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Safety Risk Recognition Method Based on Abnormal Scenarios

BUILDINGS 2022年第5期12卷 562页

作者： Li, Ziqi Song, Bo Li, Dongsheng Dalian Univ Technol Sch Civil Engn Dalian 116024 Peoples R China Dalian Ocean Univ Sch Marine Technol & Environm Dalian 116023 Peoples R China

Construction safety monitoring is a significant issue in practical engineering. Unfortunately, specific techniques in this field still heavily depend on artificial monitoring. To detect the abnormal scenarios during the construction process automatically, a method was proposed for the detection and localization of abnormal scenarios in time and space. The method consists of three components: (1) an I3D-AE video prediction model, which extracts the video features from multiple I3Ds and reconstructs the video by 3D deconvolution;(2) a spatial localization module AS-CAM, which determines the location of abnormal areas via back-propagating the I3D-AE;(3) a temporal parameter S-t, which can calculate the abnormal time period. The effectiveness of the method was verified with the use of a dataset, and the resulting data were plotted as ROC curves. The results indicated that the proposed method exceeded 0.9 on the frame-level test and 0.76 on the pixel-level test with the use of the AUC evaluation metric. Therefore, it can be used to assist the construction managers to improve the efficiency of construction safety management.

关键词： abnormal scenarios detection localization video prediction autoencoder construction process

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Robust and discrete matrix factorization hashing for cross-modal retrieval

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PATTERN RECOGNITION 2022年 122卷 108343-108343页

作者： Zhang, Donglin Wu, Xiao-Jun Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

Hashing based methods have gained great success for cross-modal similarity search, due to its fast query speed and low storage cost. However, there are some challenging problems that need to be further solved: 1) Many approaches are sensitive to noises and outliers, because pound 2 norm is utilized in the objec-tive function, the error may be amplified. 2) Most existing methods take relaxation or rounding scheme to generate binary codes, causing a large quantization loss. 3) Many supervised cross-media algorithms usually take a large n x n matrix to preserve the similarity relationship, leading to large calculation and making them unscalable. To mitigate these challenges, we develop a novel cross-media search algorithm, i.e., robust and discrete matrix factorization hashing, dubbed RDMH. The method takes a two-step strat-egy. In the first phase, the pound 2 , 1 norm is utilized to improve the robustness, which makes our model not sensitive to noises and outliers. We can learn the hash codes directly by the proposed discrete optimiza-tion method instead of relaxation scheme, avoiding the large quantization loss. Moreover, RDMH corre-lates the hash codes and semantic labels directly instead of manipulating the large similarity matrix. In the second phase, we propose an autoencoder strategy to learn the hash functions, more valuable infor-mation can be preserved and making the hash functions more powerful. Comprehensive experiments on several databases demonstrate the superior performance and efficacy of the developed RDMH. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Cross-modal retrieval Hashing autoencoder Discrete optimization

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Coarse-to-fine fusion for language grounding in 3D navigation

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KNOWLEDGE-BASED SYSTEMS 2023年第1期277卷

作者： Nguyen, Thanh Tin Vo, Anh H. Choi, Soo-Mi Kim, Yong-Guk Sejong Univ Dept Comp Engn Seoul South Korea

We present a new network whereby an agent navigates in the 3D environment to find a target object according to a language-based instruction. Such a task is challenging because the agent has to understand the instruction correctly and takes a series of actions to locate a target among others without colliding with obstacles. The essence of our proposed network consists of a coarse-to-fine fusion model to fuse language and vision and an autoencoder to encode visual information effectively. Then, an asynchronous reinforcement learning algorithm is used to coordinate detailed actions to complete the navigation task. Extensive evaluation using three different levels of the navigation task in the 3D Vizdoom environment suggests that our model outperforms the state-of-the-art. To see if the proposed network can deal with a real-world 3D environment for the navigation task, it is combined with Rec-BERT, which is based on REVERIE. The result suggests that it performs better, especially for unseen cases, and it is also useful to visualize what and when the agent pays attention to while it navigates in a complex indoor environment. & COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Language grounding Vision-language navigation Coarse-to-fine fusion autoencoder Reinforcement learning 3D vizdoom REVERIE

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GHRS: Graph-based hybrid recommendation system with application to movie recommendation

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期200卷 1页

作者： Darban, Zahra Zamanzadeh Valipour, Mohammad Hadi Monash Univ Fac Informat Technol Melbourne Australia Ostadkar Co Dept Engn & Prod Tehran Iran

Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. While most existing recommender systems rely either on a content based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. This paper proposes a recommender system method using a graph-based model associated with the similarity of users' ratings in combination with users' demographic and location information. By utilizing the advantages of autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) outperformed many existing recommendation algorithms on recommendation accuracy. Also, the method achieved significant results in the cold-start problem. All experiments have been performed on the MovieLens dataset due to the existence of users' side information.

关键词： Recommendation system Deep learning Graph-based modeling autoencoder Cold-start

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W-shaped network: a lightweight network for real-time infrared and visible image fusion

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JOURNAL OF ELECTRONIC IMAGING 2023年第6期32卷 063005-063005页

作者： Zhang, Tingting Du, Huiqian Xie, Min Beijing Inst Technol Sch Integrated Circuits & Elect Beijing Peoples R China

autoencoder (AE) is widely used in image fusion. However, AE-based fusion methods usually use the same encoder to extract the features of images from different sensors/modalities without considering the differences between them. In addition, these methods cannot fuse the images in real time. To solve these problems, an end-to-end fusion network is proposed for fast infrared image and visible image fusion. We design an end-to-end W-shaped network (W-Net), which consists of two independent encoders, one shared decoder and skip connections. The two encoders extract the representative features of images from different sources respectively, and the decoder combines the hierarchical features from corresponding layers and reconstructs the fused image without using additional fusion layer or any handcrafted fusion rules. Skip connections are added to help retain the details and salient features in the fused image. Specifically, W-Net is lightweight, with fewer parameters than the existing AE-based methods. The experimental results show that our fusion network performs well in terms of subjective and objective visual assessments compared with other state-of-the-art fusion methods. It can fuse the images very fast (e.g., the fusion time of 20 pairs of images in the TNO dataset is 0.871 to 1.081 ms), operating above real-time speed.

关键词： image fusion autoencoder multi-scale features lightweight network

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Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records

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JOURNAL OF BIOMEDICAL INFORMATICS 2019年 99卷 103291-000页

作者： Huang, Li Shea, Andrew L. Qian, Huining Masurkar, Aditya Deng, Hao Liu, Dianbo Tsinghua Univ Acad Arts & Design Beijing 10084 Peoples R China Tsinghua Univ Future Lab Beijing 10084 Peoples R China MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Beijing Univ Technol Coll Appl Math & Phys Sci Beijing 100124 Peoples R China Northeastern Univ Sch Engn Boston MA 02115 USA Massachusetts Gen Hosp Dept Anesthesia Crit Care & Pain Med Boston MA 02115 USA Johns Hopkins Univ Sch Publ Hlth Baltimore MD USA Boston Childrens Hosp Boston MA 02115 USA Harvard Univ Med Sch Boston MA 02115 USA

Electronic medical records (EMRs) support the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events. But so far most algorithms have been centralized, taking little account of the decentralized, non-identically independently distributed (non-IID), and privacy-sensitive characteristics of EMRs that can complicate data collection, sharing and learning. To address this challenge, we introduced a community-based federated machine learning (CBFL) algorithm and evaluated it on non-IID ICU EMRs. Our algorithm clustered the distributed data into clinically meaningful communities that captured similar diagnoses and geographical locations, and learnt one model for each community. Throughout the learning process, the data was kept local at hospitals, while locally-computed results were aggregated on a server. Evaluation results show that CBFL outperformed the baseline federated machine learning (FL) algorithm in terms of Area Under the Receiver Operating Characteristic Curve (ROC AUC), Area Under the Precision-Recall Curve (PR AUC), and communication cost between hospitals and the server. Furthermore, communities' performance difference could be explained by how dissimilar one community was to others.

关键词： Distributed clustering autoencoder Federated machine learning Non-IID Critical care

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Unsupervised Anomaly Detection via Nonlinear Manifold Learning

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第11期24卷 111008-111008页

作者： Yousefpour, Amin Shishehbor, Mehdi Foumani, Zahra Zanjani Bostanabad, Ramin Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection. The majority of existing anomaly detection methods either are exclusively developed for (semi) supervised settings, or provide poor performance in unsupervised applications where there are no training data with labeled anomalous samples. To bridge this research gap, we introduce a robust, efficient, and interpretable methodology based on nonlinear manifold learning to detect anomalies in unsupervised settings. The essence of our approach is to learn a low-dimensional and interpretable latent representation (aka manifold) for all the data points such that normal samples are automatically clustered together and hence can be easily and robustly identified. We learn this low-dimensional manifold by designing a learning algorithm that leverages either a latent map Gaussian process (LMGP) or a deep autoencoder (AE). Our LMGP-based approach, in particular, provides a probabilistic perspective on the learning task and is ideal for high-dimensional applications with scarce data. We demonstrate the superior performance of our approach over existing technologies via multiple analytic examples and real-world datasets.

关键词： anomaly detection manifold learning novelty detection Gaussian process uncertainty quantification autoencoder artificial intelligence data-driven engineering inverse methods for engineering applications machine learning for engineering applications

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Alzheimer's disease prediction based on continuous feature representation using multi-omics data integration

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2022年 223卷

作者： Abbas, Zeeshan Tayara, Hilal Chong, Kil To Jeonbuk Natl Univ Dept Elect & Informat Engn Jeonju 54896 South Korea Air Univ Inst Av & Aeronaut IAA Islamabad 44000 Pakistan Jeonbuk Natl Univ Sch Int Engn & Sci Jeonju 54896 South Korea Jeonbuk Natl Univ Adv Elect & Informat Res Ctr Jeonju 54896 South Korea

Alzheimer's disease (AD) is a neurological disease characterized by complex molecular pathways and neural tissue complexity. Investigation into its molecular structure and mechanisms are ongoing, and no therapeutically useful genetic risk factors have been identified. As a result, brain images such as magnetic resonance imaging (MRI) and cognitive testing have been used to diagnose AD. Recently, various independent studies have generated and evaluated large-scale omics data from various brain regions, including the prefrontal cortex. Therefore, strategies for detecting or predicting AD must be developed using these data. In addition, integration of these omics data can be a valuable resource for gaining a more thorough understanding of the disease. This study developed a machine-learning-based approach for predicting AD using DNA-methylation and gene expression datasets. It is one of the challenging tasks to manage these data while building a prediction model since these contain tens of thousands of features and have a high dimensional and low sample size (HDLSS) characteristic. To solve this dilemma, we employed an autoencoder (AE) to generate minimized and continuous feature representation. We used multiple machine-learning approaches to predict AD after receiving the encoded data and calculated the accuracy and area under the curve (AUC). Furthermore, we showed that combining DNA methylation and gene expression data can increase the prediction accuracy. Finally, we compared our method to state-of-the-art technique and found that the proposed methodology outperformed it by improving the accuracy and AUC by 9.5 and 10.6%, respectively.

关键词： Alzheimer disease autoencoder DNA methylation Gene expression Machine learning

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Deep attributed network representation learning of complex coupling and interaction

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KNOWLEDGE-BASED SYSTEMS 2021年 212卷 106618-106618页

作者： Li, Zhao Wang, Xin Li, Jianxin Zhang, Qingpeng Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Key Lab Cognit Comp & Applicat Tianjin 300350 Peoples R China Bank Shizuishan Internet Finance Dept Yinchuan 750000 Ningxia Peoples R China Deakin Univ Sch Informat Technol Geelong Vic 3221 Australia City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Networks that can describe complex systems in nature are increasingly coupled and interacted, and effective modeling on complex coupling and interaction information is an important research direction of artificial intelligence. Representation learning provides us with a paradigm to solve such issues, but the current network representation learning methods are difficult to capture the coupling and interaction information in complex networks. In this paper, we propose a novel deep attributed network representation learning model framework (RolEANE), which can effectively preserve the highly nonlinear coupling and interactive network topological structure and attribute information. We design two different structural role proximity enhancement strategies for the deep autoencoder in the model framework, so that it can efficiently capture network topological structure and attribute information. In addition, the neighbor-modified Skip-Gram model in our model framework can efficiently and seamlessly integrate network topological structure and attribute information, and the selection of an appropriate representation learning output strategy can significantly improve the final performance of the algorithm. The experiments on four real datasets show that our method consistently outperforms the state-of-the-art network representation learning methods. On the node classification task, the average performance is improved by 4.52%-10.28% than the optimal baseline method;on the link prediction task, the average performance is 4.63% higher than the optimal baseline method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Network representation learning Attributed network autoencoder Structural role proximity

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Inverse design of a topological phononic beam with interface modes

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2023年第1期56卷 015106-015106页

作者： Muhammad, Oluwaseyi Ogun, Oluwaseyi Kennedy, John Trinity Coll Dublin Dept Mech Mfg & Biomed Engn Coll Green Dublin 02 Ireland

Inspired by the idea of topological mechanics and geometric phase, the topological phononic beam governed by topological invariants has seen growing research interest due to generation of a topologically protected interface state that can be characterized by geometric Zak phase. The interface mode has maximum amount of wave energy concentration at the interface of topologically variant beams with minimal losses and decaying wave energy fields away from it. The present study has developed a deep learning based autoencoder (AE) to inversely design topological phononic beam with invariants. By applying the transfer matrix method, a rigorous analytical model is developed to solve the wave dispersion relation for longitudinal and bending elastic waves. By determining the phase of the reflected wave, the geometric Zak phase is determined. The developed analytical models are used for input data generation to train the AE. Upon successful training, the network prediction is validated by finite element numerical simulations and experimental test on the manufactured prototype. The developed AE successfully predicts the interface modes for the combination of topologically variant phononic beams. The study findings may provide a new perspective for the inverse design of metamaterial beam and plate structures in solid and computational mechanics. The work is a step towards deep learning networks suitable for the inverse design of phononic crystals and metamaterials enabling design optimization and performance enhancements.

关键词： phononic crystals metamaterials deep learning autoencoder interface mode

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