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A low-time-consumption image encryption combining 2D parametric Pascal matrix chaotic system and elementary operation 一种结合二维参数帕斯卡矩阵混沌系统和基本运算的低耗时图像加密方法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-28 DOI: 10.1016/j.jksuci.2024.102169
Jun Lu , Jiaxin Zhang , Dezhi An , Dawei Hao , Xiaokai Ren , Ruoyu Zhao

The rapid development of the big data era has resulted in traditional image encryption algorithms consuming more time in handling the huge amount of data. The consumption of time cost needs to be reduced while ensuring the security of encryption algorithms. With this in mind, the paper proposes a low-time-consumption image encryption (LTC-IE) combining 2D parametric Pascal matrix chaotic system (2D-PPMCS) and elementary operation. First, the 2D-PPMCS with robustness and complex chaotic behavior is adopted. Second, the SHA-256 hash values are applied to the chaotic sequences generated by 2D-PPMCS, which are processed and applied to image permutation and diffusion encryption. In the permutation stage, the pixel matrix is permutation encrypted based on the permutation matrix generated from the chaotic sequences. For diffusion encryption, elementary operations are utilized to construct the model, such as exclusive or, modulo, and arithmetic operations (addition, subtraction, multiplication, and division). After analyzing the security experiments, the LTC-IE algorithm ensures security and robustness while reducing the time cost consumption.

大数据时代的快速发展导致传统图像加密算法在处理海量数据时耗费更多时间。在保证加密算法安全性的同时,还需要降低时间成本的消耗。有鉴于此,本文提出了一种结合二维参数帕斯卡矩阵混沌系统(2D-PPMCS)和基本运算的低耗时图像加密(LTC-IE)。首先,采用具有鲁棒性和复杂混沌行为的二维参数帕斯卡矩阵混沌系统。其次,将 SHA-256 哈希值应用于 2D-PPMCS 生成的混沌序列,经过处理后应用于图像置换和扩散加密。在置换阶段,根据混沌序列生成的置换矩阵对像素矩阵进行置换加密。在扩散加密阶段,利用基本运算来构建模型,如排他性或、模和算术运算(加、减、乘、除)。经过安全实验分析,LTC-IE 算法在降低时间成本消耗的同时,确保了安全性和鲁棒性。
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引用次数: 0
An efficient authentication scheme syncretizing physical unclonable function and revocable biometrics in Industrial Internet of Things 在工业物联网中同步物理不可克隆功能和可撤销生物识别技术的高效认证方案
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-28 DOI: 10.1016/j.jksuci.2024.102166
Xinying Yu , Kejun Zhang , Zhufeng Suo , Jun Wang , Wenbin Wang , Bing Zou

Biometric recognition is extensive for user security authentication in the Industrial Internet of Things (IIoT). However, the potential leakage of biometric data has severe repercussions, such as identity theft or tracking. Existing authentication schemes primarily focus on protecting biometric templates but often overlook the “one-authentication multiple-access” mode. As a result, these schemes still confront challenges related to privacy leakage and low efficiency for users who frequently access the server. In this regard, this paper proposes an efficient authentication scheme syncretizing physical unclonable function (PUF) and revocable biometrics in IIoT. Specifically, we design a revocable biometric template generation method syncretizing the user’s biometric data and the device’s PUF to enhance the security and revocability of the dual identity information. Given the generated revocable biometric template and the secret sharing, our scheme implements secure authentication and key negotiation between users and servers. Additionally, we establish an access boundary and an authentication validity period to permit multiple accesses following one authentication, thus significantly decreasing the computational cost of the user-side device. We leverage BAN logic and the ROR model to prove our scheme’s security. Informal security analysis and performance comparison demonstrate that our scheme satisfies more security features with higher authentication efficiency.

生物识别技术在工业物联网(IIoT)中广泛应用于用户安全认证。然而,生物识别数据的潜在泄漏会造成严重影响,如身份盗用或跟踪。现有的身份验证方案主要侧重于保护生物识别模板,但往往忽略了 "一次验证多次访问 "模式。因此,对于频繁访问服务器的用户来说,这些方案仍然面临着隐私泄露和效率低下的挑战。为此,本文提出了一种将物理不可克隆函数(PUF)和可撤销生物识别技术同步应用于物联网的高效身份验证方案。具体来说,我们设计了一种可撤销生物识别模板生成方法,将用户的生物识别数据与设备的 PUF 同步,以增强双重身份信息的安全性和可撤销性。鉴于生成的可撤销生物识别模板和秘密共享,我们的方案实现了用户和服务器之间的安全认证和密钥协商。此外,我们还建立了访问边界和认证有效期,允许在一次认证后进行多次访问,从而大大降低了用户端设备的计算成本。我们利用 BAN 逻辑和 ROR 模型来证明我们方案的安全性。非正式的安全性分析和性能比较表明,我们的方案能以更高的验证效率满足更多的安全特性。
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引用次数: 0
An electricity price and energy-efficient workflow scheduling in geographically distributed cloud data centers 地理分布式云数据中心的电价和节能工作流调度
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-28 DOI: 10.1016/j.jksuci.2024.102170
Mehboob Hussain , Lian-Fu Wei , Amir Rehman , Abid Hussain , Muqadar Ali , Muhammad Hafeez Javed

The cloud computing platform has become a favorable destination for running cloud workflow applications. However, they are primarily complicated and require intensive computing. Task scheduling in cloud environments, when formulated as an optimization problem, is proven to be NP-hard. Thus, efficient task scheduling plays a decisive role in minimizing energy costs. Electricity prices fluctuate depending on the vending company, time, and location. Therefore, optimizing energy costs has become a serious issue that one must consider when building workflow applications scheduling across geographically distributed cloud data centers (GD-CDCs). To tackle this issue, we have suggested a dual optimization approach called electricity price and energy-efficient (EPEE) workflow scheduling algorithm that simultaneously considers energy efficiency and fluctuating electricity prices across GD-CDCs, aims to reach the minimum electricity costs of workflow applications under the deadline constraints. This novel integration of dynamic voltage and frequency scaling (DVFS) with energy and electricity price optimization is unique compared to existing methods. Moreover, our EPEE approach, which includes task prioritization, deadline partitioning, data center selection based on energy efficiency and price diversity, and dynamic task scheduling, provides a comprehensive solution that significantly reduces electricity costs and enhances resource utilization. In addition, the inclusion of both generated and original data transmission times further differentiates our approach, offering a more realistic and practical solution for cloud service providers (CSPs). The experimental results reveal that the EPEE model produces better success rates to meet task deadlines, maximize resource utilization, cost and energy efficiencies in comparison to adapted state-of-the-art algorithms for similar problems.

云计算平台已成为运行云工作流应用程序的有利去处。然而,它们主要比较复杂,需要密集的计算。如果将云环境中的任务调度表述为一个优化问题,则证明它是一个 NP 难问题。因此,高效的任务调度对能源成本最小化起着决定性作用。电价随自动售货机公司、时间和地点的不同而波动。因此,在跨地理分布云数据中心(GD-CDC)构建工作流应用调度时,优化能源成本已成为一个必须考虑的重要问题。为解决这一问题,我们提出了一种名为 "电价与能效(EPEE)工作流调度算法 "的双重优化方法,该算法同时考虑了跨 GD-CDC 的能效和波动电价,目的是在截止日期限制下实现工作流应用的最低电费。与现有方法相比,这种将动态电压和频率调整(DVFS)与能源和电价优化相结合的新方法是独一无二的。此外,我们的 EPEE 方法包括任务优先级排序、截止日期分区、基于能效和价格多样性的数据中心选择以及动态任务调度,它提供了一个全面的解决方案,可显著降低电费成本并提高资源利用率。此外,我们的方法还包含了生成数据和原始数据的传输时间,这使我们的方法更加与众不同,为云服务提供商(CSP)提供了更现实、更实用的解决方案。实验结果表明,与适用于类似问题的最先进算法相比,EPEE 模型在满足任务期限要求、最大化资源利用率、成本和能源效率方面具有更高的成功率。
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引用次数: 0
Systematic review of deep learning solutions for malware detection and forensic analysis in IoT 对用于物联网恶意软件检测和取证分析的深度学习解决方案进行系统审查
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-27 DOI: 10.1016/j.jksuci.2024.102164
Siraj Uddin Qureshi , Jingsha He , Saima Tunio , Nafei Zhu , Ahsan Nazir , Ahsan Wajahat , Faheem Ullah , Abdul Wadud

The swift proliferation of Internet of Things (IoT) devices has presented considerable challenges in maintaining cybersecurity. As IoT ecosystems expand, they increasingly attract malware attacks, necessitating advanced detection and forensic analysis methods. This systematic review explores the application of deep learning techniques for malware detection and forensic analysis within IoT environments. The literature is organized into four distinct categories: IoT Security, Malware Forensics, Deep Learning, and Anti-Forensics. Each group was analyzed individually to identify common methodologies, techniques, and outcomes. Conducted a combined analysis to synthesize the findings across these categories, highlighting overarching trends and insights.This systematic review identifies several research gaps, including the need for comprehensive IoT-specific datasets, the integration of interdisciplinary methods, scalable real-time detection solutions, and advanced countermeasures against anti-forensic techniques. The primary issue addressed is the complexity of IoT malware and the limitations of current forensic methodologies. Through a robust methodological framework, this review synthesizes findings across these categories, highlighting common methodologies and outcomes. Identifying critical areas for future investigation, this review contributes to the advancement of cybersecurity in IoT environments, offering a comprehensive framework to guide future research and practice in developing more robust and effective security solutions.

物联网(IoT)设备的迅速扩散给维护网络安全带来了巨大挑战。随着物联网生态系统的扩展,它们越来越多地吸引恶意软件攻击,因此需要先进的检测和取证分析方法。本系统综述探讨了深度学习技术在物联网环境下恶意软件检测和取证分析中的应用。文献分为四个不同的类别:物联网安全、恶意软件取证、深度学习和反取证。对每一组进行了单独分析,以确定共同的方法、技术和结果。本系统综述确定了几项研究空白,包括需要全面的物联网特定数据集、跨学科方法的整合、可扩展的实时检测解决方案以及针对反取证技术的先进对策。研究的主要问题是物联网恶意软件的复杂性和当前取证方法的局限性。通过一个强大的方法论框架,本综述综合了这些类别的研究结果,突出了共同的方法和成果。本综述确定了未来调查的关键领域,为推进物联网环境中的网络安全做出了贡献,提供了一个全面的框架,指导未来的研究和实践,以开发更强大、更有效的安全解决方案。
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引用次数: 0
Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets 将 PCA 与深度学习模型相结合用于股市预测:土耳其股票市场分析
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-27 DOI: 10.1016/j.jksuci.2024.102162
Taner Uçkan

Financial data such as stock prices are rich time series data that contain valuable information for investors and financial professionals. Analysis of such data is critical to understanding market behaviour and predicting future price movements. However, stock price predictions are complex and difficult due to the intense noise, non-linear structures, and high volatility contained in this data. While this situation increases the difficulty of making accurate predictions, it also creates an important area for investors and analysts to identify opportunities in the market. One of the effective methods used in predicting stock prices is technical analysis. Multiple indicators are used to predict stock prices with technical analysis. These indicators formulate past stock price movements in different ways and produce signals such as buy, sell, and hold. In this study, the most frequently used ten different indicators were analyzed with PCA (Principal Component Analysis. This study aims to investigate the integration of PCA and deep learning models into the Turkish stock market using indicator values and to assess the effect of this integration on market prediction performance. The most effective indicators used as input for market prediction were selected with the PCA method, and then 4 different models were created using different deep learning architectures (LSTM, CNN, BiLSTM, GRU). The performance values of the proposed models were evaluated with MSE, MAE, MAPE and R2 measurement metrics. The results obtained show that using the indicators selected by PCA together with deep learning models improves market prediction performance. In particular, it was observed that one of the proposed models, the PCA-LSTM-CNN model, produced very successful results.

股票价格等金融数据是丰富的时间序列数据,其中包含对投资者和金融专业人士有价值的信息。分析这些数据对于理解市场行为和预测未来价格走势至关重要。然而,由于这些数据中包含大量噪声、非线性结构和高波动性,股票价格预测非常复杂和困难。这种情况虽然增加了准确预测的难度,但也为投资者和分析师发现市场机会提供了一个重要领域。技术分析是预测股票价格的有效方法之一。技术分析使用多种指标来预测股票价格。这些指标以不同的方式表述过去的股价走势,并产生买入、卖出和持有等信号。在本研究中,使用 PCA(主成分分析法)对最常用的十种不同指标进行了分析。本研究旨在调查利用指标值将 PCA 和深度学习模型整合到土耳其股市的情况,并评估这种整合对市场预测性能的影响。使用 PCA 方法选出了最有效的市场预测输入指标,然后使用不同的深度学习架构(LSTM、CNN、BiLSTM、GRU)创建了 4 个不同的模型。利用 MSE、MAE、MAPE 和 R2 测量指标评估了所建模型的性能值。结果表明,将 PCA 选定的指标与深度学习模型结合使用可提高市场预测性能。其中,PCA-LSTM-CNN 模型取得了非常成功的结果。
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引用次数: 0
A self-supervised entity alignment framework via attribute correction 通过属性校正的自监督实体对齐框架
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-26 DOI: 10.1016/j.jksuci.2024.102167
Xin Zhang , Yu Liu , Hongkui Wei , Shimin Shan , Zhehuan Zhao

Entity alignment (EA), aiming to match entities with the same meaning across different knowledge graphs (KGs), is a critical step in knowledge fusion. Existing EA methods usually encode the multi-aspect features of entities as embeddings and learn to align the embeddings with supervised learning. Although these methods have achieved remarkable results, two issues have not been well addressed. Firstly, these methods require pre-aligned entity pairs to perform EA tasks, limiting their applicability in practice. Secondly, these methods overlook the unique contribution of digital attributes to EA tasks when utilising attribute information to enhance entity features. In this paper, we propose a self-supervised entity alignment framework via attribute correction. Specifically, we first design a highly effective seed pair generator based on multi-aspect features of entities to solve the labour-intensive problem of obtaining pre-aligned entity pairs. Then, a novel alignment mechanism via attribute correction is proposed to address the problem that different types of attributes have different contributions to the EA task. Extensive experiments on real-world datasets with semantic features demonstrate that our framework outperforms state-of-the-art (SOTA) EA tasks.

实体配准(EA)旨在匹配不同知识图谱(KG)中具有相同含义的实体,是知识融合的关键步骤。现有的实体配准方法通常将实体的多方面特征编码为嵌入,并通过有监督的学习对嵌入进行配准。虽然这些方法取得了显著的成果,但有两个问题还没有得到很好的解决。首先,这些方法需要预先对齐实体对才能执行 EA 任务,这限制了它们在实践中的适用性。其次,这些方法在利用属性信息增强实体特征时,忽略了数字属性对 EA 任务的独特贡献。在本文中,我们提出了一种通过属性校正进行自我监督的实体配准框架。具体来说,我们首先设计了一种基于实体多方面特征的高效种子对生成器,以解决获取预对齐实体对这一劳动密集型问题。然后,我们提出了一种通过属性校正的新型配准机制,以解决不同类型的属性对 EA 任务有不同贡献的问题。在具有语义特征的真实数据集上进行的大量实验表明,我们的框架优于最先进的(SOTA)EA 任务。
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引用次数: 0
Abnormal lower limb posture recognition based on spatial gait feature dynamic threshold detection 基于空间步态特征动态阈值检测的异常下肢姿势识别
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-22 DOI: 10.1016/j.jksuci.2024.102161
Shengrui Zhang, Ling He, Dan Liu, Chuan Jia, Dechao Zhang

Lower limb rehabilitation training often involves the use of assistive standing devices. However, elderly individuals frequently experience reduced exercise effectiveness or suffer muscle injuries when utilizing these devices. The ability to recognize abnormal lower limb postures can significantly enhance training efficiency and minimize the risk of injury. To address this, we propose a model based on dynamic threshold detection of spatial gait features to identify such abnormal postures. A human-assisted standing rehabilitation device platform was developed to build a lower limb gait depth dataset. RGB data is employed for keypoint detection, enabling the establishment of a 3D lower limb posture recognition model that extracts gait, time, spatial features, and keypoints. The predicted joint angles, stride length, and step frequency demonstrate errors of 4 %, 8 %, and 1.3 %, respectively, with an average confidence of 0.95 for 3D key points. We employed the WOA-BP neural network to develop a dynamic threshold algorithm based on gait features and propose a model for recognizing abnormal postures. Compared to other models, our model achieves a 96 % accuracy rate in recognizing abnormal postures, with a recall rate of 83 % and an F1 score of 90 %. ROC curve analysis and AUC values reveal that the WOA-BP algorithm performs farthest from the pure chance line, with the highest AUC value of 0.89, indicating its superior performance over other models. Experimental results demonstrate that this model possesses a strong capability in recognizing abnormal lower limb postures, encouraging patients to correct these postures, thereby reducing muscle injuries and improving exercise effectiveness.

下肢康复训练通常需要使用辅助站立装置。然而,老年人在使用这些设备时经常会出现运动效果下降或肌肉受伤的情况。识别异常下肢姿势的能力可显著提高训练效率,并将受伤风险降至最低。为此,我们提出了一种基于空间步态特征动态阈值检测的模型来识别此类异常姿势。我们开发了一个人体辅助站立康复设备平台,以建立下肢步态深度数据集。利用 RGB 数据进行关键点检测,从而建立了一个可提取步态、时间、空间特征和关键点的三维下肢姿势识别模型。预测的关节角度、步长和步频误差分别为 4%、8% 和 1.3%,三维关键点的平均置信度为 0.95。我们利用 WOA-BP 神经网络开发了一种基于步态特征的动态阈值算法,并提出了一种识别异常姿势的模型。与其他模型相比,我们的模型识别异常姿势的准确率达到 96%,召回率为 83%,F1 分数为 90%。ROC 曲线分析和 AUC 值显示,WOA-BP 算法的表现距离纯机会线最远,最高 AUC 值为 0.89,表明其性能优于其他模型。实验结果表明,该模型具有很强的识别异常下肢姿势的能力,可鼓励患者纠正这些姿势,从而减少肌肉损伤,提高锻炼效果。
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引用次数: 0
A formal specification language and automatic modeling method of asset securitization contract 资产证券化合同的形式化规范语言和自动建模方法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-21 DOI: 10.1016/j.jksuci.2024.102163
Yang Li , Kai Hu , Jie Li , Kaixiang Lu , Yuan Ai

Asset securitization is an important financial derivative involving complicated asset transfer operations. Therefore, digitizing traditional asset securitization contracts will improve efficiency and facilitate reliability verification. Furthermore, accurate and verifiable requirement description is essential for collaborative development between financial professionals and software engineers. A domain specific language for writing asset securitization contract has been proposed. This solves the problem of difficulty for financial professionals to directly write smart contract by simplifying writing rules. However, due to existing design of the language focused on some simple scenarios, it is insufficient and informal to describe various detailed scenarios. What is more, there are still many reliability issues, such as verifying the correctness of the logical properties of the contract and ensuring the consistency between the contract text and the contract code, within the language in the generation and execution of smart contracts. To overcome the challenges stated above, we extend, simplify and innovate the syntax subset of the domain specific language and name it AS-SC (Asset Securitization – Smart Contract), which can be used by financial professionals to accurately describe requirements. Besides, because formal methods are math-based techniques that describe system properties and can generate programs in a more formal and reliable manner, we propose a semantic consistent code conversion method, named AS2EB, for converting from AS-SC to Event-B, a common and useful formal language. AS2EB method can be used by software engineers to verify requirements. The combination of AS-SC and AS2EB ensures consistency and reliability of the requirements, and reduces the cost of repeated communications and later testing. Taking the credit asset securitization contract as case study, the feasibility and rationality of AS-SC and AS2EB are validated. In addition, by carrying out experiments on three randomly selected real cases in different classic scenarios, we show high-efficiency and reliability of AS2EB method.

资产证券化是一种重要的金融衍生工具,涉及复杂的资产转移操作。因此,将传统的资产证券化合同数字化将提高效率并促进可靠性验证。此外,准确、可验证的需求描述对于金融专业人员和软件工程师之间的合作开发至关重要。有人提出了一种用于编写资产证券化合同的特定领域语言。这通过简化编写规则,解决了金融专业人士难以直接编写智能合约的问题。然而,由于该语言的现有设计侧重于一些简单的场景,在描述各种详细场景时显得不够充分和不正规。此外,在智能合约的生成和执行过程中,该语言还存在许多可靠性问题,如验证合约逻辑属性的正确性、确保合约文本与合约代码的一致性等。为了克服上述挑战,我们对特定领域语言的语法子集进行了扩展、简化和创新,并将其命名为 AS-SC(资产证券化-智能合约),可供金融专业人士准确描述需求。此外,由于形式化方法是基于数学的技术,可以描述系统属性,并能以更形式化、更可靠的方式生成程序,因此我们提出了一种语义一致的代码转换方法,命名为AS2EB,用于将AS-SC转换为常用且有用的形式化语言Event-B。AS2EB 方法可用于软件工程师验证需求。AS-SC 和 AS2EB 的结合确保了需求的一致性和可靠性,降低了反复沟通和后期测试的成本。以信贷资产证券化合同为例,验证了 AS-SC 和 AS2EB 的可行性和合理性。此外,通过对随机抽取的三个不同经典场景的真实案例进行实验,我们展示了 AS2EB 方法的高效性和可靠性。
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引用次数: 0
DAW-FA: Domain-aware adaptive weighting with fine-grain attention for unsupervised MRI harmonization DAW-FA:用于无监督磁共振成像协调的具有细粒度注意力的领域感知自适应加权法
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-21 DOI: 10.1016/j.jksuci.2024.102157
Linda Delali Fiasam , Yunbo Rao , Collins Sey , Stacy E.B. Aggrey , Seth Larweh Kodjiku , Kwame Opuni-Boachie Obour Agyekum , Annicet Razafindratovolahy , Isaac Adjei-Mensah , Chiagoziem Chima Ukwuoma , Francis Sam

Magnetic resonance (MR) imaging often lacks standardized acquisition protocols across various sites, leading to contrast variations that reduce image quality and hinder automated analysis. MR harmonization improves consistency by integrating data from multiple sources, ensuring reproducible analysis. Recent advances leverage image-to-image translation and disentangled representation learning to decompose anatomical and contrast representations, achieving consistent cross-site harmonization. However, these methods face two significant drawbacks: imbalanced contrast availability during training affects adaptation performance, and insufficient utilization of spatial variability in local anatomical structures limits model adaptability to different sites. To address these challenges, we propose Domain-aware Adaptive Weighting with Fine-Grain Attention (DAW-FA) for Unsupervised MRI Harmonization. DAW-FA incorporates an adaptive weighting mechanism and enhanced self-attention to mitigate MR contrast imbalance during training and account for spatial variability in local anatomical structures. This facilitates robust cross-site harmonization without requiring paired inter-site images. We evaluated DAW-FA on MR datasets with varying scanners and acquisition protocols. Experimental results show DAW-FA outperforms existing methods, with an average increase of 1.92 ± 0.56 in Peak Signal-to-Noise Ratio (PSNR) and 0.023 ± 0.011 in Structural Similarity Index Measure (SSIM). Additionally, we demonstrate DAW-FA’s impact on downstream tasks: Alzheimer’s disease classification and whole-brain segmentation, highlighting its potential clinical relevance.

磁共振(MR)成像在不同部位往往缺乏标准化的采集方案,导致对比度差异,从而降低图像质量并妨碍自动分析。磁共振协调通过整合多个来源的数据来提高一致性,确保分析的可重复性。最近的进展是利用图像到图像的转换和分离表征学习来分解解剖和对比度表征,从而实现一致的跨部位协调。然而,这些方法面临两个重大缺陷:训练过程中对比度可用性的不平衡会影响适应性能,对局部解剖结构的空间变异性利用不足会限制模型对不同部位的适应性。为了应对这些挑战,我们提出了用于无监督磁共振成像协调的领域感知自适应细粒度加权(DAW-FA)。DAW-FA 结合了自适应加权机制和增强型自我注意,以减轻训练过程中磁共振对比度的不平衡,并考虑局部解剖结构的空间变异性。这有助于实现稳健的跨部位协调,而无需配对的部位间图像。我们在不同扫描仪和采集协议的磁共振数据集上对 DAW-FA 进行了评估。实验结果表明,DAW-FA 优于现有方法,峰值信噪比(PSNR)平均提高了 1.92 ± 0.56,结构相似性指数(SSIM)平均提高了 0.023 ± 0.011。此外,我们还展示了 DAW-FA 对下游任务的影响:阿尔茨海默病分类和全脑分割,突出了其潜在的临床意义。
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引用次数: 0
SARD: Fake news detection based on CLIP contrastive learning and multimodal semantic alignment SARD:基于 CLIP 对比学习和多模态语义配准的假新闻检测
IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2024-08-14 DOI: 10.1016/j.jksuci.2024.102160
Facheng Yan, Mingshu Zhang, Bin Wei, Kelan Ren, Wen Jiang

The automatic detection of multimodal fake news can be used to effectively identify potential risks in cyberspace. Most of the existing multimodal fake news detection methods focus on fully exploiting textual and visual features in news content, thus neglecting the full utilization of news social context features that play an important role in improving fake news detection. To this end, we propose a new fake news detection method based on CLIP contrastive learning and multimodal semantic alignment (SARD). SARD leverages cutting-edge multimodal learning techniques, such as CLIP, and robust cross-modal contrastive learning methods to integrate features of news-oriented heterogeneous information networks (N-HIN) with multi-level textual and visual features into a unified framework for the first time. This framework not only achieves cross-modal alignment between deep textual and visual features but also considers cross-modal associations and semantic alignments across different modalities. Furthermore, SARD enhances fake news detection by aligning semantic features between news content and N-HIN features, an aspect largely overlooked by existing methods. We test and evaluate SARD on three real-world datasets. Experimental results demonstrate that SARD significantly outperforms the twelve state-of-the-art competitors in fake news detection, with an average improvement of 2.89% in Mac.F1 score and 2.13% in accuracy compared to the leading baseline models across three datasets.

多模态假新闻的自动检测可用于有效识别网络空间的潜在风险。现有的多模态假新闻检测方法大多侧重于充分利用新闻内容中的文本和视觉特征,从而忽视了充分利用新闻社会语境特征,而社会语境特征在提高假新闻检测能力方面发挥着重要作用。为此,我们提出了一种基于 CLIP 对比学习和多模态语义对齐(SARD)的新型假新闻检测方法。SARD 利用前沿的多模态学习技术(如 CLIP)和稳健的跨模态对比学习方法,首次将面向新闻的异构信息网络(N-HIN)特征与多层次的文本和视觉特征整合到一个统一的框架中。该框架不仅实现了深度文本和视觉特征之间的跨模态对齐,还考虑了不同模态之间的跨模态关联和语义对齐。此外,SARD 还通过对齐新闻内容和 N-HIN 特征之间的语义特征来增强假新闻检测,而现有方法在很大程度上忽略了这一点。我们在三个真实世界的数据集上对 SARD 进行了测试和评估。实验结果表明,在假新闻检测方面,SARD 明显优于 12 个最先进的竞争对手,在三个数据集上,与领先的基线模型相比,Mac.F1 分数平均提高了 2.89%,准确率平均提高了 2.13%。
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引用次数: 0
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Journal of King Saud University-Computer and Information Sciences
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