmentation approach has been studied during the past years and different segmentation techniques have been proposed, but this task remains a challenge and improvements are still required to further increase segmentation accuracy. In this work, an automatic, deep learning based approach is introduced, which is adaptable and it is able to handle smaller databases, including heterogeneous data. The method starts with a preprocessing to highlight the liver area using probability density function based estimation and supervoxel segmentation. Then, a modification of the 3D U-Net is introduced, which is called 3D RP-UNet and applies the ResPath in the 3D network. Finally, with liver-heart separation and morphological steps, the segmentation results are further refined. Segmentation results on three public databases showed that the proposed method performs robustly and achieves good segmentation performance compared to other state-of-the-art approaches in the majority of the evaluation metrics.
Dynamic Domain Generalization

Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt to the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features. To enable this process, DomainMix is exploited to simulate data from diverse domains during teaching the meta-adjuster to adapt to the agnostic target domains. This learning mechanism urges the model to generalize to different agnostic target domains via adjusting the model without training. Extensive experiments demonstrate the effectiveness of our proposed method. Code is available: https://github.com/MetaVisionLab/DDG
Information granulation-based fuzzy partition in decision tree induction

In this study, an interval information granulation-based fuzzy partition (InterIG-FP) method is established to define fuzzy items in the framework of fuzzy decision tree induction. The proposed method first employs the principle of justifiable granularity to build information granules in terms of different classes on different condition attributes, where the number of information granules is equal to the number of classes. Then, the average values of the representative samples determined by each information granule are utilized to define the membership functions of fuzzy items for each condition attribute. Finally, an interval information granulation-based fuzzy decision tree (InterIG-FDT) is constructed based on the predefined fuzzy items in a top-down recursive way. The experiments illustrate the effectiveness, comparability and immunity capability of the proposed method. The effectiveness is verified by some comparative analysis with several traditional no-fuzzy decision trees. Furthermore, under the same constructing framework, some fuzzy decision trees with different fuzzy partition methods are studied to show the comparability on classification accuracies and the immunity capability on noise data, respectively.
Detection of Face Mask: A Systematic Approach

The COVID-19 coronavirus pandemic is causing health crises around the world. According to the World Health Organization (WHO), wearing a face mask is an effective means of protection in public places. In most public gatherings such as shopping centers, theaters, parks, it is increasingly necessary to make sure that people in the crowd are wearing masks. Developing an artificial intelligence solution that determines regardless of whether an individual is wearing a cover and letting it in will be great help for the society. In this case, a simple face mask detection system is built using deep learning techniques such as machine learning and persuasive neural network. The model is built with machine learning and OpenCV libraries often used for real-time applications. This model can also be used to develop complete software that scans each person before going to a public meeting
On the relativity of time: Implications and challenges of data drift on long-term effective android malware detection

The vast body of research in the Android malware detection domain has demonstrated that machine learning can provide high performance for mobile malware detection. However, the learning models have been usually evaluated with data sets encompassing short time frames, generating doubts about the feasibility of these models in operational settings that deal with the ever-evolving malware threat landscape. Although a limited number of studies have developed concept drift resilient models for handling data drift, they have never considered the impact of different timestamps on the detection solutions. Timestamps are critical to locating the data samples within the historical timeline. Different timestamping approaches may locate samples differently, which, in turn, can significantly impact the performance of the model and, consequently, the adaptive capabilities of the system to concept drift. In this study, we conducted a comprehensive benchmarking that compares the detection performance of six distinct timestamping approaches for static and dynamic feature sets. Our experiments have demonstrated that timestamp selection is an important decision that has a significant impact on concept drift modeling and the long-term performance of the model regardless of the feature type used for model construction.