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A Systematic Literature Review of Cyber Security Monitoring in Maritime

IEEE Access 2025年 13卷 85307-85329页

作者： Vaarandi, Risto Tsiopoulos, Leonidas Visky, Gabor Rehman, Muaan Ur Bahsi, Hayretdin Tallinn University of Technology Centre for Digital Forensics and Cyber Security Department of Software Science Tallinn19086 Estonia Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ86011 United States

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. Although several literature review 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. 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. © 2013 IEEE.

关键词： Network intrusion

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Visualizing NASDAQ Composite Index Historical Data Using Tableau 6th

Visualizing NASDAQ Composite Index Historical Data Using Tab...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Safuan, Halawati Abd Jalil Nyein, Chit Ko Ko Riskhan, Basheer Ashfaq, Farzeen School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science SCS Taylors University Subang Jaya Malaysia

ISBN: (纸本)9789819780891

The study explores the visualization and analysis of historical data for the NASDAQ Composite Index (^IXIC) using Tableau. It examines trends in daily closing prices and volume distribution and compares performance with the NYSE index. Findings reveal significant fluctuations in the NASDAQ index over 22 years, suggesting potential future price changes. Forecasted graphs offer insights into potential economic decline and recovery, highlighting the dynamic nature of the market. Volume distribution analysis identifies periods of heightened trading activity. Comparative performance analysis emphasizes different trends observed in the NASDAQ and NYSE indices. Due to market unpredictability and external influences, additional research is crucial for validation. This study showcases the value of data visualization and analysis in finance through Tableau. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inflation

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Leveraging LLMs for optimised feature selection and embedding in structured data: A case study on graduate employment classification

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Computers and Education: Artificial Intelligence 2025年 8卷

作者： Haque, Radiah Goh, Hui-Ngo Ting, Choo-Yee Quek, Albert Hasan, M.D. Rakibul Faculty of Computing and Informatics Multimedia University Cyberjaya 63100 Malaysia

The application of Machine Learning (ML) for predicting graduate student employability is a growing area of research, driven by the need to align educational outcomes with job market requirements. In this context, this paper investigates the application of Large Language Models (LLMs) for tabular data transformation and embedding, specifically using Bidirectional Encoder Representations from Transformers (BERT), to enhance the performance of ML models in binary classification tasks for student employability prediction. The primary objective is to determine whether converting structured data into text format improves model accuracy. The study involves several ML models including Artificial Neural Networks (ANN), CatBoost, and BERT classifier. The focus is on predicting the employment status of graduate students based on demographic, academic, and graduate tracer study data, collected from over 4000 university graduates. Feature selection methods, including Boruta and Extra Tree Classifier (ETC) are employed to identify the optimal feature set, guided by a sliding window algorithm for automatic feature selection. The models are trained in four stages: 1) original dataset without feature selection or word embedding, 2) dataset with selected optimal features, 3) transformed data with word embedding, and 4) transformed data with feature selection applied both before and after word embedding. The baseline model (without feature selection and embedding) achieved the highest accuracy with the ANN model (79%). Subsequently, applying ETC for feature selection improved accuracy, with CatBoost achieving 83%. Further transformation with BERT-based embeddings raised the highest accuracy to 85% using the BERT classifier. Finally, the optimal accuracy of 88% was obtained by applying feature selection before and after embedding, with the BERT-Boruta model. The findings from this study demonstrate that using the dual-stage feature selection approach in combination with BERT embedding

关键词： Students

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The Impact of Influencers Message Frames on Followers Trolling 24

The Impact of Influencers Message Frames on Followers Trolli...

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24th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2024

作者： Sun, Honglei Fichman, Pnina School of Information Management Nanjing University Nanjing Jiangsu China Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798400710933

Trolling has become an integral part of social media interactions that focus on social and ideological posts. As influencers posts set the social and ideological agenda for their followers, our study aims to examine the impact of influencers message frames on followers trolling reactions. Using the goal (Gain, Loss) and cultural frames (Individualist, Collectivists) we analyzed 3,050 followers comments on 160 influencers posts on the Sina Weibo platform. We found that social media influencers adopted the Loss-Individualist frame in most of their posts, and when they adopted the Gain-Individualist frame and the Loss-Collectivist frame, their posts attracted more followers trolling reactions, while posts under the Gain-Collectivist frame were less susceptible to trolling than the three other frames. We conclude by articulating the theoretical implications of our findings and providing suggestions for future research directions. © 2024 Copyright held by the owner/author(s).

关键词： Social psychology

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Analysing Zero-Shot Readability-Controlled Sentence Simplification 31

Analysing Zero-Shot Readability-Controlled Sentence Simplifi...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Barayan, Abdullah Camacho-Collados, Jose Alva-Manchego, Fernando School of Computer Science and Informatics Cardiff University United Kingdom Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9798891761964

Readability-controlled text simplification (RCTS) rewrites texts to lower readability levels while preserving their meaning. RCTS models often depend on parallel corpora with readability annotations on both source and target sides. Such datasets are scarce and difficult to curate, especially at the sentence level. To reduce reliance on parallel data, we explore using instruction-tuned large language models for zero-shot RCTS. Through automatic and manual evaluations, we examine: (1) how different types of contextual information affect a model's ability to generate sentences with the desired readability, and (2) the trade-off between achieving target readability and preserving meaning. Results show that all tested models struggle to simplify sentences (especially to the lowest levels) due to models' limitations and characteristics of the source sentences that impede adequate rewriting. Our experiments also highlight the need for better automatic evaluation metrics tailored to RCTS, as standard ones often misinterpret common simplification operations, and inaccurately assess readability and meaning preservation. © 2025 Association for Computational Linguistics.

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Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model With BERT Embedding and Data Augmentation

Depression Detection Using a Hybrid CNN-BiGRU-BiLSTM Model W...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Saadat M. Alhashmi Saleema Hashim Ahmed Mohamed Khedr College of Computing and Informatics University of Sharjah UAE

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. Also called major depressive disorder or clinical depression, it affects how you feel, think, and behave and can lead to a variety of emotional and physical problems. Automatic detection, as in a clinical interview can be derived from various modalities which includes video, audio, and text. Among them, detection from text data has emerged as a crucial task in mental health monitoring. In this study, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Gated Recurrent Units (BiGRU), and Bidirectional Long Short-Term Memory (BiLSTM) layers to enhance depression detection accuracy. Using BERT embedding for robust feature extraction, the proposed model processes social media or clinical text data to identify signs of depression. Additionally, we incorporate a novel data augmentation technique using synonym replacement to address data imbalance and improve generalization. Evaluations are conducted using key performance metrics, including accuracy, precision, recall, and F1 score. With an accuracy of 86.2 %, the results demonstrate that the combined CNN-BiGRU-BiLSTM architecture, alongside BERT embedding and augmented data, significantly improves classification performance compared to traditional models. This approach shows promise in contributing to more effective automatic depression detection systems.

关键词： Deep learning Accuracy Social networking (online) Bidirectional long short term memory Bidirectional control Depression Data augmentation Encoding Data models Convolutional neural networks

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Leveraging SAM for Automatic Prostate Segmentation on Micro-Ultrasound Images

Leveraging SAM for Automatic Prostate Segmentation on Micro-...

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Radcliffe, Olivia Lawford-Wickham, Imogen A. Wilson, Paul F.R. Abolmaesumi, Purang Mousavi, Parvin Medical Informatics Laboratory School of Computing Queen's University Kingston Canada Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (纸本)9781510686021

PURPOSE: Deep learning is promising for enabling accurate and automatic prostate segmentation. Existing deep learning segmentation model approaches often rely on large training datasets for good generalization. We aim to adapt pre-trained foundation models to enable accurate, data-efficient, and robust prostate segmentation. METHODS: We adapt the promptable foundation model SAM1 for prostate segmentation on rotational micro-ultrasound scans2 of the prostate. We design novel prompting strategies to provide the model with enhanced 3D context awareness to improve segmentation. RESULTS: Our model, which we call SliceTrack-SAM, outperforms prior state-of-the-art in micro-ultrasound prostate segmentation. We achieve a Dice coefficient of 94.0% and Hausdorff distance 1.76 mm on the MicroSegNet dataset. Qualitative analysis and ablation studies further validate the success of our approach. CONCLUSION: Transfer learning from pre-trained foundation models can alleviate the challenge of data scarcity and improve the generalization of medical imaging deep learning systems. Prompts provide a flexible and effective way to leverage auxiliary information, such as 3D context, when using these models. © 2025 SPIE.

关键词： Image segmentation

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Arthropodia-V1: Biodiversity Dataset for Semantic Segmentation, Quality Estimation, and Observation Extraction 5

Arthropodia-V1: Biodiversity Dataset for Semantic Segmentati...

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5th International Conference on Advanced Research in computing, ICARC 2025

作者： Ayoob, Mohamed Maul, Tomas Chandesa, Tissa Informatics Institute of Technology School of Computing Colombo Sri Lanka The University of Nottingham Malaysia School of Computer Science Semenyih Malaysia

ISBN: (纸本)9798331530983

This paper introduces a novel biodiversity image dataset named Arthropodia, which focuses on three crucial tasks: image quality estimation, semantic segmentation, and observation extraction (image similarity). Arthropodia is collected from tropical Malaysian forests and encompasses a diverse range of arthropod species captured under various lighting and environmental conditions. We manually annotate the dataset for each task, making it suitable for benchmarking and developing AI models applicable to biodiversity and environmental monitoring. To demonstrate its utility, we present baseline results using state-of-the-art deep learning models for each task. Our findings indicate that Arthropodia serves as a valuable resource for advancing research in the field of biodiversity conservation and ecological studies through automated image processing and analysis. The open-access availability of Arthropodia fosters further contributions from the research community, paving the way for the development of robust and accurate AI solutions for biodiversity monitoring and preservation. © 2025 IEEE.

关键词： Semantic Segmentation

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Zirkus Empathico 2.0: a multiplayer serious mobile game for children with autism spectrum disorder (ASD), with a focus on enhancing social and emotional development

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Multimedia Tools and Applications 2025年 1-24页

作者： Hassan, Ahmed Pinkwart, Niels Shafi, Muhammad Department of Computer Science and Informatics Berlin School of Business and Innovation Karl Marx Strasse 97 - 99 Berlin12043 Germany Institute for Informatic Humboldt University Berlin Rudower Chaussee 25 Berlin Berlin12489 Germany School of Computing Ulster University Belfast Campus United Kingdom Faculty of Computing & Information Technology Sohar University Sohar Oman

This study explores the effectiveness of the multiplayer serious mobile game "Zirkus Empathico (ZE) 2.0" in enhancing social-emotional behaviors in children diagnosed with autism spectrum disorder (ASD). It aims to assess how such games can foster emotional recognition, empathy, and prosocial behavior, thereby supporting the holistic development of children with ASD. An eight-week randomized controlled trial (RCT) was conducted between January and April 2021 at autism centers in Islamabad, Pakistan, and Berlin, Germany. The study involved 107 children aged 5–11 years, with 60 diagnosed with ASD and 47 typically developing children serving as an Active Control Group (ACG). Participants were engaged in sessions using the bilingual"ZE 2.0"game, incorporating multiplayer and emotional learning modules. The intervention required 120 min of weekly training supplemented by daily sessions. Post-intervention assessments demonstrated significant improvements in the ASD group compared to the control group in areas of empathy and emotional awareness, as evidenced by higher scores on the Griffith Empathy Measure (GEM) and multiplayer module evaluations. Notable was the ability of participants to apply learned social skills in dynamic, real-world contexts, which suggests the game’s effectiveness transcends the controlled settings typically associated with ASD interventions. ZE 2.0 adds to the social and teamwork competencies of children with ASD. Besides encouraging peers to be proactive in communications and cooperation, the multiplayer mode provides an immersion learning atmosphere for facilitating social and emotional development. Such an outcome underlines the perspectives of serious games for use as a framework for the treatment and education of children who have ASD. In this manner, serious games can be utilized as a supplementary or alternative means of treating ASD. Future research may focus on distinguishing the effects of intervention from those of other therapy modalit

关键词： Serious games

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A stacked ensemble approach for symptom-based monkeypox diagnosis

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Computers in Biology and Medicine 2025年 191卷 110140-110140页

作者： Nagro, Shimaa College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The recent monkeypox outbreak has raised global health concerns. Caused by a virus, it is characterized by symptoms such as skin lesions. Early detection is critical for treatment and controlling its spread. This study uses advanced machine learning and deep learning techniques, including Tab Transformer, Long Short-Term Memory, XGBoost, LightGBM, and a Stacking Classifier, to predict the presence of the virus based on patient symptoms. The performance of these models is evaluated using accuracy, precision, recall, and F1-score metrics. The experiments reveal that the Stacking Classifier significantly outperforms the other models, achieving an accuracy of 87.29 %, precision of 86.12 %, recall of 87.47 %, and an F1 score of 87.89 %. Additionally, applying Conditional Tabular GAN to generate synthetic data helps address data imbalance issues, further improving model robustness. These results highlight the proposed approach's potential for timely, accurate monkeypox detection, aiding in effective disease management and control. © 2025 The Author

关键词： Deep learning

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