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5th International Conference on Signal Processing and Machine Learning (CONF SPML 2025)

作者： Haonan Deng Xinyu Zhang Yiyi Lu Zhongxu Ye Institute of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 People's Republic of China Department of Computing Science University of Glasgow Glasgow G11 6QT UK Duke Kunshan University Suzhou 215316 People's Republic of China Department of Computer and Information Technology Purdue University West Lafayette 47907 USA

ISBN: (数字)9781837243143

Pulmonary embolism is a life-threatening illness caused by blockages in the pulmonary arteries, usually due to blood clots. This condition requires accurate diagnosis on time in order to prevent critical complications like cardiac arrest or chronic pulmonary hypertension. As a result, this study explores the capability of data fusion in a multimodal scenario to improve diagnostic accuracy in PE through the integration of Electronic Medical Records with imaging data from CT Pulmonary Angiography. Deep learning models such as PENet, ResNet3D_50, ResNext3D_101, and DenseNet were used for the analysis of imaging data. The EMR data were analyzed using logistic regression and other machine learning methods. A Late Fusion approach that combined the outputs from the EMR and imaging models, using averages, resulted in a diagnostic accuracy with an AUC of 0.94. This is a perfect example where combined clinical and imaging features addressed the shortcomings of one-modality approaches. Advanced fusion techniques, such as MISA, were also tried, but simpler strategies often performed better than them when the amount of data was limited. Overall, the results point out the promise of multimodal fusion in advancing PE diagnostics and also take note of the selection of an appropriate strategy for clinical application.

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Revolutionizing Modern Networks: Advances in AI, Machine Learning, and Blockchain for Quantum Satellites and UAV-based Communication

arXiv

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arXiv 2023年

作者： Kumar, Adarsh Awad, Ali Ismail Sharma, Gaurav Krishnamurthi, Rajalakshmi Jain, Saurabh Srikanth, P. Sharma, Kriti Hedabou, Mustapha Sood, Surinder School of Computer Science University of Petroleum and Energy Studies Uttrakhand Dehradun India College of Information Technology United Arab Emirates University United Arab Emirates Department of Computer Science and Engineering Jaypee Institute of Information Technology Uttar Pradesh Noida India School of Engineering and Technology Sushant University Haryana Gurugram India DRDO Banglore India College of Computing Mohammed VI Polytechnic University Benguerir Morocco Department of Electrical Computer and Software Engineering University of Auckland New Zealand

Quantum communication is the most secure technique of transmitting data available today. Fiber communication lines and satellite-to-ground links have served as the basis for the most successful quantum networks that have been developed so far. Using a UAV, satellite or both for free-space quantum communication reduces the need for permanent ground connections and takes advantage of the lower loss limit in space, which makes it more efficient. This work surveys the recent development in Quantum Satellites and Quantum UAVs-based networks. Here, the importance of the latest technologies, including quantum artificial intelligence, blockchain quantum machine learning, quantum satellites and quantum UAVs, are explored from network perspectives. Further, this work discussed the role of satellite-based images and artificial intelligence. © 2023, CC BY.

关键词： Satellites

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Keywords-driven web APIs group recommendation for automatic app service creation process

Keywords-driven web APIs group recommendation for automatic ...

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作者： Gong, Wenwen Lv, Chao Duan, Yucong Liu, Zengguang Khosravi, Mohammad R. Qi, Lianyong Dou, Wanchun College of Information and Electrical Engineering China Agricultural University Beijing China China Telecom Smart Home Competence Center Nanjing China E-Surfing Smart Home Technology Co. Ltd Nanjing China School of Computer Science and Cyberspace Security Hainan University Haikou China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Engineering Persian Gulf University Bushehr Iran Telecommunications Group Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran School of Information Science and Engineering Qufu Normal University Rizhao China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

With the ever-increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision-makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords-driven web APIs recommendation approach called keywords-driven and compatibility-aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real-world web APIs dataset, that is, PW dataset crawled from ***. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work. © 2020 John Wiley & Sons Ltd

关键词： Application programs

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Federated Deep Recommendation System Based on Multi-View Feature Embedding

Federated Deep Recommendation System Based on Multi-View Fea...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Xinna Wang Shunmei Meng Yanran Chen Qiyan Liu Rui Yuan Qianmu Li Department of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Business School University of Auckland Auckland New Zealand Computer Engineering University of Toronto Toronto Canada

ISBN: (纸本)9781665473316

The application of recommendation systems online services is becoming more and more extensive. However, most existing recommendation algorithms centralize multi-party information into a central processor, which may lead to the risk of privacy leakage. And many enterprises or institutions still have the problem that data cannot be shared. Federated learning has been introduced into recommendation algorithms for privacy- aware distributed learning. A typical federated learning is that each client uses local data to train a shared model, the server uses their gradient information to form a global model, and then each client updates. In this paper, we propose a federated deep recommendation algorithm called FedHe-mlp that applies a federated deep learning for data privacy protection, and combines heterogeneous information network (HIN) and matrix factorization technique for better prediction performance. First, each client obtains heterogeneous information through meta- paths, then we combine matrix factorization and heterogeneous information to mine the latent features and heterogeneous features of each client. Finally, We propose a deep neural network that considers features from multiple views. Extensive experiments on three public datasets demonstrate that FedHe- mlp can provide excellent convergence speed, recommendation accuracy, and communication efficiency while preserving data privacy.

关键词： Deep learning Training Data privacy Privacy Analytical models Federated learning Neural networks

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Regularized Hypothesis-Induced Wasserstein Divergence for Unsupervised Domain Adaptation

SSRN

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SSRN 2023年

作者： Si, Lingyu Dong, Hongwei Qiang, Wenwen Zheng, Changwen Yu, Junzhi Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Engineering Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Unsupervised domain adaptation (UDA) aims to align the data distributions of source and target domains to learn domain-invariant feature representations. The performance of such representations heavily relies on the measurement of the discrepancy between the distributions. Recent UDA methods utilize the hypothesis-based measurement, also known as classifier-induced divergence, to quantify the difference between the two domain distributions, for instance, H∆H-divergence. Nevertheless, the classifier-induced divergences face several challenges, including the fake covariate alignment problem and the inconsistency problem, rendering them inadequate for discerning the dissimilarity between different categories of output by the classifier. This paper proposes a novel method called Regularized Hypothesis-Induced Wasserstein Divergence-Based Representation Learning for Unsupervised Domain Adaptation (RHWD) to address these issues. Specifically, RHWD introduces a new hypothesis-induced Wasserstein divergence (HWD) to effectively quantify the difference between the two distributions, which aims to overcome the fake covariate alignment problem. Furthermore, RHWD includes a consistency regularization term to mitigate the inconsistency problem. We train RHWD adversarially in three iterative steps. Additionally, we provide empirical evaluations on standard benchmarks, which demonstrate the effectiveness of our proposed © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Out-of-Distribution Detection by Principal Component Correspondence

Out-of-Distribution Detection by Principal Component Corresp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoyuan Guan Zhiyong Gan Ling Deng Wei Shi Jiankang Chen Shenshen Bu Chunliang Zhao Jianfang Hu Yuren Zhou Wei-Shi Zheng Ruixuan Wang School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Network Business Group China Unicom Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China School of Data and Science Qingdao University of Science and Technology Qingdao China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Out-of-distribution (OOD) detection is vital for the safe application of intelligent systems in real-world scenarios. This paper proposes an enhancement to OOD detection by leveraging the consistency in cognition between two models, both pretrained on in-distribution (ID) data. Specifically, for a given test sample, we first apply Principal Component Analysis (PCA)-based projection on the feature vectors from each model. These obtained feature vectors (with correlation between dimensions decoupled by PCA projection) are then aligned using a multiple linear mapping, which is fitted using the least squares method on the training data. We hypothesize that the regression error for OOD data will be larger than that for ID data, making it a useful metric for OOD detection. Our experimental results demonstrate the effectiveness of this method. When combined with existing robust baselines, our approach achieves state-of-the-art performance in OOD detection.

关键词： Training Measurement Analytical models Training data Vectors Data models Intelligent systems Facial features Principal component analysis Least mean squares methods

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A unified framework of FPT approximation algorithms for clustering problems 31

A unified framework of FPT approximation algorithms for clus...

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31st International Symposium on Algorithms and Computation, ISAAC 2020

作者： Feng, Qilong Zhang, Zhen Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College PA United States Department of Computer Science and Engineering State University of New York Buffalo United States

ISBN: (纸本)9783959771733

In this paper, we present a framework for designing FPT approximation algorithms for many k-clustering problems. Our results are based on a new technique for reducing search spaces. A reduced search space is a small subset of the input data that has the guarantee of containing k clients close to the facilities opened in an optimal solution for any clustering problem we consider. We show, somewhat surprisingly, that greedily sampling O(k) clients yields the desired reduced search space, based on which we obtain FPT(k)-time algorithms with improved approximation guarantees for problems such as capacitated clustering, lower-bounded clustering, clustering with service installation costs, fault tolerant clustering, and priority clustering. © Qilong Feng, Zhen Zhang, Ziyun Huang, Jinhui Xu, and Jianxin Wang.

关键词： Approximation algorithms

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Dual Branch CNN and Transformer for Cardiac Atherosclerotic Plaque Classification

Dual Branch CNN and Transformer for Cardiac Atherosclerotic ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Haijun Lei Jia Zhao Guanjie Tong Longjiang Zhang Huaqiang Su Baiying Lei College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Radiology Jinling Hospital Medical School of Nanjing University Shenzhen University Nanjing China School of Biomedical Engineering& Shenzhen University Medical School Shenzhen University Shenzhen China

Accurate classification of coronary artery plaques can provide effective assistance for the diagnosis of coronary artery disease(CAD). The task of coronary artery plaque classification remains extremely challenging due to the complex anatomical structure and background of coronary arteries. 3D convolution still has limitations in feature modeling, so this study builds a dual branch bridge network based on convolution neural network (CNN) and Transformer framework, and fused the local feature extraction ability of convolution and the global modeling ability of Transformer through the bridge communication module. By using a shift attention (SA) module at the intersection of dual branch information to utilizes minimal computational complexity to fuse feature maps from both branches. The ghost plus (GP) module was aimed at balancing the enormous computational power issues of building rich semantic information and training difficulties in 3D Transformers. The proposed method have demonstrated the effectiveness through a large number of comparative and ablation experiment.

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A systematic literature review on phishing and anti-phishing techniques

arXiv

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arXiv 2021年

作者： Arshad, Ayesha Ur Rehman, Attique Javaid, Sabeen Ali, Tahir Muhammad Sheikh, Javed Anjum Azeem, Muhammad Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science Gulf University of Science and Technology Kuwait

Phishing is the number one threat in the world of internet. Phishing attacks are from decades and with each passing year it is becoming a major problem for internet users as attackers are coming with unique and creative ideas to breach the security. In this paper, different types of phishing and anti-phishing techniques are presented. For this purpose, the Systematic Literature Review(SLR) approach is followed to critically define the proposed research questions. At first 80 articles were extracted from different repositories. These articles were then filtered out using Tollgate Approach to find out different types of phishing and anti-phishing techniques. Research study evaluated that spear phishing, Email Spoofing, Email Manipulation and phone phishing are the most commonly used phishing techniques. On the other hand, according to the SLR, machine learning approaches have the highest accuracy of preventing and detecting phishing attacks among all other anti-phishing approaches. © 2021, CC BY-SA.

关键词： computer crime

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Extended Multi-stream Temporal-attention Module for Skeleton-based Human Action Recognition (HAR)

arXiv

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arXiv 2024年

作者： Mehmood, Faisal Guo, Xin Chen, Enqing Akbar, Muhammad Azeem Khan, Arif Ali Ullah, Sami School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou450001 China Henan Xintong Intelligent IOT Co. Ltd. No. 1-303 Intersection of Ruyun Road and Meihe Road Henan Zhengzhou450007 China Department of Software Engineering LUT University Lahti15100 Finland M3S Empirical Software Engineering Research University of Oulu Oulu90570 Finland Department of Computer Science Government College University Faisalabad Punjab Faisalabad38000 Pakistan

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data. GCN model's hierarchical structure and human action recognition input diversity make this a problematic approach;(II) Bone length and orientation are understudied due to their significance and variance in HAR. For this purpose, we introduce an Extended Multi-stream Temporal-attention Adaptive GCN (EMS-TAGCN). By training the network topology of the proposed model either consistently or independently according to the input data, this data-based technique makes graphs more flexible and faster to adapt to a new dataset. A spatial, temporal, and channel attention module helps the adaptive graph convolutional layer focus on joints, frames, and features. Hence, a multi-stream framework representing bones, joints, and their motion enhances recognition accuracy. Our proposed model outperforms the NTU RGBD for CS and CV by 0.6% and 1.4%, respectively, while Kinetics-skeleton Top-1 and Top-5 are 1.4% improved, UCF-101 has improved 2.34% accuracy and HMDB-51 dataset has significantly improved 1.8% accuracy. According to the results, our model has performed better than the other models. Our model consistently outperformed other models, and the results were statistically significant that demonstrating the superiority of our model for the task of HAR and its ability to provide the most reliable and accurate results. © 2024, CC BY.

关键词： Input output programs

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