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3DSN-Net: A 3-D Scale-Aware convNet With Nonlocal Context Guidance for Kidney and Tumor Segmentation From CT Volumes

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IEEE Transactions on Cybernetics 2024年第5期54卷 3299-3312页

作者： Wu, Huisi Zhang, Baiming Li, Zhuoying Qin, Jing Lee, Tong-Yee Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Hong Kong Polytechnic University Centre for Smart Health School of Nursing Hong Kong Hong Kong National Cheng-Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Automatic kidney and tumor segmentation from CT volumes is a critical prerequisite/tool for diagnosis and surgical treatment (such as partial nephrectomy). However, it remains a particularly challenging issue as kidneys and tumors often exhibit large-scale variations, irregular shapes, and blurring boundaries. We propose a novel 3-D network to comprehensively tackle these problems;we call it 3DSN-Net. Compared with existing solutions, it has two compelling characteristics. First, with a new scale-aware feature extraction (SAFE) module, the proposed 3DSN-Net is capable of adaptively selecting appropriate receptive fields according to the sizes of targets instead of indiscriminately enlarging them, which is particularly essential for improving the segmentation accuracy of the tumor with large scale variation. Second, we propose a novel yet efficient nonlocal context guidance (NCG) mechanism to capture global dependencies to tackle irregular shapes and blurring boundaries of kidneys and tumors. Instead of directly harnessing a 3-D NCG mechanism, which makes the number of parameters exponentially increase and hence the network difficult to be trained under limited training data, we develop a 2.5D NCG mechanism based on projections of feature cubes, which achieves a tradeoff between segmentation accuracy and network complexity. We extensively evaluate the proposed 3DSN-Net on the famous KiTS dataset with many challenging kidney and tumor cases. Experimental results demonstrate our solution consistently outperforms state-of-the-art 3-D networks after being equipped with scale aware and NCG mechanisms, particularly for tumor segmentation. © 2013 IEEE.

关键词： Feature extraction

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Detection of Conspiracy vs. Critical Narratives and Their Elements using NLP 25

Detection of Conspiracy vs. Critical Narratives and Their El...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Albladi, Aish Seals, Cheryl D. Department of Computer Science and Software Engineering Auburn University AuburnAL United States

The growing use of digital media or social media has given a platform to the people to deliver their ideas and viewpoints openly. It is facilitating the rapid spread of contrasting opinions openly. Ultimately, this has developed echo chambers or polarized platforms. These platforms either encourage conspiracy theories or critical narratives. So, nowadays, it has become crucial for people to be aware of being part of such narratives. Moreover, it is also necessary for the organisations to identify the conspiracy creators or the ones who do not possess a conspiracy mentality. This can be made possible by the use of natural language processing techniques to do this task automatically. This study presents the solutions to the two most pressing challenges in today's era. The subtask one of this study provides a solution for the identification of conspiracy narratives and critical narratives using the BERT model. Our model achieved an MCC score of 0.80. Secondly, the other subtask helps to recognize the span of the topic relevant to the important constituents of both categories of oppositional narratives, i.e., agent, facilitator, victim, campaigner, objective, negative effect. The overall accuracy of the model is 95%. These models can be used for the development of an algorithm that can automatically classify the conspiracy theories and critical narratives. © 2024 Copyright for this paper by its authors.

关键词： Social networking (online)

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Deep learning based water leakage detection for shield tunnel lining

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Frontiers of Structural and Civil engineering 2024年第6期18卷 887-898页

作者： Shichang LIU Xu XU Gwanggil JEON Junxin CHEN Ben-Guo HE College of Computer Science Sichuan UniversityChengdu 610065China School of Computer Science and Engineering Northeastern UniversityShenyang 110004China Department of Embedded Systems Engineering Incheon National UniversityIncheon 22012Korea School of Software Dalian University of TechnologyDalian 116621China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern UniversityShenyang 110819China

Shield tunnel lining is prone to water leakage,which may further bring about corrosion and structural damage to the walls,potentially leading to dangerous *** avoid tedious and inefficient manual inspection,many projects use artificial intelligence(Al)to detect cracks and water leakage.A novel method for water leakage inspection in shield tunnel lining that utilizes deep learning is introduced in this *** proposal includes a ConvNeXt-S backbone,deconvolutional-feature pyramid network(D-FPN),spatial attention module(SPAM).and a detection *** can extract representative features of leaking areas to aid inspection *** further improve the model's robustness,we innovatively use an inversed low-light enhancement method to convert normally illuminated images to low light ones and introduce them into the training *** experiments are performed,achieving the average precision(AP)score of 56.8%,which outperforms previous work by a margin of 5.7%.Visualization illustrations also support our method's practical effectiveness.

关键词： water leakage detection deep learning deconvolutional-feature pyramid spatial attention

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Visual News Ticker Surveillance Approach from Arabic Broadcast Streams

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computers, Materials & Continua 2023年第3期74卷 6177-6193页

作者： Moeen Tayyab Ayyaz Hussain Usama Mir M.Aqeel Iqbal Muhammad Haneef Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science Quaid-i-Azam UniversityIslamabad44000Pakistan Department of Computer Science Senior Member IEEEUniversity of WindsorN9B 3P4Canada Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan Department of Electrical Engineering Foundation University IslamabadIslamabad44000Pakistan

The news ticker is a common feature of many different news networks that display headlines and other *** ticker recognition applications are highly valuable in e-business and news surveillance for media regulatory *** this paper,we focus on the automatic Arabic Ticker Recognition system for the Al-Ekhbariya news *** primary emphasis of this research is on ticker recognition methods and storage *** that end,the research is aimed at character-wise explicit segmentation using a semantic segmentation technique and words identification *** proposed learning architecture considers the grouping of homogeneousshaped *** incorporates linguistic taxonomy in a unified manner to address the imbalance in data distribution which leads to individual ***,experiments with a novel ArabicNews Ticker(Al-ENT)dataset that provides accurate character-level and character components-level labeling to evaluate the effectiveness of the suggested *** proposed method attains 96.5%,outperforming the current state-of-the-art technique by 8.5%.The study reveals that our strategy improves the performance of lowrepresentation correlated character classes.

关键词： Arabic text recognition optical character recognition deep convolutional network SegNet LeNet

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of computer science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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Neural Operator-Based Framework for Time Efficient Denoising of Displacement Fields in Ultrasound Elastography

Neural Operator-Based Framework for Time Efficient Denoising...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhu, Yihong Peng, Bo School of Computer Science and Software Engineering Southwest Petroleum University Chengdu610500 China

ISBN: (纸本)9781665410205

In ultrasound elastography, the noise present in the measured displacement fields has been a critical factor affecting the quality of the strain or elastic distribution reconstruction. Existing partial differential equation (PDE) based denoising algorithms can effectively remove noise to some extent but are limited by slow solving speeds. To address this challenge, we introduce a neural operator-based framework for denoising measured displacement fields in ultrasound elastography. By utilizing neural operators to learn a general solution operator for the denoising equation, the framework aims to achieve fast and accurate denoising of displacement fields. Experiments conducted on simulated data and tissue-mimicking phantom data demonstrate that this method performs comparably to traditional methods across various metrics. In particular, the proposed approach achieves processing speeds approximately 30 to 577 times faster than the finite difference method (FDM) in various data dimensions, making it a highly effective solution for rapid and scalable data processing in ultrasound elastography. © 2024 IEEE.

关键词： Ultrasonic applications

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Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model Contrastive Detection

Fed-BRMC: Byzantine-Robust Federated Learning via Dual-Model...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Gan, Xiaoyun Lu, Peng Gan, Shanyu Xian, Youqian Peng, Kaichen Liu, Peng Li, Dongcheng School of Computer Science and Engineering School of Software Guangxi Normal University Guilin China

ISBN: (纸本)9781665410205

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning. © 2024 IEEE.

关键词： Adversarial machine learning

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Unsupervised Insider Threat Detection Using Multi-Head Self-Attention Mechanisms 4

Unsupervised Insider Threat Detection Using Multi-Head Self-...

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4th Intelligent Cybersecurity Conference, ICSC 2024

作者： Bertrand, Simon Germain, Pascal Tawbi, Nadia Laval University Dept. of Computer Science & Software Engineering Québec Canada

ISBN: (纸本)9798350354775

We propose an unsupervised insider threat detection system that learns normal user behaviors through audit data using neural networks equipped with multi-head self-attention mechanisms. The attention mechanisms learn precise normal user behaviors using large event windows. The key idea is to consider a sequence abnormal when it exhibits some events that are not likely to happen given the preceding events sequence. In addition, our method provides insights into detected threats through an event-based scoring system to facilitate threat understanding by security experts. Furthermore, the proposed solution does not require intensive audit logs pre-processing, such as manual domain knowledge-rich features extraction and data balancing. Thus, the proposed approach is easy to implement and use across organizations regardless of their expertise level. The proposed solution also provides the ability to detect threats in real-time. On the benchmark dataset CERT version 4.2, our solution, combined with hyperparameter search by Bayesian optimization, outperforms the previous state-of-the-art approaches with an area under the ROC curve of 97.23%, a recall of 91.51%, an accuracy of 93.13% and a false positive rate of 6.8%. © 2024 IEEE.

关键词： Deep neural networks

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Applied Technology Research on the Construction of Bidirectional Knowledge Graph Curriculum System Based on SmartKG 6

Applied Technology Research on the Construction of Bidirecti...

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6th International Workshop on Artificial Intelligence and Education, WAIE 2024

作者： Jiang, Su Luo, Wei Cui, Hengji School of Computer Science and Software Engineering Dalian Neusoft University of Information Dalian China

ISBN: (纸本)9798350378894

Amidst the rapid advancements in artificial intelligence technology, it is imperative to apply these technological developments to the realm of education to enhance information-based teaching methodologies. This article introduces a knowledge management framework that aims to visualize curriculum knowledge systems using knowledge graphs in the educational sector. Taking courses like Fundamentals of computer Systems as exemplars, the article demonstrates the techniques for constructing knowledge graphs. Additionally, SmartKG's ease of deployment and application allows students to construct their own visual knowledge graphs, fostering a deeper comprehension of knowledge and fostering the profound integration of AI and teaching. Through the analysis of the curriculum system and students' self-constructed knowledge graphs, the efficacy of utilizing knowledge graphs in learning has been validated, propelling the application and practice of intelligent classroom teaching. © 2024 IEEE.

关键词： Knowledge graph

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A Brief Review on Prediction Methods for Cloud Resource Management 9

A Brief Review on Prediction Methods for Cloud Resource Mana...

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9th IEEE International Conference on Smart Cloud, SmartCloud 2024

作者： Kuang, Chenxing Qiu, Yunyun Cao, Weipeng Xiao, Zhijiao Ming, Zhong Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen China

ISBN: (纸本)9798350389500

The complexity of apps running on cloud platforms is evident in their nature. Every application has distinct needs for processing power and memory at various times. In order to effectively cater to tenants' varied computing requirements while reducing inefficiencies in resource usage, cloud service providers need to be able to predict workload variations with high accuracy and evaluate how cloud apps use resources. In the recent past, there has been a significant increase in research and engineering efforts in cloud computing. These efforts are focused on developing methods to address important issues such as predicting changes in workloads, assessing resource demands, perceiving quality of service (QoS), and detecting anomalies. By reviewing these algorithms in depth, this paper aims to provide academics a better grasp of the most recent developments in this area. © 2024 IEEE.

关键词： Quality of service

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