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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Multichannel Seismic Deconvolution via 2D K-SVD and Convolutional Sparse Coding 3

Multichannel Seismic Deconvolution via 2D K-SVD and Convolut...

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3rd International Meeting for Applied Geoscience and Energy, IMAGE 2023

作者： Zhang, Guiqian Gao, Xiayu Zou, Bangli Wang, Yaojun Chen, Yingzhu China College of Computer Science and Software Engineering Shenzhen University China

Seismic deconvolution encounters problems of multiplicity and instability. The regularization technique based on various prior knowledge is used to constrain the range of solutions and improve the stability of deconvolution results. We use dictionary learning as a technical means to consider both vertical resolution and lateral continuity issues in reflectivity inversion problem. Considering the drawbacks of patch-based operations in 1D dictionary learning, we adopt 2D K-SVD and convolutional sparse coding (CSC) based on global feature extraction as the dictionary learning methods. We divide the seismic profile into low-frequency component and high-frequency component, and reconstruct their corresponding reflectivity with 2D K-SVD and CSC, respectively. Finally, the seismic deconvolution with high resolution and strong continuity is obtained by summing the results of each component deconvolution. The effectiveness of the proposed method is verified by Marmousi model data. © 2023 Society of Exploration Geophysicists and the American Association of Petroleum Geologists.

关键词： Reflection

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Efficient and Verifiable Blockchain Data Query Scheme Based on External Database 20

Efficient and Verifiable Blockchain Data Query Scheme Based ...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chunli, Zhao Mingxing, H.E. Yaxin, Tang School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798350318982

Although the development of blockchain is getting faster and faster, and the application scenarios are becoming more and more extensive, it still faces problems such as low efficiency of data query and single query function. The database has a powerful and mature data management system, which has a natural advantage in solving these problems. Therefore, based on the method of external database, this paper extracts blockchain data and stores it off-chain, and introduces third-party service providers to provide users with diversified query services. To ensure that users can obtain correct and complete query results from service providers, this paper uses an authentication data structure to design a result verification scheme, which supports users to further verify the results after obtaining the query results. The experimental results show that this scheme can provide users with efficient query service and users can verify the results. © 2023 IEEE.

关键词： Blockchain

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Medicine and Disease Association Prediction via Attention-Based Medical Heterogeneous Information Network Representation Learning

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 69-78页

作者： Zhang, Bin Yang, Dan Lin, Zhihuang Master of Computer Science and Software Engineering Department University of Science and Technology Liao Ning Anshan China School of Computer Science and Software Engineering University of Science and Technology Liao Ning Anshan China

Knowledge of medication and disease has been rapidly accumulated. Also, an increasing number of researchers have paid more attention to predicting medicine-disease associations by machine learning methods. The associations of entities in the medical heterogeneous information network involve different association types. In this paper, we propose MHIN-MD, a representation learning of medical heterogeneous network model to solve the association between medicines and diseases in medical heterogeneous information network. Specifically, we first construct a medical heterogeneous information network contains diseases, medicines, diagnosis, etc. Next, we design a neural network model to learn the feature vector representation of medical nodes information. We try to find the association between medicines and diseases through the characteristics of medicines structure and the interaction between medicines. Using the Euclidean distance to calculate medicine similarity and disease similarity, generates a set of similar nodes between medicines and diseases to prediction association between medicines and diseases. Finally, our extensive experiments on MIMIC- III datasets and DrugBank datasets, the result demonstrates that MHIN-MD can outperform baselines in the association prediction. © 2022. All Rights Reserved.

关键词： Diagnosis

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Impact of Feature Encoding on Malware Classification Explainability 15

Impact of Feature Encoding on Malware Classification Explain...

引用

15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Manai, Elyes Mejri, Mohamed Fattahi, Jaouhar Laval University Department of Computer Science & Software Engineering Quebec Canada

ISBN: (纸本)9798350321388

This paper investigates the impact of feature encoding techniques on the explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a malware classification dataset, we trained an XGBoost model and compared the performance of two feature encoding methods: Label Encoding (LE) and One Hot Encoding (OHE). Our findings reveal a marginal performance loss when using OHE instead of LE. However, the more detailed explanations provided by OHE compensated for this loss. We observed that OHE enables deeper exploration of details in both global and local contexts, facilitating more comprehensive answers. Additionally, we observed that using OHE resulted in smaller explanation files and reduced analysis time for human analysts. These findings emphasize the significance of considering feature encoding techniques in XAI research and suggest potential for further exploration by incorporating additional encoding methods and innovative visualization approaches. © 2023 IEEE.

关键词： Signal encoding

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Design and Implementation of the Chinese Character Font Recognition System Based on Binary Convolutional Encoding and Decoding Network 5

Design and Implementation of the Chinese Character Font Reco...

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5th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2023

作者： Zhang, Xiangyang School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9798350333442

With the in-depth research of computer vision and natural language processing, Chinese character font recognition technology has been widely used. However, due to the large number, complex shape, and changeable style of Chinese characters, the traditional methods based on image feature extraction and classifier design are difficult to deal with the differences among different fonts effectively. Based on the above situation, this paper mainly studies designing and implementing the Chinese character font recognition system based on a binary convolutional encoding and decoding network (BCEDN). First, this paper introduces the principle and structure of Convolutional Encoding and Decoding Networks (CEDN) and how they can be applied to image generation tasks. Second, this paper proposes a binarization strategy to convert real-valued weights and activation functions in CEDN into binary weights and activation functions, thereby reducing the model's storage space and computational complexity while maintaining high generation quality. Then, the BCEDN model is trained and tested on this dataset. Finally, this paper analyses the performance of the BCEDN model on the Chinese character font recognition task through the results of quantitative and qualitative experiments and compares them with other related methods. The proposed BCEDN method has the advantages of high efficiency, stability, and scalability, having vital research significance in the field of Chinese character font recognition. © 2023 IEEE.

关键词： Convolution

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Blur-Deblur Algorithm for Pressure-Sensitive Paint Image Based on Variable Attention Convolution

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computers, Materials & Continua 2025年第3期82卷 5239-5256页

作者： Ruizhe Yu Tingrui Yue Lei Liang Zhisheng Gao School of Computer and Software Engineering Xihua UniversityChengdu610039China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing210016China Low Speed Aerodynamics Institute China Aerodynamics Research and Development CenterMianyang621000China

In the PSP(Pressure-Sensitive Paint),image deblurring is essential due to factors such as prolonged camera exposure times and highmodel velocities,which can lead to significant image *** deblurring methods applied to PSP images often suffer from limited accuracy and require extensive computational *** address these issues,this study proposes a deep learning-based approach tailored for PSP image *** that PSP applications primarily involve the accurate pressure measurements of complex geometries,the images captured under such conditions exhibit distinctive non-uniform motion blur,presenting challenges for standard deep learning models utilizing convolutional or attention-based *** this paper,we introduce a novel deblurring architecture featuring multiple DAAM(Deformable Ack Attention Module).These modules provide enhanced flexibility for end-to-end deblurring,leveraging irregular convolution operations for efficient feature extraction while employing attention mechanisms interpreted as multiple 1×1 convolutions,subsequently reassembled to enhance ***,we incorporate a RSC(Residual Shortcut Convolution)module for initial feature processing,aimed at reducing redundant computations and improving the learning capacity for representative shallow *** preserve critical spatial information during upsampling and downsampling,we replace conventional convolutions with wt(Haar wavelet downsampling)and dysample(Upsampling by Dynamic Sampling).This modification significantly enhances high-precision image *** integrating these advanced modules within an encoder-decoder framework,we present the DFDNet(Deformable Fusion Deblurring Network)for image blur removal,providing robust technical support for subsequent PSP data *** evaluations on the FY dataset demonstrate the superior performance of our model,achieving competitive results on the GOPRO and HIDE datasets.

关键词： Pressure-sensitive paint deep learning image deblurring typeset variable attention convolution

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Anomaly Detection in Dynamic Graphs Via Long Short-Term Temporal Attention Network

Anomaly Detection in Dynamic Graphs Via Long Short-Term Temp...

引用

2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Gao, Zu-Kang Ma, Le-Ming Li, Mei-Ting Zhao, Gen Li, Mark Jun-Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350303780

Anomaly detection in dynamic graphs has drawn increasing attention in social networks, e-commerce, and cybersecurity etc. Capturing the evolution patterns using temporal features is crucial for dynamic graphs anomaly detection. However, existing approaches do not thoroughly consider the long-term and short-term temporal features, which lead to the loss of information about the evolution patterns of dynamic graphs. In this paper, we propose a end-to-end an anomaly detection algorithm in dynamic graphs via Long Short-term Temporal Attention network (GraphLSTA). GraphLSTA captures the evolution patterns of dynamic graphs by effectively extracting and fusing the long-term and short-term temporal features through a recurrent attention mechanism, improving the detection performance. © 2023 IEEE.

关键词： Anomaly detection

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A Multi-constraint 3D Mesh Models Deformation Method

A Multi-constraint 3D Mesh Models Deformation Method

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Chuangchuang Chen, Xiaobing Lin, Jianchu Guo, Shuxin Wang, Rongrong Gu, Yinxi Huaiyin Insititute of Technology Faculty of Computer and Software Engineering Huai'an China

ISBN: (纸本)9798350306293

Aiming at the problems of too many control vertices and difficult operation of the traditional free deformation technique, a multi-constraint 3D mesh models deformation method is proposed. Firstly, the input model is embedded into a sparse tetrahedral control mesh, and the positional relationship between the model and the control mesh is established using tetrahedral coordinates. Secondly, skeleton nodes are set on the model to generate skeleton segments, control mesh vertices are bound to the skeleton segments, the control mesh is deformed by manipulating the rotation and translation of the skeleton nodes, and finally the new positions of the model vertices are computed based on the control mesh. Since the skeleton drives a sparse tetrahedral control mesh, the accuracy of model deformation will be reduced, resulting in a series of problems in the deformed model. In order to optimize the model deformation, this paper sets the influence range of the control mesh on the model deformation by the weight of the constraint region, adds point constraints to the control mesh vertices to set the change range of this vertex, and finally calculates the correction factor according to the stretch constraints and volume constraints, and optimally adjusts the position of the control meshes vertices. The experimental results show that this method can make up for the problems of the traditional free deformation technique, which is complicated and unintuitive to operate, and improve the accuracy of deforming the model. © 2023 IEEE.

关键词： Mesh generation

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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