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A Brain Disease Classification Framework with Temporal Attribute and Feature Fusion 19

A Brain Disease Classification Framework with Temporal Attri...

引用

2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Yunjing Zhang, Li Wang, Xiaoxiao Jing, Ming Zhang, Guangwei Dong, Anming Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350346558

The fMRI functional imaging technology has become an important means to detect brain diseases. It mainly diagnoses whether there are lesions in the patient's brain region by monitoring the changes of the blood oxygen level (BOLD) in the brain region. Existing brain disease classification techniques are mainly achieved through network construction, feature extraction and classification model construction. But in the existing methods, almost no one uses the time attribute. In this paper, we propose a brain disease diagnosis framework with temporal attribute and feature fusion, use temporal and BOLD signal's changes in different time periods to construct a brain connectivity network. We extract frequent sequences and sequences' connection rules in the subjects' brain connection network, and finally use pseudo-binary matrix to fuse the two features and finish classification model construction. Experiment turns out that we achieve better accuracy than other schemes. © 2022 IEEE.

关键词： Feature extraction

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Visual Representation and Layout Optimization for Comparison of Dynamic Graph 19

Visual Representation and Layout Optimization for Comparison...

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作者： Zhang, Li Wang, Xiaoxiao Liu, Yunjing Zhang, Guangwei Jing, Ming Yu, Jiguo Qilu University of Technology School of Computer Science and Technology Jinan China Big Data Institute Qilu University of Technology Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350346558

The comparison between temporal graph data and the representation of the results are very helpful for in-depth understanding of the difference between the two data. However, limited by display technology, it is more difficult to visually analyze the comparison of temporal graph data. Based on the analysis of the temporal characteristics of the graph data, this paper summarizes and reorganizes the data that needs to be compared, and efficiently displays the difference in the time sequence of the graph data by means of graphic embedding. This paper introduces the data processing process, visual element design and layout optimization algorithm in detail, and verifies it on real data. Finally, the evaluation experiment proves that the proposed method is more efficient in comparison of temporal graph data. © 2022 IEEE.

关键词： Embeddings

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Incentive Mechanism of Blockchain-Based Reverse Auction for Federated Learning

Incentive Mechanism of Blockchain-Based Reverse Auction for ...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Bo Cui Li Dang Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Federated learning is a novel machine learning paradigm in which the model is trained on local data by distributed clients. Most of the current research on federated learning assumes that clients are unconditionally providing data and training models, and little consideration has been given to how to incentivize clients with high-quality data to participate in the model training task. Therefore, this paper proposes a blockchain-based federated learning incentive mechanism combining data quality verification and reverse auction. Firstly, by verifying the quality of client data, the client with high-quality data that meets the task requirements is selected, and then the client sends its bid for the task to the reverse auction smart contract. Secondly, some clients with the best performance in different training phases are selected to participate in the task training using smart contracts, and a certain number of clients are selected to form a committee among the unselected participants. The committee members are responsible for validating the local model parameters of the clients while receiving validation rewards. Finally, we conduct simulation experiments on two datasets separately, and the experimental results demonstrate the effectiveness of our proposal.

关键词： Training Federated learning data integrity Smart contracts data models Reliability Proposals

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A Study on a Reduced Indicator System for the Network Security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

引用

2023 International Conference on Networking and Network Applications, NaNA 2023

作者： Zhang, Hongbin Bai, Yikang Zhao, Dongmei Liu, Bin Xu, Ying Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (纸本)9798350327380

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time. © 2023 IEEE.

关键词： Network security

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Hybrid Deep Learning Approach for Nonfunctional Software Requirements Classifications 3

Hybrid Deep Learning Approach for Nonfunctional Software Req...

引用

3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Rahman, Kiramat Ghani, Anwar Ahmad, Rashid Sajjad, Syed Haseeb International Islamic University Dept. of Software Engineering Islamabad Pakistan Big Data Research Center Jeju National University Jeju Korea Republic of Inernational Islamic University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350332391

This paper explores the concept of algorithmic hybridization, which involves combining various machine learning (ML) algorithms to enhance performance by utilizing the benefits of both simultaneously. This study presents a framework that utilizes a combination of long short-term memory (LSTM) and bidirectional LSTM (BiLSTM) with artificial neural networks (ANN) to classify non-functional requirements (NFR). The task of NFR classification is challenging due to the scarcity of supervised learning data. The effectiveness of the proposed approach was assessed by comparing the performance of our integrated model with that of single LSTM and BiLSTM models. To conduct this evaluation, we combined two datasets consisting of 1000 non-functional requirements (NFR). The experimental findings revealed that the proposed approach is effective as the hybrid models exhibited better precision, recall, and F-1 score compared to its counterparts. © 2023 IEEE.

关键词： Requirements engineering

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Multi-scale Variable Window Attention Network for Continuous Sign Language Recognition

Multi-scale Variable Window Attention Network for Continuous...

引用

2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Zhang, Nan Wei, Chao Guo, Leming Cheng, Zeyu Zhao, Zhao Wu, Yangcan Xue, Wanli Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Emergency Centre Tianjin China Tianjin University of Technology School of Mechanical Engineering Tianjin China Shaanxi Big Data Group Co. Ltd Shaanxi China Industry Research Institute Co Shaanxi Logistics Group Shaanxi China

ISBN: (纸本)9798350376739

Sign language is primarily conveyed through hands and face movements. Therefore, a pivotal challenge in continuous sign language recognition (CSLR) is the meticulous modeling and interpreting of visual cues emanating from hands and facial movements. However, contemporary CSLR methods have exhibited limitations in adequately capturing information from both the hands and facial regions, often overlooking the continuity, and correlations across multiple consecutive frames. To mitigate these shortcomings, we propose the Multi-scale Variable Window Attention Network (MVAN). This framework fuses the strengths of an improved Efficient Multi-scale Attention (iEMA) with Variable Window Adaptation (VWA) attention. Specifically, iEMA first scans information spatially at multiple scales to simultaneously capture information from areas such as hands and faces, and models spatial continuity interactions between them, enhancing spatial features. Secondly, VWA dynamically changes its window size at the temporal level to adaptively capture complex temporal changes in hands and face movements in successive frames. Thus, VWA can effectively model the continuity and correlation between hands and face movements to have the ability to capture sign language movements of different lengths. Comprehensive experimental results demonstrate that our proposed MVAN strengthens the visual feature extraction of hands and face, improving the recognition performance. © 2024 IEEE.

关键词： Convolutional neural networks

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M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Network for Segmenting Pathology Lesion of the Focal Lymphocytic Sialadenitis 1

M2CF-Net: A Multi-Resolution and Multi-Scale Cross Fusion Ne...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Han, Hao Lu, Feng Deng, Yanhan Luo, Xiaofang Jin, Hai Tu, Wei Xie, Xia School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan430074 China Tongji Medical College Huazhong University of Science and Technology Tongji Hospital Wuhan430022 China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350358780

In the medical realm, the pivotal role of pathological Whole Slide Images (WSIs) in detecting cancer, tracking disease progression, and evaluating treatment efficacy is indisputable. Nevertheless, the identification and quantification of lesion areas in these gigapixel WSIs present a significant challenge due to their substantial size and the intricate details of lesions. To address these issues, we developed a novel multi-resolution and multi-scale cross fusion network (M2CF-Net), adept at managing large-scale pathological WSIs and capturing both fine details and context. Our model particularly focuses on segmenting local lymphocyte infiltration lesions in pathological WSIs of patients diagnosed with primary Sjogren's syndrome. By employing a patch-based training approach and combining interconnected elements via a multi-scale fusion technique, we enhance our model's capacity to detect and analyze structures and features in minor salivary gland section WSIs. Extensive experiments and ablation studies conducted on real-world clinical datasets affirm our model's superior accuracy in identifying lymphocyte-infiltrated regions over state-of-the-art models, with a performance improvement of up to 4.32% in terms of the Dice Similarity Coefficient. © 2023 IEEE.

关键词： Pathology

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Visual Analysis of research on Blockchain

Visual Analysis of Research on Blockchain

引用

2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Liu, Yunjing Jing, Ming Wang, Xiaoxiao Zhang, Li Qilu University of Technology School of Computer Science and Technology Jinan China Qilu University of Technology Big Data Institute Jinan China Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

With the development of big data, people focus on data security and privacy, blockchain has been paid more and more attention. Compared to pre-2013, the number of blockchain literature publications has increased. Scholars from different research fields have deeply explored blockchain technology. In this paper, we collected blockchain literature data from 2013 to 2020, and then we conducted a quantitative analysis of publications, author groups, research content and future research content. For the current research status, we also discussed the problems facing blockchain. We started by mining representative authors, and revealed current status of blockchain research, author's team cooperation, and blockchain's development content. We can provide research objectives and inspiration for new researchers, and give research routes for the future development of blockchain. © 2022 IEEE.

关键词： Blockchain

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The Construction and Application of Deep Integration model of Information Technology and Undergraduate Teaching 5

The Construction and Application of Deep Integration model o...

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5th International Conference on Education Technology Management, ICETM 2022

作者： Feng, Youling Kang, Xin Yang, Yanan Han, Yi Sheng, Rui Zhou, Jinpin Sun, Tiezheng Jilin University of Finance and Economics School of Management Science and Information Engineering Business Big Data Research Center of Jilin Province Changchun130117 China

ISBN: (纸本)9781450398015

At the background of Information Techonology 2.0 and "Internet+Education", E-education is imperative for undergraduate education. In this article we construct a combined activity of student-centered, through higher school, educator, student triple dimension and a deep integration model of undergraduate education and IT. We also seek for IT combined education's defect from knowledge, capability and quality on the subject of the administrator, executor and accepter. In the meanwhile, we analyze the reason of defect and propose reforming solutions to improve the integration of IT and undergraduate education. Therefore, the efficiency and quality of education can also be enhanced. © 2022 ACM.

关键词： Defects

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LDVNet: Lightweight and Detail-Aware Vision Network for Image Recognition Tasks in Resource-Constrained Environments 29

LDVNet: Lightweight and Detail-Aware Vision Network for Imag...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Yuchen Liang, Hu Qiu, Ran Zhao, Shengrong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In many underwater application scenarios, recognition tasks need to be executed promptly on computationally limited platforms. However, models designed for this field often exhibit spatial locality, and existing works lack the ability to capture crucial details in images. Therefore, a lightweight and detail-aware vision network (LDVNet) for resource-constrained environments is proposed to overcome the limitations of these approaches. Firstly, in order to enhance the accuracy of target image recognition, we introduce transformer modules to acquire global information, thus addressing the issue of spatial locality inherent in traditional convolutional neural networks (CNNs). Secondly, to maintain the network's lightweight nature, we integrate the transformer module with convolutional operations, thereby mitigating the substantial parameter and floating point operations (FLOPs) overhead. Thirdly, for the efficient extraction of crucial fine-grained details from feature maps, we have devised a channel and spatial attention module (C&SA). This module aids in recognizing intricate and fine-grained visual tasks and enhances image understanding. It is seamlessly integrated into LDVNet with nearly negligible parameter overhead. The experimental results demonstrate that LDVNet outperforms other lightweight networks and hybrid networks in different recognition tasks, while being suitable for resource-constrained environments. © 2023 IEEE.

关键词： Detail-aware Image recognition Lightweight

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