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7th International Conference on Vision, Image and Signal Processing, ICVISP 2023

作者： Yuan, Ge Zhou, Jie Huang, Chucheng Gao, Can Wang, Yangbo Shen, Xinrui National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China College of Mathematics and Statistics Shenzhen University Guangdong Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

ISBN: (纸本)9781839539848

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches. © The Institution of engineering & Technology 2023.

关键词： Fuzzy clustering

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The Impact of 5G Technology on Agile Project Management: A Cross-Industry Analysis 5

The Impact of 5G Technology on Agile Project Management: A C...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Mishra, Mahesh Kumar Selvaraj, Krishnamoorthy Santosh, Kathari Aarif, Mohd Suma Christal Mary, S. Kiran Bala, B. Delta System and Software Inc 2320 Grover Cleveland Dr McKinneyTX75072 United States Encloud Service Llc Dept of Erp KellerTX United States Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Iosrp UP Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350385649

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the Technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world. © 2024 IEEE.

关键词： Decision making

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Improving Medical Predictions with Label Noise Tolerant Classification 4

Improving Medical Predictions with Label Noise Tolerant Clas...

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Vernekar, Shivam Ayyar, Arun Rajagopalan, Arun Kumar, Bijendra Mishra, Vivek Kumar Evive Software Analytics Pvt. Ltd. Bengaluru India Santa Fe Research Pvt Ltd Chennai India Evive Software Analytics Pvt. Ltd. Data Science Bengaluru India Galgotia College of Engineering and Technology Electrical and Electronics Engineering Greater Noida India

ISBN: (纸本)9781665474368

Supervised machine learning algorithms depend crucially on availability of a large pool of correctly labelled instances. However many real world datasets may contain noisy features and label values. This is even more problematic for medical datasets since noise can be asymmetric (class dependent) due to imprecise class definitions or subjective human biases during data collection. There is easy availability of a large pool of unlabelled instances which can't be directly used for training, for example raw data from medical devices like fitness trackers, data from health surveys etc. Labelling them (with respect to certain task) with the help of human experts might be a time consuming and a costly process. Besides, learning algorithms trained on noisy datasets can reduce the quality of future inferences making them unsuitable for business use-cases. In this paper we propose a semi-supervised learning based approach where we combine information embedded in unlabelled and incorrectly labelled instances with a very small set of correctly labelled instances. To the best of our knowledge this is the first work based on semi-supervised learning for reducing the noise in generated predictions using imperfect datasets. It is demonstrated through extensive experimentation using a medical diagnostic diabetes dataset, that by using only a fraction of correctly labelled instances, we are able to generate high quality predictions on unseen data. © 2022 IEEE.

关键词： Forecasting

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Crowd Anomaly Scattering Detection Based on Information Entropy 15th

Crowd Anomaly Scattering Detection Based on Information Entr...

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15th Conference on Image and Graphics Technology and Applications, IGTA 2020

作者： Feng, Fujian Liu, Shuang Pan, Yongzhen Zhang, Qian Wang, Lin College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China College of Software Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9789813360327

Crowd anomaly scattering detection is an important indicator in crowd anomalous events and plays an important role in scene event determination. At present, the special detection methods of abnormal scattering behavior cannot fully mine and make use of the specific information, and the accuracy of these methods is not sensitive to the rapid capture of abnormal scattering behavior. In this paper, we propose a crowd anomaly scattering detection method based on motion velocity and information entropy. In addition, in order to accurately determine the degree of crowd anomalous scattering, we propose a new evaluation method for crowd anomalous scattering. Finally, the performance of the proposed method is verified through UMN dataset. The experimental results show that the proposed method can accurately detect the abnormal scattering behavior in the medium-low density scenarios. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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A Novel Adaptive Perturbation-Based Method for Long-Tailed Open Sets Recognition

SSRN

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SSRN 2023年

作者： Ngueilbaye, Alladoumbaye Zhang, Qin Yang, Hongye Zhang, Xiaolin Zhang, Peng Chen, Xiaojun Huang, Joshua Zhexue Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

Real-world data often have a long-tailed and open-ended distribution. A reliable practical machine learning system need to learn from the majority classes and also generalize to minority *** achieve this, acknowledge the minority and the novel class samples, and avoid misclassifying them into one of the majority classes are of great significance. Long-tailed Open Set Recognition aims to learn from the naturally distributed long-tailed and open-ended data, by using the easy-gained head class samples to learn the fundamental information, and also try to improve the out-of-distribution generalization ability by identifying the tail class and open-set class samples as novelty to avoid the misclassification. The key challenges for long-tailed open set recognition is the tail and open-set sensitivity, since they can be easily biased towards dominant classes and perform ineffectively on close-set dataset classes. Inspired by techniques on open set recognition, in this paper, we propose a Class-aware Input Preprocessing (CLAIP) approach to improve the detection of the Out-of-distribution samples by perturbing the input images. In this approach, we employ two techniques to leverage its performance. First, we introduce the middle class samples as auxiliary data during the network training process to force each category's boundaries. Second, we add a Class-aware Input Preprocessing module that can be easily plugged into the testing process by perturbing the salient regions of the input images to further improve the out-of-distribution generalization. The experimental results show that CLAIP achieves high quality and significant performance compared to the existing solutions. © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Securing Blockchain Using Propagation Chain Learning 5th

Securing Blockchain Using Propagation Chain Learning

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Jin, Shifeng Zhai, Yijun Zhong, Yuxuan Cui, Jifu Xu, Lei Sun, Hongxia Lei, Yan School of Big Data and Software Engineering Chongqing University Chongqing400044 China College of Computer Science Chongqing University Chongqing400044 China School of Pre-school Education Chongqing College of International Business and Economics Chongqing401520 China Qingdao Penghai Software Co. Ltd. Qingdao266000 China Haier Smart Home Co. Ltd. Qingdao266000 China Qingdao Haidacheng Purchasing Service Co. Ltd. Qingdao266000 China

ISBN: (纸本)9789819981007

Smart contract vulnerabilities are the most common and severe type of blockchain vulnerability, which may result in very serious economic and property losses. Vulnerability detection and repair are necessary to ensure the security of the blockchain. Currently, the-state-of-art smart contract vulnerability detection methods (e.g. Oyente and Securify) use heuristics based on human-designed algorithms, which have certain shortcomings in different application scenarios. Therefore, this paper proposes a smart contract vulnerability detection method, i.e. CuVuD, which uses Propagation Chain Learning to solve the current vulnerability detection problem. This method first parses the source code, then obtains and trims the propagation chain of smart contracts, and finally detects vulnerabilities in smart contracts. To verify the effectiveness of CuVuD, this paper compares the CuVuD method with seven the-state-of-art smart contract vulnerability detection methods on a large-scale smart contract dataset based on the Solidity language. The experimental results show that CuVuD’s effectiveness in detecting smart contract vulnerabilities is significantly higher than seven the-state-of-art smart contract vulnerability detection methods, significantly improving the ability to detect vulnerabilities. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Research on Deep Learning-Based Algorithms for Medical Image Characterisation

Research on Deep Learning-Based Algorithms for Medical Image...

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data science and Information System (ICDSIS), IEEE International Conference on

作者： Yunxia Yu Zhiyong Qi Ling Xu Xiong Zhou Big Data Research Center Jingchu University of Technology Jingmen China College of Computer Science Wuhan Vocational College of Software and Engineering Wuhan China Shayang People’s Hospital Jingmen China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Lung disease is a worldwide health problem, and accurate image diagnosis of lung disease is crucial for patient treatment and management. In recent years, deep learning has made significant progress in the field of medical image analysis, in which Graph Neural Networks (GNNs), as a special deep learning model, show great potential in lung disease diagnosis. In this study, we propose a graph neural network-based model for lung disease diagnosis, aiming to automate the analysis of lung images and lesion detection. First, we represent the lung images as a graph structure, where each pathogenic region represents a node in the graph. Then, the adjacency matrix and node features of the graph are constructed, and the image is subjected to feature extraction and representation learning using a graph neural network, which enables the model to capture the structural and contextual information in the image. To further improve the performance of the model, we introduce multilayer graph convolution and multilayer perceptron to enhance the sensitivity and differentiation ability of the graph neural network for lung lesions. We conducted experimental evaluations based on publicly available lung image datasets and compared them with traditional machine learning methods as well as other deep learning models. The experimental results show that the proposed graph neural network model achieves excellent performance on the lung disease diagnosis task.

关键词： Deep learning Analytical models Sensitivity Pulmonary diseases Lung Feature extraction Graph neural networks

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AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes

arXiv

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arXiv 2025年

作者： Li, Zhenteng Lian, Sheng Pan, Dengfeng Wang, Youlin Liu, Wei College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350002 China School of Software East China Jiaotong University Nanchang330013 China

Object detection in Unmanned Aerial Vehicle (UAV) images poses significant challenges due to complex scale variations and class imbalance among objects. Existing methods often address these challenges separately, overlooking the intricate nature of UAV images and the potential synergy between them. In response, this paper proposes AD-Det, a novel framework employing a coherent coarse-to-fine strategy that seamlessly integrates two pivotal components: Adaptive Small Object Enhancement (ASOE) and Dynamic Class-balanced Copy-paste (DCC). ASOE utilizes a high-resolution feature map to identify and cluster regions containing small objects. These regions are subsequently enlarged and processed by a fine-grained detector. On the other hand, DCC conducts object-level resampling by dynamically pasting tail classes around the cluster centers obtained by ASOE, maintaining a dynamic memory bank for each tail class. This approach enables AD-Det to not only extract regions with small objects for precise detection but also dynamically perform reasonable resampling for tail-class objects. Consequently, AD-Det enhances the overall detection performance by addressing the challenges of scale variations and class imbalance in UAV images through a synergistic and adaptive framework. We extensively evaluate our approach on two public datasets, i.e., VisDrone and UAVDT, and demonstrate that AD-Det significantly outperforms existing competitive alternatives. Notably, AD-Det achieves a 37.5% Average Precision (AP) on the VisDrone dataset, surpassing its counterparts by at least 3.1%. Code will be available at https://***/gentlezachary/AD-Det. Copyright © 2025, The Authors. All rights reserved.

关键词： Vehicle detection

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TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED BACKDOOR WATERMARK 12

TOWARDS FAITHFUL XAI EVALUATION VIA GENERALIZATION-LIMITED B...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ya, Mengxi Li, Yiming Dai, Tao Wang, Bin Jiang, Yong Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China The State Key Laboratory of Blockchain and Data Security Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China College of Computer Science and Software Engineering Shenzhen University China Guangzhou Intelligence Communications Technology Co. Ltd. China

Saliency-based representation visualization (SRV) (e.g., Grad-CAM) is one of the most classical and widely adopted explainable artificial intelligence (XAI) methods for its simplicity and efficiency. It can be used to interpret deep neural networks by locating saliency areas contributing the most to their predictions. However, it is difficult to automatically measure and evaluate the performance of SRV methods due to the lack of ground-truth salience areas of samples. In this paper, we revisit the backdoor-based SRV evaluation, which is currently the only feasible method to alleviate the previous problem. We first reveal its implementation limitations and unreliable nature due to the trigger generalization of existing backdoor watermarks. Given these findings, we propose a generalization-limited backdoor watermark (GLBW), based on which we design a more faithful XAI evaluation. Specifically, we formulate the training of watermarked DNNs as a min-max problem, where we find the 'worst' potential trigger (with the highest attack effectiveness and differences from the ground-truth trigger) via inner maximization and minimize its effects and the loss over benign and poisoned samples via outer minimization in each iteration. In particular, we design an adaptive optimization method to find desired potential triggers in each inner maximization. Extensive experiments on benchmark datasets are conducted, verifying the effectiveness of our generalization-limited watermark. Our codes are available at https://***/yamengxi/GLBW. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Deep neural networks

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Deep Learning in Automatic Recognition of Mongolian Compound Nouns

Deep Learning in Automatic Recognition of Mongolian Compound...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Ha Si Du La Yang Ding College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China College of Computer Science and Technology Inner Mongolia Normal University Hohhot China School of Software and Big Data Inner Mongolia Electronic Information Vocational Technical College Hohhot China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

Mongolian compound nouns often become the vehicle for the creation of new concepts and terms due to their diverse combinations and flexible expressive power. In the field of natural language shallow syntactic analysis, the recognition of Mongolian compound nouns plays an important role, and its performance will directly affect the processing effect of related research. In the current academic research, the research on automatic recognition of Mongolian compound nouns is still insufficient, especially in the automatic recognition of Mongolian compound nouns using deep learning technology, which is almost in a blank state. In this study, we firstly construct a Mongolian compound noun annotated corpus, and on the basis of the constructed annotated corpus, we propose a Mongolian compound noun automatic recognition model based on deep learning for the fact that there is no deep learning method for recognising Mongolian compound nouns at present. The model takes Conditional Random Field (CRF) as the basic framework to make full use of contextual information by considering the joint probability distribution of the whole sequence. During the model construction process, the TextCNN model is incorporated and by designing a multi-scale convolutional kernel, the model is able to effectively capture the local features of Mongolian compound nouns. The experimental results show that the automatic Mongolian compound noun recognition model based on deep learning proposed in this study performs well on the Mongolian compound noun recognition task, with an F1 value of 93.40%.

关键词： Deep learning Natural languages Syntactics Conditional random fields Probability distribution Pattern recognition Compounds Kernel Context modeling

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