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Deep transformer and few‐shot learning for hyperspectral image classification

CAAI Transactions on Intelligence Technology 2023年第4期8卷 1323-1336页

作者： Qiong Ran Yonghao Zhou Danfeng Hong Meiqiao Bi Li Ni Xuan Li Muhammad Ahmad College of Information Science and Technology Beijing University of Chemical TechnologyBeijingChina Key Laboratory of Computational Optical Imaging Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina China Greatwall Technology Group Co. LtdShenzhenChina Department of Computer Science National University of Computer and Emerging SciencesChiniotPakistan

Recently,deep learning has achieved considerable results in the hyperspectral image(HSI)***,most available deep networks require ample and authentic samples to better train the models,which is expensive and inefficient in practical *** few‐shot learning(FSL)methods generally ignore the potential relationships between non‐local spatial samples that would better represent the underlying features of *** solve the above issues,a novel deep transformer and few‐shot learning(DTFSL)classification framework is proposed,attempting to realize fine‐grained classification of HSI with only a few‐shot ***,the spatial attention and spectral query modules are introduced to overcome the constraint of the convolution kernel and consider the information between long‐distance location(non‐local)samples to reduce the uncertainty of ***,the network is trained with episodes and task‐based learning strategies to learn a metric space,which can continuously enhance its modelling ***,the developed approach combines the advantages of domain adaptation to reduce the variation in inter‐domain distribution and realize distribution *** three publicly available HSI data,extensive experiments have indicated that the proposed DT‐FSL yields better results concerning state‐of‐the‐art algorithms.

关键词： deep learning feature extraction hyperspectral image classification

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BiDect: Leveraging cascaded Bidirectional Long Short-Term Memory for enhanced contextual modeling in plant disease classification

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Journal of Agricultural, Biological and Environmental Statistics 2025年 1-23页

作者： Medjadba, Yasmine Drid, Hamza Bardou, Dalal Zhan, Ying LAMIE Laboratory Computer science department Batna 2 University Batna Algeria Computer Science and Math department Abbes Laghrour University khenchela Algeria School of Computer and Software Nanyang Institute of Technology Nanyang China

Plant diseases pose a serious threat to global food security and economic stability due to significant crop losses. Timely detection and management are essential to reduce these losses. Unlike manual methods, which are slow and error-prone, deep learning (DL) provides accurate, automated solutions that save both time and effort. This study presents BiDect, a Patch Context Modeling approach using cascaded Bidirectional Long Short-term memory (Bi-LSTM) for plant disease detection and classification. Images are first divided into smaller patches to capture local fea-tures that may be missed in global analysis. Individual Bi-LSTM networks process these patch features to capture spatial dependencies. The final Bi-LSTM layer combines these local insights to identify global patterns, enhancing classification accuracy. This cascaded approach focuses on processing smaller patches, enabling the model to capture intricate patterns effectively, which is particularly beneficial for large-scale datasets. We evaluated the method on diverse datasets, testing its performance in both real-world fields and controlled laboratory conditions. Comparison with state-of-the-art models, such as ResNet, DenseNet, InceptionV3, Xception, and VGG19, demonstrated that the cascaded BiLSTM network, combined with CNN features, outperforms these models, achieving superior classification accuracy for real-world applications.

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Testing the Unknown: A Framework for OpenMP Testing via Random Program Generation

Testing the Unknown: A Framework for OpenMP Testing via Rand...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Laguna, Ignacio Chapman, Patrick Parasyris, Konstantinos Georgakoudis, Giorgis Rubio-Gonzalez, Cindy Lawrence Livermore National Laboratory Center for Applied Scientific Computing United States University of California Department of Computer Science Davis United States

ISBN: (纸本)9798350355543

We present a randomized differential testing approach to test OpenMP implementations. In contrast to previous work that manually creates dozens of verification and validation tests, our approach is able to randomly generate thousands of tests, exposing OpenMP implementations to a wide range of program behaviors. We represent the space of possible random OpenMP tests using a grammar and implement our method as an extension of the Varity program generator. By generating 1,800 OpenMP tests, we find various performance anomalies and correctness issues when we apply them to three OpenMP implementations: GCC, Clang, and Intel. We also present several case studies that analyze the anomalies and give more details about the classes of tests that our approach creates. © 2024 IEEE.

关键词： differential testing OpenMP random program generation software testing

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Enhancing Financial Accessibility: A Tailored UPI Payment Application for Divyangjan 10

Enhancing Financial Accessibility: A Tailored UPI Payment Ap...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Bhowmik, Biswajit Sudhama, Kruthika K. Dongala, Joshitha R. Antony, Reshma T. Girish, K.K. National Institute of Technology Karnataka Maharshi Sushrut Cas Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Surathkal575025 India

ISBN: (纸本)9798350384369

The emergence of financial technology (FinTech) has transformed the financial sector, introducing a new era characterized by state-of-the-art technologies that enhance speed, affordability, and accessibility. The proliferation of the internet and smartphones has further accelerated this transformation, fostering greater connectivity and global interaction. Subsequently, these advancements have significantly expanded financial inclusion, ensuring access to financial services for previously under-served populations. While the rise of FinTech has propelled financial inclusion for many, individuals with disabilities have not experienced commensurate improvements in their financial accessibility. As the banking sector increasingly migrates to online platforms, people with disabilities encounter barriers stemming from inaccessible websites, mobile applications, and online banking services. This paper introduces a specialized UPI payment application designed explicitly for individuals with disabilities. The objective is to integrate this underserved demographic into the digital financial landscape, fostering financial inclusion and enhancing access to essential financial services. © 2024 IEEE.

关键词： Decentralized finance

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A Novel Deep Learning Framework for Contraband Items Recognition in Smart City Applications 2nd

A Novel Deep Learning Framework for Contraband Items Recogni...

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2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Khalid, Shehzad Ahmed, Zubair Noor, Ayesha Sedik, Ahmed Asif, Waqar Department of Computer Engineering Bahria University Islamabad Pakistan Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh Saudi Arabia Computer Science and Cyber Security University of West London London United Kingdom

ISBN: (纸本)9789819783441

Ensuring safety and security is paramount in today’s complex environment, and the effective detection of contraband items plays a pivotal role in achieving this objective. Contraband items, ranging from illegal substances to unauthorized goods, pose a threat to public safety, security, and the overall well-being of smart city inhabitants. Such items are currently detected by human operator reviewing the images from X-ray baggage scanners. However, manual detection of contraband items is inherently challenging and time-consuming resulting in significant delays at crowded places such as airports, train-stations, shopping malls etc. Moreover, there is a significant risk of overlooking certain items that could pose potential harm. To address these challenges, there is a growing demand for intelligent systems for contraband items detection that can efficiently and accurately detect items whilst minimizing false negatives. Automated deep learning solutions offer a sophisticated and technologically advanced approach to enhance the accuracy and speed of the detection process. In our pursuit to address this challenge comprehensively, we have obtained an X-ray Imaging Dataset specifically curated for this purpose. The dataset includes five types of objects including guns, knives, pliers, scissors, and wrenches that are typically banned to carry along. In this paper, we have proposed a deep learning-based approach to efficiently and accurately detect contraband items from X-ray images. The proposed approach is based on YOLO architectures that has been shown to perform better for object detection in variety of domains both in terms of accuracy and real-time performance. We have evaluated different versions of YOLO to select the version that works best for contraband item detection from X-ray images. Yolo-v8 has shown superior performance followed by Yolo-v5 in terms of accuracy. Challenges regarding class imbalance have been addressed using data augmentation especially for clas

关键词： Deep learning

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Testing Constraint Checking Implementations via Principled Metamorphic Transformations 31

Testing Constraint Checking Implementations via Principled M...

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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Gao, Mingchen Wang, Huiyan Xu, Chang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9798350330663

Constraint checking techniques are being widely used for ensuring the consistency of software artifacts during their development and evolution (e.g., detecting inconsistency in an application's running contexts or identifying rule violation in the code being developed). Typically, consistency constraints are formulated and checked upon the changes of software artifacts under checking. When any constraint is violated, an inconsistency is said to occur and then follow-up actions can be taken to remedy the problem. Currently, various constraint checking techniques have been proposed and implemented with sophisticated mechanisms for higher efficiency and scalability. However, these implementations could be far from being well tested due to their oracle problems, i.e., hardly able to tell what the checking result should be, given any software artifacts and their consistency constraints to check. In this paper, we leverage metamorphic testing and propose a family of metamorphic relations catered for testing constraint checking implementations. We dedicatedly design these relations by following the sensitivity principle via a fine-granularity control and the diversity principle via input-oriented transformations. Our experiments reported promising results (disclosing 80 % mutation bugs and five real bugs) without the need of any manual labeling. © 2024 IEEE.

关键词： Software testing

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Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Compositional Optimization 38

Gradient-Free Methods for Nonconvex Nonsmooth Stochastic Com...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Zhuanghua Luo, Luo Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore CNRS@CREATE Ltd. 1 Create Way #08-01 CREATE Tower 138602 Singapore School of Data Science Fudan University Shanghai Key Laboratory for Contemporary Applied Mathematics China Department of Computer Science National University of Singapore Singapore

Stochastic compositional optimization (SCO) problems are popular in many real-world applications, including risk management, reinforcement learning, and meta-learning. However, most of the previous methods for SCO require the smoothness assumption on both the outer and inner functions, which limits their applications to a wider range of problems. In this paper, we study the SCO problem in that both the outer and inner functions are Lipschitz continuous but possibly nonconvex and nonsmooth. In particular, we propose gradient-free stochastic methods for finding the (δ, ϵ)-Goldstein stationary points of such problems with non-asymptotic convergence rates. Our results also lead to an improved convergence rate for the convex nonsmooth SCO problem. Furthermore, we conduct numerical experiments to demonstrate the effectiveness of the proposed methods. © 2024 Neural information processing systems foundation. All rights reserved.

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Multi-Classification Segmentation Method of Gastric Cancer Pathological Images Based on Deep Learning

Multi-Classification Segmentation Method of Gastric Cancer P...

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2024 lEEE International Conference on Advanced Information, Mechanical Engineering, Robotics and Automation, AIMERA 2024

作者： Zhou, Hehu Pan, Jingshan Na, Li Ding, Qingyan Zhou, Chengjun Du, Wantong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China The Second Hospital of Shandong University Department of Pathology Jinan China

ISBN: (纸本)9798350343335

Gastric cancer is a serious health threat, and pathological imaging is important in detecting it. These images can assist doctors in accurately determining the location of the cancer, thereby providing an important reference for clinical decision-making. In the field of image processing, deep learning technology is leading to more and more excellent segmentation models. The Trans-Unet model has achieved success in the image segmentation. However, when applying this model to gastric cancer pathological section data, the segmentation boundary appears jagged. We propose three possible solutions. First, we designed an attention connection module to replace the skip connections in the model to enhance the prediction accuracy of the model. Second, we designed a prediction processing unit that takes the model's prediction results as input and uses a Conditional Random Field (CRF) for further prediction calculations. To enable the model to meet the requirements of multi-classification of images, we designed a dynamic weight selection module to enhance the model's accuracy for multi-classification tasks. After our optimization, the optimized model improved by 8% on the DSC evaluation index and 39% on the HD evaluation index. In addition, the jagged boundary problem in the prediction results has also been effectively improved. Through comparative experiments and erosion experiments, we found that the improved method enhances the accuracy of model prediction and reduces the jagged results at the boundary. © 2024 IEEE.

关键词： Health risks

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Research on Error Handling Techniques for Speech Interaction 3

Research on Error Handling Techniques for Speech Interaction

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3rd IEEE International Conference on computer Systems, ICCS 2023

作者： Ma, Rui Zou, Xuechao Tao, Pin Tu, Yixiang Qinghai University Department of Computer Technology and Applications Xining China Ministry of Education Key Laboratory of Pervasive Computing Beijing China Tsinghua University Department of Computer Science and Technology Beijing China

ISBN: (纸本)9798350313666

With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech interaction systems when faced with recognition or comprehension challenges. A case study focusing on the common task of 'electronic spreadsheet editing' is conducted, wherein an experimental environment is developed by combining an open-source neural network code repository with a third-party speech recognition interface. Drawing from historical experience, a 'triplet' paradigm is constructed to parse user command texts, and an improved approach for intelligent selection of error recovery strategies is proposed. This approach automatically determines the appropriate strategy for different speech interaction errors based on user logs. Experiments on 20 users demonstrate that our proposed intelligent error handling techniques for speech interaction, exhibit performance similar to empirically designed systems in practical tasks. Moreover, they significantly enhance user satisfaction and task completion efficiency when utilizing the system. © 2023 IEEE.

关键词： Speech recognition

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Historical Analysis of Financial Fraud and Its Future 1

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International Conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Girish, K.K. Bhowmik, Biswajit Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory National Institute of Technology Karnataka Surathkal Mangalore575025 India

ISBN: (数字)9789819720040

ISBN: (纸本)9789819720033

As the world is sailing toward a highly advanced digital financial culture with the advent of financial technologies (FinTech), more and more people are now under the shore of financial inclusion. Subsequently, new opportunities are created in the financial sector, making it possible for people who were previously unbanked or underbanked to access financial services. However, the rise in financial fraud and its potential implications are creating a rift in the financial sector, resulting in substantial economic losses across the globe. This paper provides an in-depth comprehension of financial fraud, encompassing its historical perspectives and ramifications. After that, factors contributing to fraudulent behavior are highlighted. In addition, the paper presents a comprehensive framework for fraud classification and accentuates the impacts of financial fraud. Furthermore, the paper underscores the aspects influencing future occurrences of financial fraud, enabling the formulation of proactive strategies to prevent and mitigate it. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Losses

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