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International Conference on computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms

Software Supply Chain Vulnerabilities Detection in Source Co...

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2022 IEEE International Conference on big data, big data 2022

作者： Akter, Mst Shapna Faruk, Md Jobair Hossain Anjum, Nafisa Masum, Mohammad Shahriar, Hossain Sakib, Nazmus Rahman, Akond Wu, Fan Cuzzocrea, Alfredo Department of Computer Science Kennesaw State University United States Department of Information Technology Kennesaw State University United States Department of Applied Data Science San Jose State University United States Department of Computer Science and Software Engineering Auburn University United States Department of Computer Science Tuskegee University United States IDEA Lab University of Calabria Rende Italy

ISBN: (纸本)9781665480451

The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SSC attacks execute during the software development processes lead to vulnerabilities in software products targeting downstream customers and even involved stakeholders. Machine Learning approaches are proven in detecting and preventing software security vulnerabilities. Besides, emerging quantum machine learning can be promising in addressing SSC attacks. Considering the distinction between traditional and quantum machine learning, performance could be varies based on the proportions of the experimenting dataset. In this paper, we conduct a comparative analysis between quantum neural networks (QNN) and conventional neural networks (NN) with a software supply chain attack dataset known as ClaMP. Our goal is to distinguish the performance between QNN and NN and to conduct the experiment, we develop two different models for QNN and NN by utilizing Pennylane for quantum and TensorFlow and Keras for traditional respectively. We evaluated the performance of both models with different proportions of the ClaMP dataset to identify the f1 score, recall, precision, and accuracy. We also measure the execution time to check the efficiency of both models. The demonstration result indicates that execution time for QNN is slower than NN with a higher percentage of datasets. Due to recent advancements in QNN, a large level of experiments shall be carried out to understand both models accurately in our future research. © 2022 IEEE.

关键词： Supply chains

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAAI Conference on Artificial intelligence, AAAI 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Embeddings

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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EdgeMove: Pipelining Device-Edge Model Training for Mobile intelligence 23

EdgeMove: Pipelining Device-Edge Model Training for Mobile I...

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2023 World Wide Web Conference, WWW 2023

作者： Dong, Zeqian He, Qiang Chen, Feifei Jin, Hai Gu, Tao Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia School of Computing Macquarie University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers' training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers' training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme. © 2023 ACM.

关键词： Internet of things

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Expedited Block Transmission in Blockchain Network by using Clusters

Expedited Block Transmission in Blockchain Network by using ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiaoqi Hua Jian Chen Peiyun Zhang Zhangjie Fu Haibin Zhu Kezhong Lu Jigang Ren School of Software Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics of Ministry of Education and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Mathematics Nipissing University North Bay ON Canada School of Big Data and Artificial Intelligence Chizhou University Chizhou China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

关键词： Throughput Blockchains Peer-to-peer computing Delays Synchronization Cybernetics

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Optimizing Ethanol Production in Escherichia Coli Using a Hybrid of Particle Swarm Optimization and Artificial Bee Colony 6

Optimizing Ethanol Production in Escherichia Coli Using a Hy...

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6th International Conference on Advances in Artificial intelligence, ICAAI 2022

作者： Dzulkalnine, Mohamad Faiz Mohamad, Mohd Saberi Choon, Yee Wen Remli, Muhammad Akmal Alashwal, Hany Artificial Intelligence and Bioinformatics Research Group Faculty of Computing Universiti Teknologi Malaysia Johor81310 Malaysia Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University P.O. Box 17666 Al Ain Abu Dhabi United Arab Emirates Big Data Analytics Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Data Science Universiti Malaysia Kelantan Kelantan Kota Bharu16100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al-Ain United Arab Emirates

ISBN: (纸本)9781450396943

Metabolic engineering for biomass production using microorganisms' cell has received considerable attention in recent years. This is due to the biomass products being extensively used in the field of food additives, supplements, pharmaceuticals, and polymer materials. In this paper, ethanol production in Escherichia coli (E. coli) is the desired product. Sugarcane and corn are often used to produce ethanol. However, one of the problems to produce adequate amounts of ethanol is that large areas are needed to plant sugarcane and corn. Furthermore, the amount of time for the process of dry milling and wet milling is high, which are 40 to 50 hours and 24 to 48 hours, respectively. The wet laboratory is also having limitation on the production of ethanol in microorganisms because the amount of the ethanol produced is not satisfying. Hence, a lot of metabolic engineering techniques is introduced to enhance the production of ethanol in E. coli, such as gene knockout strategy, but the production is yet to meet the demand. Therefore, this paper proposes a hybrid algorithm of Particle Swarm Optimization with the Artificial Bee Colony algorithm (PSOABC) to identify the optimal set of gene knockout strategy to improve the ethanol production in E. coli. A list of genes to knockout, production of the desired product, and growth rate are presented in this paper. PSOABC has shown better performance in terms of production, growth rate and accuracy. © 2022 Association for Computing Machinery.

关键词： Ethanol

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features

Evolutionary Multiobjective Feature Selection Assisted by Un...

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Congress on Evolutionary Computation

作者： Xuan Duan Songbai Liu Junkai Ji Lingjie Li Qiuzhen Lin Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets.

关键词： Accuracy Evolutionary computation Multitasking Feature extraction Classification algorithms Task analysis Knowledge transfer

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Nusec and Midesec: datasets for Histopathological Image Analysis

SSRN

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SSRN 2024年

作者： Nemati, Nooshin Samet, Refik Sak, Serpil Hancer, Emrah Kirmizi, Bilge Ayca Yildirim, Zeynep Traoré, Mohamed Department of Computer Engineering Ankara University Ankara06830 Turkey Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Software Engineering Mehmet Akif Ersoy University Burdur15039 Turkey Department of Pathology Faculty of Medicine Ankara University Ankara06080 Turkey

Hematoxylin and Eosin (H&E) staining stands as the gold standard protocol for the preparationof histopathology tissue samples in breast cancer research. This staining technique facilitates theidentification and differentiation of various cell types within the tissue, providing crucial informationfor pathologists. While deep learning algorithms have shown promise in automating the analysis ofH&E images, their effectiveness relies on extensive datasets for training. Unfortunately, there is ascarcity of publicly available datasets specifically tailored for nuclei and mitosis detection. To addressthis gap, this paper introduces two publicly available datasets, namely NuSeC (Nuclei Segmentationand Classification) and MiDeSeC (Mitosis Detection, Segmentation, and Classification) to serve asvaluable resources for building deep learning models for histopathological image analysis. The NuSeCdataset comprises 75 images for training and an additional 25 images for testing, encompassing around6000 nuclei structures, and the MiDeSeC dataset involves 35 training and 15 test image sets with morethan 500 mitoses. In both datasets, the QuPath tool as a pixel-level annotator is utilized to generatemask images. Moreover, we design two deep learning-based methodologies to conduct experimentalanalysis on the NuSeC and MiDeSeC datasets. The results indicate the potential to achieve substantialperformance on these datasets, facilitating the construction of new models or analysis of existing ones © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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A Novel Machine Learning Based Framework for Bridge Condition Analysis

A Novel Machine Learning Based Framework for Bridge Conditio...

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2022 IEEE International Conference on big data, big data 2022

作者： Masum, Mohammad Anjum, Nafisa Hossain Faruk, Md Jobair Shahriar, Hossain Sakib, Nazmus Valero, Maria Karim, M.A. Rahman, Akond Wu, Fan Cuzzocrea, Alfredo San Jose State University Department of Applied Data Science San Jose United States Kennesaw State University Department of Information Technology United States Kennesaw State University Department of Software Engineering and Game Development United States Kennesaw State University Department of Civil and Environmental Engineering United States Auburn University Department of Computer Science and Software Engineering United States Tuskegee University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9781665480451

Bridges play a vital part in the transportation system by ensuring the connectedness of transportation systems, which is critical for a country's social and economic prosperity by offering daily mobility to the people. However, according to the American Society of Civil Engineers (ASCE 2017), many U.S. bridges are in critical condition, raising safety issues, with 9.1 and 13.6 percent of the country's 614,387 bridges, respectively, structurally defective, and functionally obsolete. Every day, 178 million people traverse these structurally defective bridges. Furthermore, the average annual failure rate is expected to be between 87 and 222. Bridge breakdowns have disastrous repercussions, and in many cases, result in death. While bridge authorities strive to improve bridge conditions, budget limits make it difficult to make cost-effective maintenance decisions. Bridge authorities distribute limited repair resources based on projected future bridge conditions. As a result, building a data-driven, autonomous, and effective bridge condition prediction model is critical for improving maintenance decision-making. In this paper, we present a novel bridge condition prediction framework using advanced Machine Learning (ML) algorithms on the National Bridge Inventory (NBI) dataset. The framework consists of two stages, where the most informative features from the NBI dataset are selected using the Recursive Feature Elimination process and in the 2nd step, ML classifiers are applied to the selected features for bridge condition prediction. The experimental results show that the proposed framework can effectively predict bridge conditions by producing highly accurate results in terms of accuracy, precision, recall, and f1-score. © 2022 IEEE.

关键词： Forecasting

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