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The impact of toxic trolling comments on anti-vaccine YouTube videos (vol 14, 5088, 2024)

SCIENTIFIC REPORTS 2024年第1期14卷 1-9页

作者： Miyazaki, Kunihiro Uchiba, Takayuki Kwak, Haewoon An, Jisun Sasahara, Kazutoshi Luddy School of Informatics Computing and Engineering Indiana University Bloomington Bloomington IN USA Sugakubunka Co. Ltd. Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan

Anti-vaccine trolling on video-hosting websites hinders efforts to increase vaccination rates by using toxic language and threatening claims to intimidate people and promote vaccine hesitancy. However, there is a shortage of research investigating the effects of toxic messages on these platforms. This study focused on YouTube anti-vaccine videos and examined the relationship between toxicity and fear in the comment section of these videos. We discovered that highly liked toxic comments were associated with a significant level of fear in subsequent comments. Moreover, we found complex patterns of contagion between toxicity and fear in the comments. These findings suggest that initial troll comments can evoke negative emotions in viewers, potentially fueling vaccine hesitancy. Our research bears essential implications for managing public health messaging and online communities, particularly in moderating fear-mongering messages about vaccines on social media.

关键词： Emotional contagion Fearful comment Toxic comment Vaccine hesitancy YouTube videos

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models

arXiv

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arXiv 2024年

作者： Zarharan, Majid Wullschleger, Pascal Kia, Babak Behkam Pilehvar, Mohammad Taher Foster, Jennifer School of Computing Dublin City University Ireland Iran University of Science and Technology Iran School of Computer Science and Informatics Cardiff University United Kingdom Lucerne School of Computer Science and Information Technology Switzerland

This paper presents a comprehensive analysis of explainable fact-checking through a series of experiments, focusing on the ability of large language models to verify public health claims and provide explanations or justifications for their veracity assessments. We examine the effectiveness of zero/few-shot prompting and parameter-efficient fine-tuning across various open and closed-source models, examining their performance in both isolated and joint tasks of veracity prediction and explanation generation. Importantly, we employ a dual evaluation approach comprising previously established automatic metrics and a novel set of criteria through human evaluation. Our automatic evaluation indicates that, within the zero-shot scenario, GPT-4 emerges as the standout performer, but in few-shot and parameter-efficient fine-tuning contexts, open-source models demonstrate their capacity to not only bridge the performance gap but, in some instances, surpass GPT-4. Human evaluation reveals yet more nuance as well as indicating potential problems with the gold explanations. © 2024, CC BY.

关键词： Public health

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ActivityMamba: A CNN-Mamba hybrid neural network for efficient human activity recognition

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IEEE Transactions on Mobile computing 2025年

作者： Luo, Fei Li, Anna Jiang, Bin Khan, Salabat Wu, Kaishun Wang, Lu Great Bay University School of Computing and Information Technology Dongguan China Lancaster University School of Computing & Communications United Kingdom University of Petroleum College of Oceanography and Space Informatics Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Guangzhou China

Current research in human activity recognition primarily emphasizes enhancing accuracy, with limited exploration into computational efficiency and hardware compatibility. Recently, Mamba has sparked substantial interest within the realm of deep learning. Mamba is a hardware-aware algorithm enabling very efficient training and inference. Researchers are applying Mamba to various tasks, demonstrating significant promise in both language and vision tasks. It is worthwhile to investigate the use of Mamba for efficient human activity recognition. In this paper, we proposed a hybrid neural network that integrates CNN and visual Mamba, called ActivityMamba. The SE-Mamba block in ActivityMamba utilizes both CNN's local and Mamba's global context modeling while keeping computation and memory efficiency. We evaluated the ActivityMamba on five public benchmark datasets collected by using three different sensing techniques. ActivityMamba achieved higher performance than vision transformers, vision Mamba, and CNNs with fewer FLOPs and parameters. It sets a new SOTA on all five datasets, which are 91.78% OA and 89.13% F1 on the USC-HAD dataset, 99.19% OA and 98.64% F1 on the UT-HAR dataset, 99.82% OA and F1 on the DIAT dataset, 98.59% OA and 98.65% F1 on the UCI-HAR dataset, and 95.41% OA and 93.14% F1 on the UniMib dataset. Our work is the first to investigate the CNN-Mamba hybrid network for efficient human activity recognition. © 2002-2012 IEEE.

关键词： Active learning

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Lesion region segmentation via weakly supervised learning

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Quantitative Biology 2022年第3期10卷 239-252页

作者： Ran Yi Rui Zeng Yang Weng Minjing Yu Yu-Kun Lai Yong-Jin Liu Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Computer Science and Informatics Cardiff UniversityCardiffCF103ATUnited Kingdom

Background:Image-based automatic diagnosis of field diseases can help increase crop yields and is of great ***,crop lesion regions tend to be scattered and of varying sizes,this along with substantial intraclass variation and small inter-class variation makes segmentation ***:We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region ***,a two-branch network is designed for joint disease classification and seed region *** generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder *** from previous works that use an encoder in the segmentation network,the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions,which is the major challenge in image-based diagnosis of field *** further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network,making use of the extracted seed ***:Experimental results show that our system achieves better lesion region segmentation results than state of the *** addition to crop images,our method is also applicable to general scattered object *** demonstrate this by extending our framework to work on the PASCAL VOC dataset,which achieves comparable performance with the state-of-the-art DSRG(deep seeded region growing)***:Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task,but also shows its capability to perform well on more general tasks.

关键词： weakly supervised learning lesion segmentation disease detection semantic segmentation agriculture

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A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Recognition to Enhance Learning Experience 23rd

A Novel DAAM-DCNNs Hybrid Approach to Facial Expression Rec...

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23rd International Conference on Computational Science, ICCS 2023

作者： Alfred, Rayner Pailus, Rayner Henry Obit, Joe Henry Lim, Yuto Sukirno, Haviluddin Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia School of Information Science Japan Advanced Institute of Science and Technology Access 1-1 Asahidai Ishikawa Nomi923-1292 Japan Department of Informatics Universitas Mulawarman Kalimantan Timur Samarinda Indonesia

ISBN: (纸本)9783031360268

Many machine learning models are applied on facial expression classification and there are three main issues affecting the performance of any algorithms in classifying emotions based on facial expressions, and these issues include image illumination, image quality and partial features recognition. Many approaches have been proposed to handle these issues. Unfortunately, one of the main challenges in detecting and classifying facial expression process is minimal differences of features between different types of emotions that can be used to differentiate these different types of emotions. Thus, there is a need to enrich each type of emotion with more relevant extracted features by having a more effective approach to extract features that can be used to represent each type of emotions more effectively and efficiently. This work addresses the issue of improving the emotion recognition accuracy by introducing a novel hybrid approach that combines the Depth Active Appearance Model (DAAM) and Deep Convolutional Neural Networks (DCNNs). The proposed DAAM and DCNNs model can be used to assist one in identifying emotions and classify learner involvement and interest in the topic which are plotted as feedback to the instructor to improve learner experience. The proposed method is evaluated on two publicly available datasets namely, JAFFE and CK+ and the results are compared to the state-of-the-art results. The empirical study showed that the proposed DAMM-CNNs hybrid method managed to perform the face expression recognition with 97.4% for the JAFFE dataset and 96.9% for the CK+ dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Contrastive Learning

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A Survey on Image Captioning Using Object Detection and NLP

A Survey on Image Captioning Using Object Detection and NLP

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Advanced Research in computing (ICARC), International Conference on

作者： Vathila De Silva T.G.D.K. Sumanathilaka School of Computing Informatics Institute of Technology Colombo 06 Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

Recent years have seen the emergence of image captioning as a revolutionary technical development that seamlessly blends computer vision and natural language processing. The integration of various domains facilitates the production of captivating captions for pictures, promoting a more profound comprehension of visual material. This study offers a thorough analysis of the rapidly developing field of image captioning, examining its uses in various settings such as social media, online platforms, assistive technology, and content indexing. It emphasizes the critical importance of advanced techniques like You Only Look Once (YOLO) for accurate item detection and Natural Language Processing (NLP) for creating subtle captions. While NLP gives the resulting text a layer of contextual depth, YOLO guarantees precise object detection, which helps with caption accuracy. Using these cutting-edge methods boosts the overall efficacy of image captioning systems and represents a noteworthy trend in the literature. Examining the evolution of image captioning, the review paper highlights how important it is for multimodal comprehension. Image captioning is a transformative tool that enhances the readability of visual content and influences contemporary digital experiences in various contexts. The development of image captioning is summarized in this abstract, which also highlights the tendency to use cutting-edge techniques for more accurate and nuanced caption production.

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Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Using clinical entity recognition for curating an interface ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kollapally, Navya Martin Dehkordi, Mahshad Koohi H. Perl, Yehoshua Geller, James Deek, Fadi P. Liu, Hao Keloth, Vipina K. Elhanan, Gai Einstein, Andrew J. Zhou, Shuxin Kean University Department of Computer Science Union United States New Jersey Institue of Technology Department of Computer Science Newark United States New Jersey Institue of Technology Department of DataScience Newark United States New Jeresy Institue of Technology Department of Informatics Newark United States Montclair State University School of Computing Montclair United States Yale University Section of Biomedical Informatics and DataScience New Haven United States University of Nevada School of Medicine Nevada United States Columbia Medical Center Dept. of Medicine New York United States

ISBN: (纸本)9798350386226

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting. © 2024 IEEE.

关键词： Electronic health record

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A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation

arXiv

引用

arXiv 2024年

作者： Abeysiriwardana, Miuru Sumanathilaka, Deshan School of Computing Informatics Institute of Technology Colombo06 Sri Lanka Department of Computer Science Swansea University Swansea Wales United Kingdom

This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP), highlighting the complexity of linguistic phenomena such as polysemy and homonymy and their implications for computational models. Focusing extensively on Word Sense Disambiguation (WSD), it outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet. The paper introduces cutting-edge methodologies like word sense extension (WSE) and neuromyotonic approaches, enhancing disambiguation accuracy by predicting new word senses. It examines specific applications in biomedical disambiguation and language-specific optimisation and discusses the significance of cognitive metaphors in discourse analysis. The research identifies persistent challenges in the field, such as the scarcity of sense-annotated corpora and the complexity of informal clinical texts. It concludes by suggesting future directions, including using large language models, visual WSD, and multilingual WSD systems, emphasising the ongoing evolution in addressing lexical complexities in NLP. This thinking perspective highlights the advancement in this field to enable computers to understand language more accurately. © 2024, CC BY.

关键词： Natural language processing systems

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Analysing Zero-Shot Readability-Controlled Sentence Simplification

arXiv

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arXiv 2024年

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

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. © 2024, CC BY.

关键词： Zero-shot learning

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