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arXiv 2025年

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahşi, Hayretdin Department of Software Science Tallinn University of Technology Tallinn Estonia School of Informatics Computing and Cyber Systems Northern Arizona University United States

In recent years, many cyber incidents have happened in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several systematization-of-knowledge papers have been published in the maritime field, none of the previous studies has focused on cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. The current article addresses this research gap and surveys the methods, algorithms, tools and architectures used for cyber security monitoring in the maritime sector. For the survey, a systematic literature review of cyber security monitoring studies is conducted in this article, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The first contribution of this article is the bibliometric analysis of related literature and the identification of the main research themes in previous works. For that purpose, our article presents a taxonomy for existing studies which highlights the main properties of maritime cyber security monitoring research. The second contribution of this article is an in-depth analysis of previous works and the identification of research gaps and limitations in existing literature. Based on our findings, we outline future research directions for cyber security monitoring in the maritime field. © 2025, CC BY.

关键词： Network intrusion

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Deepwalk-aware graph convolutional networks

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Science China(Information Sciences) 2022年第5期65卷 81-95页

作者： Taisong JIN Huaqiang DAI Liujuan CAO Baochang ZHANG Feiyue HUANG Yue GAO Rongrong JI Media Analytics and Computing Lab Department of Computer Science and Technology School of InformaticsXiamen University Media Analytics and Computing Lab Department of Artificial Intelligence School of InformaticsXiamen University School of Automation Science and Electrical Engineering Beihang University Tencent Youtu Lab School of Software Tsinghua University

Graph convolutional networks(GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph *** fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer,and Pubmed citation networks.

关键词： graph convolutional networks global information fusion node classification

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Enhancing Data Security in Federated Learning with Dilithium

Enhancing Data Security in Federated Learning with Dilithium

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Quoc Bao Phan Hien Nguyen Phap Duong Ngoc Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University USA The University of Danang – Vietnam-Korea University of Information and Communication Technology Vietnam

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Federated learning (FL) enables multiple parties to collaboratively train machine learning models while preserving data privacy. However, securing communication within FL frameworks remains a significant challenge due to potential vulnerabilities to data breaches and integrity attacks. This paper proposes a novel approach using Dilithium, a robust digital signature framework, to enhance data security in FL. By integrating Dilithium into FL protocols, this study demonstrates robust communication security, preventing data tampering and unauthorized access, thereby promoting safer and more efficient collaborative model training across distributed networks. Furthermore, our approach incorporates an optimized client selection algorithm and a parallelized GPU-based training process that reduces latency and ensures seamless synchronization among participants. Experimental results demonstrate that our system achieves a total processing time of 6.891 seconds, significantly outperforming the 10.24 seconds of normal FL and 12.32 seconds of FL-Dilithium systems on the same computing platforms. Additionally, the proposed model achieves an accuracy of 94%, surpassing the 93% of the normal FL.

关键词： Training Data privacy Accuracy Federated learning Telemedicine Data security Collaboration Data models Digital signatures Testing

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Relationship Prompt Learning is Enough for Open-Vocabulary Semantic Segmentation 38

Relationship Prompt Learning is Enough for Open-Vocabulary S...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Li, Jiahao Lu, Yang Xie, Yuan Qu, Yanyun School of Informatics Xiamen University China Institute of Artificial Intelligence Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computer Science and Technology East China Normal University China Chongqing Institute of East China Normal University China

Open-vocabulary semantic segmentation (OVSS) aims to segment unseen classes without corresponding labels. Existing Vision-Language Model (VLM)based methods leverage VLM's rich knowledge to enhance additional explicit segmentation-specific networks, yielding competitive results, but at the cost of extensive training cost. To reduce the cost, we attempt to enable VLM to directly produce the segmentation results without any segmentation-specific networks. Prompt learning offers a direct and parameter-efficient approach, yet it falls short in guiding VLM for pixel-level visual classification. Therefore, we propose the Relationship Prompt Module (RPM), which generates the relationship prompt that directs VLM to extract pixel-level semantic embeddings suitable for OVSS. Moreover, RPM integrates with VLM to construct the Relationship Prompt Network (RPN), achieving OVSS without any segmentation-specific networks. RPN attains state-of-the-art performance with merely about 3M trainable parameters (2% of total parameters). © 2024 Neural information processing systems foundation. All rights reserved.

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Hybrid Emotion Recognition: Enhancing Customer Interactions Through Acoustic and Textual Analysis

arXiv

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arXiv 2025年

作者： Wewelwala, Sahan Hewage Sumanathilaka, T.G.D.K. School of Computing Informatics Institute of Technology 06 Colombo Sri Lanka Department of Computer Science Swansea University Wales Swansea United Kingdom

This research presents a hybrid emotion recognition system integrating advanced Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs) to analyze audio and textual data for enhancing customer interactions in contact centers. By combining acoustic features with textual sentiment analysis, the system achieves nuanced emotion detection, addressing the limitations of traditional approaches in understanding complex emotional states. Leveraging LSTM and CNN models for audio analysis and DistilBERT for textual evaluation, the methodology accommodates linguistic and cultural variations while ensuring real-time processing. Rigorous testing on diverse datasets demonstrates the system's robustness and accuracy, highlighting its potential to transform customer service by enabling personalized, empathetic interactions and improving operational efficiency. This research establishes a foundation for more intelligent and human-centric digital communication, redefining customer service standards. © 2025, CC BY.

关键词： Emotion Recognition

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A Brief Survey on Data Aggregation and Data Compression Models using Blockchain Model in Wireless Sensor Network

A Brief Survey on Data Aggregation and Data Compression Mode...

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Bandlamudi Naga Sudheer Kamepalli Sujatha School of Computing and Informatics Vignan's Foundation for Science Technology & Research Vadlamudi Guntur Andhra Pradesh

Most of the time, nodes in Wireless Sensor Networks (WSNs) are set up in an unmonitored, potentially dangerous setting. Battery life, storage space, processing power, and network connectivity are all severely lacking in these nodes. Therefore, it is difficult to protect a WSN without negatively impacting the efficiency of the network. The issues listed above can be effectively addressed with a well-designed plan that makes use of Data Aggregation (DA) in conjunction with a robust security system In this work, we provide a thorough analysis of Secure Data Aggregation (SDA) in WSNs, highlighting both the security benefits sought and the challenges currently faced by this approach. Both established and up-and-coming network topologies are covered, with comparisons drawn between them to highlight key differences in application landscapes and security provisions afforded by each. Compressive data gathering (CDG), which employs Compressive Sensing (CS), can significantly enhance energy effieiency with the advantages gained from data compression. The data collection performance and longevity of Internet of Things (IoT) based WSNs are found to be significantly improved by CS based data collecting techniques. However, in hostile and sophisticated network deployments, they encounter significant difficulties with key distribution and adversary attacks. This research explores how wireless sensor networks' security measures affect their ability to collect and analyze data. This research provides a brief survey on data aggregation and data compression using blockchain in WSN for secure data transmission.

关键词： Wireless communication Wireless sensor networks Data compression Data aggregation Data models Blockchains Sensors

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Automated Fire Hydrant Monitoring and Maintenance using IoT Data Analytics and Naive Bayes Classifier

Automated Fire Hydrant Monitoring and Maintenance using IoT ...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sarasu, R. Nandagopal, V. Shibu, N.V. Lakshminarayanan, Suresh Raj, T Saju Rajmohan, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Chennai India Mohan Babu University School of Engineering Department of Electrical and Electronics Engineering Tirupathi India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Chennai India University of Technology and Applied Sciences Department of Engineering & Electronics South Al Batina Oman Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350375442

This paper introduces a new method for managing fire hydrants that uses a monitoring system based on the Internet of Things (IoT) and a Naive Bayes Classifier (NBC) for predictive maintenance. Water pressure, flow rate, and the physical state of fire hydrants are among the essential factors for the proposed system continually collecting data using an IoT sensor network. The system analyzes real-time data using NBC. These predictive capabilities reduce the potential of hydrant breakdowns during emergencies through preventive maintenance potential. It introduces a reliable IoT infrastructure for fire hydrant monitoring, improving the acquired data's granularity and timeliness. NBC provides a powerful instrument for early problem detection, significantly improving maintenance effectiveness. Since the technology is compatible with existing fire department procedures, it can easily integrate into current operational processes. Better public safety, more efficient use of resources, and more dependable fire hydrants are potential outcomes of responsive and proactive data-driven maintenance techniques. The findings improve overall fire safety and operational efficiency, with a 25% decrease in maintenance expenditures and a 40% increase in issue detection accuracy. © 2024 IEEE.

关键词： Predictive maintenance

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A novel analytical model for estimating vehicle delay at isolated signalised intersections

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International Journal of Simulation and Process Modelling 2022年第1-2期19卷 71-85页

作者： Qiao, Feng Liu, Huixin Luo, Dan Sun, Haochen Chen, Yinong School of Information and Control Engineering Shenyang Institute of Technology Fushun113122 China Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang100168 China School of Information Engineering Henan University of Science and Technology Henan Luoyang471000 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287-8809 United States

This paper proposes a novel analytical model to estimate the average vehicle delay at signalised intersections under saturated or oversaturated conditions based on the investigation and analysis of the existing methods to deal with the problems arising in the process of acceleration, deceleration, and the transmissibility of the cycle-by-cycle average vehicle delay. The proposed model employs and combines the operating and queuing characteristics of vehicles to produce the analytic formula. To verify the effectiveness of the proposed model, simulation experiments are conducted, and the error rates and the correlation coefficients are investigated, which confirm that the proposed model possesses certain significant advantages over the existing models in saturated and oversaturated conditions. The results of research work show that the proposed model can provide transportation engineers or professionals with an effective tool for analysing, timing and managing the saturated or oversaturated signalised intersections. © 2022 Inderscience Enterprises Ltd.

关键词： Analytical models

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Color-Name Aware Optimization to Enhance the Perception of Transparent Overlapped Charts

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IEEE Transactions on Visualization and Computer Graphics 2024年 PP卷 PP页

作者： Lu, Kecheng Zhu, Lihang Wang, Yunhai Zeng, Qiong Song, Weitao Reda, Khairi Renmin University of China School of Information China Shandong University School of Computer Science and Technology China Beijing Institute of Technology School of Optics and Photonics Zhengzhou Research Institute China Indiana University Indianapolis Luddy School of Informatics and Computing United States

Transparency is commonly utilized in visualizations to overlay color-coded histograms or sets, thereby facilitating the visual comparison of categorical data. However, these charts often suffer from significant overlap between objects, resulting in substantial color interactions. Existing color blending models struggle in these scenarios, frequently leading to ambiguous color mappings and the introduction of false colors. To address these challenges, we propose an automated approach for generating optimal color encodings to enhance the perception of translucent charts. Our method harnesses color nameability to maximize the association between composite colors and their respective class labels. We introduce a color-name aware (CNA) optimization framework that generates maximally coherent color assignments and transparency settings while ensuring perceptual discriminability for all segments in the visualization. We demonstrate the effectiveness of our technique through crowdsourced experiments with composite histograms, showing how our technique can significantly outperform both standard and visualization-specific color blending models. Furthermore, we illustrate how our approach can be generalized to other visualizations, including parallel coordinates and Venn diagrams. We provide an open-source implementation of our technique as a web-based tool. © 1995-2012 IEEE.

关键词： Simulated annealing

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UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via Visual Foundation Models Prior 38

UniDSeg: Unified Cross-Domain 3D Semantic Segmentation via V...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wu, Yao Xing, Mingwei Zhang, Yachao Luo, Xiaotong Xie, Yuan Qu, Yanyun School of Informatics Xiamen University China Institute of Artificial Intelligence Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Computer Science and Technology East China Normal University China Chongqing Institute of East China Normal University China

3D semantic segmentation using an adapting model trained from a source domain with or without accessing unlabeled target-domain data is the fundamental task in computer vision, containing domain adaptation and domain generalization. The essence of simultaneously solving cross-domain tasks is to enhance the generalizability of the encoder. In light of this, we propose a groundbreaking universal method with the help of off-the-shelf Visual Foundation Models (VFMs) to boost the adaptability and generalizability of cross-domain 3D semantic segmentation, dubbed UniDSeg. Our method explores the VFMs prior and how to harness them, aiming to inherit the recognition ability of VFMs. Specifically, this method introduces layer-wise learnable blocks to the VFMs, which hinges on alternately learning two representations during training: (i) Learning visual prompt. The 3D-to-2D transitional prior and task-shared knowledge is captured from the prompt space, and then (ii) Learning deep query. Spatial Tunability is constructed to the representation of distinct instances driven by prompts in the query space. Integrating these representations into a cross-modal learning framework, UniDSeg efficiently mitigates the domain gap between 2D and 3D modalities, achieving unified cross-domain 3D semantic segmentation. Extensive experiments demonstrate the effectiveness of our method across widely recognized tasks and datasets, all achieving superior performance over state-of-the-art methods. Remarkably, UniDSeg achieves 57.5%/54.4% mIoU on "A2D2/sKITTI" for domain adaptive/generalized tasks. Code is available at https://***/Barcaaaa/UniDSeg. © 2024 Neural information processing systems foundation. All rights reserved.

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