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A multi-model approach to construction site safety: Fault trees, Bayesian networks, and ontology reasoning

Expert systems with Applications 2025年 288卷

作者： Shi, Donghui Gan, Shuling Zurada, Jozef Guan, Jian Wang, Feilong Weichbroth, Pawel School of Electronics and Information Engineering Anhui Jianzhu University Anhui Province Hefei230601 China Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving Anhui Jianzhu University Anhui Hefei230022 China Department of Information Systems Analytics and Operations College of Business University of Louisville 2301 S 3rd St LouisvilleKY40292 United States School of Electronic Science and Engineering Hunan University of Information Technology Hunan Changsha410151 China Department of Software Engineering Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Narutowicza 11/12 pomorskie Gdansk80-233 Poland

With the rapid expansion of the construction industry, accidents on construction sites have been increasingly common. Among these, collapses of earthwork foundation pits are particularly significant due to the sheer weight of the collapsed materials and the vast affected areas, resulting in substantial casualties and economic losses. This study aims to determine the causes of these construction safety and collapse incidents and understand their relationships to enable effective supervision and prevention during construction. Initially, the LDA model is used to categorize historical construction safety accident reports, and text mining is applied utilizing R language platform and the TF-IDF measure to extract keywords related to accident causative factors from historical accident reports. Following this, risk factors are evaluated to find the basic, intermediate, and top events of the accident, constructing a fault tree of casualties from earthwork foundation pit collapse (EFPC) accidents and analyzing the structural significance of risk factors. The fault tree is converted into a Bayesian network through image and numerical mapping, allowing the analysis of node sensitivity and the prediction of top event probability for informed construction accident prediction and prevention. Lastly, the study constructs an ontology knowledge base and knowledge graph in the realm of building safety, and establishes an ontology reasoning model using the Pellet reasoning machine and SWRL reasoning rules. The novel approach in this study involves integrating multiple advanced methodologies and provides a comprehensive framework and knowledge foundation for enhancing construction accident prediction and prevention as well as understanding and mitigating the risk-causing factors of earthwork foundation pit collapses in the construction industry. © 2025 Elsevier Ltd

关键词： Construction industry

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Quantum software engineering: A New Genre of Computing

arXiv

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arXiv 2022年

作者： Akbar, Muhammad Azeem Khan, Arif Ali Mahmood, Sajjad Rafi, Saima Department of Software Engineering LUT University Lappeenranta Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90570 Finland Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia University of Murcia Department of Informatics and Systems Murcia Spain

Quantum computing (QC) is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially tackle the limitations of classical computing. Over the last few years, major technology giants have invested in developing hardware and programming frameworks to develop quantum-specific applications. QC hardware technologies are gaining momentum, however, operationalizing the QC technologies trigger the need for software-intensive methodologies, techniques, processes, tools, roles, and responsibilities for developing industrial-centric quantum software applications. This paper presents the vision of the quantum software engineering (QSE) life cycle consisting of quantum requirements engineering, quantum software design, quantum software implementation, quantum software testing, and quantum software maintenance. This paper particularly calls for joint contributions of software engineering research and industrial community to present real-world solutions to support the entire quantum software development activities. The proposed vision facilitates the researchers and practitioners to propose new processes, reference architectures, novel tools, and practices to leverage quantum computers and develop emerging and next generations of quantum software. © 2022, CC BY.

关键词： software design

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Zero-Shot Audio Captioning Using Soft and Hard Prompts

arXiv

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arXiv 2024年

作者： Zhang, Yiming Xu, Xuenan Du, Ruoyi Liu, Haohe Dong, Yuan Tan, Zheng-Hua Wang, Wenwu Ma, Zhanyu The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Electronic Systems Aalborg University Aalborg9220 Denmark The Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom

In traditional audio captioning methods, a model is usually trained in a fully supervised manner using a human-annotated dataset containing audio-text pairs and then evaluated on the test sets from the same dataset. Such methods have two limitations. First, these methods are often data-hungry and require time-consuming and expensive human annotations to obtain audio-text pairs. Second, these models often suffer from performance degradation in cross-domain scenarios, i.e., when the input audio comes from a different domain than the training set, which, however, has received little attention. We propose an effective audio captioning method based on the contrastive language-audio pre-training (CLAP) model to address these issues. Our proposed method requires only textual data for training, enabling the model to generate text from the textual feature in the cross-modal semantic space. In the inference stage, the model generates the descriptive text for the given audio from the audio feature by leveraging the audio-text alignment from CLAP. We devise two strategies to mitigate the discrepancy between text and audio embeddings: a mixed-augmentation-based soft prompt and a retrieval-based acoustic-aware hard prompt. These approaches are designed to enhance the generalization performance of our proposed model, facilitating the model to generate captions more robustly and accurately. Extensive experiments on AudioCaps and Clotho benchmarks show the effectiveness of our proposed method, which outperforms other zero-shot audio captioning approaches for in-domain scenarios and outperforms the compared methods for cross-domain scenarios, underscoring the generalization ability of our method. © 2024, CC BY.

关键词： Semantics

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A Vision of DevOps Requirements Change Management Standardization

arXiv

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arXiv 2022年

DevOps (development and operations) aims to shorten the software development process and provide continuous delivery with high software quality. To get the potential gains of DevOps, the software development industry considering global software development (GSD) environment to hire skilled human resources and round-the-clock working hours. However, due to the lack of frequent communication and coordination in GSD, the planning and managing of the requirements change process becomes a challenging task. As in DevOps, requirements are not only shaped by development feedback but also by the operations team. This means requirements affect development, development affects operations and operations affect requirements. However, DevOps in GSD still faces many challenges in terms of requirement management. The purpose of this research project is to develop a DevOps requirement change management and implementation maturity model (DevOps-RCMIMM) that could assist the GSD organizations in modifying and improving their requirement management process in the DevOps process. The development of DevOps-RCMIMM will be based on the existing DevOps and RCM literature, industrial empirical study, and understanding of factors that could impact the implementation of the DevOps requirement change management process in the domain of GSD. This vision study presents the initial results of a systematic literature review that will contribute to the development of maturity levels of the proposed DevOps-RCMIMM. © 2022, CC BY.

关键词： Standardization

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GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

arXiv

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arXiv 2022年

作者： Song, Siyang Song, Yuxin Luo, Cheng Song, Zhiyuan Kuzucu, Selim Jia, Xi Guo, Zhijiang Xie, Weicheng Shen, Linlin Gunes, Hatice The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Baidu Inc Beijing100193 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Engineering Middle East Technical University Ankara Turkey School of Computer Science University of Birmingham Birmingham United Kingdom

Graph is powerful for representing various types of real-world data. The topology (edges’ presence) and edges’ features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, task-specific and crucial relationship cues may be disregarded by such manually defined topology and single-value edge features. In this paper, we propose the first general graph representation learning framework (called GRATIS) which can generate a strong graph representation with a task-specific topology and task-specific multi-dimensional edge features from any arbitrary input (i.e., graph or non-graph data). To learn each edge’s presence and multi-dimensional feature, our framework takes both of the corresponding vertices pair and their global contextual information into consideration, enabling the generated graph representation to have a globally optimal message passing mechanism for different down-stream tasks. The principled investigation results achieved for various graph analysis tasks (e.g., graph classification, vertex classification, and link prediction) on 11 graph and non-graph datasets show that our GRATIS can not only largely enhance pre-defined graphs but also learns a strong graph representation for non-graph data, with clear performance improvements on all tasks. In particular, the learned topology and multi-dimensional edge features provide complementary task-related cues for graph analysis tasks. Our framework is effective, robust and flexible, and is a plug-and-play module that can be combined with different backbones and Graph Neural Networks (GNNs) to generate a task-specific graph representation from various graph and non-graph data. Our code is made publicly available at https://***/SSYSteve/Learning-Graph-Representation-with-Task-specific-Topology-and-Multi-dimensional-Edge-Feature

关键词： Graph neural networks

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HARQ-IR Aided Short Packet Communications: BLER Analysis and Throughput Maximization

arXiv

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arXiv 2023年

作者： He, Fuchao Shi, Zheng Yang, Guanghua Li, Xiaofan Ye, Xinrong Ma, Shaodan the School of Intelligent Systems Science and Engineering and GBA B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China the School of Physics and Electronic Information Anhui Normal University Wuhu241002 China the State Key Laboratory of Internet of Things for Smart City the Department of Electrical and Computer Engineering University of Macau China

This paper introduces hybrid automatic repeat request with incremental redundancy (HARQ-IR) to boost the reliability of short packet communications. The finite blocklength information theory and correlated decoding events tremendously preclude the analysis of average block error rate (BLER). Fortunately, the recursive form of average BLER motivates us to calculate its value through the trapezoidal approximation and Gauss–Laguerre quadrature. Moreover, the asymptotic analysis is performed to derive a simple expression for the average BLER at high signal-to-noise ratio (SNR). Then, we study the maximization of long term average throughput (LTAT) via power allocation meanwhile ensuring the power and the BLER constraints. For tractability, the asymptotic BLER is employed to solve the problem through geometric programming (GP). However, the GP-based solution underestimates the LTAT at low SNR due to a large approximation error in this case. Alternatively, we also develop a deep reinforcement learning (DRL)based framework to learn power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process, which is solved by integrating deep deterministic policy gradient (DDPG) with subgradient method. The numerical results finally demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Novel 6dof Pose Estimation Method Using Transformer Fusion

SSRN

引用

SSRN 2023年

作者： Wang, Huafeng Zhang, Haodu Liu, Wanquan Hu, Zhimin Gao, Haoqi Lv, Weifeng Gu, Xianfeng School of Information Technology North China University of Technology Beijing100122 China School of Software Beihang University Beijing100191 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510335 China University of Wisconsin Madison United States Department of Computer Science Stony Brook University NY11794-2424 United States

Integrating heterogeneous information in multimodal tasks presents challenges in effectively leveraging complementary information. This is particularly evident in the context of 6D pose estimation, where the effective utilization of RGB-D data is crucial for extracting supplementary information that complements 2D and 3D data. Another challenge is aligning modalities implicitly to obtain robust estimation results. To tackle these challenges, we propose a Transformer-based fusion module that facilitates hierarchical fusion across modalities at different stages. This approach effectively combines disparate features and complements global information in a non-convolutional manner. Additionally, we employ an attentional residual structure to address two primary challenges. Firstly, it mitigates information loss that may occur due to the stacking depth of layers. Secondly, it leverages attention weights to adapt the output of the preceding layer, thereby enhancing modal alignment. We validate our approach on two benchmark datasets: LineMOD [1] and YCB-Video [2]. Our method outperforms most existing methods on the LineMOD dataset and achieves state-of-the-art results on the YCB-Video dataset, showcasing its superior generalization ability. © 2023, The Authors. All rights reserved.

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

引用

IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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A Domain-Agnostic Approach for Characterization of Lifelong Learning systems

arXiv

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arXiv 2023年

作者： Baker, Megan M. New, Alexander Aguilar-Simon, Mario Al-Halah, Ziad Arnold, Sébastien M.R. Ben-Iwhiwhu, Ese Brna, Andrew P. Brooks, Ethan Brown, Ryan C. Daniels, Zachary Daram, Anurag Delattre, Fabien Dellana, Ryan Eaton, Eric Fu, Haotian Grauman, Kristen Hostetler, Jesse Iqbal, Shariq Kent, Cassandra Ketz, Nicholas Kolouri, Soheil Konidaris, George Kudithipudi, Dhireesha Learned-Miller, Erik Lee, Seungwon Littman, Michael L. Madireddy, Sandeep Mendez, Jorge A. Nguyen, Eric Q. Piatko, Christine Pilly, Praveen K. Raghavan, Aswin Rahman, Abrar Ramakrishnan, Santhosh Kumar Ratzlaff, Neale Soltoggio, Andrea Stone, Peter Sur, Indranil Tang, Zhipeng Tiwari, Saket Vedder, Kyle Wang, Felix Xu, Zifan Yanguas-Gil, Angel Yedidsion, Harel Yu, Shangqun Vallabha, Gautam K. Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd. LaurelMD20723 United States Teledyne Scientific Company Intelligent Systems Laboratory 19 T.W. Alexander Drive NC27709 United States Department of Computer Science University of Texas at Austin AustinTX United States Department of Computer Science University of Southern California Los AngelesCA United States Department of Computer Science Loughborough University Loughborough United Kingdom Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States SRI International 201 Washington Rd PrincetonNJ United States University of Texas at San Antonio San AntonioTX United States Department of Computer Science University of Massachusetts Amherst AmherstMA United States Sandia National Laboratories AlbuquerqueNM United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Department of Computer Science Brown University ProvidenceRI United States Information and Systems Sciences Laboratory HRL Laboratories 3011 Malibu Canyon Road MalibuCA90265 United States Department of Computer Science Vanderbilt University NashvilleTN United States Argonne National Laboratory 9700 S Cass Ave LemontIL United States

Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed. This critical gap may be addressed through the development of "Lifelong Learning" systems that are capable of 1) Continuous Learning, 2) Transfer and Adaptation, and 3) Scalability. Unfortunately, efforts to improve these capabilities are typically treated as distinct areas of research that are assessed independently, without regard to the impact of each separate capability on other aspects of the system. We instead propose a holistic approach, using a suite of metrics and an evaluation framework to assess Lifelong Learning in a principled way that is agnostic to specific domains or system techniques. Through five case studies, we show that this suite of metrics can inform the development of varied and complex Lifelong Learning systems. We highlight how the proposed suite of metrics quantifies performance trade-offs present during Lifelong Learning system development - both the widely discussed Stability-Plasticity dilemma and the newly proposed relationship between Sample Efficient and Robust Learning. Further, we make recommendations for the formulation and use of metrics to guide the continuing development of Lifelong Learning systems and assess their progress in the future. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside"

arXiv

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arXiv 2024年

作者： Santina, Cosimo Della Corbato, Carlos Hernandez Sisman, Burak Leiva, Luis A. Arapakis, Ioannis Vakalellis, Michalis Vanderdonckt, Jean D’Haro, LuisFernando Manzi, Guido Becchio, Cristina Elamrani, Aïda Alirezaei, Mohsen Castellano, Ginevra Dimarogonas, Dimos V. Ghosh, Arabinda Haesaert, Sofie Soudjani, Sadegh Stroeve, Sybert Verschure, Paul Bacciu, Davide Deroy, Ophelia Bahrami, Bahador Gallicchio, Claudio Hauert, Sabine Sanz, Ricardo Lanillos, Pablo Iacca, Giovanni Sigg, Stephan Gasulla, Manel Steels, Luc Sierra, Carles Department of Cognitive Robotics Delft University of Technology Delft Netherlands Oberpfaffenhofen Germany Department of Computer Science University of Luxembourg Luxembourg 4 Telefónica I+D Spain AEGIS IT Research GmbH Germany Université catholique de Louvain Belgium Speech Technology and Machine Learning Group ETSI de Telecomunicación Universidad Politécnica de Madrid Madrid Spain Alien Venture Studio Alien Technology Transfer Rome Italy Hamburg Germany Institut Jean Nicod École Normale Supérieure Paris France Research Technology and Development Department Digital Industry Software Helmond Netherlands Department of Information Technology Uppsala University Uppsala Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Max Planck Institute for Software Systems Kaiserslautern Germany Department of Electrical Engineering Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Amsterdam Netherlands Donders Centre for Neuroscience Radboud University Nijmegen Netherlands Computer Science Department University of Pisa Pisa Italy Faculty of Philosophy Philosophy of Science The Study of Religion Ludwig Maximilian University of Munich Munich Germany School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Autonomous Systems Laboratory Universidad Politécnica de Madrid Spain Madrid Spain Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Information and Communications Engineering Aalto University Espoo Finland Department of Electronic Engineering Universitat Politècnica de Catalunya Barcelona Spain Studio Stelluti Brussels Belgium Barcelona Spain

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices that are connected with projects funded by the EIC Pathfinder Challenge called "Awareness Inside", a nonrecurring call for proposals within Horizon Europe that was designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems. © 2024, CC BY.

关键词： Robotics

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