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Courtroom-FND: a multi-role fake news detection method based on argument switching-based courtroom debate

Journal of King Saud University - computer and Information sciences 2025年第3期37卷 1-29页

作者： Jin, Weiqiang Su, Dafu Tao, Tao Wang, Xiujun Wang, Ningwei Zhao, Biao School of Computer Science and Technology Anhui University of Technology Anhui Ma’anshan China School of Information and Communications Engineering Xi’an Jiaotong University Shaanxi Xi’an China

With the proliferation of the internet and social media, the spread of fake news has become a global issue, posing serious challenges to the research of Fake News Detection (FND) methods. With advancements in Artificial Intelligence (AI), large language models (LLMs) have become increasingly evident across various industries, especially in natural language processing (NLP). LLM-based FND approaches, including Chain-of-Thought (CoT), self-reflection, and in-context learning (ICL) prompting paradigms, has shown promise but still faces challenges in effectively handling complex and nuanced content. For example, CoT paradigm faces error propagation issues, self-reflection methods suffer from the Degeneration-of-Thought (DoT) problem, and ICL paradigm is highly dependent on the quality of the provided context. To address these issues, we propose a multi-role detection method based on courtroom debates. This method involves two attorneys, representing the prosecution and the defense, as well as a judge, simulating a debate process on the authenticity of the news. First, the prosecution attempts to prove that the news is fake, while the defense tries to prove that the news is genuine. The judge evaluates the evidence presented by both sides to reach a conclusion. Next, the prosecution and defense switch roles, with each attempting to argue from the opposite standpoint, and the judge evaluates the arguments again. Finally, the judge synthesizes all arguments to issue a verdict. Extensive experiments across multiple challenging scenarios (e.g., controversial news and misleading media posts) show that this debate-based framework achieves up to 9%-11% higher accuracy than advanced LLM baselines, revealing how role switching significantly enhances detection performance. Moreover, our findings indicate that incorporating diverse perspectives reduces cognitive bias, but also highlight that LLM-based judges remain susceptible to inherent biases-especially if pretrained data includ

关键词： Cognitive bias mitigation Courtroom debate framework Fake news detection Large language models Role switching and fairness in AI

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Analysis of Brain Health Using Machine Learning and Artificial Intelligence Technology: Modern Drug Discovery Perspective 1

Analysis of Brain Health Using Machine Learning and Artifici...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Thirunagalingam, Arunkumar Usha, M.S. Geetha, S. Dhanamathi, A. Dhivya, S. Thendral, M.Tamil Business Intelligence and Reporting B. S. Abdur Rahman Crescent Institute of Science and Technology Department of CSE Chennai600048 India VS.B. ENGINEERING COLLEGE Department of Computer Science and Engineering Karur India Roever Engineering College Department of CSE Perambalur621220 India Sri Venkateswara College of Engineering Department of Computer Science and Engineering Sriperumbudur India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India

ISBN: (纸本)9798331518578

The creation and discovery of pharmaceuticals may be considered the most important translational science activity that improves human invulnerability and happiness. In the pharmaceutical sector, strategies to reduce costs and speed up the development of new drugs have sparked a rigorous and fascinating brainstorming session. The use of classified big data in conjunction with remarkably improved computer power and cloud storage has enabled the application of artificial intelligence (AI), particularly the deep-learning (DL) component, in all domains. In healthy individuals, ML evaluation of neuroimaging data may accurately predict ordered age;illness and mental impairment have been linked to departures from normal brain maturation. Convolutional neural networks (CNN), a DL-based predictive display technique, were applied to both pre-handled and raw T1-weighted X-ray data in order to further evaluate the eligibility of 'brain-predicted age' as a biomarker of individual contrasts in the brain ageing process. Brain-predicted age is a very accurate, remarkably stable, and genetically valid trait that exhibits assurance as a biomarker of brain maturation. Additionally, age may be accurately predicted from raw T1-MRI data, which greatly reduces the amount of time needed to compute fresh data and accelerates the process of delivering immediate data on brain health in clinical settings. © 2025 IEEE.

关键词： Electronic health record

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A Smart Irrigation System for Coconut Farming using IoT 3

A Smart Irrigation System for Coconut Farming using IoT

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Russia, S. Mohanapriya, B. Gowrishankar, V. Parimala Devi, M. Pradeep, K.K. Brindha, P. Velalar College of Engineering and Technology Department of Computer Science and Engineering India Velalar College of Engineering and Technology Department of Electronics and Communication Engineering Erode India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Coconut farming is an essential agricultural practice that contributes significantly to the global economy by providing valuable products such as coconut water, oil, and meat. However, the management of coconut plantations faces numerous challenges, including labor shortages, pest control, and the demand for precise irrigation and fertilization. This paper proposes an integrated smart farming system that leverages advanced sensor technologies and automation to address these challenges. The system employs precision irrigation and fertigation methods, facilitated by a centralized motor controller and solenoid valves, to deliver water and nutrients efficiently to the base of each coconut tree. A pH sensor monitors soil acidity or alkalinity, ensuring optimal nutrient availability and promoting sustainable farming practices by reducing chemical inputs. Additional features include a tree motion sensor and a tilt sensor for monitoring tree stability under adverse weather conditions. Soil moisture sensors, temperature, humidity, and light intensity sensors provide real-time data to enhance decision-making. Farmers can remotely monitor and control the system via a Node-RED dashboard, receiving alerts through platforms like Telegram. By integrating these technologies, the proposed system aims to optimize resource utilization, enhance crop yield, and reduce the environmental impact of coconut farming. © 2025 IEEE.

关键词： Soil moisture

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RRHF-V: Ranking Responses to Mitigate Hallucinations in Multimodal Large Language Models with Human Feedback 31

RRHF-V: Ranking Responses to Mitigate Hallucinations in Mult...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Guoqing Zhang, Fu Lin, Jinghao Lu, Chenglong Cheng, Jingwei School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Multimodal large language models (MLLMs) demonstrate strong capabilities in multimodal understanding, reasoning, and interaction but still face the fundamental limitation of hallucinations, where they generate erroneous or fabricated information. To mitigate hallucinations, existing methods annotate pair-responses (one non-hallucination vs one hallucination) using manual methods or GPT-4V, and train alignment algorithms to improve the correspondence between images and text. More critically, an image description often involve multiple dimensions (e.g., object attributes, posture, and spatial relationships), making it challenging for the model to comprehensively learn multidimensional information from pair-responses. To this end, in this paper, we propose RRHF-V, which is the first using rank-responses (one non-hallucination vs multiple ranking hallucinations) to mitigate multimodal hallucinations. Instead of using pair-responses to train the model, RRHF-V expands the number of hallucinatory responses, so that the responses with different scores in a rank-response enable the model to learn rich semantic information across various dimensions of the image. Further, we propose a scene graph-based approach to automatically construct rank-responses in a cost-effective and automatic manner. We also design a novel training objective based on rank loss and margin loss to balance the differences between hallucinatory responses within a rank-response, thereby improving the model's image comprehension. Experiments on two MLLMs of different sizes and four widely used benchmarks demonstrate that RRHF-V is effective in mitigating hallucinations and outperforms the DPO method based on pair-responses. © 2025 Association for Computational Linguistics.

关键词： Semantics

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Enhanced Federated RL with NEAT, Markov Chains, and Gaussian Processes

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Procedia computer science 2025年 260卷 742-751页

作者： Robert McMenemy Kallinatha H D Hamsaveni M Sachin D N Software Engineer Independent Researcher Glasgow Area Scotland UK Dept of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

Reinforcement Learning (RL) is a machine learning approach in which an agent learns to make decisions in an environment to maximize a cumulative reward. When combined with NeuroEvolution of Augmenting Topologies (NEAT), RL offers several advantages. NEAT is a genetic algorithm that optimizes the development of artificial neural networks by modifying both their structure and weights. When integrated with RL, NEAT can improve the learning process by merging evolutionary optimization with RL techniques. NEAT has demonstrated significant potential in evolving neural networks for RL tasks. However, traditional centralized training methods encounter scalability and data privacy issues. This paper investigates the integration of NEAT with Federated Learning (FL) and its enhancement with Markov Chains and Gaussian Processes to address certain issues. We propose a new framework that combines NEAT for neural network evolution with TensorFlow Federated (TFF) for decentralized training across multiple clients. Our approach is assessed using the BipedalWalker-v3 environment from OpenAI Gym. The experimental results show that our federated NEAT framework, augmented with Markov Chains and Gaussian Processes, achieves competitive performance while maintaining data privacy and reducing computational overhead on central servers. Additionally, we implement parallelization techniques using concurrent futures to enhance the efficiency of NEAT generations.

关键词： Federated Learning NEAT Reinforcement Learning TensorFlow Federated NeuroEvolution Privacy Decentralized Training Parallelization Markov Chains Gaussian Processes

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Securing the Chain: A Comprehensive Review of Blockchain Security Fundamentals and Challenges 8th

Securing the Chain: A Comprehensive Review of Blockchain Sec...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Kumar, S. Senthil Rajaprakash, S. TamilNadu Chennai India Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu Chennai India

ISBN: (纸本)9789819786947

Due to its potential to revolutionize a variety of industries by providing transaction records that are decentralized and immutable, blockchain technology has received a lot of attention in recent years. However, numerous security issues that put at risk the integrity and dependability of distributed ledger systems impede the widespread use of blockchain. This review paper looks at the many different security problems that blockchain technology has, from basic flaws to new threats. Normal security chances, including 51% assaults, Sybil assaults, twofold spending, and brilliant agreement weaknesses, are examined exhaustively, alongside certifiable instances of safety breaks. In addition, the paper investigates existing mitigation and security solutions like smart contract auditing, cryptographic methods, and improvements to consensus algorithms. Also, arising patterns and future headings in blockchain security, for example, protection improving advancements, versatility arrangements, and quantum-safe cryptography, are talked about to expect and address developing dangers. Through contextual analyses and industry viewpoints, the paper gives experiences into the down-to-earth ramifications of blockchain security issues across different areas. At last, the survey closes by accentuating the significance of progressing exploration and cooperation to improve the security stance of blockchain networks and work with their broad reception in the computerized economy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart contract

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Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Relation Triplet Extraction 31

Re-Cent: A Relation-Centric Framework for Joint Zero-Shot Re...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Li, Zehan Zhang, Fu Lyu, Kailun Cheng, Jingwei Peng, Tianyue School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University China

ISBN: (纸本)9798891761964

Zero-shot Relation Triplet Extraction (ZSRTE) aims to extract triplets from the context where the relation patterns are unseen during training. Due to the inherent challenges of the ZSRTE task, existing extractive ZSRTE methods often decompose it into named entity recognition and relation classification, which overlooks the interdependence of two tasks and may introduce error propagation. Motivated by the intuition that crucial entity attributes might be implicit in the relation labels, we propose a Relation-Centric joint ZSRTE method named Re-Cent. This approach uses minimal information, specifically unseen relation labels, to extract triplets in one go through a unified model. We develop two span-based extractors to identify the subjects and objects corresponding to relation labels, forming span-pairs. Additionally, we introduce a relation-based correction mechanism that further refines the triplets by calculating the relevance between span-pairs and relation labels. Experiments demonstrate that Re-Cent achieves state-of-the-art performance with fewer parameters and does not rely on synthetic data or manual labor. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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AI-Powered Approaches for Identifying Misinformation in Social Media Platforms

AI-Powered Approaches for Identifying Misinformation in Soci...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Ashish Kumar Bhuvan Chandra Joshi Narendra Pal Singh Kamini Tanwar Divya Joshi Department of Computer Science and Engineering Sharda University Gr. Noida India Department of Computer Science and Engineering Invertis University Bareilly India Department of Computer Science and Engineering SRMS CET Bareilly India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The proliferation of social media and online information dissemination has led to a significant worry regarding fake news. False news detection is a difficult problem requiring a multidisciplinary approach combining natural language processing, machine learning, and social network research. This article provides a comprehensive review of the major area by classifying the research on false news identification into three categories: feature-based approaches, machine learning methods, and social network analysis methods. We look into many feature types in news, such as textual, social context, and metadata characteristics, that are utilized to identify bogus news. We look at various machine learning techniques, including supervised learning and unique algorithm approaches that have been applied to detect bogus news. Furthermore, we delineate plausible remedies for the issues and limitations of the existing false news identification system.

关键词： Machine learning algorithms Social networking (online) Reviews Supervised learning Machine learning Metadata Natural language processing Fake news Cognitive systems Bioinformatics

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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

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Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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AN 8-WAY RF FEED NETWORK FOR 5.3 GHZ C-BAND SAR: DESIGN, SIMULATION, AND ANALYSIS USING STANDARD METHODS

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 21-43页

作者： Kothapudi, Venkata Kishore Thokala, Niharika Vallabhaneni, Ramyasri Sanaka, Poojitha Kumar, Vijay Pappula, Lakshman Gottapu, Srinivasa Kiran Bhattacharyya, Arka Guntur District Andhra Pradesh Vadlamudi522213 India Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Electrical Electronics and Communication Engineering GITAM School of Technology GITAM University Andhra Pradesh Visakhapatnam530045 India School of Science and Engineering Engineering and Computer Science Department Benedict College 1600 Harden St ColumbiaSC29204 United States

This study proposes the design and analysis of an eight-way power divider for unequal division at 5.3 GHz for C-band frequency. Many transmission line pieces make up the current power divider. These transmission lines were printed on a substrate material (RT/duroid 5880, 1.6 mm thick, permittivity 2.2, and loss tangent 0.0002). The eight-way power divider’s unequal amplitudes are constructed using Taylor’s 1-parameter method to achieve the side projection levels –17 dB down as from the main beam. The proposed power divider consists of using two transmission lines with a 3/4 frequency connected to branched microstriplines, providing excellent isolation and matching between the power divider’s two output ports. This power divider is composed of three stages. A 1:2 power divider is used in the first stage, a 1:4 power divider is used in the second stage, and a 1:8 power divider is used in the third stage. According to the simulation results, 1:2 delivers half of the input power, 1:4 produces one-fourth of the power, and 1:8 delivers one-eighth of the input power. All of these assessments were done with the advanced design system (ADS) simulation program. According to both simulated and measured data, the return loss exceeds –35 dB, and the isolation across the output terminals exceeds –40 dB. The proposed circuit operates effectively at C-band frequencies in terms of return loss and isolation (4 GHz and 8 GHz). © 2025 by Begell House, Inc.

关键词： Integrated circuit design

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