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IEEE Conference on Decision and Control

作者： Sandamal Weerasinghe Tamas Abraham Tansu Alpcan Sarah M. Erfani Christopher Leckie Benjamin I. P. Rubinstein School of Computing and Information Systems University of Melbourne Australia Defence Science and Technology Group Australia Department of Electrical and Electronic Engineer-ing University of Melbourne Australia

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The vulnerability of machine learning models to adversarial perturbations has motivated a significant amount of research under the broad umbrella of adversarial machine learning. Sophisticated attacks may cause learning algorithms to discover decision functions or make decisions with poor predictive performance. In this context, there is a growing body of literature that uses local intrinsic dimensionality (LID), a local metric that describes the minimum number of latent variables required to describe each data point, for detecting adversarial samples and subsequently mitigating their effects. The research to date has tended to focus on using LID as a practical defence method, often without fully explaining why LID can detect adversarial samples. In this paper, we derive a lower-bound and an upper-bound for the LID value of a perturbed data point and demonstrate that the bounds, in particular the lower-bound, have a positive correlation with the magnitude of the perturbation. Hence, we demonstrate that data points that are perturbed by a large amount would have large LID values compared to unperturbed samples, thus providing a theoretical justification for the use of LID in prior literature. Furthermore, our empirical validation demonstrates the utility of the bounds on benchmark datasets.

关键词： Measurement Machine learning algorithms Correlation Perturbation methods Benchmark testing Prediction algorithms Adversarial machine learning

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

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Journal of Computer science & technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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MAM: Multimodel Attention Mechanism for Social Media Natural Disaster Management Tweet Classification

MAM: Multimodel Attention Mechanism for Social Media Natural...

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ACS/IEEE International Conference on Computer Systems and Applications

作者： M. Sangeetha R. Manjula Devi Bhisham Sharma Subrata Chowdhury Imed Ben Dhaou Department of Information Technology Dr.N.G.P. Institute of Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamilnadu India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Chittoor Andra Pradesh India Department of Computer Science Hekma School of Engineering Computing and Design Dar Al-Hekma University Jeddah Saudi Arabia

People have been using social media as a category for exchanging content for decades. It has revolutionized communication and enhanced the sharing of information during emergency situations. The key features of social media are collective action, connectivity, comprehensiveness, and clarity. Consequently, it performed a significant function in natural disaster management by keeping track of and reporting disaster-related incidents. The volume and diversity of the data acquired from social media during a natural disaster pose the greatest challenge. For natural disaster management it is extremely difficult to derive actionable information from the data collected from social platforms. Various strategies have been presented in the literature to address the difficulties posed by social media for natural disaster management. The proposed work is a semi-supervised machine learning model for detecting and classifying tweets. The proposed work centre’s on preparing the data by performing cleansing and various transformations, generating word embedding vectors with DistillBERT, using the vision transformer, the image model was constructed. The attention mechanism is utilized for text and image model integration. In the proposed work the data pertaining to seven distinct natural disasters, such as cyclones, floods, and earthquakes are analyzed. A novel decision diffusion technique is proposed for classifying them into informative and non-informative groups and evaluating the accuracy of the results. The MAM model increases the accuracy to 97% for crisisMMD dataset.

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Hybrid Algorithm and Forecasting technology of Network Public Opinion Based on BP Neural Network 9th

Hybrid Algorithm and Forecasting Technology of Network Publi...

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9th International Workshop of Advanced Manufacturing and Automation, IWAMA 2019

作者： Zhang, Ronghua Liu, Changzheng Ma, Hongliang Intelligent and Distributed Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Information Science and Technology Shihezi University Shihezi832000 China

ISBN: (纸本)9789811523403

Based on the hybrid algorithm and BP neural network, the forecasting technology of network public opinion was studied. After detailed introduction of the research background and significance, the concept and development of hybrid algorithm, Hybrid algorithm is analyzed in detail. The user network public opinion classification model under the clustering algorithm is established. Then the data mining experiment of Sina Weibo users’ network public opinion is conducted. Using an improved BP neural network algorithm, unsupervised user network clustering and evaluation of the results, finally, the user’s Internet public opinion is divided into six categories. The characteristic preferences of each user group are analyzed, with recommendations provided for operation recommendation and promotion. © Springer Nature Singapore Pte Ltd 2020.

关键词： Data mining

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Diabetes disease prediction using decision tree for feature selection 1

Diabetes disease prediction using decision tree for feature ...

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1st International Conference on Advances in Computational science and Engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Muthukumaran, V. Thimmia Raja, J. Joseph, Rose Bindu Munirathanam, Meram Balajee, J.M. Vellore Institute of Technology Vellore India Department of Mathematics School of Applied Science REVA University Bengaluru - 64 India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Department of Mathematics Christ Academy Institute for Advance Studies Department of Mathematics Rajiv Gandhi University of Knowledge Technologies School of Information Technology and Engineering Vellore Institute of Technology Vellore India

In this paper more than one approaches are evaluated to optimise machine learning models for diabetes disease diagnosis. The main goal is to sort the medical data computation and choose the most suitable parameters to construct a faster and more perfect model using feature selection. Reducing the number of features to construct a model could direct to more useful machine learning algorithms which helps the doctors to focus on what are the most significant assessment to take into story. Feature selection is one of the process in machine learning which choose a subset of topical features namely variables for construction of models. In this research paper we use three feature selection techniques like Recursive Feature Elimination (RFE), Genetic Algorithm (GA) and Burota Package. After using feature selection at the end we use Decision Tree to predict the diagnosis Diabetes using a dataset named Pima Indian Diabetes Dataset and verify the performance of result model. © Published under licence by IOP Publishing Ltd.

关键词： Feature Selection

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Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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science China(information sciences) 2020年第3期63卷 288-292页

作者： Wei LI Linfeng LIAO Dawu GU Shan CAO Yixin WU Jiayao LI Zhihong ZHOU Zheng GUO Ya LIU Zhiqiang LIU School of Computer Science and Technology Donghua University Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Key Laboratory of Integrate Administration Technologies for Information Security School of Microelectronics Shanghai Jiao Tong University Department of Computer Science and Engineering University of Shanghai for Science and Technology

Dear editor,The Midori lightweight cipher can be applied in the RFID tags and other low-resource devices to protect information on the Internet. This cipher was proposed by Banik et al. and presented at the ASIACRYPT conference [1]. It has a Substitution Permutation Network structure with a 128-bit keysize, and two block-size variants, 64 bits

关键词： CFA Ciphertext-only fault analysis on the Midori lightweight cryptosystem

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Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review

arXiv

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arXiv 2024年

作者： Lopez-Ramos, Luis M. Leiser, Florian Rastogi, Aditya Hicks, Steven Strümke, Inga Madai, Vince I. Budig, Tobias Sunyaev, Ali Hilbert, Adam Holistic Systems department Simula Metropolitan Center for Digital Engineering Oslo Norway Department of Economics and Management Karlsruhe Institute of Technology Germany Department of Neuroradiology University Hospital Bonn Germany Department of Computer Science Faculty of Informatics Norwegian University of Science and Technology Trondheim Norway QUEST Center for Responsible Research Charité - Universitätsmedizin Berlin Berlin Germany School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom ETH Zurich Switzerland School of Computation Information and Technology Technical University of Munich Germany Charité - Universitätsmedizin Berlin Germany

The joint implementation of Federated learning (FL) and Explainable artificial intelligence (XAI) will allow training models from distributed data and explaining their inner workings while preserving important aspects of privacy. Towards establishing the benefits and tensions associated with their interplay, this scoping review maps those publications that jointly deal with FL and XAI, focusing on publications where an interplay between FL and model interpretability or post-hoc explanations was found. In total, 37 studies met our criteria, with more papers focusing on explanation methods (mainly feature relevance) than on interpretability (mainly algorithmic transparency). Most works used simulated horizontal FL setups involving 10 or fewer data centers. Only one study explicitly and quantitatively analyzed the influence of FL on model explanations, revealing a significant research gap. Aggregation of interpretability metrics across FL nodes created generalized global insights at the expense of node-specific patterns being diluted. 8 papers addressed the benefits of incorporating explanation methods as a component of the FL algorithm. Studies using established FL libraries or following reporting guidelines are a minority. More quantitative research and structured, transparent practices are needed to fully understand their mutual impact and under which conditions it happens. © 2024, CC BY.

关键词： Federated learning

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EmoDNN: Understanding emotions from short texts through a deep neural network ensemble

arXiv

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arXiv 2021年

作者： Kamran, Sara Zall, Raziyeh Kangavari, Mohammad Reza Hosseini, Saeid Rahmani, Sana Hua, Wen School of Computer Engineering Iran University of Science and Technology The Faculty of Computing and Information Technology Sohar University Oman The School of Information Technology and Electrical Engineering University of Queensland Australia

The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security. Affective computing, as an interdisciplinary research field, linking artificial intelligence to cognitive inference, is capable to exploit emotion-oriented knowledge from brief contents. The textual contents convey hidden information such as personality and cognition about corresponding authors that can determine both correlations and variations between users. Emotion recognition from brief contents should embrace the contrast between authors where the differences in personality and cognition can be traced within emotional expressions. To tackle this challenge, we devise a framework that, on the one hand, infers latent individual aspects, from brief contents and, on the other hand, presents a novel ensemble classifier equipped with dynamic dropout convnets to extract emotions from textual context. To categorize short text contents, our proposed method conjointly leverages cognitive factors and exploits hidden information. We utilize the outcome vectors in a novel embedding model to foster emotion-pertinent features that are collectively assembled by lexicon inductions. Experimental results show that compared to other competitors, our proposed model can achieve a higher performance in recognizing emotion from noisy contents. © 2021, CC BY.

关键词： Speech recognition

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Inter- and intra-uncertainty based feature aggregation model for semi-supervised histopathology image segmentation

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Cui, Hui Sun, Changming Song, Yang Zheng, Jiangbin Cao, Leilei Wei, Leyi Su, Ran School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia CSIRO Data61 Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Software Shandong University Shandong China School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China

Acquiring pixel-level annotations is often limited in applications such as histology studies that require domain expertise. Various semi-supervised learning approaches have been developed to work with limited ground truth annotations, such as the popular teacher-student models. However, hierarchical prediction uncertainty within the student model (intra-uncertainty) and image prediction uncertainty (inter-uncertainty) have not been fully utilized by existing methods. To address these issues, we first propose a novel inter- and intra-uncertainty regularization method to measure and constrain both inter- and intra-inconsistencies in the teacher-student architecture. We also propose a new two-stage network with pseudo-mask guided feature aggregation (PG-FANet) as the segmentation model. The two-stage structure complements with the uncertainty regularization strategy to avoid introducing extra modules in solving uncertainties and the aggregation mechanisms enable multi-scale and multi-stage feature integration. Comprehensive experimental results over the MoNuSeg and CRAG datasets show that our PG-FANet outperforms other state-of-the-art methods and our semi-supervised learning framework yields competitive performance with a limited amount of labeled data. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medical Image Arbitrary-Scale Super Resolution

CuNeRF: Cube-Based Neural Radiance Field for Zero-Shot Medic...

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International Conference on Computer Vision (ICCV)

作者： Zixuan Chen Lingxiao Yang Jian-Huang Lai Xiaohua Xie School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread attention, aiming to supersample medical volumes at arbitrary scales via a single model. However, existing MIASSR methods face two major limitations: (i) reliance on high-resolution (HR) volumes and (ii) limited generalization ability, which restricts their applications in various scenarios. To overcome these limitations, we propose Cube-based Neural Radiance Field (CuNeRF), a zero-shot MIASSR framework that is able to yield medical images at arbitrary scales and free viewpoints in a continuous domain. Unlike existing MISR methods that only fit the mapping between low-resolution (LR) and HR volumes, CuNeRF focuses on building a continuous volumetric representation from each LR volume without the knowledge of the corresponding HR one. This is achieved by the proposed differentiable modules: cube-based sampling, isotropic volume rendering, and cube-based hierarchical rendering. Through extensive experiments on magnetic resource imaging (MRI) and computed tomography (CT) modalities, we demonstrate that CuNeRF can synthesize high-quality SR medical images, which outperforms state-of-the-art MISR methods, achieving better visual verisimilitude and fewer objectionable artifacts. Compared to existing MISR methods, our CuNeRF is more applicable in practice.

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