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Sentiment Analysis on Memes: A Review 模因的情感分析综述
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-25 DOI: 10.1111/exsy.70133
Ravi Kumar Routhu, Ujwala Baruah

This review explores the field of Sentiment Analysis on Memes, examining the methodologies employed to analyse the emotions expressed in widely shared online images. We discuss the various architectures used in sentiment analysis, review existing datasets and highlight shared tasks that facilitate model evaluation. The review also addresses the challenges specific to this domain, such as the interpretation of humour and sarcasm, which add complexity to sentiment analysis in the context of memes. A key focus of this review is the need for novel datasets that better capture the unique nature of memes, particularly those that blend text and images with cultural and emotional nuances. Existing benchmark datasets often fall short in representing the diversity of meme formats and regional variations, highlighting the necessity for more comprehensive datasets. Looking forward, we anticipate advancements in analytical methodologies and the development of such specialised datasets, which would significantly enhance the accuracy and depth of sentiment analysis models. This review serves as a comprehensive resource for researchers and practitioners interested in advancing the study of sentiment analysis in the evolving field of memes.

本综述探讨了模因情感分析领域,研究了用于分析广泛共享的在线图像中表达的情感的方法。我们讨论了情感分析中使用的各种架构,回顾了现有的数据集,并强调了促进模型评估的共享任务。这篇综述还解决了这一领域特有的挑战,例如幽默和讽刺的解释,这增加了模因背景下情感分析的复杂性。本综述的一个重点是需要新的数据集,以更好地捕捉模因的独特性,特别是那些将文本和图像与文化和情感细微差别混合在一起的数据集。现有的基准数据集往往不足以代表模因格式的多样性和区域差异,这突出了需要更全面的数据集。展望未来,我们期待分析方法的进步和此类专业数据集的开发,这将大大提高情感分析模型的准确性和深度。本综述为有兴趣在不断发展的模因领域中推进情感分析研究的研究人员和实践者提供了全面的资源。
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引用次数: 0
Traversal Learning Coordination for Lossless and Efficient Distributed Learning 无损高效分布式学习的遍历学习协调
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-25 DOI: 10.1111/exsy.70141
Erdenebileg Batbaatar, Jeonggeol Kim, Yongcheol Kim, Young Yoon

In this paper, we introduce Traversal Learning (TL), a novel approach designed to address the problem of decreased quality encountered in popular distributed learning (DL) paradigms such as Federated Learning (FL), Split Learning (SL) and SplitFed Learning (SFL). Traditional FL often suffers an accuracy drop during aggregation due to its averaging function, while SL and SFL face increased loss due to the independent gradient updates on each split network. TL adopts a unique strategy where the model traverses the nodes during forward propagation (FP) and performs backward propagation (BP) at the orchestrator, effectively implementing centralised learning (CL) principles within a distributed environment. The orchestrator is tasked with generating virtual batches and planning the model's sequential node visits during FP, aligning them with the ordered index of the data within these batches. We conducted experiments on six datasets representing diverse characteristics across various domains. Our evaluation demonstrates that TL is on par with classic CL approaches in terms of accurate inference, thereby offering a viable and robust solution for DL tasks. TL outperformed other DL methods and improved accuracy by 7.85% for independent and identically distributed (IID) datasets, macro F1-score by 1.06% for non-IID datasets, accuracy by 2.05% for text classification and AUC by 1.41% and 2.82% for medical and financial datasets, respectively. By effectively preserving data privacy while maintaining performance, TL represents a significant advancement in DL methodologies.

在本文中,我们介绍了遍历学习(TL),这是一种新颖的方法,旨在解决流行的分布式学习(DL)范式(如联邦学习(FL),分裂学习(SL)和分裂学习(SFL))中遇到的质量下降问题。传统FL由于其平均功能,在聚合过程中往往会导致精度下降,而SL和SFL由于每个分裂网络上的独立梯度更新而面临更大的损失。TL采用一种独特的策略,其中模型在前向传播(FP)期间遍历节点,并在编排器上执行后向传播(BP),从而在分布式环境中有效地实现集中式学习(CL)原则。编排器的任务是生成虚拟批,并在FP期间规划模型的顺序节点访问,将它们与这些批中的数据的有序索引对齐。我们在六个数据集上进行了实验,这些数据集代表了不同领域的不同特征。我们的评估表明,在准确推断方面,TL与经典CL方法相当,从而为DL任务提供了一个可行且健壮的解决方案。TL优于其他DL方法,在独立同分布(IID)数据集上的准确率提高了7.85%,在非IID数据集上的宏观f1得分提高了1.06%,在文本分类上的准确率提高了2.05%,在医疗和金融数据集上的AUC分别提高了1.41%和2.82%。通过在保持性能的同时有效地保护数据隐私,TL代表了DL方法的重大进步。
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引用次数: 0
Twitter and Sentiment Analysis for Wildfire Heat Mapping 野火热图的Twitter和情感分析
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-25 DOI: 10.1111/exsy.70147
Catarina Silva, Isabel Carvalho, Bruno Ferreira, João Cabral Pinto, Alberto Cardoso, Hugo Gonçalo Oliveira

Nowadays, automated intelligent systems play an increasingly vital role in aiding decision-making processes across various fields. Firefighting represents a crucial area where accurate information gathering is paramount for efficient resource allocation. Social media platforms as Twitter (or X) have emerged as valuable sources of real-time data, often referred to as ‘citizen science’, offering additional insights alongside traditional data sources. In this work, we introduce a novel pipeline that leverages Natural Language Processing (NLP) techniques and Twitter data, utilising transformer models to identify and monitor wildfire incidents. Expanding on this approach, we incorporate sentiment analysis to provide deeper insights into public perceptions and emotions related to fire events. Additionally, we present visual representations of geographic data through heat mapping, potentially aiding firefighters in making informed decisions. By integrating advanced NLP techniques with social media data, our approach presents a promising strategy for enhancing wildfire management efforts.

如今,自动化智能系统在辅助各个领域的决策过程中发挥着越来越重要的作用。消防是一个至关重要的领域,准确的信息收集对于有效的资源分配至关重要。像Twitter(或X)这样的社交媒体平台已经成为实时数据的宝贵来源,通常被称为“公民科学”,除了传统数据源之外,还提供了额外的见解。在这项工作中,我们引入了一种利用自然语言处理(NLP)技术和Twitter数据的新型管道,利用变压器模型来识别和监测野火事件。在此方法的基础上,我们结合了情绪分析,以更深入地了解公众对火灾事件的看法和情绪。此外,我们通过热图呈现地理数据的可视化表示,可能有助于消防员做出明智的决策。通过将先进的自然语言处理技术与社交媒体数据相结合,我们的方法为加强野火管理工作提供了一个有希望的策略。
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引用次数: 0
An Overview of Q-Learning and Deep Q-Learning for an Autonomous Multi-UAV Wireless Network 自主多无人机无线网络的q -学习和深度q -学习综述
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-24 DOI: 10.1111/exsy.70143
Vikash Chandra Sharma, Saugata Roy

In various fields, including environmental monitoring, communication, surveillance, and disaster response, unmanned aerial vehicles (UAVs) are increasingly essential. Autonomous multi-UAV wireless networks (MUWNs), where multiple UAVs work together, offer powerful solutions in these fields. However, making these networks fully autonomous is challenging, especially concerning real-time decision-making, coordination, and resource management. In the field of literature, there is a demand for a comprehensive survey of recent developments in Q-learning (QL) and deep Q-learning (DQL) within MUWNs. To address this gap, we provide a thorough evaluation of QL and DQL approaches, focusing on their application in autonomous MUWNs. We highlight their roles in route planning, communication relays, and network optimization, discussing advantages and limitations. The survey also explores key challenges, including scalability, energy efficiency, and real-time adaptability, and reviews how QL/DQL enhancements address them. In particular, this paper provides an overview of some QL and DQL applications in MUWNs, such as data retrieval, monitoring, aggregation, resource allocation, and task scheduling to support wireless connectivity, UAV-assisted autonomous trajectory planning, navigation, security, and jamming avoidance. The use of these technologies enables the effective use of UAVs in smart cities, monitoring of industrial complexes, agricultural surveys, and border security. Additionally, using the knowledge gathered from our review, we identify and discuss several open challenges.

在各种领域,包括环境监测、通信、监视和灾害响应,无人驾驶飞行器(uav)越来越重要。多架无人机协同工作的自主多无人机无线网络(MUWNs)为这些领域提供了强大的解决方案。然而,使这些网络完全自主是具有挑战性的,特别是在实时决策、协调和资源管理方面。在文献领域,需要对MUWNs中Q-learning (QL)和深度Q-learning (DQL)的最新发展进行全面调查。为了解决这一差距,我们对QL和DQL方法进行了全面的评估,重点关注它们在自治MUWNs中的应用。我们强调了它们在路由规划、通信中继和网络优化中的作用,并讨论了它们的优点和局限性。该调查还探讨了关键挑战,包括可伸缩性、能源效率和实时适应性,并回顾了QL/DQL增强如何解决这些问题。特别地,本文概述了MUWNs中的一些QL和DQL应用,例如数据检索、监控、聚合、资源分配和任务调度,以支持无线连接、无人机辅助的自主轨迹规划、导航、安全和干扰避免。这些技术的使用使无人机能够在智慧城市、工业综合体监控、农业调查和边境安全中有效使用。此外,利用从我们的审查中收集到的知识,我们确定并讨论了几个开放的挑战。
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引用次数: 0
Emotional Climate Recognition in Speech-Based Conversations: Leveraging Deep Bispectral Image Analysis and Affect Dynamics 基于语音的对话中的情绪气候识别:利用深度双谱图像分析和影响动力学
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-23 DOI: 10.1111/exsy.70146
Ghada Alhussein, Mohanad Alkhodari, Shiza Saleem, Ahsan H. Khandoker, Leontios J. Hadjileontiadis

The growing availability of conversational data across multiple platforms has intensified interest in dynamic emotion recognition. Speech plays a pivotal role in shaping the emotional climate (EC) of peer conversations. We propose DeepBispec, the first framework to integrate deep bispectral image analysis with affect dynamics (AD) for speech-based EC recognition. Bispectrum representations capture nonlinear and non-Gaussian speech characteristics, while AD descriptors model temporal emotion fluctuations. Evaluated on K-EmoCon, IEMOCAP and SEWA datasets, DeepBispec consistently improved EC classification performance. For example, on K-EmoCon, arousal accuracy increased from 79.0% (bispectrum only) to 81.4% (with AD), while valence accuracy improved from 76.8% to 77.5%; similar trends were observed for IEMOCAP and SEWA. DeepBispec outperformed strong CNN, LSTM, and Transformer baselines, demonstrating robust cross-lingual performance across seven languages. These findings highlight its potential for real-world applications such as mental health monitoring, affect-aware learning platforms and empathetic dialogue systems.

跨多个平台的会话数据的日益可用性增强了对动态情感识别的兴趣。言语在同伴对话的情绪氛围(EC)的形成中起着关键作用。我们提出了DeepBispec,这是第一个将深度双谱图像分析与影响动力学(AD)相结合的框架,用于基于语音的EC识别。双谱表示捕获非线性和非高斯语音特征,而AD描述符模拟时间情绪波动。在K-EmoCon, IEMOCAP和SEWA数据集上进行评估,DeepBispec持续提高EC分类性能。例如,在K-EmoCon上,唤醒准确率从79.0%(双谱)提高到81.4% (AD),效价准确率从76.8%提高到77.5%;IEMOCAP和SEWA也观察到类似的趋势。DeepBispec的表现优于强大的CNN、LSTM和Transformer基线,在7种语言中表现出强大的跨语言性能。这些发现突出了它在现实世界中的应用潜力,如心理健康监测、情感感知学习平台和移情对话系统。
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引用次数: 0
Continuous Time Markov Chain for Smartwatch Sensors 智能手表传感器的连续时间马尔可夫链
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-23 DOI: 10.1111/exsy.70144
Iti Chaturvedi, Wei Liang Seow, Amber Hogarth, Luca Adornetto, Erik Cambria

Time-series forecasting is essential for predicting events in the future and for tracking objects. The conventional recurrent neural network model needs to pad the target with zeros when handling long inputs, resulting in a loss in accuracy. Recently, it was proposed to divide a time series input into patches and merge the learned weights. However, such a model is difficult to interpret. In this article, we consider a mixture of continuous and discrete Markov states to model long-range time dependencies. For example, in a vehicle, each gear level can be a discrete state and the throttle input is continuously controlled to maximise the efficiency of the engine. Data collected from the sensor is prone to noise due to component faults or external disturbances. Hence, we apply a stability constraint to select samples for training. We validate our algorithm on three datasets: (1) Apple Watch, (2) Car engine and (3) Election tweets. On all datasets, we achieve an improvement in the range of 5%–20% in the F-measure. Furthermore, the features learned are easy to explain in terms of real-world scenarios.

时间序列预测对于预测未来事件和跟踪目标至关重要。传统的递归神经网络模型在处理长输入时需要用零填充目标,导致精度下降。最近,有人提出将时间序列输入分割成小块并合并学习到的权重。然而,这样的模型很难解释。在本文中,我们考虑连续和离散马尔可夫状态的混合来建模长期时间依赖性。例如,在车辆中,每个档位都可以是离散状态,并且油门输入是连续控制的,以最大限度地提高发动机的效率。由于元件故障或外部干扰,从传感器收集的数据容易产生噪声。因此,我们应用稳定性约束来选择训练样本。我们在三个数据集上验证了我们的算法:(1)Apple Watch, (2) Car engine和(3)Election tweets。在所有数据集上,我们在f度量中实现了5%-20%的改进。此外,学习到的特征很容易用现实世界的场景来解释。
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引用次数: 0
RETRACTION: The Model of Tibetan Thangka Sales under Blockchain Technology
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-22 DOI: 10.1111/exsy.70137

Retraction: Y. Fang, “ The Model of Tibetan Thangka Sales under Blockchain Technology”, Expert Systems 41, no. 5 (2024): e12989. https://doi.org/10.1111/exsy.12989.

The above article, published online on 09 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. In addition, further investigation by the editors found that the article's topic was not within the scope of the journal. In view of the clear evidence of compromised peer review, the parties agreed that the paper must be retracted. The author did not respond to our notice regarding the retraction.

中文信息学报,2009(5):379 - 379。https://doi.org/10.1111/exsy.12989。上述文章于2022年3月9日在线发表在Wiley在线图书馆(http://onlinelibrary.wiley.com/)上,经期刊主编David Camacho;及约翰威利父子有限公司。经过出版商的调查,双方得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。此外,编辑进一步调查发现,该文章的主题不在期刊的范围内。鉴于有明确的证据表明同行评议受到了损害,双方同意必须撤回这篇论文。作者没有回应我们关于撤稿的通知。
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引用次数: 0
RETRACTION: Analysis and Design of Financial Data Mining System Based on Fuzzy Clustering 基于模糊聚类的金融数据挖掘系统分析与设计
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-21 DOI: 10.1111/exsy.70136

Retraction: H. Li, “ Analysis and Design of Financial Data Mining System Based on Fuzzy Clustering”, Expert Systems 41, no. 5 (2024): e13031. https://doi.org/10.1111/exsy.13031.

The above article, published online on 20 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The author did not respond to our notice regarding the retraction.

撤稿:李辉,“基于模糊聚类的金融数据挖掘系统分析与设计”,《专家系统》,第41期。5 (2024): e13031。https://doi.org/10.1111/exsy.13031。上述文章于2022年3月20日在线发表在Wiley在线图书馆(http://onlinelibrary.wiley.com/)上,经期刊主编David Camacho;及约翰威利父子有限公司。经过出版商的调查,双方得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者没有回应我们关于撤稿的通知。
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引用次数: 0
RETRACTION: Linking AI Supply Chain Strength to Sustainable Development and Innovation: A Country-Level Analysis 将人工智能供应链实力与可持续发展和创新联系起来:国家层面的分析
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-21 DOI: 10.1111/exsy.70138

Retraction: H. Wang, “ Linking AI Supply Chain Strength to Sustainable Development and Innovation: A Country-Level Analysis”, Expert Systems 41, no. 5 (2024): e12973. https://doi.org/10.1111/exsy.12973.

The above article, published online on 03 March 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The author did not respond to our notice regarding the retraction.

引用本文:王辉,“人工智能供应链实力与可持续发展和创新的关系:基于国家层面的分析”,《专家系统》第41期。5 (2024): e12973。https://doi.org/10.1111/exsy.12973。上述文章于2022年3月3日在线发表在Wiley在线图书馆(http://onlinelibrary.wiley.com/)上,经期刊主编David Camacho;及约翰威利父子有限公司。经过出版商的调查,双方得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者没有回应我们关于撤稿的通知。
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引用次数: 0
RETRACTION: Dynamic Relationship Network and International Management of Enterprise Supply Chain by Particle Swarm Optimization Algorithm under Deep Learning 基于深度学习的粒子群优化算法的动态关系网络与企业供应链国际化管理
IF 2.3 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2025-09-21 DOI: 10.1111/exsy.70135

Retraction: M. Chen and W. Du, “ Dynamic Relationship Network and International Management of Enterprise Supply Chain by Particle Swarm Optimization Algorithm under Deep Learning”, Expert Systems 41, no. 5 (2024): e13081. https://doi.org/10.1111/exsy.13081.

The above article, published online on 18 June 2022, in Wiley Online Library (http://onlinelibrary.wiley.com/), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley & Sons Ltd. Following an investigation by the publisher, the parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The authors disagree with the retraction.

引用本文:陈敏、杜伟,“基于深度学习的粒子群优化算法的动态关系网络与企业供应链国际化管理”,《专家系统》,第41期。5 (2024): e13081。https://doi.org/10.1111/exsy.13081。上述文章于2022年6月18日在线发表在Wiley在线图书馆(http://onlinelibrary.wiley.com/)上,经期刊主编David Camacho;及约翰威利父子有限公司。经过出版商的调查,双方得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。作者不同意撤稿。
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引用次数: 0
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