Reviewerly/exHarmony huggingface dataset:
Anomaly detection of aviation data bus based on SAE and IMD

Authors:
raw_author_name: Li Huang
raw_affiliation_string: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China
raw_author_name: Ge Hongjuan
raw_affiliation_string: College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract: To detect remote terminal (RT) spoofing attacks on MIL-STD-1553B data bus and prevent the network paralysis of integrated avionics system (IAS) caused by misjudgment, an anomaly detection method of aviation data bus based on the combination of sparse autoencoder (SAE) and integrated mahalanobis distance (IMD) is proposed. Aiming at the communication traffic training set with only normal data, an unsupervised learning algorithm SAE is used to train a model that only represents normal behavior. To combine the feature information of each layer within SAE, the IMD, which can measure the similarity between data characteristics, is used to obtain the anomaly score of test data, and the comprehensive anomaly score (CAS) is obtained by considering the reconstruction error between SAE input and output. To solve the problem that data distribution and detection requirements were not considered in a single threshold, a heuristic multi-threshold selection method is proposed, which maximizes the performance of the classifier by considering the accuracy, youden index (YI), and F1. The experimental results demonstrate the effectiveness and feasibility of the method.
Defining and visualizing process execution variants from partially ordered event data

Authors:
raw_author_name: Daniel Schuster
raw_affiliation_string: Fraunhofer Institute for Applied Information Technology FIT, Data Science and Artificial Intelligence, Schloss Birlinghoven, Sankt Augustin, 53757, Germany; RWTH Aachen University, Chair of Process and Data Science, Aachen, Germany
raw_author_name: Sebastiaan J. van Zelst
raw_affiliation_string: Fraunhofer Institute for Applied Information Technology FIT, Data Science and Artificial Intelligence, Schloss Birlinghoven, Sankt Augustin, 53757, Germany; RWTH Aachen University, Chair of Process and Data Science, Aachen, Germany
raw_author_name: Wil M.P. van der Aalst
raw_affiliation_string: Fraunhofer Institute for Applied Information Technology FIT, Data Science and Artificial Intelligence, Schloss Birlinghoven, Sankt Augustin, 53757, Germany; RWTH Aachen University, Chair of Process and Data Science, Aachen, Germany

Abstract: The execution of operational processes generates event data stored in enterprise information systems. Process mining techniques analyze such event data to obtain insights vital for decision-makers to improve the reviewed process. In this context, event data visualizations are essential. We focus on visualizing variants describing process executions that are control flow equivalent. Such variants are an integral concept for process mining and are used, e.g., for data exploration and filtering. We propose high-level and low-level variants covering different levels of abstraction and present corresponding visualizations. Compared to existing variant visualizations, we support partially ordered event data and allow for heterogeneous temporal information per event, i.e., we support both time intervals and time points. We evaluate our contributions using automated experiments showing practical applicability to real-life event data. Finally, we present a user study indicating significantly improved usefulness and ease of use of the proposed high-level variant visualization compared to existing variant visualizations for typical analysis tasks.
Augmented reality guided autonomous assembly system: A novel framework for assembly sequence input validations and creation of virtual content for AR instructions development

Authors:
raw_author_name: Eswaran M.
raw_affiliation_string: Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal 609 609, India
raw_author_name: V.S.S. Vara Prasad
raw_affiliation_string: Department of Mechanical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem 534 101, India
raw_author_name: M.V.A. Raju Bahubalendruni
raw_affiliation_string: Industrial Robotics and Manufacturing Automation Laboratory, Department of Mechanical Engineering, National Institute of Technology Puducherry, Karaikal 609 609, India

Abstract: In recent years, industries are striving to adapt immersive technologies in the real-time assembly shop floor setting to achieve effective outcomes in terms of enabling the worker to handle the complex cognitive assembly task execution/training circumstances. The manufacturers are presently facing difficulties with the process of Augmented reality (AR) instruction creation for the required product assembly system. The existing AR instruction development process demands highly skilled experts and more time consumption. Hence, the manufacturers are presently relying on third parties to acquire the required AR instructions by providing the necessary assembly particulars as input. Where, the cost of developing AR instruction is another big challenging concern. Predominantly, the significance of providing exact assembly input is vital for the experts to develop the appropriate AR content without any errors. An automated approach is developed in this study to resolve the difficulties with assembly sequence input for the AR instructions development process. The automated approach affords four different provisions that include validation of an assembly sequence input prior to the AR instructions development stage, suggestions for assembly sequence input generation, simulation of validated assembly sequence in the Virtual reality (VR) environment for user perception, and generation of virtual content for AR visualizations. Subsequently, the generated digital contents from the automated approach are imparted into the AR Unity platform to develop the required AR instructions for the product assembly system. Notably, this study explores the AR customized marker technique to visualize the AR content in the user's view of the physical context. Finally, the AR guidelines are built as apps for the selected 11-part assembly and deployed on the shop floor for the user to perceive the AR content through visualization devices for execution of assembly operations in a real-time context. In addition, the effectiveness of the automated approach is analyzed with the existing approach, and the consequences are explored through the process of receiving feedback from the experts about the error rate, cognitive level, and creation time.
Brain-inspired GCN: Modularity-based Siamese simple graph convolutional networks

Authors:
raw_author_name: Xiao Yao
raw_affiliation_string: The College of IOT Engineering, Hohai University, Changzhou, 213022, Jiangsu, China

Abstract: In graph representation learning, Graph Convolutional Networks (GCNs) and their variants have received much attention. However, GCNs encounter oversmoothing as the models get deeper, limiting their ability to aggregate node representations within high-order neighborhood. Inspired by the modular structure of the brain network, we propose Modularity-based Siamese Simple Graph Convolution (MS-SGC), a Siamese network architecture that incorporates the characteristics of brain modular structure into graph convolutional networks. Spectral clustering is leveraged to detect the modular structure in the graph, and then the weight of cross edges between modules is lowered. Siamese network is adopted to combine the modularity-preserved graph representation with the original graph representation, improving classification performance and reducing oversmoothing. Furthermore, a graph convolution method that functions as a linear low-pass graph filter is elaborated through spectral analysis to preserve the similarity of nodes within the same module and further alleviate the oversmoothing problem. We validate the effectiveness of MS-SGC on citation networks and extend our experimental analysis to various downstream tasks. Extensive experiments demonstrate that MS-SGC outperforms state-of-the-arts while reducing the time and computation complexity for node classification. Moreover, MS-SGC achieves competitive performance compared to other state-of-the-arts in node clustering, community prediction and text classification tasks.
Grassmannians of codes

Authors:
raw_author_name: Ilaria Cardinali
raw_affiliation_string: Dep. Information Engineering and Mathematics, University of Siena, Via Roma 56, I-53100 Siena, Italy
raw_author_name: Luca Giuzzi
raw_affiliation_string: D.I.C.A.T.A.M., University of Brescia, Via Branze 43, I-25123 Brescia, Italy

Abstract: Consider the point line-geometry Pt(n,k) having as points all the [n,k]-linear codes having minimum dual distance at least t+1 and where two points X and Y are collinear whenever X∩Y is a [n,k−1]-linear code having minimum dual distance at least t+1. We are interested in the collinearity graph Λt(n,k) of Pt(n,k). The graph Λt(n,k) is a subgraph of the Grassmann graph and also a subgraph of the graph Δt(n,k) of the linear codes having minimum dual distance at least t+1 introduced in [9]. We shall study the structure of Λt(n,k) in relation to that of Δt(n,k) and we will characterize the set of its isolated vertices. We will then focus on Λ1(n,k) and Λ2(n,k) providing necessary and sufficient conditions for them to be connected.
Are virtual reality applications effective for construction safety training and education? A systematic review and meta-analysis

Authors:
raw_author_name: Siu Shing Man
raw_affiliation_string: School of Design, South China University of Technology, Guangzhou, China

Abstract: Virtual reality (VR) gains attention in construction safety training because it allows users to simulate real activities without the risks of real activities. However, a literary work comprehensively describing the effectiveness of VR in construction safety training and education (CSTE) is lacking.This study provides a systematic review of the research related to VR applications for CSTE over the past decade using meta-analysis techniques. Standardized mean differences between traditional training methods and VR training were grouped by measurement. Potential moderators possibly affecting the effectiveness of VR in CSTE were analyzed.Results showed that VR is significantly more effective in construction training and education than traditional methods. The effectiveness of VR was 0.593, 0.432, and 0.777 higher than that of traditional methods for behaviors, skills, and experience measurements, respectively. The training context and mean work experience of trainees were two important moderators that significantly affected the effectiveness of VR in CSTE (p < 0.001).The presented results suggested the need for targeted development and management of VR technology in the construction industry and the early promotion of VR for general safety training among young, inexperienced construction workers.
RepDehazeNet: Dual subnets image dehazing network based on structural re-parameterization

Authors:
raw_author_name: Xiaozhong Luo
raw_affiliation_string: College of Information and Network Safety, People's Public Security University of China, Beijing 100038, China; Science and Technology Information Network Division, Beijing Police College, Beijing 102202, China
raw_author_name: Han Zhong
raw_affiliation_string: College of Information and Network Safety, People's Public Security University of China, Beijing 100038, China
raw_author_name: Junjie Lu
raw_affiliation_string: Information Center, Beijing Vocational College of Agriculture, Beijing 100031, China
raw_author_name: Chen Meng
raw_affiliation_string: R&D Office, Beijing Bee Connection Technology Co., Ltd., Beijing 100085, China
raw_author_name: Xu Han
raw_affiliation_string: Science and Technology Information Network Division, Beijing Police College, Beijing 102202, China

Abstract: In recent times, there has been notable and swift advancement in the field of image dehazing. Several deep learning techniques have demonstrated remarkable proficiency in resolving homogeneous dehazing issues. Nonetheless, the current dehazing approaches are generally formulated to deal with homogeneous haze, which is often undermined in real-world scenarios due to the uncertain haze dispersion. In this paper, we propose a dehazing model named RepDehazeNet by combining a structurally Reparameterization Encoder-Decoder subnet and a Full-Resolution Attention subnet. To be specific, the structural reparameterization idea is introduced into the encoder–decoder subnet to strengthen the feature extraction of dehazed images and improve the feature extraction speed. RepDehazeNet is compared with seven SOTA models on different datasets in terms of PSNR, SSIM, parameter quantity, and inference time. Compared to the DW-GAN model, the proposed RepDehazeNet model reduces the number of parameters by 2.7 million, and improves the inference speed by 90.3%, while achieving a higher PSNR of 0.5 dB on the NH-Haze2021 dataset. The experimental results demonstrate that the proposed RepDehazeNet model can effectively improve the real-time performance, accuracy of dehazing synthesized and nonhomogeneous haze images.
A supervised parallel optimisation framework for metaheuristic algorithms

Authors:
raw_author_name: Lloyd Fletcher
raw_affiliation_string: UKAEA, Culham Science Centre, Abingdon, Oxfordshire, OX14 3DB, United Kingdom

Abstract: A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption - Evolution Strategy (CMA-ES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.
A transition from porphyry Mo to vein-style Zn-Pb mineralization: Insights from hydrothermal fluid evolution of the Chalukou deposit, NE China

Authors:
raw_author_name: Lu-Ying Jin
raw_affiliation_string: China Copper Corporation Limited, Beijing 100082, China; Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, P.O. Box 9825, Beijing 100029, China
raw_author_name: Ke-Zhang Qin
raw_affiliation_string: Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, P.O. Box 9825, Beijing 100029, China; University of the Chinese Academy of Sciences, Beijing 100049, China
raw_author_name: Jun-Xing Zhao
raw_affiliation_string: Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, P.O. Box 9825, Beijing 100029, China
raw_author_name: Zhen-Zhen Li
raw_affiliation_string: China Institute of Disaster Prevention, Sanhe 065201, China
raw_author_name: Guang-Ming Li
raw_affiliation_string: Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, P.O. Box 9825, Beijing 100029, China

Abstract: To reveal the hydrothermal evolution from porphyry Mo to vein-style Zn-Pb mineralization, the Chalukou deposit in NE China with these two typical mineralization overprinted is selected for detailed research. From early to late hydrothermal veins, the quartz cathodoluminescence intensities change from bright to dark and the textures also evolve. The Ti concentrations of quartz in these veins change from high to moderate and then to low values. The fluid inclusion petrography, microthermometry and stable isotope analyses indicate intermediate-density magma-derived fluids (δ18Owater = +4.7 to + 6.2‰, δD = −132 to −136‰) exsolved at high temperatures (560° to 600 °C) with low to moderate salinity (2.4 to 8.0 wt% NaCl equiv) containing minor CO2 (<5.7 mol %). Early minor molybdenite occurred under lithostatic conditions at high temperatures (540° to 580 °C) during fluids pH change and early magnetite precipitation. Later main molybdenite precipitation at moderate temperatures (350° to 430 °C), result from fluid cooling and involving of meteoric water under the transition from lithostatic to hydrostatic conditions. The precipitation of pyrite in transitional veins at moderate temperatures (330° to 420 °C), and the late sphalerite-galena from a lower-temperature fluid (180° to 300 °C) is consistent with the cooling of magmatic fluid, and gradually dilution with meteoric water under hydrostatic conditions. It is deduced the transition from porphyry Mo to vein-style Zn-Pb mineralization is likely driven by progressive decrease in temperature of the magmatic fluids and the increasing influx of meteoric water.
3D face reconstruction from a single image based on hybrid-level contextual information with weak supervision

Authors:
raw_author_name: Yang Liu
raw_affiliation_string: School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
raw_author_name: Teng Ran
raw_affiliation_string: School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
raw_author_name: Liang Yuan
raw_affiliation_string: College of Information Science and Technology, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China; School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
raw_author_name: Kai Lv
raw_affiliation_string: School of Mechanical Engineering, Xinjiang University, Urumqi 830017, China
raw_author_name: Guoquan Zheng
raw_affiliation_string: College of Information Science and Technology, and Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China

Abstract: Currently, deep learning-based 3D face reconstruction methods have shown promising results. However, they ignore the contextual information of the face, which is a topologically unified entirety. This paper proposes a 3D face reconstruction approach based on hybrid-level contextual information. Firstly, we suggest a regression network with contextual modeling capability at the feature level, PPR-CNet, which adopts a preferential parameter regression to regress the 3DMM parameters dynamically based on their various impacts on the reconstructed 3D face. Furthermore, we design a contextual landmark loss to constrain the face geometry at the landmark level. We introduce a differentiable renderer combined with multiple loss functions for weakly-supervised training. Quantitative experiments on two benchmarks show our method outperforms several SOTA methods. Extensive qualitative experiments indicate that our method performs efficiently in realism, facial proportion, and occlusion.
Trust in a ‘trust-free’ system: Blockchain acceptance in the banking and finance sector

Authors:
raw_author_name: QingQiu Gan
raw_affiliation_string: Department of Business Administration, Anhui University, Hefei, People's Republic of China
raw_author_name: Raymond Yiu Keung Lau
raw_affiliation_string: Department of Information Systems, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong Special Administrative Region

Abstract: Blockchain is a disruptive technology that revolutionizes traditional business models and revamps relationships between financial transaction counterparties. While the volume of literature on blockchains has been rapidly growing in recent years, research on the relationship between trust and blockchain acceptance at the individual level is underexplored. Our study aims to fill this research gap by investigating the determinants of blockchain acceptance in the banking and finance sector from the affordance theory. We extend UTAUT model with trust-related constructs (trust in technology and trust among communities), advocate a research framework to examine the impact of trust on blockchain acceptance. Quantitative data were collected from 218 employees at Chinese banks, and structural equation modeling was applied to analyze the data. Our empirical analysis revealed that these trust relationships are positively associated with the intention to accept blockchain technology. The results of polynomial regressions and the response surface support the significance of the trust congruence effect. We make a theoretical contribution by extending the UTAUT with two trust relationships in a Fintech context. The results can also be used as a guideline for the practitioners, inspiring them to consider the critical determinants of blockchains acceptance in blockchain implementation.
Robust weapon detection in dark environments using Yolov7-DarkVision

Authors:
raw_author_name: Pavinder Yadav
raw_affiliation_string: Department of Mathematics and Scientific Computing, National Institute of Technology, Hamirpur, Himachal Pradesh, 177005, India
raw_author_name: Nidhi Gupta
raw_affiliation_string: Department of Computer Applications, National Institute of Technology, Kurukshetra, Haryana, 136119, India
raw_author_name: Pawan Kumar Sharma
raw_affiliation_string: Department of Mathematics and Scientific Computing, National Institute of Technology, Hamirpur, Himachal Pradesh, 177005, India

Abstract: Currently, weapon detection through video surveillance has been extensively studied using deep learning techniques by researchers. However, limited attention has been given to the detection of weapons in night time or dark scenarios. This paper aims to address this gap by proposing a novel approach for weapon detection specifically modified for low-light conditions. The authors demonstrate accurate and robust detection of weapons in challenging nighttime environments by modifying the deep learning model YOLOv7. The YOLOv7-DarkVision model has been developed by combining a brightening algorithm with the advanced image processing techniques and architecture of YOLOv7. A dataset of 15,367 images were collected for training of the model, along with five dark videos from various sources for performance evaluation. The derived detection model, which has a precision score of 95.50% and an F1-Score of 93.41%, performs absolutely well as a weapon detector.