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Annual IEEE Systems Conference

作者： Suman Bunia Owen Campbell Arthur Carvalho Vamsi Alluri Department of Computer Science and Software Engineering Miami University Oxford Ohio USA Farmer School of Business Miami University Oxford Ohio USA Freelance Blockchain Protocol Architect North Carolina USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The healthcare transport sector has progressed considerably in recent years, yet it continues to face persistent challenges such as payment, staffing, record redundancy, and resource waste. This paper proposes a blockchain-based auction system called Smart Contract enabled Fair, Secure, and Transparent Auction (SCeFSTA) that aims to address these problems and provide fair competition for healthcare transportation. SCeFSTA employs blockchain technology to create a transparent and secure auction-based platform that provides secure, immediate payment for services and promotes competition. It allows healthcare transportation providers to bid for services, ensuring that only cost-effective providers are selected. The smart contract ensures payments are made upon service completion, enhancing security for patients and providers. We follow a design science research approach to design the system after interviewing several key stakeholders in the healthcare transportation community. The blockchain-based data ledger reduces redundancy and waste while providing enhanced privacy and security. SCeFSTA has the potential to benefit the healthcare transportation field by creating a fair and efficient system that improves performance. We demonstrated the prototype to the first responder community, who understood the effectiveness. Through a series of rigorous system evaluations, we demonstrate that the proposed system could be deployed to healthcare transportation such as ambulance hire, and inter-facility patient transfer.

关键词： Data privacy Smart contracts Transportation Termination of employment Prototypes Medical services Blockchains

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NESec: A Modified-UI Honeyword Generation Strategy for Mitigating Targeted Guessing Attacks 7

NESec: A Modified-UI Honeyword Generation Strategy for Mitig...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Chakraborty, Nilesh Shojafar, Mohammad Chen, Zhuangzhuang Leung, Victor C. M. Li, Jianqiang Zulkernine, Mohammad School of Computing Queen's University K7L 3N6 Canada Institute for Communication Systems University of Surrey 5GIC & 6GIC GU27XH United Kingdom College of Computer Science and Software Engineering Shenzhen University 518060 China

ISBN: (纸本)9798350342871

By harnessing Graphics Processing Unit (GPU), Field-programmable Gate Array (FPGA), and advanced cracking techniques, the success rates of server-side threats on passwords have reached unprecedented levels. Honeywords, also known as decoy passwords, have emerged as a promising detection strategy against this threat scenario. However, existing work falls short in creating a trap to counter targeted guessing attackers (TgA) who exploit users' personal information to bypass honeyword-based traps. In this paper, we introduce two fundamental honeyword generation modules, namely NSecure-modifiedUI and ESecure-modifiedUI. Building upon these primary modules, we propose a hybrid honeyword-based strategy named NESec, which significantly enhances the ability to detect TgA 's activities. A comparative analysis showcases the usability advantages and security benefits of the proposed NESec approach. © 2023 IEEE.

关键词： Graphics processing unit

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VMeta: A QoS Dataset for Metaverse Services 6th

VMeta: A QoS Dataset for Metaverse Services

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Ge, Zhiyuan Jin, Huiying Dong, Hai Ji, Shunhui Li, Jiajia Yang, Shuhan Wang, Qi Zhang, Pengcheng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210000 China College of Computer Science and Software Engineering Hohai University Nanjing210000 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210000 China School of Computing Technologies RMIT University Melbourne3000 Australia

ISBN: (纸本)9789819614134

The metaverse is undergoing repaid evolution with metaverse services emerging as a significant focal point of research across virtual reality, augmented reality, and metaverse social networking. However, a key challenge lies in the scarcity of datasets that accurately represent metaverse services, impeding the advancement of metaverse service technologies and applications. To address this challenge, we have categorized metaverse services into virtual scene services and meta-services. Subsequently, we introduced a metaverse service dataset called "VMeta". VMeta encompasses comprehensive information about virtual scene services and meta-services, including essential QoS (Quality of Service) data. This dataset serves as a valuable resource for researchers exploring various aspects of metaverse services, with the ultimate goal of enhancing user satisfaction in the metaverse. We have conducted evaluations and tests on VMeta to optimize metaverse QoS. The results demonstrate its substantial value for metaverse service research. Our datasets are available at https://***/Gelieziman/VMeta. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model

arXiv

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arXiv 2024年

作者： Tian, Yuxin Yang, Mouxin Li, Yunfan Liu, Dayiheng Ren, Xingzhang Peng, Xi Lv, Jiancheng College of Computer Science Sichuan University Chengdu China School of Software and Microelectronics Peking University Beijing China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu China

Recent studies applied Parameter Efficient Fine-Tuning techniques (PEFTs) to efficiently narrow the performance gap between pre-training and downstream. There are two important factors for various PEFTs, namely, the accessible data size and fine-tunable parameter size. A natural expectation for PEFTs is that the performance of various PEFTs is positively related to the data size and fine-tunable parameter size. However, according to the evaluation of five PEFTs on two downstream vision-language (VL) tasks, we find that such an intuition holds only if the downstream data and task are not consistent with pre-training. For downstream fine-tuning consistent with pre-training, data size no longer affects the performance, while the influence of fine-tunable parameter size is not monotonous. We believe such an observation could guide the choice of training strategy for various PEFTs. © 2024, CC BY-NC-SA.

关键词： Luminance

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Sara: Controllable Makeup Transfer with Semantic-Guided Alignment and Region-Adaptive Normalization

SSRN

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SSRN 2024年

作者： Zhong, Xiaojing Huang, Xinyi Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China SenseTime Research Hong Kong

Makeup transfer is a process of transferring the makeup style from a reference image to the source image, while preserving the source image's identity. In addition to achieving fine-grained control over the makeup transfer, incorporating semantic alignment into the transformation is crucial, as the poses of the reference and source images are often inconsistent. We propose a novel Semantic-guided Alignment and Region-Adaptive normalization framework (SARA) to effectively transfer makeup styles under misaligned poses, offering flexible control to meet the demands of real-world applications, such as partial transfer, intensity adjustment, and makeup removal. Specifically, SARA comprises three modules: Firstly, we propose a semantic-guided alignment module to explicitly construct dense correspondence between the reference image and the target semantic map, employing unbalanced optimal transport matching to handle semantic region mismatches. Secondly, a region-adaptive normalization module is responsible for dynamically combining the warped style features with shape-independent style codes obtained by region-wise average pooling, mitigating feature loss during the alignment. Lastly, a makeup fusion module progressively fuses the identity features with the makeup styles to render the final output image. Furthermore, we combine optimal transport with histogram matching to generate the pseudo ground truth, which is used to facilitate the transfer in terms of both spatial alignment and color distribution. Experimental results show that our proposed SARA outperforms existing methods on two public datasets. © 2024, The Authors. All rights reserved.

关键词： Semantics

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SCAF-DG: A Multi-site Medical Image Denoising With A Domain-Generalized Spatial-Channel Attention Fusion

SCAF-DG: A Multi-site Medical Image Denoising With A Domain-...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Linda Delali Fiasam Yunbo Rao Collins Sey Benjamin Klugah-Brown Obed Nartey Tettey Esther Stacy E.B. Aggrey Isaac Osei Agyemang Isaac Adjei-Mensah Yang Yang School of Information and Software Engineering University of Electronic Science Technology of China Chengdu China School of Life Science and Technology University of Electronic Science Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science Technology of China Chengdu China Sichuan Institute of Computer Sciences Chengdu China

Images from multiple medical sites usually contain varying noise levels that can affect the generalization performance of the denoising models. Domain generalization (DG), which seeks to learn a model that can generalize to an unseen test domain, has not been extensively explored in medical image denoising for multi-site images. We present an approach for multi-site medical image denoising, which addresses the issue of varying noise levels across different imaging sites. Our proposed method, called SCAF-DG (Spatial-Channel Attention Fusion for Domain Generalization), employs two attention units to extract both domain-specific channel features and mutually-invariant spatial features from the noisy images. These features are then combined to form a rich feature space. Additionally, we utilized skip connections to preserve structural information during the denoising process. We evaluate SCAF-DG on publicly available brain magnetic resonance image (MRI) datasets from three different imaging sites, and demonstrate an average increase of 1.01 in peak signal-to-noise ratio (PSNR) and 0.016 in structural similarity index measure (SSIM) for denoising images. Our results demonstrate that SCAF-DG has the potential to generalize well to new, unseen test domains, and outperforms existing state-of-the-art denoising models on multi-site medical images.

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Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection

arXiv

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arXiv 2024年

作者： Yao, Siyuan Sun, Hao Xiang, Tian-Zhu Wang, Xiao Cao, Xiaochun School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications Beijing100876 China Inception Institute of Artificial Intelligence G42 Bayanat Abu Dhabi United Arab Emirates School of Software Beihang University Beijing100083 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen518107 China

Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged objects and the background region, it is extremely challenging to precisely distinguish the camouflaged objects by existing approaches. In this paper, we propose a hierarchical graph interaction network termed HGINet for camouflaged object detection, which is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Specifically, we first design a region-aware token focusing attention (RTFA) with dynamic token clustering to excavate the potentially distinguishable tokens in the local region. Afterwards, a hierarchical graph interaction transformer (HGIT) is proposed to construct bi-directional aligned communication between hierarchical features in the latent interaction space for visual semantics enhancement. Furthermore, we propose a decoder network with confidence aggregated feature fusion (CAFF) modules, which progressively fuses the hierarchical interacted features to refine the local detail in ambiguous regions. Extensive experiments conducted on the prevalent datasets, i.e. COD10K, CAMO, NC4K and CHAMELEON demonstrate the superior performance of HGINet compared to existing state-of-the-art methods. Our code is available at https://***/Garyson1204/HGINet. Copyright © 2024, The Authors. All rights reserved.

关键词： Object recognition

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Multi-scale Attention-Based Feature Pyramid Networks for Object Detection 11th

Multi-scale Attention-Based Feature Pyramid Networks for Obj...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Zhao, Xiaodong Chen, Junliang Liu, Minmin Ye, Kai Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783030873547

Feature pyramid network (FPN) is widely used for multi-scale object detection. While lots of FPN based methods have been proposed to improve detection performance, there exists semantic difference between cross-scale features. Therefore, simple connections bring spatial or channel information loss, while excessive connections bring extra parameters and inference cost. Besides, the fusion of too many features may lead to the information decay and feature aliasing. To deal with the above problems, we propose the Multi-scale Attention-based Feature Pyramid Networks (MAFPN), to fully exploit spatial and channel information and generate a better feature representation for each level from multi-scale features. Taking scale, spatial and channel information into consideration at the same time, MAFPN can process multi-scale input more comprehensively than most conventional methods. The experimental results show that our MAFPN can improve the detection performance of both two-stage and one-stage detectors with an acceptable increase of inference cost. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Galliot: Path Merging Based Betweenness Centrality Algorithm on GPU 42

Galliot: Path Merging Based Betweenness Centrality Algorithm...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Zheng, Zhigao Zhao, Chen Xie, Peichen Dum, Bo School of Computer Science Wuhan University Wuhan 430072 China National Engineering Research Center for Multimedia Software Wuhan University Wuhan 430072 Chin. Institute of Artificial Intelligence Wuhan University Wuhan 430072 Chin. Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan 430072 China Hubei Luojia Laboratory Wuhan 430072 China

ISBN: (纸本)9798350334142

Betweenness centrality (BC) is widely used to measure a vertex's significance by using the frequency of a vertex appearing in the shortest path between other vertices. However, most recent algorithms in BC computation suffer from the problem of high auxiliary memory consumption. To reduce BC computing's memory consumption, we propose a path-mergingbased algorithm called Galliot to calculate the BC values on GPU, which aims to minimize the on-board memory consumption and enable the BC computation of large-scale graphs. The proposed algorithm requires O(n) space and runs in O(mn) time on unweighted graphs. We present the theoretical principle for the proposed path merging method. Moreover, we propose a locality-oriented policy to maintain and update the worklist to improve GPU data locality. In addition, we conducted extensive experiments on NVIDIA GPUs to show the performance of Galliot. The results show that Galliot can process the larger graphs, which have 11.32× more vertices and 5.67× more edges than the graphs that recent works. Moreover, Galliot can achieve up to 38.77× speedup over the existing methods. © 2023 IEEE.

关键词： Graphics processing unit

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A method to generate context information sets from analysis results with a unified abstraction model based on an extension of data enrichment scheme

A method to generate context information sets from analysis ...

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作者： Park, Yoosang Mun, Jonghyeok Choi, Jongsun Choi, Jaeyoung Kim, Hoda School of Computer Science and Engineering Soongsil University Seoul Korea Republic of National Program of Excellence in Software center Chosun University Gwangju Korea Republic of

Context-awareness techniques generally deal with steps for processing information that can be gathered from perspectives of objects in real-world scenes in accordance with resolving heterogeneous devices and providing meaningful context information in recent computing domains such as Internet of Things, cloud computing, edge computing, and big data analysis. However, analysis applications are necessary to be improved in a way of cooperative approaches because they are independently executed far away from context-aware systems. This means that the stages are applied to process the data or the derived results, are isolated. That leads difficulties of management and integration for other software modules. As a result, a computing paradigm called edge computing has been suggested to compromise such isolations. For processing context information, it is necessary to establish knowledge parts with validation processes against the isolations. In this paper, we suggest a method for processing datasets in a view of building knowledge parts. The suggested method uses an extensible data enrichment scheme, which represents the relations for the object. In the experiment section, applications of the proposed method are shown in one of the healthcare community centers, and the data abstraction with the analysis jobs can be processed for each real-world object. © 2020 John Wiley & Sons, Ltd.

关键词： Knowledge representation

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