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IEEE International Conference on Systems, Man and Cybernetics

作者： Zhenyu Tong Chenxi Luo Tiejian Luo School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computing Engineering and Mathematics La Trobe University Melbourne VIC Australia

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

From search engines like Google to advanced applications such as Retrieval Augmented Generation integrating large language Model (LLM), Information Retrieval (IR) serves a crucial role. To facilitate the development of increasingly large and complex code program projects, researchers introduce IR systems into the code domain. Unfortunately, although IR systems achieve significant success in retrieving natural language corpus and query, they face challenges when tasked with retrieving corpus consisting of code sequences. Primarily, in practical applications, most code sequences lack corresponding natural language annotations, which are known as low-resource scenarios, hindering the training of neural network-based retriever in IR systems. Furthermore, modern IR systems often overlook the structural features of code sequences, which may be beneficial for understanding these sequences. Additionally, the length of most code sequences exceeds that of equivalent natural language expressions, complicating the processing of relationships between code and natural language sequences. To address these challenges, we propose a novel IR system CSFIR, which leverages Code-Speclflc Features to augment IR. For the prevalent issue of unlabeled code sequences in low-resource scenarios, we employ a supervised fine-tuned LLM as a generator to generate natural language queries for unlabeled code sequence. Subsequently, we extract structural features from the abstract syntax tree of code sequence using graph convolution networks and integrate these features to enhance the original retriever. Finally, given the adverse effects of lengthy code sequences on generators, we propose a subtree segmentation algorithm, which reduces the length of code sequences without compromising their original meaning, thereby enhancing the quality of queries generated by the generator. We conduct comparative experiments to ascertain the efficacy of our method. Regarding Recall@100, our CSFIR system improves f

关键词： Training Codes Convolution large language models Natural languages Retrieval augmented generation Syntactics Search engines Feature extraction Generators

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Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

Dynamic Resource Allocation for Metaverse Applications with ...

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IEEE Conference on Wireless Communications and Networking

作者： Nam H. Chu Diep N. Nguyen Dinh Thai Hoang Khoa T. Phan Eryk Dutkiewicz Dusit Niyato Tao Shu School of Electrical and Data Engineering University of Technology Sydney Australia School of Engineering and Mathematical Sciences La Trobe University Melbourne Australia School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computer Science and Software Engineering Auburn University USA

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

关键词： Deep learning Metaverse Heuristic algorithms Simulation Reinforcement learning Quality of service Dynamic scheduling

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Mix of Experts language Model for Named Entity Recognition

arXiv

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arXiv 2024年

作者： Chen, Xinwei Li, Kun Guo, Jiangjian Song, Tianyou Department of Electronical and Computer Engineering University of Illinois at Urbana Champaign Urbana United States Department of Computer Science University of Illinois at Urbana Champaign Urbana United States Department of Computer Science and Engineering University of California La Jolla San Diego United States Department of Computer Science Columbia University New York United States

Named Entity Recognition (NER) is an essential steppingstone in the field of natural language processing. Although promising performance has been achieved by various distantly supervised models, we argue that distant supervision inevitably introduces incomplete and noisy annotations, which may mislead the model training process. To address this issue, we propose a robust NER model named BOND-MoE based on Mixture of Experts (MoE). Instead of relying on a single model for NER prediction, multiple models are trained and ensembled under the Expectation-Maximization (EM) framework, so that noisy supervision can be dramatically alleviated. In addition, we introduce a fair assignment module to balance the document-model assignment process. Extensive experiments on real-world datasets show that the proposed method achieves state-of-the-art performance compared with other distantly supervised NER. Copyright © 2024, The Authors. All rights reserved.

关键词： Natural language processing systems

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SESAME: Toward Medical Image Segmentation via Foundation Model-assisted Semi-supervised Learning

SESAME: Toward Medical Image Segmentation via Foundation Mod...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiyun Hu Junao Shen Jinkang Ji Xinyu Wang Tian Feng Hui Cui School of Software Technology Zhejiang University Hangzhou China Department of Computer Science and Info. Tech. La Trobe University Melbourne Australia

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical image segmentation is essential for diagnosis but requires expensive and time-consuming labeled data. Semi-supervised learning (SSL) mitigates this issue by using unlabeled data to improve generalization. However, current SSL methods encounter issues with inadaptive perturbations and low-quality pseudo-labels. Vision foundation models, such as SAM, have shown promise in segmentation. We propose SESAME, an SSL method integrating SAM and U-Net to improve labeling accuracy through a foundation model-assisted pipeline. In particular, we introduce a reliability score to address low-quality pseudo-labels and employ strategies for utilizing both reliable and unreliable images. Reliable images are associated with refined pseudo-labels via a conflict resolving strategy, whereas unreliable ones undergo a mutual region swapping strategy. Extensive experiments demonstrate that our SESAME outperforms representative methods for medical image segmentation.

关键词： Image segmentation Image resolution Foundation models Perturbation methods Biological system modeling Pipelines Semisupervised learning Reliability labeling Medical diagnostic imaging

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An Effective Deep Learning Technique for Analyzing COVID - 19 Using X-Ray Images 15th

An Effective Deep Learning Technique for Analyzing COVID - 1...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Uma Maheswari, V. Aluvalu, Rajanikanth Guduri, Manisha Kantipudi, M. V. V. Prasad Department of Computer Science and Engineering Chaitanya Bharati Institute of Technology Telangana Hyderabad India Pune India School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States University Pune412115 India

ISBN: (纸本)9783031810794

The COVID - 19 disease is a extremely contagious viral infection with significant global health insinuations. COVID has a negative impact on the world economy. If positive patients are identified early, the proliferation of this pandemic infection can be slackened. COVID-19 disease prophecy is useful for finding the people with health issues who are at risk of COVID. The Deep Learning and machine learning approaches for COVID prediction using X-ray images have the possibility to be tremendously helpful in addressing the shortage of clinicians and doctors in rural areas. This research presents a deep layered Convolutional Neural Network (CNN) for recognizing the COVID-19 patients by training the system with the datasets of around 6000 chest X-ray images collected from various sources. We are categorized them as normal or COVID - impacted. Our model trained the X-ray images of chest to find these patients. And it also predicted well with the help of image analysis and deep learning by showcasing the better results comparing with the state-of-the-art methods in terms of the average accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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BioBERT-based Deep Learning and Merged ChemProt-DrugProt for Enhanced Biomedical Relation Extraction

arXiv

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arXiv 2024年

作者： McInnes, Bridget T. Tang, Jiawei Mahendran, Darshini Nguyen, Mai H. Department of Computer Science Virginia Commonwealth University RichmondVA United States Department of Computer Science & Engineering University of California La Jolla San DiegoCA United States

This paper presents a methodology for enhancing relation extraction from biomedical texts, focusing specifically on chemical-gene interactions. Leveraging the BioBERT model and a multi-layer fully connected network architecture, our approach integrates the ChemProt and DrugProt datasets using a novel merging strategy. Through extensive experimentation, we demonstrate significant performance improvements, particularly in CPR groups shared between the datasets. The findings underscore the importance of dataset merging in augmenting sample counts and improving model accuracy. Moreover, the study highlights the potential of automated information extraction in biomedical research and clinical practice. © 2024, CC BY-NC-ND.

关键词： Network architecture

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Multi-view attribute learning and context relationship encoding enhanced segmentation of lung tumors from CT images

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computers in Biology and Medicine 2024年 177卷 108640-108640页

作者： Xuan, Ping Chu, Xiuqiang Cui, Hui Nakaguchi, Toshiya Wang, Linlin Ning, Zhiyuan Ning, Zhiyu Li, Changyang Zhang, Tiangang Department of Computer Science and Technology Shantou University Shantou China School of Computer Science and Technology Heilongjiang University Harbin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Center for Frontier Medical Engineering Chiba University Chiba Japan Department of Radiation Oncology Shandong First Medical University and Shandong Academy of Medical Sciences Jinan China School of Electrical and Information Engineering The University of Sydney Sydney Australia Sydney Polytechnic Institute Sydney Australia School of Mathematical Science Heilongjiang University Harbin China

Graph convolutional neural networks (GCN) have shown the promise in medical image segmentation due to the flexibility of representing diverse range of image regions using graph nodes and propagating knowledge via graph edges. However, existing methods did not fully exploit the various attributes of image nodes and the context relationship among their attributes. We propose a new segmentation method with multi-similarity view enhancement and node attribute context learning (MNSeg). First, multiple views were formed by measuring the similarities among the image nodes, and MNSeg has a GCN based multi-view image node attribute learning (MAL) module to integrate various node attributes learnt from multiple similarity views. Each similarity view contains the specific similarities among all the image nodes, and it was integrated with the node attributes from all the channels to form the enhanced attributes of image nodes. Second, the context relationships among the attributes of image nodes are formulated by a transformer-based context relationship encoding (CRE) strategy to propagate these relationships across all the image nodes. During the transformer-based learning, the relationships were estimated based on the self-attention on all the image nodes, and then they were encoded into the learned node features. Finally, we design an attention at attribute category level (ACA) to discriminate and fuse the learnt diverse information from MAL, CRE, and the original node attributes. ACA identifies the more informative attribute categories by adaptively learn their importance. We validate the performance of MNSeg on a public lung tumor CT dataset and an in-house non-small cell lung cancer (NSCLC) dataset collected from the hospital. The segmentation results show that MNSeg outperformed the compared segmentation methods in terms of spatial overlap and the shape similarities. The ablation studies demonstrated the effectiveness of MAL, CRE, and ACA. The generalization ability of M

关键词： Signal encoding

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Quantum Computing Society of the Philippines: An NGO Perspective on Promoting Quantum Information, science, and Technology in the Philippines 5

Quantum Computing Society of the Philippines: An NGO Perspec...

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5th IEEE International Conference on Quantum Computing and engineering, QCE 2024

作者： Lopez, Dylan Josh Domingo Peramo, Elmer Ayson, Jabez Mendoza Masongsong, Angela Nicole S. Raquel, lance Dominic C. Corpus, Bobby O. Department of Computer Technology De La Salle University Manila Philippines Advanced Science and Technology Institute Department of Science and Technology Quezon City Philippines Electronics Engineering Department Don Honorio Ventura State University Pampanga Philippines STEM Department De La Salle University Integrated School Manila Philippines Department of Physics University of San Carlos Cebu City Philippines Quantum Computing Society of the Philippines Manila Philippines

ISBN: (纸本)9798331541378

The development of Quantum Information science and Technology (QIST) as an emerging technology among developed and developing countries has caught the attention of governments, industries, and educational institutions. However, not all countries have equal starting points in promoting QIST amongst different sectors. Several players in various countries promote QIST as a discipline and education. Although significant developments in quantum technology are focused in Western countries, great innovation can also be seen in Eastern countries, specifically Asia. The Philippines is one of several developing countries in Asia that is in the process of adopting QIST in multiple sectors. In this paper, the Quantum Computing Society of the Philippines (QCSP) is introduced as one of the arms of the country for promoting QIST to the public. Further in this paper, the initial impact of QIST promotion on various communities across the Philippines through multiple activities and educational efforts is explained. Several critical success stories for different audiences were described, wherein initial acceptance of QIST was evident through hackathons and lecture series. Events held show improvement in interest and proficiency in the foundations of QIST. © 2024 IEEE.

关键词： Quantum computers

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Load-balancing system for SDN data center based on improved ant colony optimization

Load-balancing system for SDN data center based on improved ...

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2024 International Conference on computer Network Security and Software engineering, CNSSE 2024

作者： Zhou, Yue Wu, Wenlong Information Engineering Department Anhui Industry Polytechnic Anhui Tongling244000 China Graduate School La Consolacion University Philippines Bulacan Malolos3000 Philippines Computer Science College Guangzhou College of Applied Science and Technology Guangdong Zhaoqing526072 China

ISBN: (纸本)9781510680234

The load balancing strategy in the traditional network architecture system is limited to the limitations of the closed operating system of the network device and cannot effectively meet various communication environments. In SDN, a new network architecture with open programming, the load balancing system architecture of the SDN data center is designed. The traffic classification module is used to distinguish large and small flows. A load balancing mechanism based on the improved ant colony optimization is proposed for the forwarding of large flows. A weighted ECMP load balancing mechanism is used for the forwarding of small flows. The improved ant colony optimization considers the difference in link load status and optimizes the parameters of the ant colony optimization. Through experimental simulation and comparison with traditional load balancing algorithms ECMP and DLB algorithms, the advantages of the optimized ant colony optimization in terms of delay jitter, average link utilization and other indicators are verified. © 2024 SPIE.

关键词： Ant colony optimization

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Joint Device and Training Scheduling for Wireless Federated Learning

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IEEE Internet of Things Journal 2025年第12期12卷 18806-18819页

作者： Chen, Xiaobing Zhou, Xiangwei Zhang, Hongchao Sun, Mingxuan Zhao, Taibiao Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States Louisiana State University Department of Mathematics Baton RougeLA70803 United States Louisiana State University Division of Computer Science and Engineering Baton RougeLA70803 United States

The advent of ubiquitous computing devices in the Internet of Things (IoT) has resulted in an explosion of data. Traditional centralized machine learning models face challenges including limited bandwidth in wireless environments and privacy concerns due to their data aggregation approach. Federated learning addresses these challenges via decentralizing model training across numerous devices, leveraging model updates to enhance privacy and reduce communication overhead. To improve its cost efficiency, current research focuses on minimizing either time or energy costs but rarely both, and does not jointly optimize the parameters of device and training scheduling in the presence of system and data heterogeneity inherent in IoT networks. In our paper, we first introduce a multi-group transmission scheme and propose a comprehensive device scheduling framework, Group Scheduling on Orthogonal Frequency-Division Multiple Access (GS-OFDMA), to address time bottlenecks. Then we formulate a joint optimization problem for device and training scheduling that minimizes the total cost of training while ensuring model convergence. To tackle the resulting mixed integer nonlinear programming problem, we develop an iterative algorithm. Experimental results show that our approach significantly reduces the total cost by at least 35% across various real-world datasets and data distributions in comparison with random participant selection. The proposed GS-OFDMA protocol also exhibits higher time efficiency over other device scheduling schemes. © 2014 IEEE.

关键词： Federated learning

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