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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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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Deepfake Detection: Emerging Techniques and Evolving Challenges 12

Deepfake Detection: Emerging Techniques and Evolving Challen...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Natarajan, Krishnaraj Mathur, Abeer Bhargava, Kshitiz Singh, Manvendra Tejpal, Moulik Department of Database Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350387315

Several newly developed techniques and tools for manipulating images, audio, and videos have been introduced as an outcome of the recent and rapid breakthroughs in AI, machine learning, and deep learning. While most applications for these techniques or tools are in the fields of entertainment and education, some individuals with unlawful intent have also been benefited from them. These individuals use such techniques for various purposes, including the spread of misleading information and unnecessary propaganda, the incitement of political instability, hate and unrest, as well as for purposes of torture and blackmail. These high-quality and convincing manipulated images, audio, or videos are commonly referred to as ‘Deepfakes’. Since then, various solutions to the problems raised by Deepfakes have been proposed in academic studies. This literature review contains relevant publications that offered a variety of approaches to give an updated summary of the research activities in different types of Deepfake attacks, their detection, and countermeasures. It also assesses the effectiveness of the detection capabilities of different techniques with various datasets and algorithms applied in Deepfake detection, while also outlining the various benefits and drawbacks of various methodologies. © 2024 IEEE.

关键词： Deep learning

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SFPL: Improving CRS via Prompt Learning based Semantic Fusion Module 4

SFPL: Improving CRS via Prompt Learning based Semantic Fusio...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Song, Ao Lei, Zhou Chen, Shengbo Shanghai University College of Computer Engineering and Science Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China

ISBN: (纸本)9798350363586

With the popularity of the Internet and the massive increase of content production, users are faced with massive information and content, and it is often difficult to accurately and efficiently find the content that meets their needs. This information overload motivates the emergence of recommendation systems that help users filter and recommend relevant content. Dialogue recommendation systems (CRS) aim to dynamically recommend high-quality items to users while eliciting user preferences by means of dialogue. CRS mainly consists of two parts, namely, the dialogue module and the recommendation module. An efficient CRS is to solve the problems existing in the two sub-tasks of dialogue and recommendation, namely, generating appropriate responses in dialogue and predicting user favorite items in recommendation. In addition, it is necessary to adopt a suitable method to integrate these two modules to ensure the accuracy of CRS recommendation. This paper proposes an efficient way of semantic fusion module to solve the integration problem of two modules, and proves the effectiveness of our method through experiments © 2024 IEEE.

关键词： Adversarial machine learning

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A dynamic and resource sharing virtual network mapping algorithm

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Digital Communications and Networks 2023年第5期9卷 1101-1112页

作者： Xiancui Xiao Xiangwei Zheng Ji Bian Cun Ji Xinchun Cui School of Information Science and Engineering Shandong Normal UniversityJinan250014China School of Information Engineering Shandong Management UniversityJinan250358China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014China School of Computer Science and Technology Qufu Normal UniversityRizhao276800China

Network virtualization can effectively establish dedicated virtual networks to implement various network ***,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and *** addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation *** on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology *** the node mapping process,three properties of the node are used to measure its mapping ***,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link ***,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource *** former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision *** results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenu

关键词： Network virtualization VNRs Network frameworks Dynamic resource allocation Resource sharing

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Boosting Write Performance of KV Stores: An NVM -Enabled Storage Collaboration Approach 40

Boosting Write Performance of KV Stores: An NVM -Enabled Sto...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi He, Jiajian Sun, Kaoyi Dong, Yunhao Chen, Jiaxian Ma, Chenlin Zhou, Amelie Chi Mao, Rui College of Computer Science and Software Engineering Shenzhen University China Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This 'ZigZag' shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes. © 2024 IEEE.

关键词： Flash memory

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Speed Harmonic based Saturation Free Inductance Modeling and Estimation of Interior PMSM Using Measurements Under One Load Condition

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CES Transactions on Electrical Machines and Systems 2025年第1期9卷 91-99页

作者： Guodong Feng Yuting Lu Zhe Tong Beichen Ding Guishan Yan Chunyan Lai the School of Intelligent Systems Engineering Sun Yat-sen UniversityShenzhenChina the School of Advanced Manufacturing Sun Yat-sen University the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) China the Department of Electrical and Computer Engineering Concordia UniversityQCCanada

For permanent magnet synchronous machines(PMSMs),accurate inductance is critical for control design and condition *** to magnetic saturation,existing methods require nonlinear saturation model and measurements from multiple load/current conditions,and the estimation is relying on the accuracy of saturation model and other machine parameters in the *** harmonic produced by harmonic currents is inductance-dependent,and thus this paper explores the use of magnitude and phase angle of the speed harmonic for accurate inductance *** estimation models are built based on either the magnitude or phase angle,and the inductances can be from d-axis voltage and the magnitude or phase angle,in which the filter influence in harmonic extraction is considered to ensure the estimation *** inductances can be estimated from the measurements under one load condition,which is free of saturation ***,the inductance estimation is robust to the change of other machine *** proposed approach can effectively improve estimation accuracy especially under the condition with low current *** and comparisons are conducted on a test PMSM to validate the proposed approach.

关键词： PMSM Inductance estimation Speed harmonic High frequency signal injection

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LeaderKV: Improving Read Performance of KV Stores via Learned Index and Decoupled KV Table 40

LeaderKV: Improving Read Performance of KV Stores via Learne...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Wang, Yi Yuan, Jianan Wu, Shangyu Liu, Huan Chen, Jiaxian Ma, Chenlin Qin, Jianbin Shenzhen University College of Computer Science and Software Engineering China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350317152

Log-structured merge-tree (LSM-tree) is a storage architecture widely used in key-value (KV) stores. To enhance the read efficiency of LSM-tree, recent works utilize the learned index to learn the mapping between keys and locations. However, in existing learned-index-aided KV stores, inefficient design of the learned index and disk access significantly impact the read performance. How to design a learned KV store to improve index efficiency and minimize disk access remains a critical problem. This paper presents LeaderKV, a read-optimized LSM-tree-based KV store. LeaderKV employs decoupled KV tables (DK-Table) and efficient learned indexes for data retrieval. DKTables are storage files in Leader Kvbecause they avoid reading irrelevant data in collaboration with learned indexes during queries. A learned index called Leader is proposed to accelerate data retrieval within DKTable. Leader is composed of precise models and approximate models. A redirect mechanism is designed to reduce the cost of mispredictions in Leader. We integrate DKTable and Leader into LeaderKV and demonstrate its effectiveness using a variety of datasets and workloads. Experimental results show that LeaderKV significantly improves the read performance compared to representative schemes. © 2024 IEEE.

关键词： Efficiency

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A model to measure the spread power of rumors

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13787-13811页

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad-Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories "importance" (28 features) and "ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Cell proliferation

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Countering Deepfakes using an Improved Advanced CNN and its Ensemble with Pretrained Models 3

Countering Deepfakes using an Improved Advanced CNN and its ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Mathur, Abeer Tejpal, Moulik Bhargava, Kshitiz Natarajan, Krishnaraj Singh, Manvendra Vellore Institute of Technology School of Computer Science & Engineering Department of Software Systems Vellore India Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India

ISBN: (纸本)9798350369083

The extensive spread of DeepFake images on the internet has emerged as a significant challenge, with applications ranging from harmless entertainment to harmful acts like blackmail, misinformation, and spreading false propaganda. To tackle this issue, this paper introduces a sophisticated DeepFake detection model designed to identify and mitigate the increase of these deceptive images. The model architecture integrates an ensemble approach, combining the strengths of two pre-trained Convolutional Neural Network (CNN) models - MobileNet and Xception - with a novel CNN architecture, the Advanced CNN (ACNN). This rigorous validation process enabled the model to achieve a high accuracy rate of 97.89% in detecting DeepFakes. The successful implementation of this ensemble CNN approach demonstrates its effectiveness in distinguishing between real and fabricated imagery with high precision. This research makes a substantial contribution to the field of digital image forensics, offering a reliable tool for stakeholders across various sectors to identify and counteract the spread of DeepFake images online. © 2024 IEEE.

关键词： Generative adversarial networks

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