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Towards Open Zero-Shot learning 21st

Towards Open Zero-Shot Learning

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Marmoreo, Federico Carrazco, Julio Ivan Davila Cavazza, Jacopo Murino, Vittorio Italian Inst Technol Pattern Anal & Comp Vis PAVIS Genoa Italy Univ Genoa Dept Marine Elect Elect & Telecommun Engn Genoa Italy Univ Verona Dept Comp Sci Verona Italy

ISBN: (纸本)9783031064302;9783031064296

In Generalized Zero-Shot learning (GZSL), unseen categories (for which no visual data are available at training time) can be predicted by leveraging their class embeddings (e.g., a list of attributes describing them) together with a complementary pool of seen classes (paired with both visual data and class embeddings). Despite GZSL is arguably challenging, we posit that knowing in advance the class embeddings, especially for unseen categories, is an actual limit of the applicability of GZSL towards real-world scenarios. To relax this assumption, we propose Open Zero-Shot learning (OZSL) as the problem of recognizing seen and unseen classes (as in GZSL) while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided. We formalize the OZSL problem introducing evaluation protocols, error metrics and benchmark datasets. We also tackle the OZSL problem by proposing and evaluating the idea of performing unknown feature generation.

关键词： Zero-Shot learning Open-Set

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Machine learning KNIME Based Diseases Prediction for Healthcare Analytics 21

Machine Learning KNIME Based Diseases Prediction for Healthc...

引用

21st IEEE India Council international conference, INDICON 2024

作者： Devi, M. Akila Bapuram, Apoorva Kumar, Abhishek Banoth, Yakub Dwivedi, Santosh K. Kumar, Amarjit National Institute of Technology Warangal India Gnitc Hyderabad India

ISBN: (纸本)9798350391282

This study explores an innovative application of the KNIME Analytics Platform to create a predictive model for various diseases, including diabetes, heart conditions, asthma, kidney disorders, and skin cancer. The dataset, obtained from Kaggle, provides a comprehensive collection of patient data essential for accurate predictions. The research focused on designing a robust predictive framework utilizing machine learning techniques such as Random Forest, Naive Bayes, and Logistic Regression. To improve model effectiveness, advanced data preprocessing and feature engineering methods were implemented. The primary aim was to develop a reliable system for predicting multiple diseases based on patient attributes, supporting the development of tools for better medical decision-making and patient management. This work underscores the potential of KNIME as a flexible analytics tool, demonstrating its effectiveness in advancing disease prediction and enhancing healthcare strategies. © 2024 IEEE.

关键词： Prediction models

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Few-Shot Class-Incremental learning with Meta-Learned Class structures 21

Few-Shot Class-Incremental Learning with Meta-Learned Class ...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Zheng, Guangtao Zhang, Aidong Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

ISBN: (纸本)9781665424271

learning continually from few-shot examples is a hallmark of human intelligence but it poses a great challenge for deep neural networks since they commonly suffer from catastrophic forgetting and overfitting. In this paper, we tackle this challenge in the few-shot class-incremental learning (FSCIL) setting, where a sequence of few-shot learning sessions containing disjoint sets of classes is created for a model to incrementally learn new classes, and the model should avoid forgetting information of old classes. Simply accumulating information of all learned classes will severely degrade the performance of the model since new classes are not learned to be discriminative across different sessions and they tend to be biased with limited training examples. To address this problem, we introduce class structures to regularize the learned classes on how they should distribute in the embedding space such that they are distinctive with each other within and across different learning sessions. Concretely, these class structures are encoded in a subspace where an alignment kernel aligns a learned class with class structures by moving it along the base vectors of the subspace. We sample incremental tasks in the training to simulate incremental learning and formulate the training as a meta-learning process to learn generalizable class structures across many incremental tasks. Experimental results on the CIFAR100, minilmageNet, and CUB200 datasets demonstrate the effectiveness of our method in combating overfitting and catastrophic forgetting.

关键词： class-incremental learning few-shot learning meta-learning classification

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Comparison of Different Supervised and Self-supervised learning Techniques in Skin Disease Classification 21st

Comparison of Different Supervised and Self-supervised Learn...

引用

21st international conference on Image Analysis and Processing (ICIAP)

作者： Cino, Loris Mazzeo, Pier Luigi Distante, Cosimo Univ Salento Via Monteroni Sn I-73100 Lecce Italy DHITECH ISASI CNR Via Monteroni Sn I-73100 Lecce Italy

ISBN: (纸本)9783031064272;9783031064265

For years now, The international Skin Imaging Collaboration has been providing datasets of dermoscopic images. Several studies show that dermoscopy provides improved diagnostic accuracy, in comparison to standard photography. Excellent results have been obtained that even exceed human performance. In this paper we broke the state of the art for the dataset provided for the ISIC 2019 challenge. In this work were compared the performance of various convolutional networks, various data augmentations, and various cost functions and optimizers. Results obtained using transfer learning from IMAGENET were compared with the performance obtained using BYOL (bootstrap your own latent), a self-supervised technique. Moreover, it has been demonstrated that self-supervised learning techniques can be used in this field improving the performance of the network compared to training from scratch. We were obtained a balanced multiclass accuracy (BCA) of 87% in the test and validation dataset and with a top-2 accuracy of 97%.

关键词： Skin disease classification CNN Self-supervised learning

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GSEK: Graph structured Neural Networks with Energy Graph Kernel 21

GSEK: Graph Structured Neural Networks with Energy Graph Ker...

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21st international conference on Wavelet Analysis and Pattern Recognition, ICWAPR 2023

作者： Xu, Wei Cui, Qing-Zhe Ge, Wei Zhou, Biao Wang, Xiao-Feng Tong, He Xu, Li-Xiang Tang, Yuan-Yan College of Artificial Intelligence and Big Data Hefei University Hefei230601 China Chinese people's Liberation Army Aviation School Department of Basic Beijing101123 China Zhuhai UM Science and Technology Research Institute FST University of Macau Macau China

ISBN: (纸本)9798350303810

Graph neural networks (GNNs) have become an increasingly popular framework for describing graph-structured data using deep learning. However, traditional GNNs may not accurately map features for graphs that contain noise or missing information. In order to address this issue, we propose a new method called Graph structured Neural Networks with Energy Graph Kernel (GSEK) for classification. GSEK defines energy properties for nodes and extracts node subgraphs using maximum energy random wandering to ensure the centrality of the subgraphs' structure. It then uses Wasserstein Weisfeiler-Lehman (WWL) graph kernel to calculate isomorphism coefficients for subgraphs of different node pairs, which are used to define weight matrices and achieve graph reconstruction. Unlike traditional methods that solely focus on node features, GSEK places greater emphasis on the local structure of the graph. Our experiments on six publicly available graph classification datasets demonstrate that our proposed model outperforms other benchmark models. © 2023 IEEE.

关键词： Graph classification Graph kernel Graph neural networks Graph structure learning

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Legitimacy: An Ensemble learning Model for Credibility Based Fake News Detection 21

Legitimacy: An Ensemble Learning Model for Credibility Based...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Ramkissoon, Amit Neil Goodridge, Wayne Univ West Indies St Augustine Dept Comp & Informat Technol St Augustine Trinidad Tobago

ISBN: (纸本)9781665424271

The unadulterated spread of fake news, especially via social media platforms, can have serious deleterious effects on our data driven society. Fake news can be classified using a variety of methods. Predicting and detecting fake news has proven to be challenging even for machine learning algorithms. This research presents, Legitimacy a unique ensemble machine learning model to accomplish the task of Credibility Based Fake News Detection The Legitimacy ensemble combines the learning potential of a Two Class Boosted Decision Tree and a Two Class Neural Network. The ensemble technique follows a pseudo mixture-of-experts methodology. For the gating model, an instance of Logistic Regression is implemented. This study validates Legitimacy using a standard dataset with features relating to the credibility of news publishers to predict fake news. These features are analysed using the ensemble algorithm. The results of these experiments are examined using four evaluation methodologies. The analysis reveals positive performance with the use of the ensemble ML method. Based upon our selected dataset, the Legitimacy ensemble model has proven to be most appropriate for the purpose of Credibility Based Fake News Detection.

关键词： Decision Trees Ensemble learning Fake News Detection Logistic Regression Neural Networks

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learning Exercises Recommendation for Online Judge System Based on Deep Knowledge Tracing 21

Learning Exercises Recommendation for Online Judge System Ba...

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21st international conference on New Trends in intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Dai, Kaiyu Zuo, Xiaorui Liu, Fan Zhang, Rui National Demonstration Center for Experimental Computer Education School of Computer Science Fudan University Shanghai China School of Economics Fudan University Shanghai China Software School of Fudan University Shanghai China

ISBN: (纸本)9781643683164

Online judge systems are widely used in programming courses and competitions, but they lack personalized resource recommendations. In this paper, the deep knowledge tracing (DKT) model with transformer network serving as the RNN block is used to model the learning state of programming learners, and the topic recommendation is modeled as Markov decision process, and the problem set recommendation strategy is optimized by maximum expectation algorithm. Compared with other traditional knowledge tracing models and DKT with LstM network through experiments and data analysis, the algorithm adopted in this paper shows better effect. © 2022 The authors and IOS Press. All rights reserved.

关键词： Markov processes

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Untrained Deep learning Techniques for Channel Estimation With Limited Pilots in RIS-Enhanced SIMO-OFDM Systems

Untrained Deep Learning Techniques for Channel Estimation Wi...

引用

Consumer Communications and Networking conference, CCNC IEEE

作者： Fan-Shuo Tseng Tsang-Yi Wang Pin-Chu Yu Inst. of Communications Engineering National Sun Yat-sen University Kaohsiung Taiwan

The estimation of channel state information (CSI) is a well-known challenge in reconfigurable intelligent surface (RIS)-assisted communication systems due to the limited signal processing capabilities of the RIS node. Conventional methods often resort to employing a substantial number of pilot sequences to obtain the necessary degrees of freedom, leading to a significant reduction in the data transmission rate. In this study, we present deep learning-based channel estimator designed for scenarios with limited pilots in an RIS-assisted single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system. Specifically, we introduce an untrained deep neural network (DNN) based on the deep image prior (DIP) network. This DIP-based DNN is employed to denoise the estimated channel using orthogonal matching pursuit (OMP), ultimately achieving a more accurate channel estimation. Numerical results from our experiments demonstrate that the proposed untrained DIP-based DNN estimator offers significant improvements in accuracy when compared to both conventional OMP-based approaches and training-based DNN estimators.

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data Visualization Literacy: Multimodal skills in the Age of Internet of Things (IoT) 22

Data Visualization Literacy: Multimodal skills in the Age of...

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7th international conference on Information and Education Innovations, ICIEI 2022

作者： Mahmud, Malissa Maria Wong, Shiau Foong Wong, Shiau San Ismail, Othman Ramachandiran, Chandra Reka Healthy Ageing and Well-Being Research Cluster Academic Enhancement Division Sunway University Selangor Malaysia Center for American Education Sunway University Selangor Malaysia Faculty of Business Help University Kuala Lumpur Malaysia Akademi Pengajian Bahasa Universiti Teknologi MARA Selangor Malaysia School of Electrical and Computer Engineering Xiamen University Malaysia

ISBN: (纸本)9781450396196

The global networked infrastructure, also known as the Internet of Things (IoT), has been embedded into various hardware and software to enable communication capabilities and serves as one of the significant influences that shape the education sector. IoT advances fundamental data visualization technologies as a game-changer for the coveted skills required for 21st century graduates. Despite the discernible pre-requisites to possess the skills, there is a knowledge and pedagogic gap where a large portion of the existing curriculum does not integrate the expected skills to successfully bridge students with the real-world and practical application. A non-experimental research design was employed in this study to examine the significance of data visualization literacy (DVL) for the students' learning process. The overarching findings underscore the importance of visual learningĝ€¯as a preferred learning approach. © 2022 ACM.

关键词： data visualization

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A Robust Algorithm to Unifying Offline Causal Inference and Online Multi-armed Bandit learning 21

A Robust Algorithm to Unifying Offline Causal Inference and ...

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21st IEEE international conference on data Mining (IEEE ICDM)

作者： Tang, Qiao Xie, Hong Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781665423984

Utilizing offline logged data to improve sequential or online decision making is drawing more and more attention. VirUCB is one of the latest notable algorithmic framework in this research line, and it has both sound theoretical guarantee and nice empirical performance. However, regarding VirUCB, it is still unclear: (1) how imbalanced offline logged data influences the decision making accuracy;(2) how to schedule offline logged data across the decision making horizon so as to reduce offline logged data consumption. We show that with imbalanced offline logged data, VirUCB can have a learning speed slower than the baseline algorithm without offline logged data. This finding inspires us to design RobVirUCB algorithm, which is robust against such imbalanced data, i.e., still maintains a fast learning speed. RobVirUCB adaptively selects "useful" offline logged data to speed up learning and it has theoretical guarantees on regret. Finally, we design EffVirUCB algorithm, which reduces offline logged data consumption of RobVirUCB. EffVirUCB schedules the offline logged data to the decision round that the decision maker may select suboptimal arms and it has theoretical guarantees on regret. Extensive experiments on both synthetic data and real-world data validate the superior performance of RobVirUCB and EffVirUCB.

关键词： Decision making

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