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Modelling information warfare dynamics to counter propaganda using a nonlinear differential equation with a PINN-based learning approach

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1527-1538页

作者： Pandey, Rashmikiran Pandey, Mrinal Nazarov, Alexey Nikolaevich Department of Radio Engineering and Cybernetics Moscow Institute of Physics and Technology Moscow Russian Federation Federal Research Center Computer Science and Control Russian Academy of Science Moscow Russian Federation

The widespread dissemination of misinformation and propaganda has become a crucial issue in societal conversations. This study presents an innovative framework to counter propaganda within information warfare using a modified logistic differential equation integrated with a Physics-Informed Neural Network (PINN). Employing the LIAR dataset, which reflects real-world political discourse, our model captures the intricate dynamics of propaganda. The novel approach effectively estimates underlying dynamics and unknown parameters, yielding superior accuracy (97.32%) compared to benchmark models. A parameter sensitivity analysis reveals the significant impact of the spread rate parameter, shedding light on aggressive propaganda dissemination. This research paves the way for robust methods in combating propaganda, demonstrating the effectiveness and superiority of the proposed PINN-based model over established benchmark models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Information warfare Nonlinear differential equations PINN Propaganda Random forest classifier TF-IDF

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Hybrid Digital-Analog Semantic Communications

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2478-2492页

作者： Xie, Huiqiang Qin, Zhijin Han, Zhu Letaief, Khaled B. Jinan University College of Information Science and Technology Guangzhou China Tsinghua University Department of Electronic Engineering Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China University of Houston Department of Electrical and Computer Engineering Houston United States The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong

Digital and analog semantic communications (SemCom) face inherent limitations such as data security concerns in analog SemCom, as well as leveling-off and cliff-edge effects in digital SemCom. In order to overcome these challenges, we propose a novel SemCom framework and a corresponding system called HDA-DeepSC, which leverages a hybrid digitalanalog approach for multimedia transmission. This is achieved through the introduction of analog-digital allocation and fusion modules. To strike a balance between data rate and distortion, we design new loss functions that take into account long-distance dependencies in the semantic distortion constraint, essential information recovery in the channel distortion constraint, and optimal bit stream generation in the rate constraint. Additionally, we propose denoising diffusion-based signal detection techniques, which involve carefully designed variance schedules and sampling algorithms to refine transmitted signals. Through extensive numerical experiments, we will demonstrate that HDA-DeepSC exhibits robustness to channel variations and is capable of supporting various communication scenarios. Our proposed framework outperforms existing benchmarks in terms of peak signal-to-noise ratio and multi-scale structural similarity, showcasing its superiority in semantic communication quality. © 1983-2012 IEEE.

关键词： Digital communication systems

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NCU-IISR: Enhancing Biomedical Question Answering with GPT-4 and Retrieval Augmented Generation in BioASQ 12b Phase B 25

NCU-IISR: Enhancing Biomedical Question Answering with GPT-4...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Chih, Bing-Chen Han, Jen-Chieh Tsai, Richard Tzong-Han Department of Computer Science and Information Engineering National Central University Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taiwan

In this paper, we introduce our system and submissions in BioASQ 12b phase b [1], highlighting a significant improvement with GPT-4 and the integration of Retrieval Augmented Generation (RAG) techniques. We describe our prompt engineering methods and the experimental procedures followed. Because GPT-4 has proven effectiveness in generating answers and its ability in the biological domain, our system utilizes GPT-4 to address biomedical question-answering (QA). Leveraging OpenAI's ChatCompletions API, we refined previous prompt engineering approaches [2] for BioASQ 11b phase b. This year, the addition of RAG techniques significantly improved the information retrieval capabilities of our system. Consequently, our latest submission employed what we experimented to be the most effective prompts and techniques, achieving excellent performance across multiple metrics in the fourth batch. © 2024 Copyright for this paper by its authors.

关键词： Question answering

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TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya 30

TIGQA: An Expert-Annotated Question-Answering Dataset in Tig...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Teklehaymanot, Hailay Kidu Fazlija, Dren Ganguly, Niloy Patro, Gourab K. Nejdl, Wolfgang L3S Research Center Leibniz University Hannover Germany Department of Computer Science and Engineering IIT Kharagpur India

ISBN: (纸本)9782493814104

The absence of explicitly tailored, accessible annotated datasets for educational purposes presents a notable obstacle for NLP tasks in languages with limited resources. This study initially explores the feasibility of using machine translation (MT) to convert an existing dataset into a Tigrinya dataset in SQuAD format. As a result, we present TIGQA, an expert-annotated dataset containing 2,685 question-answer pairs covering 122 diverse topics such as climate, water, and traffic. These pairs are from 537 context paragraphs in publicly accessible Tigrinya and Biology books. Through comprehensive analyses, we demonstrate that the TIGQA dataset requires skills beyond simple word matching, requiring both single-sentence and multiple-sentence inference abilities. We conduct experiments using state-of-the-art MRC methods, marking the first exploration of such models on TIGQA. Additionally, we estimate human performance on the dataset and juxtapose it with the results obtained from pre-trained models. The notable disparities between human performance and the best model performance underscore the potential for future enhancements to TIGQA through continued research. Our dataset is freely accessible via the provided link to encourage the research community to address the challenges in the Tigrinya MRC. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： domain specific QA Low resource QA dataset Tigrinya QA dataset

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Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

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IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

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LongDocFACTScore: Evaluating the Factuality of Long Document Abstractive Summarisation 30

LongDocFACTScore: Evaluating the Factuality of Long Document...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bishop, Jennifer A. Xie, Qianqian Ananiadou, Sophia National Centre for Text Mining Department of Computer Science The University of Manchester United Kingdom Artificial Intelligence Research Center Tokyo Japan

ISBN: (纸本)9782493814104

Maintaining factual consistency is a critical issue in abstractive text summarisation, however, it cannot be assessed by traditional automatic metrics used for evaluating text summarisation, such as ROUGE scoring. Recent efforts have been devoted to developing improved metrics for measuring factual consistency using pre-trained language models, but these metrics have restrictive token limits, and are therefore not suitable for evaluating long document text summarisation. Moreover, there is limited research and resources available for evaluating whether existing automatic evaluation metrics are fit for purpose when applied in long document settings. In this work, we evaluate the efficacy of automatic metrics for assessing the factual consistency of long document text summarisation. We create a human-annotated data set for evaluating automatic factuality metrics, LongSciVerify, which contains fine-grained factual consistency annotations for long document summaries from the scientific domain. We also propose a new evaluation framework, LongDocFACTScore, which is suitable for evaluating long document summarisation. This framework allows metrics to be efficiently extended to any length document and outperforms existing state-of-the-art metrics in its ability to correlate with human measures of factuality when used to evaluate long document summarisation data sets. We make our code and LongSciVerify data set publicly available: https://***/jbshp/LongDocFACTScore. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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Fabrication of a Revolutionary Pick-and-Place Robot with Omnidirectional Mobility 3

Fabrication of a Revolutionary Pick-and-Place Robot with Omn...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Rahman, Md Hasibur Haque, Samiul Monir, Md Fayed Al Uddin, Mohammad Faisal Alam, Saadia Binte Independent University Bangladesh Department of Computer Science Engineering Dhaka Bangladesh Independent University of Bangladesh RIoT Research Center Dhaka Bangladesh

ISBN: (纸本)9798350374766

The paper outlines the creation of an innovative robot capable of omnidirectional movement, enhancing its ability to navigate confined industrial spaces. This robot, uniquely designed for picking and placing tasks, underwent a comprehensive development process, including planning, design, fabrication, and programming. It represents a notable advancement in the field of industrial robotics, with potential applications in environments where traditional robots may be less effective. The discussion also covers possible future improvements and broader applications of this technology. © 2024 IEEE.

关键词： Pulse width modulation

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Comparative Analysis of Two Pico Satellites: Performance, Sensor Integration, and Communication Technologies. 3

Comparative Analysis of Two Pico Satellites: Performance, Se...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Rahman, Md Hasibur Hasan, Mostofa Fahim Smaron, J.M. Sadik-Ul Islam Ahmed, Sadia Uddin, Mohammad Faisal Alam, Saadia Binte Independent University Bangladesh Department of Computer Science Engineering Dhaka Bangladesh Independent University Bangladesh RIoT Research Center Dhaka Bangladesh

ISBN: (纸本)9798350374766

In this research, we evaluate the effectiveness of two Pico satellite designs - a can-satellite model and a cubesatellite model - focusing on their performance during controlled parachute descents from a specific altitude. The study centers on embedded sensors in these satellites, which collect environmental and kinematic data throughout the descent. These sensors track temperature, humidity, barometric pressure, gyroscope readings, acceleration, and magnetometer data, offering detailed insights into each design's descent dynamics. The primary goal is to assess how the can and cubePico satellites withstand descent pressures and their data gathering efficacy during parachute-assisted landings. This evaluation is crucial for understanding how different sensor integration impact the performance and adaptability of picosatellites in freefall conditions. Additionally, the research includes a comparative analysis of two communication devices: the cube satellite equipped with an NRF24L01+LNA, and the can satellite with an ESP32S wireless development board. This comparison aims to identify which communication technology is more suitable for the challenging environment of parachute-assisted descent, with a focus on the reliability and quality of data transmission. © 2024 IEEE.

关键词： Parachutes

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Back to School: Translation Using Grammar Books

Back to School: Translation Using Grammar Books

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Hus, Jonathan Anastasopoulos, Antonios Department of Computer Science George Mason University VA United States Archimedes AI Unit Athena Research Center Athens Greece

ISBN: (纸本)9798891761643

Machine translation systems for high resource languages perform exceptionally well and produce high quality ***, the vast majority of languages lack the quantity of parallel sentences needed to train such *** under-represented languages are not entirely without resources, as bilingual dictionaries and grammar books may be available as linguistic reference *** current large language models (LLMs) supporting near book-length contexts, we can use the available material to ensure advancements are shared among all of the world's *** this paper, we use dictionaries and grammar books to improve machine *** evaluate on 16 typologically diverse low-resource languages, showing encouraging improvements. © 2024 Association for Computational Linguistics.

关键词： Machine translation

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