Reviewerly/exHarmony huggingface dataset:
Assessing Priorities in Engineering Design Through Augmented Reality

Authors:
raw_author_name: Logan Perry
raw_affiliation_string: University of Nebraska - Lincoln

Abstract: Abstract Abstract Authentic learning is fast becoming one of the most effective educational instructional methods for teaching engineering education. One of the issues that students must face with transitioning into the industry is the application of school knowledge to their real-life practice. Engineering students are expected to think critically, come up with original solutions, and collaborate with other team members on projects. Often, traditional teaching methods are not enough to build these capacities in young engineers, which has led to the introduction of new instructional methods such as the incorporation of virtual reality as an authentic learning technique. Several authentic learning techniques have been used in the past to introduce students to virtual reality. While VR substitutes reality, transporting you to a different environment, AR enhances reality by superimposing information onto what is already visible. Hence, incorporating AR in authentic learning activity has the potential to help understand how users view the real world and make informed decisions to improve workplace decisions. This paper serves as a literature review for a larger study that investigates how engineering students set priorities in engineering design to assess their preparedness for industry. The larger study seeks to answer the following research questions: • How do students perceive their undergraduate education prepares them to set priorities in engineering design?" • How do authentic learning activities through AR change the way students think about design? To answer the research questions, the advancement of teaching framework on professional preparation across professions known as the "three apprenticeships model" will be used as a guiding theoretical lens. To explore the research purpose, 10 civil engineering undergraduate students from a university in the Midwest will be assessed on their knowledge via an authentic learning activity to redesign a children's playground using augmented reality based on five assessment metrices (cost, fun, sustainability, time, and safety). Feedback will be gathered through semi-structured interviews and analyzed through thematic analysis. The objective of this study is to give students exposure into real-world practice to understand industry requirements even before they graduate from school. This paper will present a brief literature review on the topic, while more details about the methodology, results, and future directions will be presented in a future paper.
Challenges in the Application of Artificial Intelligence in Education for Sustainable Engineering

Authors:
raw_author_name: Alicia Perdigones
raw_affiliation_string: Universidad Politécnica de Madrid, Spain
raw_author_name: José Luis García
raw_affiliation_string: Universidad Politécnica de Madrid, Spain
raw_author_name: Fernando R. Mazarrón
raw_affiliation_string: Universidad Politécnica de Madrid, Spain

Abstract: The aim of this chapter is to provide university educators with a range of ideas and alternatives for the use of artificial intelligence (AI) tools in their teaching to integrate concepts of sustainability, which are currently highly demanded in the professional activity. Artificial intelligence has revolutionized many areas of economic activity, offering new solutions and/or optimizing certain processes. Training students in these tools is essential for successful integration into the labor market in a technologically evolving era. This training must be carried out in coordination with educational policies, and within a teaching program that includes ethical aspects in the handling of these tools. Artificial intelligence can be key to improving sustainability in multiple areas of engineering as shown in various examples and ideas in this work.
Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Authors:
raw_author_name: Vladimir Stanovov
raw_affiliation_string: Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia
raw_author_name: Eugene Semenkin
raw_affiliation_string: Institute of Informatics and Telecommunication, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, Russia

Abstract: Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algorithm. The main reason for this is that differential evolution is highly sensitive to the scaling factor and crossover rate parameters. In this study, a novel adaptation technique is proposed which uses the success rate to replace the popular success history-based adaptation for scaling factor tuning. In particular, the scaling factor is sampled with a Cauchy distribution, whose location parameter is set as an nth order root of the current success rate, i.e., the ratio of improved solutions to the current population size. The proposed technique is universal and can be applied to any differential evolution variant. Here it is tested with several state-of-the-art variants of differential evolution, and on two benchmark sets, CEC 2017 and CEC 2022. The performed experiments, which include modifications of algorithms developed by other authors, show that in many cases using the success rate to determine the scaling factor can be beneficial, especially with relatively small computational resource.
A Vision Enhancement and Feature Fusion Multiscale Detection Network

Authors:
raw_author_name: Chengwu Qian
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China
raw_author_name: Jiangbo Qian
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China
raw_author_name: Chong Wang
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China
raw_author_name: Xulun Ye
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China
raw_author_name: Caiming Zhong
raw_affiliation_string: Ningbo University, 818 Fenghua Road, Ningbo, 315211, China

Abstract: Abstract In the field of object detection, there is often a high level of occlusion in real scenes, which can very easily interfere with the accuracy of the detector. Currently, most detectors use a convolutional neural network (CNN) as a backbone network, but the robustness of CNNs for detection under cover is poor, and the absence of object pixels makes conventional convolution ineffective in extracting features, leading to a decrease in detection accuracy. To address these two problems, we propose VFN (A Vision Enhancement and Feature Fusion Multiscale Detection Network), which first builds a multiscale backbone network using different stages of the Swin Transformer, and then utilizes a vision enhancement module using dilated convolution to enhance the vision of feature points at different scales and address the problem of missing pixels. Finally, the feature guidance module enables features at each scale to be enhanced by fusing with each other. The total accuracy demonstrated by VFN on both the PASCAL VOC dataset and the CrowdHuman dataset is better than that of other methods, and its ability to find occluded objects is also better, demonstrating the effectiveness of our method.The code is available at https://github.com/qcw666/vfn .
A Novel Framework for Image Matching and Stitching for Moving Car Inspection under Illumination Challenges

Authors:
raw_author_name: Andreas El Saer
raw_affiliation_string: Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece
raw_author_name: Lazaros Grammatikopoulos
raw_affiliation_string: Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece
raw_author_name: Giorgos Sfikas
raw_affiliation_string: Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece
raw_author_name: George Karras
raw_affiliation_string: School of Rural, Surveying and Geoinformatics Engineering, National Technical University of Athens, 15780 Athens, Greece
raw_author_name: Elli Petsa
raw_affiliation_string: Department of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece

Abstract: Vehicle exterior inspection is a critical operation for identifying defects and ensuring the overall safety and integrity of vehicles. Visual-based inspection of moving objects, such as vehicles within dynamic environments abounding with reflections, presents significant challenges, especially when time and accuracy are of paramount importance. Conventional exterior inspections of vehicles require substantial labor, which is both costly and prone to errors. Recent advancements in deep learning have reduced labor work by enabling the use of segmentation algorithms for defect detection and description based on simple RGB camera acquisitions. Nonetheless, these processes struggle with issues of image orientation leading to difficulties in accurately differentiating between detected defects. This results in numerous false positives and additional labor effort. Estimating image poses enables precise localization of vehicle damages within a unified 3D reference system, following initial detections in the 2D imagery. A primary challenge in this field is the extraction of distinctive features and the establishment of accurate correspondences between them, a task that typical image matching techniques struggle to address for highly reflective moving objects. In this study, we introduce an innovative end-to-end pipeline tailored for efficient image matching and stitching, specifically addressing the challenges posed by moving objects in static uncalibrated camera setups. Extracting features from moving objects with strong reflections presents significant difficulties, beyond the capabilities of current image matching algorithms. To tackle this, we introduce a novel filtering scheme that can be applied to every image matching process, provided that the input features are sufficient. A critical aspect of this module involves the exclusion of points located in the background, effectively distinguishing them from points that pertain to the vehicle itself. This is essential for accurate feature extraction and subsequent analysis. Finally, we generate a high-quality image mosaic by employing a series of sequential stereo-rectified pairs.
Graph transformer against graph perturbation by flexible-pass filter

Authors:
raw_author_name: Yonghua Zhu
raw_affiliation_string: School of Computer Science, University of Auckland, New Zealand
raw_author_name: Jincheng Huang
raw_affiliation_string: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Yang Chen
raw_affiliation_string: School of Computer Science, University of Auckland, New Zealand
raw_author_name: Robert Amor
raw_affiliation_string: School of Computer Science, University of Auckland, New Zealand
raw_author_name: Michael Witbrock
raw_affiliation_string: NAOInstitute, University of Auckland, New Zealand

Abstract: Graph perturbation hinders graph models in real applications, and thus, defense methods against graph perturbation have been attracting increasing attention. However, current defense methods limit expressiveness and demand expert knowledge. To overcome these issues, in this paper, we propose a flexible-frequency graph transformer, building on the powerful expressive ability of self-attention. Specifically, we design a frequency-extraction self-attention with three heads to extract multi-frequency representations, i.e., low-frequency representation, hybrid-frequency representation, and high-frequency representation. We then design an adaptive fusion method to combine diverse representations for outputting a flexible-frequency representation. This improves the model’s expressive ability by using comprehensive information to enhance defense ability against graph perturbation. To achieve this, we adaptively capture the robust graph filters within self-attention, eliminating the need for expert knowledge. To further enhance the effectiveness of self-attention, we incorporate graph learning to capture graph information before conducting node representation learning through self-attention. Furthermore, we theoretically analyze the feasibility of our proposed method. Extensive experimental results demonstrate that our proposed method offers a dynamic and effective defense against graph perturbation compared to existing state-of-the-art methods.
Laplace Expansion for Bartlett--Nanda--Pillai Test Statistic and Its Error Bound

Authors:
raw_author_name: V. V. Ulyanov

Abstract: We construct asymptotic expansions for the distribution function of the Bartlett--Nanda--Pillai statistic under the condition that the null linear hypothesis is valid in a multivariate linear model. Computable estimates of the accuracy of approximation are obtained via the Laplace approximation method, which is generalized to integrals for matrix-valued functions.
An Introduction to SGTPPR: Sparse Geochemical Tectono‐Magmatic Setting Probabilistic MembershiP DiscriminatoR

Authors:
raw_author_name: Kenta Ueki
raw_affiliation_string: Research Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan
raw_author_name: Hideitsu Hino
raw_affiliation_string: The Institute of Statistical Mathematics, Tokyo, Japan
raw_author_name: Tatsu Kuwatani
raw_affiliation_string: Research Institute for Marine Geodynamics, Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan

Abstract: Abstract We present a new and easy‐to‐use geochemical tectono‐magmatic setting discriminator to calculate the probability of membership (the Sparse Geochemical Tectono‐magmatic setting Probabilistic membershiP discriminatoR, SGTPPR) that runs in Excel. It outputs the probability of membership for eight different tectono‐magmatic settings (mid‐ocean ridge, oceanic island, oceanic plateau, continental flood basalt province, intra‐oceanic arc, continental arc, island arc, and back‐arc basin) for a given volcanic rock sample based on major and selected trace element contents (SiO 2 , TiO 2 , Al 2 O 3 , Fe 2 O 3 , MgO, CaO, K 2 O, Na 2 O, Rb, Sr, Y, Zr, Nb, and Ba). We consider all possible ratios and multiplications of these contents, in addition to the contents themselves, which improves the discrimination accuracy. We use a statistical method called sparse multinomial logistic regression to construct a robust and predictive discrimination model. By imposing the sparsity, only a small number of essential variables are included in the model. The variables are objectively extracted from 287 possible geochemical variables, including all possible ratios and multiplications of the major and trace element contents. The constructed model exhibits a high classification ability, indicating that tectonic discrimination using major and selected trace elements yields a high classification ability when ratios and multiplications are considered. The system outputs the relative weights of the variables (i.e., contents, and ratios and multiplications of contents) of the input geochemical data to the calculated membership probabilities. This information can be used to evaluate and interpret the results. We apply the model to multiple samples of a geological unit, to determine the tectonic setting.
Label-efficient object detection via region proposal network pre-training

Authors:
raw_author_name: Nanqing Dong
raw_affiliation_string: Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China
raw_author_name: Linus Ericsson
raw_affiliation_string: Institute for Imaging, Data and Communications (IDCOM), School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, UK
raw_author_name: Yongxin Yang
raw_affiliation_string: School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK
raw_author_name: Ales Leonardis
raw_affiliation_string: School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK
raw_author_name: Steven McDonagh
raw_affiliation_string: Institute for Imaging, Data and Communications (IDCOM), School of Engineering, University of Edinburgh, Edinburgh, EH9 3FG, UK

Abstract: Self-supervised pre-training, based on the pretext task of instance discrimination, has fueled the recent advance in label-efficient object detection. However, existing studies focus on pre-training only a feature extractor network to learn transferable representations for downstream detection tasks. This leads to the necessity of training multiple detection-specific modules from scratch in the fine-tuning phase. We argue that the region proposal network (RPN), a common detection-specific module, can additionally be pre-trained towards reducing the localization error of multi-stage detectors. In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance. We evaluate the efficacy of our approach on benchmark object detection tasks and additional downstream tasks, including instance segmentation and few-shot detection. In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance, with largest gains found in label-scarce settings.
Sample diversity selection strategy based on label distribution morphology for active label distribution learning

Authors:
raw_author_name: Weiwei Li
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
raw_author_name: Wei Qian
raw_affiliation_string: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
raw_author_name: Lei Chen
raw_affiliation_string: School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
raw_author_name: Xiuyi Jia
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Abstract: Labeling a sample in label distribution learning is highly expensive because it involves several labels at the same time and also requires assigning an exact value as the significance of each label. Therefore, active learning, which lowers the labeling cost by actively querying the labels of the most useful data, becomes especially critical for label distribution learning. Most of the known active query algorithms are for multi-label learning, and applying them directly to label distribution learning will lose some key supervisory information and thus fail to yield satisfactory experimental results. In this paper, we propose a sample diversity selection strategy based on the label distribution morphology, which can select diverse samples with different distribution morphologies from a large number of unlabeled samples for active querying. First, we use the feature space to construct a dissimilarity matrix that describes the pairwise dissimilarity among the unlabeled samples in order to pick a subset of samples that are representative of the unlabeled dataset. Second, using the information about the label distribution morphologies provided by the predicted labels of the unlabeled samples, we design a diversity loss score for each unlabeled sample. This score reflects the degree of difference between the sample and the labeled training sample. Finally, we use a convex optimization method to select valuable samples that are diverse from the labeled samples and represent the distribution of the unlabeled samples. The results of the comparison experiments demonstrate the effectiveness of our approach.
Improving generalized zero-shot learning via cluster-based semantic disentangling representation

Authors:
raw_author_name: Yi Gao
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, 610065, China
raw_author_name: Wentao Feng
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, 610065, China
raw_author_name: Lihuo He
raw_affiliation_string: School of Electronic Engineering, Xidian University, Xi’an, 710071, China
raw_author_name: Zhenan He
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, 610065, China
raw_author_name: Jiancheng Lv
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, 610065, China
raw_author_name: Tang Chenwei
raw_affiliation_string: College of Computer Science, Sichuan University, Chengdu, 610065, China

Abstract: Generalized Zero-Shot Learning (GZSL) aims to recognize both seen and unseen classes by training only the seen classes, in which the instances of unseen classes tend to be biased towards the seen class. In this paper, we propose a Cluster-based Semantic Disentangling Representation (CSDR) method to improve GZSL by alleviating the problems of domain shift and semantic gap. First, we cluster the seen data into multiple clusters, where the samples in each cluster belong to several original seen categories, so as to facilitate fine-grained semantic disentangling of visual feature vectors. Then, we introduce representation random swapping and contrastive learning based on the clustering results to realize the disentangling semantic representations of semantic-unspecific, class-shared, and class-unique. The fine-grained semantic disentangling representations show high intra-class similarity and inter-class discriminability, which improve the performance of GZSL by alleviating the problem of domain shift. Finally, we construct the visual-semantic embedding space by the variational auto-encoder and alignment module, which can bridge the semantic gap by generating strongly discriminative unseen class samples. Extensive experimental results on four public data sets prove that our method significantly outperforms state-of-the-art methods in generalized and conventional settings.