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Correction to “Predictive Modelling of Tick Distribution: A Machine Learning Approach to Ixodes ricinus Abundance” 更正“蜱虫分布的预测建模:蓖麻伊蚊丰度的机器学习方法”
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-03 DOI: 10.1002/cpe.70600

K. Jamalpuram, M. S. Sharif, A. Nanmi, et al., “ Predictive Modelling of Tick Distribution: A Machine Learning Approach to Ixodes ricinus Abundance,” Concurrency and Computation: Practice and Experience 38, no. 1 (2026): e70496, https://doi.org/10.1002/cpe.70496.

The university name is missing for affiliation number 3 in the published article. Therefore, affiliation 3 for Ahmed Ibrahim Alzahrani and Nasser Alalwan should be updated to:

Department of Computer Science and Engineering, College of Applied Studies, King Saud University, Riyadh, Saudi Arabia.

We apologize for this error.

K. Jamalpuram, M. S. Sharif, A. Nanmi, et .,“蜱虫分布的预测建模:一种用于蓖麻蜱丰度的机器学习方法”,并发与计算:实践与经验38,第2期。1 (2026): e70496, https://doi.org/10.1002/cpe.70496.The在发表的文章中,隶属关系号3缺少大学名称。因此,Ahmed Ibrahim Alzahrani和Nasser Alalwan的隶属关系应该更新为:沙特阿拉伯利雅得沙特国王大学应用研究学院计算机科学与工程系。我们为这个错误道歉。
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引用次数: 0
Scalable SoC Architecture for Parallel-Pixel Classification of Hyperspectral Images Using Weighted-Summation Kernel SVM 基于加权求和核支持向量机的高光谱图像并行像素分类的可扩展SoC架构
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-03 DOI: 10.1002/cpe.70558
B. B. Shabarinath, Muralidhar Pullakandam

Hyperspectral image (HSI) classification demands high-performance processing and accuracy due to data dimensionality and computational complexity. The support vector machine (SVM) algorithm, which is renowned for its ability to manage high-dimensional data through kernel techniques for classification tasks effectively, has shown promising results in HSI analysis and applications. Composite kernels enhance SVM efficacy by integrating multiple kernels to capture diverse data characteristics. Existing SVM algorithms based on composite kernels are susceptible to high latency, computationally intensive operations, and poor scalability, which makes it challenging to deploy efficiently on resource-limited high-performance platforms like field-programmable gate arrays (FPGAs). The paper proposes a novel composite weighted-summation kernel (WSK) that combines polynomial and hardware friendly kernels (HFKs). The WSK kernel has been integrated with SVM, resulting in a classifier named WSK-SVM. Furthermore, a scalable FPGA-based system on chip (SoC) architecture is developed for HSI classification using WSK-SVM. The architecture implements a fast kernel approximation method to compute a HFK which eliminates multiplications. The architecture leverages spatial and temporal parallelism, efficiently aggregating processing into clusters of parallel binary WSK-SVM classifiers using the one-vs-one (OvO) methodology. The approach is scalable and achieves a level of spatial parallelism that facilitates the concurrent classification of multiple pixels. Two different SoC boards were used to implement the design. One board was based on the Xilinx Zynq-7000 and featured four parallel classifiers, and the second board was based on the Zynq UltraScale+ MPSoC and featured eleven parallel classifiers. High classification accuracy (>∖,99%) was achieved on benchmark datasets (Indian Pines, Pavia Centre, and Pavia University) using 8-bit fixed-point processing, resulting in negligible loss of floating-point accuracy. Resource utilization on the PYNQ-Z2 reached 78.37% for LUTs and 77.14% for BRAM, whereas on the ZCU-104, it was 49.76% for LUTs and 95.19% for BRAM. Real-time latency was as low as 1.26 μs$$ mu mathrm{s} $$ per pixel (ZCU-104, Pavia Centre dataset) with a throughput of up to 6.35 MBPS. The proposed architecture provides scalable and high-performance solutions for onboard HSI classification under real-time constraints.

由于高光谱图像的数据维数和计算复杂度,高光谱图像分类需要高性能的处理和精度。支持向量机(SVM)算法以其通过核技术有效地管理高维数据进行分类任务的能力而闻名,在HSI分析和应用中显示出有希望的结果。复合核通过集成多个核来捕获不同的数据特征,从而提高支持向量机的有效性。现有基于复合核的支持向量机算法存在高延迟、计算量大、可扩展性差等问题,难以在现场可编程门阵列(fpga)等资源有限的高性能平台上高效部署。提出了一种将多项式核和硬件友好核相结合的复合加权求和核。将WSK内核与SVM集成在一起,形成了WSK-SVM分类器。在此基础上,利用WSK-SVM开发了一种可扩展的基于fpga的片上系统(SoC)架构,用于HSI分类。该体系结构实现了一种快速核近似方法来计算HFK,消除了乘法。该架构利用空间和时间并行性,使用一对一(OvO)方法有效地将处理聚合到并行二进制WSK-SVM分类器的集群中。该方法具有可扩展性,并实现了一定程度的空间并行性,从而促进了多个像素的并发分类。两个不同的SoC板被用来实现设计。一块主板基于Xilinx Zynq-7000,具有四个并行分类器,另一块主板基于Zynq UltraScale+ MPSoC,具有11个并行分类器。分类精度高(&gt;∑,99%) was achieved on benchmark datasets (Indian Pines, Pavia Centre, and Pavia University) using 8-bit fixed-point processing, resulting in negligible loss of floating-point accuracy. Resource utilization on the PYNQ-Z2 reached 78.37% for LUTs and 77.14% for BRAM, whereas on the ZCU-104, it was 49.76% for LUTs and 95.19% for BRAM. Real-time latency was as low as 1.26  μ s $$ mu mathrm{s} $$ per pixel (ZCU-104, Pavia Centre dataset) with a throughput of up to 6.35 MBPS. The proposed architecture provides scalable and high-performance solutions for onboard HSI classification under real-time constraints.
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引用次数: 0
Toward Enabling Facial Emotional Expressions in Humanoid Robots 在类人机器人中实现面部情感表达
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-02 DOI: 10.1002/cpe.70554
Yongtong Zhu, Lei Li, Ji Hong, Weiye Liu, Qingdu Li, Ge Gao, Youshuang Ding, Jialang He, Na Liu, Jianwei Zhang, Ye Yuan

The ability to generate facial expressions is essential for humanoid social robots to engage in natural, human-like interactions. This capability significantly enhances the fluidity of human-robot communication and the precision of emotional expression. However, current methods rely heavily on pre-programmed behavioral patterns, which are manually implemented at considerable cost in both time and human labor. To enable humanoid robots to autonomously acquire generalized expressive capabilities, they must learn human-like expressions via self-supervised training. To address this challenge, we present a highly biomimetic robotic face equipped with physically actuated electronic facial units, alongside an end-to-end learning framework that integrates Kolmogorov-Arnold Networks (KAN) with attention mechanisms. In contrast to previous approaches, we have also developed an automated data collection system guided by expert-designed facial motion primitives, enabling the construction of a high-quality dataset. Notably, to the best of our knowledge, this constitutes the first facial expression dataset specifically designed for humanoid social robots. Extensive evaluations demonstrate that our method enables accurate and diverse facial mimicry across a range of test subjects.

生成面部表情的能力对于类人社交机器人进行自然的、类似人类的互动至关重要。这种能力大大提高了人机交流的流动性和情感表达的准确性。然而,目前的方法严重依赖于预先编程的行为模式,这些模式是手动实现的,在时间和人力方面都付出了相当大的代价。为了使类人机器人能够自主地获得广义表达能力,它们必须通过自我监督训练来学习类人表达。为了应对这一挑战,我们提出了一种高度仿生的机器人面部,配备了物理驱动的电子面部单元,以及集成了Kolmogorov-Arnold网络(KAN)和注意力机制的端到端学习框架。与之前的方法相比,我们还开发了一个由专家设计的面部运动原语指导的自动数据收集系统,从而能够构建高质量的数据集。值得注意的是,据我们所知,这构成了第一个专门为类人社交机器人设计的面部表情数据集。广泛的评估表明,我们的方法能够在一系列测试对象中实现准确和多样化的面部模仿。
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引用次数: 0
AI-Driven Threat Detection: Synergizing Edge Honeypots and IDS in SDN-Enabled Edge Computing 人工智能驱动的威胁检测:在sdn支持的边缘计算中协同边缘蜜罐和IDS
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-02 DOI: 10.1002/cpe.70457
Afef Slimani, Kamel Karoui

This paper proposes an intelligent and adaptive security framework for internet of things (IoT) environments by integrating edge Honeypots, AI-driven intrusion detection systems (IDS), and software-defined networking (SDN). The system is designed to enhance real-time threat detection, reduce false positives, and dynamically mitigate attacks. Edge Honeypots are deployed at the network perimeter to attract and analyze malicious traffic, which is then used to train AI-based IDS models. The IDS employ a hybrid detection mechanism combining signature-based and anomaly-based techniques. SDN facilitates centralized traffic control and dynamic rule updates, enabling rapid responses to new attack vectors. The framework is implemented and evaluated in a simulated SDN-IoT environment using Mininet, with several machine learning models benchmarked. The Decision Tree model achieves the highest detection accuracy (97%) for IoT threats. Experimental results demonstrate improved detection performance, reduced false positives, and enhanced adaptability through a continuous learning loop between the IDS and honeypots.

本文通过集成边缘蜜罐、人工智能驱动的入侵检测系统(IDS)和软件定义网络(SDN),为物联网(IoT)环境提出了一个智能和自适应的安全框架。该系统旨在增强实时威胁检测,减少误报,并动态减轻攻击。边缘蜜罐部署在网络边缘,用于吸引和分析恶意流量,然后用于训练基于人工智能的IDS模型。IDS采用基于签名和基于异常的混合检测机制。SDN可以实现集中的流量控制和动态的规则更新,快速响应新的攻击向量。该框架使用Mininet在模拟SDN-IoT环境中实现和评估,并对几个机器学习模型进行了基准测试。决策树模型对物联网威胁的检测准确率最高(97%)。实验结果表明,通过IDS和蜜罐之间的连续学习循环,提高了检测性能,减少了误报,增强了自适应性。
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引用次数: 0
FMF-DETR: A Frequency-Aware Multi-Scale Fusion DETR for Small Object Detection FMF-DETR:用于小目标检测的频率感知多尺度融合DETR
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-01 DOI: 10.1002/cpe.70597
Lingling Li, Yang Mei, Xuezhuan Zhao, Xiaoyan Shao, Zonghao Zhu, Shiqin Diao, Mai Xu

Small object detection, a critical technique for recognizing and localizing diminutive targets in visual data, plays a vital role in applications ranging from remote sensing and unmanned aerial vehicle (UAV) vision to autonomous driving. Current methodologies, however, face substantial challenges, including detection accuracy limitations, low-resolution image processing difficulties, background noise interference, and target occlusion issues. To address these challenges, we propose FMF-DETR, an innovative small object detection framework featuring frequency-domain feature optimization through three key components: (1) High-Low Frequency Fusion Model (HLFM), (2) Focused Diffusion Feature Pyramid Network (FDFPN), and (3) BiPathNet (BPNet). Specifically, the HLFM module enhances multiscale feature representation by emphasizing high-frequency details while suppressing low-frequency background noise. The FDFPN architecture improves detection performance in complex scenarios through multiscale feature fusion and saliency-aware diffusion. BPNet introduces a dual-path feature extraction mechanism that simultaneously enhances feature discriminability and reduces computational overhead. Through the synergistic integration of these components, the proposed framework enhances both detection accuracy and operational efficiency. Comprehensive evaluations on the VisDrone dataset demonstrate FMF-DETR's superior performance, achieving a 2.2% accuracy improvement while reducing model parameters by 13.03M and computational complexity by 97.2G FLOPs compared to baseline methods. These results validate both the effectiveness and efficiency of our proposed framework.

小目标检测是识别和定位视觉数据中微小目标的关键技术,在从遥感、无人机(UAV)视觉到自动驾驶等应用中发挥着至关重要的作用。然而,目前的方法面临着巨大的挑战,包括检测精度限制、低分辨率图像处理困难、背景噪声干扰和目标遮挡问题。为了解决这些挑战,我们提出了FMF-DETR,这是一个创新的小目标检测框架,通过三个关键组件进行频域特征优化:(1)高低频融合模型(HLFM),(2)聚焦扩散特征金字塔网络(FDFPN)和(3)BiPathNet (BPNet)。具体而言,HLFM模块通过强调高频细节同时抑制低频背景噪声来增强多尺度特征表示。FDFPN架构通过多尺度特征融合和显著性感知扩散提高了复杂场景下的检测性能。BPNet引入了一种双路径特征提取机制,在增强特征可分辨性的同时减少了计算开销。通过这些组件的协同集成,该框架提高了检测精度和操作效率。对VisDrone数据集的综合评估表明,FMF-DETR具有优越的性能,与基线方法相比,FMF-DETR的准确率提高了2.2%,同时将模型参数减少了13.03M,计算复杂度减少了97.2G FLOPs。这些结果验证了我们提出的框架的有效性和效率。
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引用次数: 0
Intelligent Data-Driven Framework for Fuel Consumption Prediction in Price Commitment Scenarios 价格承诺情景下油耗预测的智能数据驱动框架
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-01 DOI: 10.1002/cpe.70514
Boyang Li, Yu An, Xi Zhang

Accurate fuel consumption prediction is critical for effective supply chain management and decision-making in industrial engineering systems. In price commitment scenarios, the discrepancies between market prices and transaction prices necessitate the development of scenario-specific prediction methods. However, such scenarios present several challenges. Market price fluctuations can induce variations in consumption patterns, while transaction price dynamics often differ from those in traditional markets, rendering conventional prediction models inadequate. Additionally, the cold-start characteristic of business results in limited historical data, and traditional methods relying on random sampling strategies often neglect sample heterogeneity, leading to biased weight allocation. To address these issues, we propose an Adaptive Sampling LightGBM framework (ASL), which integrates an adaptive sampling strategy and post-sampling bagging to enhance model stability with limited samples. By constructing a scenario-oriented risk function, the method incorporates market price volatility intensity and the divergence between commitment and conventional scenarios as dynamic weighting factors, thereby quantifying the time-varying impact of price elasticity on consumption. Experimental results using data from two fuel stations in North China demonstrate that our method outperforms benchmark prediction methods.

在工业工程系统中,准确的燃料消耗预测对有效的供应链管理和决策至关重要。在价格承诺情景中,市场价格和交易价格之间的差异需要开发特定情景的预测方法。然而,这样的场景带来了一些挑战。市场价格波动可引起消费模式的变化,而交易价格动态往往与传统市场不同,因此传统预测模型不充分。此外,业务的冷启动特性导致历史数据有限,依赖随机抽样策略的传统方法往往忽略样本异质性,导致权重分配有偏差。为了解决这些问题,我们提出了一个自适应采样LightGBM框架(ASL),它集成了自适应采样策略和采样后装袋,以提高有限样本下模型的稳定性。该方法通过构建面向场景的风险函数,将市场价格波动强度和承诺与常规场景的差异作为动态加权因子,量化价格弹性对消费的时变影响。华北地区两个加油站数据的实验结果表明,该方法优于基准预测方法。
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引用次数: 0
A Blockchain-Based Attribute-Based Conditional Proxy Re-Encryption Scheme 基于区块链的基于属性的条件代理重加密方案
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-02-01 DOI: 10.1002/cpe.70576
Tao Feng, Xuebin Yang, Chunyan Liu, Buzhen He

To address the challenges of coarse-grained access control, collusion attack risks, and massive data storage issues in cross-departmental traffic data sharing within Intelligent Transportation Systems (ITS) scenarios, this study proposes an Attribute-Based Conditional Proxy Re-Encryption (AB-CPRE) scheme integrated with blockchain technology. This scheme employs a conditional proxy re-encryption mechanism to achieve fine-grained access control based on device attributes and access policies, thereby defending against collusion attacks by proxy nodes and malicious users. By combining the distributed ledger of blockchain and IPFS's distributed storage of IPFS, a verifiable system is constructed that includes ciphertext hashes, a complete set of device attributes, and operational conditions. This ensures data integrity while reducing the computational and storage pressure on edge servers. Security analysis demonstrates that the scheme satisfies adaptive IND-CCA security under the standard model, and performance evaluation indicates significant improvements in computational efficiency and communication overhead compared with similar schemes.

针对智能交通系统(ITS)场景下跨部门交通数据共享中存在的粗粒度访问控制、合谋攻击风险和海量数据存储等问题,本研究提出了一种结合区块链技术的基于属性的条件代理重加密(AB-CPRE)方案。该方案采用条件代理重加密机制,根据设备属性和访问策略实现细粒度的访问控制,防范代理节点和恶意用户的合谋攻击。将区块链的分布式账本与IPFS的IPFS分布式存储相结合,构建了一个包含密文哈希、一整套设备属性、操作条件的可验证系统。这确保了数据的完整性,同时减少了边缘服务器的计算和存储压力。安全性分析表明,该方案在标准模型下满足自适应IND-CCA安全性,性能评估表明,与同类方案相比,该方案在计算效率和通信开销方面有显著提高。
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引用次数: 0
ABHealChain: Enhancing Privacy and Security in Healthcare Data Sharing Through Hyperledger Fabric and Attribute-Based Access Control ABHealChain:通过超级账本结构和基于属性的访问控制增强医疗数据共享中的隐私和安全性
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-01-29 DOI: 10.1002/cpe.70592
Anita Thakur, Virender Ranga, Ritu Agarwal

In the current panorama of digital healthcare evolution and the proliferation of electronic health records (EHRs), healthcare systems face many challenges. These challenges encompass data administration and security, data exchange, and ensuring the confidentiality of medical data. Within this context, blockchain technology emerges as a promising way to address the numerous challenges associated with EHRs. Ensuring the security and confidentiality of EHRs remains a pivotal concern encompassing healthcare service recipients and providers. The compromise of a healthcare system leads to the exposure of intricately private health information. Typically stored in centralized databases, this information repository introduces susceptibilities that consequently fuel instances of cyber intrusion. To ensure the safeguarding of medical data, this research paper strategically adopts the Hyperledger Fabric (HLF) blockchain platform, which incorporates attribute-based access control mechanisms to thwart any malicious attempt at unauthorized access to sensitive information. These apprehensions regarding security have been effectively addressed by leveraging robust cryptographic protocols such as the AES-256 algorithm. This algorithm encrypts messages, transmitting the encrypted data across the network, thereby restricting visibility solely to intended recipients and providing a secure data exchange. The effectiveness of our proposed solution is rigorously evaluated using the Hyperledger Caliper tool. The evaluation encompasses pivotal performance metrics, average latency, throughput, success rate, resource consumption, and traffic.

在当前数字医疗发展的全景和电子健康记录(EHRs)的扩散,医疗保健系统面临着许多挑战。这些挑战包括数据管理和安全、数据交换以及确保医疗数据的机密性。在这种情况下,区块链技术作为解决与电子病历相关的众多挑战的一种有希望的方式出现。确保电子病历的安全性和保密性仍然是医疗保健服务接受者和提供者关注的关键问题。医疗保健系统的泄露导致错综复杂的私人健康信息暴露。通常存储在集中式数据库中,该信息存储库引入了易感性,从而引发网络入侵实例。为了确保医疗数据的安全,本研究战略性地采用了Hyperledger Fabric (HLF)区块链平台,该平台结合了基于属性的访问控制机制,以阻止任何未经授权访问敏感信息的恶意企图。通过利用健壮的加密协议(如AES-256算法),这些关于安全性的担忧已经得到了有效解决。该算法对消息进行加密,通过网络传输加密的数据,从而将可见性仅限制为预期的收件人,并提供安全的数据交换。我们提出的解决方案的有效性使用Hyperledger Caliper工具进行了严格评估。评估包括关键性能指标、平均延迟、吞吐量、成功率、资源消耗和流量。
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引用次数: 0
Sentiment Intensity Contrastive Text-Enhanced Fusion Network 情感强度对比文本增强融合网络
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-01-28 DOI: 10.1002/cpe.70593
Heng Jiang, Lianke Shi, Deyu Kong, Jiahao Hua, Honggui Shang, Lijia Chen

Multimodal sentiment analysis (MSA) has recently encountered two major challenges: non-textual modalities are often affected by noise, and sentiment intensity differences are difficult to capture. To address these issues, we propose a Sentiment Intensity Contrastive Text-Enhanced Fusion Network (SICTEF Net), which achieves deep collaboration among text, audio, and visual modalities through three key mechanisms. First, a grouped-channel-attention based Feature Enhancement Module (EMA) is designed to mitigate modality-specific noise and emphasize emotion-sensitive cues by combining spatial–channel interaction mapping with dual-branch attention fusion. Second, a text-centered cross-modal fusion mechanism is introduced, where bidirectional multi-head self-attention and a residual-enhanced encoder jointly enable complementary mappings between text and non-text modalities, thereby producing intermediate representations that preserve semantic primacy while incorporating fine-grained complementary information. Third, a sentiment-intensity weighted contrastive learning strategy dynamically assigns weights to positive and negative sample pairs according to their sentiment intensity differences, allowing the model to more precisely distinguish samples with varying degrees of similarity in the embedding space. Experimental evaluation on the CMU-MOSI and CMU-MOSEI datasets demonstrates that SICTEF Net consistently outperforms state-of-the-art baselines in binary accuracy, F1 score, seven-class accuracy, mean absolute error (MAE), and Pearson correlation. Comprehensive ablation studies further confirm the complementary benefits of EMA, the text-enhanced Transformer, and sentiment-intensity contrastive learning. These results indicate that combining text-driven deep interaction, non-text modality enhancement via channel attention, and contrastive learning can improve the accuracy and robustness of multimodal sentiment analysis.

多模态情感分析(MSA)近年来面临着两大挑战:非文本模态经常受到噪声的影响,情感强度差异难以捕捉。为了解决这些问题,我们提出了一种情感强度对比文本增强融合网络(SICTEF Net),该网络通过三个关键机制实现文本、音频和视觉模式之间的深度协作。首先,设计了基于分组通道注意力的特征增强模块(EMA),通过将空间通道交互映射与双分支注意力融合相结合,减轻模态特定噪声并强调情绪敏感线索。其次,引入了以文本为中心的跨模态融合机制,其中双向多头自关注和残差增强编码器共同实现了文本和非文本模态之间的互补映射,从而产生了在包含细粒度互补信息的同时保留语义首要性的中间表示。第三,采用情感强度加权对比学习策略,根据情感强度差异动态地为正、负样本对分配权重,使模型能够更精确地区分嵌入空间中不同相似度的样本。对CMU-MOSI和CMU-MOSEI数据集的实验评估表明,SICTEF Net在二进制精度、F1分数、七类精度、平均绝对误差(MAE)和Pearson相关性方面始终优于最先进的基线。综合消融研究进一步证实了EMA、文本增强的Transformer和情绪强度对比学习的互补益处。这些结果表明,结合文本驱动的深度交互、通过通道注意进行的非文本情态增强和对比学习可以提高多模态情感分析的准确性和鲁棒性。
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引用次数: 0
Sequence Recommendation for Mobile Application via Time Interval-Aware Attention and Contrastive Learning 基于时间间隔意识和对比学习的移动应用程序序列推荐
IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2026-01-27 DOI: 10.1002/cpe.70585
Buqing Cao, Junyi Chen, Ziming Xie, Wenyu Zhao, Sheng Lin, Longxin Zhang

Mobile application recommendation has emerged as a pivotal domain within the realm of personalized recommendation systems. Traditional mobile application sequence recommendation approaches are predominantly dedicated to the pursuit of sophisticated sequence encoders to achieve more precise representations. However, existing sequence recommendation methods primarily consider the sequential order of historical App interactions, overlooking the time intervals between applications. This oversight hinders the model's capability to fully unearth the temporal correlations in user behavior, consequently limiting the accuracy and personalization of mobile application recommendations. Moreover, the interactions between users and mobile applications are typically sparse, which weakens the model's generalization capabilities. To address these issues, we propose a novel method for mobile application sequence recommendation, incorporating time interval-aware attention and contrastive learning (called Ti-CoRe). Specifically, this approach introduces a novel sequence augmentation strategy based on similarity replacement within a contrastive learning framework. By considering textual similarities between applications, this method selectively replaces applications that possess lower similarity scores to generate augmented sequences, increasing the diversity of the sample space and mitigating data sparsity. Furthermore, integrating a time interval-aware mechanism into the BERT4Rec model, the paper presents a new T-BERT encoder. It precisely assesses the influence of fluctuating time intervals on the prediction of the subsequent mobile application, thereby ensuring a more nuanced app representation. Experiments conducted on the 360APP real dataset demonstrate that Ti-CoRe consistently outperforms various baseline models in terms of NDCG and HR metrics.

移动应用程序推荐已经成为个性化推荐系统领域的一个关键领域。传统的移动应用程序序列推荐方法主要致力于追求复杂的序列编码器,以实现更精确的表示。然而,现有的顺序推荐方法主要考虑历史应用交互的顺序顺序,忽略了应用之间的时间间隔。这种疏忽阻碍了模型充分挖掘用户行为中的时间相关性的能力,从而限制了移动应用程序推荐的准确性和个性化。此外,用户与移动应用程序之间的交互通常是稀疏的,这削弱了模型的泛化能力。为了解决这些问题,我们提出了一种新的移动应用程序序列推荐方法,该方法结合了时间间隔感知注意和对比学习(称为Ti-CoRe)。具体来说,该方法在对比学习框架中引入了一种基于相似性替换的序列增强策略。该方法通过考虑应用程序之间的文本相似性,选择性地替换具有较低相似性分数的应用程序来生成增广序列,增加了样本空间的多样性,减轻了数据稀疏性。此外,将时间间隔感知机制集成到BERT4Rec模型中,提出了一种新的T-BERT编码器。它精确地评估波动时间间隔对后续移动应用程序预测的影响,从而确保更细致入微的应用程序表示。在360APP真实数据集上进行的实验表明,在NDCG和HR指标方面,Ti-CoRe始终优于各种基线模型。
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