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DocTTT: Test-Time Training for Handwritten Document Recognition Using Meta-Auxiliary Learning

DocTTT: Test-Time Training for Handwritten Document Recognit...

引用

IEEE Workshop on Applications of computer Vision (WACV)

作者： Wenhao Gu Li Gu Ziqiang Wang Ching Yee Suen Yang Wang Department of Computer Science and Software Engineering Concordia University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particularly in scenarios with minimal annotated data available. In this paper, we introduce the DocTTT framework to address these challenges. The key innovation of our approach is that it uses test-time training to adapt the model to each specific input during testing. We propose a novel Meta-Auxiliary learning approach that combines Meta-learning and self-supervised Masked Autoencoder (MAE). During testing, we adapt the visual representation parameters using a self-supervised MAE loss. During training, we learn the model parameters using a meta-learning framework, so that the model parameters are learned to adapt to a new input effectively. Experimental results show that our proposed method significantly outperforms existing state-of-the-art approaches on benchmark datasets.

关键词： Training Metalearning Adaptation models Handwriting recognition Visualization Technological innovation computer vision Text recognition Computational modeling Layout

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Federated Learning for Detecting COVID-19 in Chest CT Images: A Lightweight Federated Learning Approach 4

Federated Learning for Detecting COVID-19 in Chest CT Images...

引用

4th International Conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Lai, Weimin Yan, Qiao College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350321951

The novel coronavirus is spreading rapidly worldwide, and finding an effective and rapid diagnostic method is apriority. Medical data involves patient privacy, and the centralized collection of large amounts of medical data is impossible. Federated learning is a privacy-preserving machine learning paradigm that can be well applied to smart healthcare by coordinating multiple hospitals to perform deep learning training without transmitting data. This paper demonstrates the feasibility of a federated learning approach for detecting COVID-19 through chest CT images. We propose a lightweight federated learning method that normalizes the local training process by globally averaged feature vectors. In the federated training process, the models' parameters do not need to be transmitted, and the local client only uploads the average of the feature vectors of each class. Clients can choose different local models according to their computing capabilities. We performed a comprehensive evaluation using various deep-learning models on COVID-19 chest CT images. The results show that our approach can effectively reduce the communication load of federated learning while having high accuracy for detecting COVID-19 on chest CT images. © 2022 IEEE.

关键词： COVID-19

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On the Implications of Human-Paper Interaction for software Requirements engineering Education 34

On the Implications of Human-Paper Interaction for Software ...

引用

34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Kamthan, Pankaj Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)1891706543

It is broadly accepted that requirements engineering is one of the most important phases of a software project, and requires tools to be effective. For a variety of reasons, paper as a tool has lasted for millennia and remains ubiquitous. This paper makes a case for a contextual, conscientious, and evidence-based use of paper in a competency-oriented approach to software requirements engineering education (REE). It argues that the prophecies for the obsolescence of paper are premature, there are unique benefits in the use of paper, and the decision to use paper should be based on [0, 1] rather than (0, 1). In this regard, a need-centered conceptual model for human-paper interaction is proposed. The characteristics of paper that make it historically unique are reported and the affordances of paper relevant to REE are discussed. The REE-related activities that benefit from viewing paper as a boundary object and using different types of paper are highlighted and illustrated by means of examples. In advocating polyliteracy, the potential for a convergence of paper and digital media towards a harmonic coexistence is underscored. © 2022 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Requirements engineering

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A Multiobjective Evolutionary Multitasking Algorithm Based on Decomposition and Multiple Knowledge Transfer 4

A Multiobjective Evolutionary Multitasking Algorithm Based o...

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4th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2022

作者： Wu, Zhongjian Zhu, Qingling Chen, Jianyong Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (数字)9781665459822

ISBN: (纸本)9781665459822

Multiobjective evolutionary multitasking (MOEMT) has become very popular in recent years, as this kind of methods aims to solve a set of multiobjective optimization problems (MOPs) simultaneously, which has been validated to be more promising than the traditional way that solves MOPs separately. However, most existing studies of MOEMT solve the MOP as a whole and use one single knowledge transfer strategy for solving all tasks, which is not so efficient as the MOP could be further decomposed into a set of subproblems and needs different strategies for knowledge transfer. To solve this problem, this paper suggests a new MOEMT algorithm (MOEMTA) based on decomposition and multiple knowledge transfer (MOEMTA-DM). First, the decomposition method is used to transform an MOP in each task into a set of subproblems, and the subproblems with greater performance improvement are chosen. Then their associated solutions will be selected to run multiple knowledge transfer strategies via both implicit and explicit ways, which can share the useful search experience between tasks. This way, computational resources can be adaptively assigned to speed up the solving of all tasks and the effect of knowledge transfer among all tasks can be improved. The effectiveness of the proposed algorithm was verified by studying benchmark multitasking MOPs (MTMOPs). Experimental results showed the proposed algorithm is more effective than other compared MOEMT algorithms. © 2022 IEEE.

关键词： Multiobjective optimization

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data 2

Integrity Estimation of Twitter Based Event Recognition Usin...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Siva Rama Lingham, N. Parameswari, M. Karthick, P. Priya, T. Subha Rathi Rajesh, G. Jagannathan, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai India School of Engineering and Technology Jeppiaar University Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events. © 2024 IEEE.

关键词： Impact testing

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A Design Framework for a Blockchain-Based Open Market Platform of Enriched Card-Based Transactional Data for Big Data Analytics and Open Banking

A Design Framework for a Blockchain-Based Open Market Platfo...

引用

12th International Conference on Data science, Technology and Applications, DATA 2023

作者： Toy, Trevor Langerman, Josef Academy of Computer Science and Software Engineering University of Johannesburg Auckland Park Johannesburg South Africa

ISBN: (纸本)9789897586644

Around a quarter of the world’s data is generated by financial institutions. The Capgemini 2022 World Payments Report predicts a 28% increase in transaction volumes from 2021 to 2026, to an estimated total of 2.122 trillion global non-cash transactions. There is a growing demand for accessible transactional data for analytical purposes and to support the rapid global adoption of Open Banking. Open banking is a collaborative business model involving customer-authorised transactional data sharing with other unaffiliated parties to allow for enhanced service and product offerings to the marketplace. This research explores utilising distributed ledger technology to facilitate the market mechanism of securely sharing data through an integrated and decentralised platform that conforms to the expected regulatory and compliance standards of the financial industry from which the data is generated. Scalable and accessible access is a core requirement of a marketplace platform for its data consumers and producers. To enable customer-authorised transactional data sharing, an incentive mechanism is proposed, which includes the data subject in the process to empower them to control access and earn money from the related transactional data that they generate. A proposed framework is defined for the development of a marketplace platform that can ultimately support the growth, prosperity and development of economies, businesses, communities and individuals, by providing accessible and relevant transactional data for big data analytics and open banking. © 2023 by SCITEPRESS–science and Technology Publications, Lda.

关键词： Marketplaces

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Mask Wearing Detection System for Epidemic Control Based on STM32 6th

Mask Wearing Detection System for Epidemic Control Based on ...

引用

6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Luoli Yadav, Amit Khan, Asif Varish, Naushad Singh, Priyanka Thakkar, Hiren Kumar Department of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University DarwinNT Australia Department of Computer Application Integral University Lucknow India Department of Computer Science and Engineering GITAM University Hyderabad502329 India Department of Computer Science and Engineering SRM University Andhra Pradesh Amravati522502 India Department of Computer Science and Engineering Pandit Deendayal Energy Univrsity Gujarat Gandhinagar382007 India

ISBN: (纸本)9789819940707

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image recognition

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Analysis of Techniques for Detection and Removal of Zero-Day Attacks (ZDA) 2nd

Analysis of Techniques for Detection and Removal of Zero-Day...

引用

2nd International Conference on Ubiquitous Security, UbiSec 2022

作者： Hamid, Khalid Iqbal, Muhammad Waseem Aqeel, Muhammad Liu, Xiangyong Arif, Muhammad Department of Software Engineering The Superior University Lahore54000 Pakistan Department of Computer Science The Superior University Lahore54000 Pakistan School of Computer Science and Cyber Engineering Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819902712

Zero-day attacks (ZDAs) are previously unknown flaws and errors in operating systems, networks, and general-purpose software. ZDAs are the cause to open security breach holes for external users or hackers for illegal operations before patches. ZDAs are different from viruses, worms and Trojans. They are very difficult to detect and remove because they are unknown to users and even programmers. Conventional defense systems are useless against such types of attacks. This paper investigates the zero-day attacks, their vulnerabilities, the intensity of these attacks and the reasons in a real-time environment. Different techniques are compared to investigate ZDAs. The hybrid technique has been used to check the behavior of data traffic coming from outside the world in the virtual environment for zero-day attacks. Static check tools (MD5 Checksum, AV-Suite, and SOPHOS) and behavioral check tools (SANDBOX) have been used for the verification and validation of results. The second comparatively best technique is Machine Learning-Based Technique which showed the best results. The results show how to choose the best solution against zero-day attacks in the real-time environment of the software. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Zero-day attack

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Resilient and Reliable Communication for First Responders with Ad-Hoc Network and MPTCP

Resilient and Reliable Communication for First Responders wi...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Cui, Yue Lim, Alvin Yan, Yufei Kulkarni, Abhishek Lan, Tianhang Computer Science and Software Engineering Auburn University Auburn United States

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

In severe hazardous situations, reliable network communication is an important infrastructure for conveying vital real-time information from sensors embedded in first responders. Important communications and emergency information must be received quickly to make critical decisions, improve situation awareness, and save lives. It is always challenging to ensure that the network is continuously available. To ensure seamless, fault-tolerant, and secure communication, we propose a new communication system for first responders. In this novel architecture, we improve the reliability of our communication networks using three techniques: Multi-Path TCP (MPTCP), Optimized Linked State Routing Protocol (OLSR) and Message Queuing Telemetry Transport (MQTT) for portable devices and cloud services. MPTCP enables rapid recovery when a TCP connection fails. OLSR enables routes through Ad Hoc networks. MQTT enables efficient transmission of sensor data. We have implemented the above reliable communication network and conducted various experiments to show improvement in reliability. The results show that our proposed method can be quickly deployed and provides reliability in the communication network despite the failure of some network infrastructures. © 2022 IEEE.

关键词： Ad hoc networks

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FedSeProto: Learning Semantic Prototype in Federated Learning 27

FedSeProto: Learning Semantic Prototype in Federated Learnin...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Lai, Yanyi Fu, Lele Liao, Tianchi Chen, Chuan Zheng, Zibin School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China School of Systems Science and Engineering Sun Yat-Sen University Guangzhou China School of Software Engineering Sun Yat-Sen University Zhuhai China

ISBN: (纸本)9781643685489

Federated learning enables multiple clients to collaboratively train a global model without revealing their local data. However, conventional federated learning often overlooks the fact that data stored on different clients may originate from diverse domains, and the resulting domain shift problem can significantly impair the performance of the global model. In this paper, we introduce Federated Semantic Prototype Learning (FedSeProto), a semantic prototype-based approach designed to address the domain shift issue in federated learning. The proposed method comprises two components: feature decoupling and feature alignment. Feature decoupling aims to learn semantic prototypes that can represent semantic information associated with specific categories, while feature alignment utilizes these semantic prototypes to facilitate learning of cross-client consistent features. Two key techniques are employed to achieve feature decoupling. On one hand, feature separation is achieved through the minimization of mutual information between semantic and domain features. On the other hand, the knowledge distillation is leveraged to ensure that both semantic and domain features carry the correct information. For feature alignment, intra-class semantic features are used to generate the local prototypes, which are further aggregated to the global prototypes. These global prototypes serve as guides during the local training process. Specifically, the local intra-class semantic features are driven to close to the corresponding global prototypes, thereby encouraging all clients to learn the globally consistent semantic features. Comprehensive experiments conducted on four challenging multi-domain datasets demonstrate the effectiveness of the proposed method compared with existing federated learning algorithms. © 2024 The Authors.

关键词： Federated learning

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