Reviewerly/exHarmony huggingface dataset:
Embedding Civil Engineering Understanding through the Use of Interactive Virtual Reality

Recent skills surveys of engineering graduates have highlighted a deficit in critical thinking among graduates. A possible solution to this is to increase the number of hands-on exercises in the curriculum. This could be carried out through the integration of 3D learning tools, specifically a virtual reality (VR) program, to effectively teach civil engineering practical studies and allow repeatable and measurable exercises for students. This study aims to assess the suitability of the VR program as an additional resource alongside existing learning exercises or a substitute for hands-on experiments when needed. The methodology involved creating a VR program, compatible with VR headsets to replicate an engineering experiment, namely the loading of a concrete beam to observe its failure. Students’ understanding of the virtual experiment was evaluated through end-of-experiment questions. The findings indicate that the VR learning tool was successful in enhancing students’ understanding of the civil engineering experiment. The immersive and interactive nature of VR contributed to a solid grasp of the concepts presented, proving its potential as a valuable educational resource. By leveraging VR technology, educational institutions can provide an engaging and effective alternative to traditional laboratory sessions, ensuring uninterrupted and high-quality learning experiences for civil engineering students.
Huber-Based Robust Tracking Method of Hypersonic Cruise Vehicle

In order to solve the tracking problem of hypersonic cruise vehicle (HCV) during high-speed flight, a robust tracking method (CHF) for hypersonic cruise vehicle is proposed. Firstly, the hypersonic cruise vehicle tracking model is established, including a vehicle motion model, a tracking model, a ground-based radar measurement model and a flicker noise model. On this basis, for the nonlinear problem of the system, the tracking method based on deterministic sampling is adopted. Aiming at the noise non-Gaussian problem caused by high-speed flight, a robust tracking method based on Huber-based hypersonic cruise vehicle is proposed. Finally, comparative simulations under various conditions are carried out to verify the effectiveness of the robust tracking method based on Huber. The simulation analysis of the tracking and positioning accuracy of hypersonic cruise aircraft was completed from the aspects of angle measurement accuracy and frame rate measurement. The comparison of CKF and CHF algorithms under different intensity flashes shows the superiority of CHF algorithm in hypersonic cruise vehicle tracking, and higher estimation accuracy and system robustness can be obtained.
Deep Learning for Integrated Speckle Reduction and Super-Resolution in Multi-Temporal SAR

In the domain of synthetic aperture radar (SAR) image processing, a prevalent issue persists wherein research predominantly focuses on single-task learning, often neglecting the concurrent impact of speckle noise and low resolution on SAR images. Currently, there are two main processing strategies. The first strategy involves conducting speckle reduction and super-resolution processing step by step. The second strategy involves performing speckle reduction as an auxiliary step, with a focus on enhancing the primary task of super-resolution processing. However, both of these strategies exhibit clear deficiencies. Nevertheless, both tasks jointly focus on two key aspects, enhancing SAR quality and restoring details. The fusion of these tasks can effectively leverage their task correlation, leading to a significant improvement in processing effectiveness. Additionally, multi-temporal SAR images covering imaging information from different time periods exhibit high correlation, providing deep learning models with a more diverse feature expression space, greatly enhancing the model’s ability to address complex issues. Therefore, this study proposes a deep learning network for integrated speckle reduction and super-resolution in multi-temporal SAR (ISSMSAR). The network aims to reduce speckle in multi-temporal SAR while significantly improving the image resolution. Specifically, it consists of two subnetworks, each taking the SAR image at time 1 and the SAR image at time 2 as inputs. Each subnetwork includes a primary feature extraction block (PFE), a high-level feature extraction block (HFE), a multi-temporal feature fusion block (FFB), and an image reconstruction block (REC). Following experiments on diverse data sources, the results demonstrate that ISSMSAR surpasses speckle reduction and super-resolution methods based on a single task in terms of both subjective perception and objective evaluation metrics regarding the quality of image restoration.
Multi UAV Trajectory Tracking Optimization Method Based on Fuzzy Graph Convolution Neural Network

Multi-UAV coordinated trajectory tracking is the most popular research problem at present. It is necessary to optimize and adjust the combined course of multiple UAVs and the motion trajectory of UAVs in real-time, and constantly pursue the dynamic optimal configuration between multiple UAVs and targets to improve and stabilize the positioning accuracy. Given the shortcomings of traditional centralized optimization methods, this paper proposes an advanced fuzzy graph convolution neural network model (FGTT). Because of the multi unmanned multi-source target and the irregular geometric structure, the graph convolution neural network can better optimize and process the trajectory tracking optimization method in non-European space. Several groups of verification show that this method can achieve higher tracking accuracy faster than traditional methods, The stability and accuracy of location and tracking are improved.
<scp>BCUIS‐Net</scp>: A breast cancer ultrasound image segmentation network via boundary‐aware and shape feature fusion

Abstract Breast cancer is a highly lethal disease with the highest mortality rate among women worldwide. Breast tumor segmentation from ultrasound images plays a critical role in enabling early detection, leading to a reduction in mortality rates. However, the challenge of ultrasound breast cancer segmentation arises from factors such as indistinct lesion boundaries, noise artifacts, and inhomogeneous intensity distribution within the lesion region. To address the bottlenecks, a novel boundary‐aware shape feature fusion network (BCUIS‐Net) is proposed to segment breast lesion in ultrasound images. Firstly, a boundary‐aware module (BAM) is put forward to accurately localize the ambiguous tumor regions and boundaries by embedding the horizontal and vertical position information into the channel attention. Subsequently, a shape feature fusion (SFF) module is presented to fuse shape features and segmentation features, in order to adaptively extract their complementary features by aggregating contextual information in an attention module. Specifically, the different levels of features from the encoder are up‐sampled to the original image size and fed into the BAM to predict the boundary map. The boundary and decoder‐generated feature maps are thereafter fused by the SFF module to exploit the complementarity between them to correct errors in segmentation and shape features, effectively eliminating false detections and noise in the features to achieve accurate segmentation of pathological regions. Finally, the shape fusion loss is derived from a combination of the binary cross‐entropy loss and the distance map loss to intelligently penalize incorrect predictions and thus improve the attention to boundary locations. The performance of the network is evaluated in two public breast ultrasound datasets. Experimental results verify that the proposed method obtains superior segmentation results and outperforms the most recent state‐of‐the‐art, in which IOU is increased by 2.15% and 2.59% on UDIAT and BUSI, respectively.
Generalizing the eight levels theorem: a journey to Mersenne prime discoveries and new polynomial classes

Mersenne primes, renowned for their captivating form as $2^p - 1$, have intrigued mathematicians for centuries. In this paper, we embark on a captivating quest to unveil the intricate nature of Mersenne primes, seamlessly integrating methods with the Eight Levels Theorem. Initially, we extend the Eight Levels Theorem and introduce an innovative approach that harmoniously combines arithmetic and differential techniques to compute the coefficients of the polynomial expansions of $x^n + y^n$ in terms of binary quadratic forms. This endeavor leads us to the genesis of novel polynomial sequences as we scrutinize the coefficients within this expansion. Our research unearths previously uncharted connections between Mersenne numbers and the derivatives of specific polynomial sequences. By forging this linkage, we not only enhance our understanding of Mersenne primes but also bridge the divide between well-established sequences in number theory and differential equations. This broadens the applicability of our findings across diverse scientific domains, revealing fresh avenues for exploration in number theory and beyond. These polynomial bridges serve as conduits between these sequences, unveiling exciting prospects for future research. This interdisciplinary exploration opens up exciting possibilities for the broader implications of Mersenne primes, extending their significance beyond the realm of pure mathematics. In our paper, we also delve into the intriguing influence of the Golden Ratio constant, which unveils segments reminiscent of the beauty found in nature, adding an unexpected dimension to the world of arithmetic. In this harmonious journey, the allure of Mersenne primes resonates through the symphony of mathematical discovery.
Experience Replay Method with Attention for Multi-agent Reinforcement Learning

To enhance the efficiency of the experience replay method, this article proposes an improvement by incorporating the past experience reward value and the timing difference error (TD error) to form a prioritized R-T experience parameter. Additionally, an attention mechanism is introduced to determine data priority based on the R-T experience parameter. This improved experience replay method is then applied to the multi-agent deep deterministic policy gradient algorithm, resulting in improved algorithm training efficiency and stability.
Using ScrutinAI for visual inspection of DNN performance in a medical use case

Abstract Our Visual Analytics (VA) tool ScrutinAI supports human analysts to investigate interactively model performance and data sets. Model performance depends on labeling quality to a large extent. In particular in medical settings, generation of high quality labels requires in depth expert knowledge and is very costly. Often, data sets are labeled by collecting opinions of groups of experts. We use our VA tool to analyze the influence of label variations between different experts on the model performance. ScrutinAI facilitates to perform a root cause analysis that distinguishes weaknesses of deep neural network (DNN) models caused by varying or missing labeling quality from true weaknesses. We scrutinize the overall detection of intracranial hemorrhages and the more subtle differentiation between subtypes in a publicly available data set.
Research on Failure Cause Analysis Method Based on Aircraft Maintenance Records

Due to the accumulation of a large amount of maintenance text information recorded by maintenance personnel based on experience in aircraft maintenance, due to its strong professionalism and diverse expression methods, intelligent analysis has not yet been achieved, making it difficult to fully utilize these data. In response to this issue, this article proposes an aircraft maintenance record analysis method based on the ALBERT-TextCNN model, which solves the fault causes to assist maintenance personnel in correct troubleshooting work. Firstly, the vectorization representation of the preprocessed text and the preliminary feature extraction are performed by using ALBERT; Secondly, the TextCNN model is used to extract deeper features to improve the accuracy of model analysis; Finally, compared with other commonly used text analysis models, the results show that the method used in this article can accurately analyze the causes of faults in aircraft maintenance records, effectively improving the accuracy and speed of analysis.
Learning from Temporal Aviation Data to Detect Anomaly Events

Recently, data analysis of aviation sensors is a hot topic because it is important to aviation safety. Many researchers propose temporal models (RNN, LSTM, etc.) to predict the accuracy performance degradation of sensors, due to the mechanical property, and filter out anomaly data of sensors. However, aerial sensor data is usually long-term data because of intensive sample rates and long runs. The traditional models usually lose the gradients during the training phase for long-sequence data. To reduce this problem, in this paper, Residual-Cascaded LSTM (RC-LSTM) is proposed. It can effectively extract the rich features from large inputs to model large-range independence. Besides, it is found that the volume of anomaly data is much less than that of normal samples. Thus, a distribution-aware shuffling scheme is designed to assist models to learn more reasonable representations for input data. A large number of experiments are performed on the simulated flight sensor dataset and two public datasets (earthquake, Lightning2). The proposed RC-LSTM is better than the most advanced method (RNN, LSTM, etc.), and its accuracy is 80%.
Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods

Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex data set, but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image data sets, such as Persian handwritten images have numerous pixels, making statistical inference difficult. Such high dimensionality property pose challenges for analysis and processing, requiring specialized techniques like clustering to extract information. Incorporating response variable information enhances clustering analysis, transforming it into a supervised method. This article evaluates a supervised clustering approach using Ridge and Lasso penalties, comparing them in analyzing a real data set while identifying important variables. We demonstrate that despite choosing a small number of variables as important variables, Lasso penalty performed relatively well in predicting the labels of new observations for this multi-class data set.
Expert-Annotated Dataset to Study Cyberbullying in Polish Language

We introduce the first dataset of harmful and offensive language collected from the Polish Internet. This dataset was meticulously curated to facilitate the exploration of harmful online phenomena such as cyberbullying and hate speech, which have exhibited a significant surge both within the Polish Internet as well as globally. The dataset was systematically collected and then annotated using two approaches. First, it was annotated by two proficient layperson volunteers, operating under the guidance of a specialist in the language of cyberbullying and hate speech. To enhance the precision of the annotations, a secondary round of annotations was carried out by a team of adept annotators with specialized long-term expertise in cyberbullying and hate speech annotations. This second phase was further overseen by an experienced annotator, acting as a super-annotator. In its initial application, the dataset was leveraged for the categorization of cyberbullying instances in the Polish language. Specifically, the dataset serves as the foundation for two distinct tasks: (1) a binary classification that segregates harmful and non-harmful messages and (2) a multi-class classification that distinguishes between two variations of harmful content (cyberbullying and hate speech), as well as a non-harmful category. Alongside the dataset itself, we also provide the models that showed satisfying classification performance. These models are made accessible for third-party use in constructing cyberbullying prevention systems.
Transparency Practice and Work Engagement: School Context

The study aimed to find out the correlation between corporate transparency practices of school management or administration and the work engagement of employees. To carry out the study and to strengthen the theory of the study, the related literature and studies were reviewed. The population of the study was all employees of the colleges and therefore the total enumeration was used. The research design of the study was descriptive assessment and descriptive correlational research design. The data were gathered through questionnaires and a weighted mean was used to measure the level of corporate transparency practices and work engagement. Pearson r was used to determine the correlation between the two variables. The study found that there is no correlation between corporate transparency practices and work engagement and therefore the hypothesis is rejected.
Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Abstract Background Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users’ feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results The usability study investigated the feasibility of using VR for genomic data analysis in domain users’ daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users’ daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.
Binary semantic segmentation for detection of prostate adenocarcinoma using an ensemble with attention and residual U-Net architectures

An accurate determination of the Gleason Score (GS) or Gleason Pattern (GP) is crucial in the diagnosis of prostate cancer (PCa) because it is one of the criterion used to guide treatment decisions for prognostic-risk groups. However, the manually designation of GP by a pathologist using a microscope is prone to error and subject to significant inter-observer variability. Deep learning has been used to automatically differentiate GP on digitized slides, aiding pathologists and reducing inter-observer variability, especially in the early GP of cancer. This article presents a binary semantic segmentation for the GP of prostate adenocarcinoma. The segmentation separates benign and malignant tissues, with the malignant class consisting of adenocarcinoma GP3 and GP4 tissues annotated from 50 unique digitized whole slide images (WSIs) of prostate needle core biopsy specimens stained with hematoxylin and eosin. The pyramidal digitized WSIs were extracted into image patches with a size of 256 × 256 pixels at a magnification of 20×. An ensemble approach is proposed combining U-Net-based architectures, including traditional U-Net, attention-based U-Net, and residual attention-based U-Net. This work initially considers a PCa tissue analysis using a combination of attention gate units with residual convolution units. The performance evaluation revealed a mean Intersection-over-Union of 0.79 for the two classes, 0.88 for the benign class, and 0.70 for the malignant class. The proposed method was then used to produce pixel-level segmentation maps of PCa adenocarcinoma tissue slides in the testing set. We developed a screening tool to discriminate between benign and malignant prostate tissue in digitized images of needle biopsy samples using an AI approach. We aimed to identify malignant adenocarcinoma tissues from our own collected, annotated, and organized dataset. Our approach returned the performance which was accepted by the pathologists.
Optimal Artificial Neural Network using Particle Swarm Optimization

Artificial neuron networks (ANNs) are widely used for data analyticS in broad areas of engineering applications and commercial services. The ANN has one to two hidden layers. In advanced ANN, multiple-layer ANN is used where the network extracts different features until it can recognize what it is looking for through deep learning approaches. Usually, a backpropagation algorithm is used to train the network and fix weights and biases associated with each network neuron. This paper proposes a particle swarm optimization (PSO) based algorithm for training ANN for better performance and accuracy. Two types of ANN models and their training using PSO have been developed. The performance of the developed models has been analyzed on a standard dataset. Also, the effectiveness and suitability of the developed approach have been demonstrated through statistics of the obtained results.