Reviewerly/exHarmony huggingface dataset:
Mapping Structurally Controlled Alterations Sparked by Hydrothermal Activity in the Fatira–Abu Zawal Area, Eastern Desert, Egypt

Abstract The Eastern Desert of Egypt suffered a protracted period of deformation triggered by cratonization of the new juvenile crust known as the Arabian Nubian Shield (ANS), which has been proposed for potential gold discoveries associated with the corresponding tectonic event. The Fatira area, on the border of Egypt's Northern and Central Eastern Deserts, is covered with metavolcanic rocks twisted by a dextral relocation of the Fatira Shear Zone (FSZ) relative to the Barud magmatic body. The recent study evaluated many deformed post‐orogenic granitic intrusions and felsite dikes associated with promising mineralization localities, notably orogenic gold deposits. The combination of various field observations and remote sensing data, followed by the analysis of aeromagnetic enhanced maps, allowed the differentiation of distinct lithologies, structural features, and hydrothermal alterations in the study area. Additionally, the integrated results obtained from the different interpretation techniques are utilized to identify and confirm the previously supposed mineralized localities in the Fatira and Abu Zawal areas and predict other matched localities. The final ASTER, Sentinel 2 hydrothermal alteration, and orientation entropy heat maps demonstrate the association between these mineralized regions and major structures related to the FSZ late stage of deformation rather than other structures studied throughout the area of interest.
Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.
Multisource traffic incident reporting and evidence management in Internet of Vehicles using machine learning and blockchain

Intelligent transportation systems require efficient ways to manage traffic incidents like accidents and traffic rule violations. Secure logging of incident related data is important in case of accidents for forensic analysis, insurance, and legal settlements. Internet of vehicles can be enhanced using machine learning techniques to detect events automatically at the vehicle and road infrastructure level and blockchain can be used as a source for immutable evidence storage and management. The work proposes a framework addressing multiple challenges in traffic incident detection and evidence management like gathering of traffic event related evidence from multiple sources, addressing fault management in embedded vehicle on board units, resiliency from malicious event reporting and handling conflicting traffic incident event reports. The framework relies on a combination of cooperative event correlation and trust model to detect malicious and erroneous reporting of traffic incidents followed by Long Short term Memory (LSTM) and Bayesian model to resolve conflicting event reports. The events are correlated and verified before being transmitting onto the blockchain for evidence management and access control by various stakeholders. Analysis is presented highlighting the improvement in efficiency achieved by convergence of the multiple approaches proposed.
Optimization of complex engineering problems using modified sine cosine algorithm

In this article, a modified version of the Sine Cosine algorithm (MSCA) is proposed to solve the optimization problem. Based on the Sine Cosine algorithm (SCA), the position update formula of SCA is redefined to increase the convergence speed, then the Levy random walk mutation strategy is adopted to improve the population diversity. In order to verify the performance of MSCA, 24 well-known classical benchmark problems and IEEE CEC2017 test suites were introduced, and by comparing MSCA with several popular methods, it is demonstrated that MSCA has good convergence and robustness. Finally, MSCA is used to address six complex engineering design problems, demonstrating the engineering utility of the algorithm.
Quantifying imbalanced classification methods for leukemia detection

Uncontrolled proliferation of B-lymphoblast cells is a common characterization of Acute Lymphoblastic Leukemia (ALL). B-lymphoblasts are found in large numbers in peripheral blood in malignant cases. Early detection of the cell in bone marrow is essential as the disease progresses rapidly if left untreated. However, automated classification of the cell is challenging, owing to its fine-grained variability with B-lymphoid precursor cells and imbalanced data points. Deep learning algorithms demonstrate potential for such fine-grained classification as well as suffer from the imbalanced class problem. In this paper, we explore different deep learning-based State-Of-The-Art (SOTA) approaches to tackle imbalanced classification problems. Our experiment includes input, GAN (Generative Adversarial Networks), and loss-based methods to mitigate the issue of imbalanced class on the challenging C-NMC and ALLIDB-2 dataset for leukemia detection. We have shown empirical evidence that loss-based methods outperform GAN-based and input-based methods in imbalanced classification scenarios.
Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online discourse, especially during the electoral periods, where except the legitimate bots used for dissemination and communication purposes, the goal is to manipulate the public opinion and the electorate towards a certain direction, specific ideology, or political party. This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. To this end, a supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm, where the hyper-parameters are tuned via cross-validation. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions by calculating feature importance, using the game theoretic-based Shapley values. Experimental evaluation on distinct Twitter datasets demonstrate the superiority of our approach, in terms of bot detection accuracy, when compared against a recent state-of-the-art Twitter bot detection method.
Ontology Reshaping for Knowledge Graph Construction: Applied on Bosch Welding Case

Automatic knowledge graph (KG) construction is widely used in industry for data integration and access, and there are several approaches to enable (semi-)automatic construction of knowledge graphs. One important approach is to map the raw data to a given knowledge graph schema, often a domain ontology, and construct the entities and properties according to the ontology. However, the existing approaches to construct knowledge graphs are not always efficient enough and the resulting knowledge graphs are not sufficiently application-oriented and user-friendly. The challenge arises from the trade-off: the domain ontology should be knowledge-oriented, to reflect the general domain knowledge rather than data particularities; while a knowledge graph schema should be data-oriented, to cover all data features. If the former is directly used as the knowledge graph schema, this can cause issues like blank nodes created due to classes unmapped to data and deep knowledge graph structures. To this end, we propose a system for ontology reshaping, which generates knowledge graph schemata that fully cover the data while also covers domain knowledge well. We evaluated our approach extensively with a user study and three real manufacturing datasets from Bosch against four baselines, showing promising results.
Standardisation Considerations for Autonomous Train Control

Abstract In this paper, we review software-based technologies already known to be, or expected to become essential for autonomous train control systems with grade of automation GoA 4 (unattended train operation) in existing open railway environments. It is discussed which types of technology can be developed and certified already today on the basis of existing railway standards. Other essential technologies, however, require modifications or extensions of existing standards, in order to provide a certification basis for introducing these technologies into non-experimental “real-world” rail operation. Regarding these, we check the novel pre-standard ANSI/UL 4600 with respect to suitability as a certification basis for safety-critical autonomous train control functions based on methods from artificial intelligence. As a thought experiment, we propose a novel autonomous train controller design and perform an evaluation according to ANSI/UL 4600. This results in the insight that autonomous freight trains and metro trains using this design could be evaluated and certified on the basis of ANSI/UL 4600 .
A Background-Impulse Kalman Filter With Non-Gaussian Measurement Noises

In the Kalman filter (KF), the estimated state is a linear combination of the one-step prediction and measurement. The two combination weights depend on the prediction mean-square error matrix and the covariances matrix of measurement noise (CMMN), respectively. When the measurement noise values are small (large), the corresponding measurement is close to (far from) the real value, and the weight should be large (small). If there is non-Gaussian measurement noise, especially heavy-tailed noise, most of the noise values are small, and a few of them are large. The occasional impulses cause the overall noise variance to be much greater than the noise without impulses. Since the overall CMMN is adopted in the KF, its performance will deteriorate. In this article, we divide the measurement noise into two parts: 1) the background noise with small variance and 2) the impulse noise with large variance. Then, we use the expectation–maximization algorithm to dynamically calculate the parameters and propose a new KF algorithm to process them separately, which is called background-impulse KF (BIKF). It can dynamically determine whether there is an impulse and eliminate its impact as much as possible. Compared with the recent maximum correntropy criterion and minimum error entropy criterion-based KFs, simulations show that the proposed BIKF works better than them with lower computational complexity.
CP$$^2$$: Copy-Paste Contrastive Pretraining for Semantic Segmentation

Recent advances in self-supervised contrastive learning yield good image-level representation, which favors classification tasks but usually neglects pixel-level detailed information, leading to unsatisfactory transfer performance to dense prediction tasks such as semantic segmentation. In this work, we propose a pixel-wise contrastive learning method called CP $$^2$$ (Copy-Paste Contrastive Pretraining), which facilitates both image- and pixel-level representation learning and therefore is more suitable for downstream dense prediction tasks. In detail, we copy-paste a random crop from an image (the foreground) onto different background images and pretrain a semantic segmentation model with the objective of 1) distinguishing the foreground pixels from the background pixels, and 2) identifying the composed images that share the same foreground. Experiments show the strong performance of CP $$^2$$ in downstream semantic segmentation: By finetuning CP $$^2$$ pretrained models on PASCAL VOC 2012, we obtain 78.6% mIoU with a ResNet-50 and 79.5% with a ViT-S. Code and models are available at https://github.com/wangf3014/CP2 .
PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection

Recent years have witnessed a trend of applying context frames to boost the performance of object detection as video object detection. Existing methods usually aggregate features at one stroke to enhance the feature. These methods, however, usually lack spatial information from neighboring frames and suffer from insufficient feature aggregation. To address the issues, we perform a progressive way to introduce both temporal information and spatial information for an integrated enhancement. The temporal information is introduced by the temporal feature aggregation model (TFAM), by conducting an attention mechanism between the context frames and the target frame (i.e., the frame to be detected). Meanwhile, we employ a Spatial Transition Awareness Model (STAM) to convey the location transition information between each context frame and target frame. Built upon a transformer-based detector DETR, our PTSEFormer also follows an end-to-end fashion to avoid heavy post-processing procedures while achieving 88.1% mAP on the ImageNet VID dataset. Codes are available at https://github.com/Hon-Wong/PTSEFormer .
LAR-SR: A Local Autoregressive Model for Image Super-Resolution

Previous super-resolution (SR) approaches often formulate SR as a regression problem and pixel wise restoration, which leads to a blurry and unreal SR output. Recent works combine adversarial loss with pixel-wise loss to train a GAN-based model or introduce normalizing flows into SR problems to generate more realistic images. As another powerful generative approach, autoregressive (AR) model has not been noticed in low level tasks due to its limitation. Based on the fact that given the structural in-formation, the textural details in the natural images are locally related without long term dependency, in this paper we propose a novel autoregressive model-based SR approach, namely LAR-SR, which can efficiently generate realistic SR images using a novel local autoregressive (LAR) module. The proposed LAR module can sample all the patches of textural components in parallel, which greatly reduces the time consumption. In addition to high time efficiency, it is also able to leverage contextual information of pixels and can be optimized with a consistent loss. Experimental results on the widely-used datasets show that the proposed LAR-SR approach achieves superior performance on the vi-sual quality and quantitative metrics compared with other generative models such as GAN, Flow, and is competitive with the mixture generative model.
Target Detection Based on Two-Stream Convolution Neural Network With Self-Powered Sensors Information

With the rapid development of artificial intelligence, a neural network is widely used in various fields. The target detection algorithm is mainly based on a neural network, but the accuracy of the target detection algorithm is greatly related to the complexity of the scene and texture. A target detection algorithm based on the RGB-D image from the perspective of the lightweight of the target detection network model and the integration of the depth map to overcome the weak environmental illumination with self-powered sensors information is proposed. This article analyzes the network model structure of YOLOv4 and MobileNet, compares the variation of parameter numbers between depthwise separable convolution and convolutional neural networks, and combines the advantages of the YOLOv4 network and the MobileNetv3 network. The main network of three effective feature layers in YOLOv4 is replaced by the MobileNetv3 network for initial feature layer extraction to strengthen the feature extraction network. At the same time, the standard convolution models in the network are replaced by depthwise separable convolution. The proposed method is compared with YOLOv4 and YOLOv4-MobileNetv3 in this article, and the experimental results show that the proposed network retains its original advantages in accuracy, but the size of the network model is about 23% of that of the YOLOv4 network model, the processing speed is about 42% higher than that of the YOLOv4 network model, and the detection accuracy can still reach 83% in the environment with poor lighting conditions.
A UAV Navigation Approach Based on Deep Reinforcement Learning in Large Cluttered 3D Environments

This paper is concerned with the problem of using deep reinforcement learning methods to enable safe navigation of UAVs in unknown large-scale environments containing a large number of obstacles. The ability of UAV to navigate autonomously is a precondition for performing disaster rescue and logistics deliveries. In this paper, the perception-constrained navigation of UAV is modeled as a partially observable Markov decision process (POMDP), and a fast recurrent stochastic valued gradient algorithm based on the actot-critic framework is designed for online solution. Compared with traditional SLAM-based and reactive obstacle avoidance-based approaches, the proposed navigation method can map the raw sensing information into navigation signals, and the learned policy deployed to the UAV can avoid the memory occupation and computational consumption required for real-time map building. Through experiments in a newly designed simulation environment, it is demonstrated that the proposed algorithm can enable the UAV to navigate safely in unknown cluttered large-scale environments and outperform the state-of-the-art DRL algorithm in terms of performance.
B-cos Networks: Alignment is All We Need for Interpretability

We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace the linear transforms in DNNs by our B-cos transform. As we show, a sequence (network) of such transforms induces a single linear transform that faithfully summarises the full model computations. Moreover, the B-cos transform introduces alignment pressure on the weights during optimisation. As a result, those induced linear transforms become highly interpretable and align with task-relevant features. Importantly, the B-cos transform is designed to be compatible with existing architectures and we show that it can easily be integrated into common models such as VGGs, ResNets, InceptionNets, and DenseNets, whilst maintaining similar performance on ImageNet. The resulting explanations are of high visual quality and perform well under quantitative metrics for interpretability. Code available at github.com/moboehle/B-cos.
Virtual Dimension—A Primer to Metaverse

The metaverse is a concept of a persistent, online, 3-D World that combines multiple virtual spaces. With Metaverse, these independent, computer-generated environments developed by different organizations come together within a single, integrated network of 3-D worlds where users can hop from one universe to another. For achieving a near-perfect virtual world, Metaverse uses numerous advanced technologies including virtual reality, augmented reality, artificial intelligence, blockchain, etc. A novel technology like metaverse gives rise to various challenges like the standing of virtual avatars in legal systems in case of injury or harm, demand for expensive resources in its upscaling, privacy of user data, and increased risk of cybercrimes. This research presents a brief overview of technologies used in the development of metaverse, the challenges, and the potential aspects of the virtual world.
Entanglement dynamics of noisy random circuits

The process by which open quantum systems thermalize with an environment is both of fundamental interest and relevant to noisy quantum devices. As a minimal model of this process, we consider a qudit chain evolving under local random unitaries and local depolarization channels. After mapping to a statistical mechanics model, the depolarization (noise) acts like a symmetry-breaking field, and we argue that it causes the system to thermalize within a timescale independent of system size. We show that various bipartite entanglement measures -- mutual information, operator entanglement, and entanglement negativity -- grow at a speed proportional to the size of the bipartition boundary. As a result, these entanglement measures obey an area law: Their maximal value during the dynamics is bounded by the boundary instead of the volume. In contrast, if the depolarization only acts at the system boundary, then the maximum value of the entanglement measures obeys a volume law. We complement our analysis with scalable simulations involving Clifford gates, for both one- and two-dimensional systems.
A Novel Metadata Based Multi-Label Document Classification Technique

From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of non-open access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, “to what extent metadata-based features can perform in contrast to content-based approaches.” In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-of-the-art techniques in the literature.