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International Conference on Advanced Learning Technologies (ICALT)

作者： Wildemarkes de Almeida da Silva Luis Carlos Costa Fonseca Sofiane Labidi José Chrystian Lima Pacheco Post-Graduation Program in Computer and Systems Engineering State University of Maranhão - UEMA São Luís Brazil Bachelor of Science and Technology Program -BICT Federal University of Maranhão - UFMA São Luís Brazil Graduation in Computer and Systems Engineering State University of Maranhão - UEMA São Luís Brazil

ISBN: (数字)9798350362053

ISBN: (纸本)9798350362060

The rapid growing application of language models (LLMs) in education offers exciting prospects for personalized learning and interactive experiences. However, a critical challenge emerges - the risk of "hallucinations," where LLMs generate factually incorrect or misleading information. This paper proposes Comparative and Cross-Verification Prompting (CCVP), a novel technique specifically designed to mitigate hallucinations in educational LLMs. CCVP leverages the strengths of multiple LLMs, a Principal Language Model (PLM) and Auxiliary Language Models (ALMs), to verify the accuracy and educational relevance of the PLM's response to a prompt. Through a series of prompts and assessments, CCVP harnesses the diverse perspectives of various LLMs and incorporates human expertise for intricate cases. This method addresses the limitations of relying on a single model and fosters critical thinking skills in learners within the educational context. We detail the CCVP approach with examples specifically applicable to educational settings, such as geography. We also discuss its strengths and limitations, including computational cost, data reliance, and ethical considerations. We highlight its potential applications in educational disciplines, including fact-checking content, detecting bias, and promoting responsible LLM use. CCVP presents a promising avenue for ensuring the accuracy and trustworthiness of LLM-generated educational content. Further research and development will refine its scalability, address potential biases, and solidify its position as a vital tool for harnessing the power of LLMs while fostering responsible knowledge dissemination in education.

关键词： Geography Solid modeling Accuracy Scalability Prevention and mitigation Computational modeling Education

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Generative AI-Enhanced Cooperative MEC of UAVs and Ground Stations for Unmanned Surface Vehicles

Generative AI-Enhanced Cooperative MEC of UAVs and Ground St...

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Annual Conference on Information sciences and Systems (CISS)

作者： Jiahao You Ziye Jia Chao Dong Qihui Wu Zhu Han The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

The increasing deployment of unmanned surface vehicles (USVs) require computational support and coverage in applications such as maritime search and rescue. Unmanned aerial vehicles (UAVs) can offer low-cost, flexible aerial services, and ground stations (GSs) can provide powerful supports, which can cooperate to help the USVs in complex scenarios. However, the collaboration between UAVs and GSs for USVs faces challenges of task uncertainties, USVs trajectory uncertainties, heterogeneities, and limited computational resources. To address these issues, we propose a cooperative UAV and GS based robust multi-access edge computing framework to assist USVs in completing computational tasks. Specifically, we formulate the optimization problem of joint task offloading and UAV trajectory to minimize the total execution time, which is in the form of mixed integer nonlinear programming and NP-hard to tackle. Therefore, we propose the algorithm of generative artificial intelligence-enhanced heterogeneous agent proximal policy optimization (GAI-HAPPO). The proposed algorithm integrates GAI models to enhance the actor network ability to model complex environments and extract high-level features, thereby allowing the algorithm to predict uncertainties and adapt to dynamic conditions. Additionally, GAI stabilizes the critic network, addressing the instability of multi-agent reinforcement learning approaches. Finally, extensive simulations demonstrate that the proposed algorithm outperforms the existing benchmark methods, thus highlighting the potentials in tackling intricate, cross-domain issues in the considered scenarios.

关键词： Adaptation models Uncertainty Heuristic algorithms Computational modeling Autonomous aerial vehicles Prediction algorithms Feature extraction Trajectory Vehicle dynamics Optimization

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Predicting Dominant Beat Frequency from Brain Responses while Listening to Music

Predicting Dominant Beat Frequency from Brain Responses whil...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Pandey, Pankaj Ahmad, Nashra Miyapuram, Krishna Prasad Lomas, Derek Computer Science and Engineering Indian Institute of Technology Gandhinagar Gujarat India Centre for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gujarat India Delft University of Technology Industrial Design Engineering for Positive AI Delft Netherlands

ISBN: (纸本)9781665401265

Modern neuroscience has shown that the brain is profoundly rhythmic and that frequencies of neural rhythms are responsive to frequencies of musical rhythms. We collected Electroencephalography (EEG) response on 12 naturalistic music stimuli (songs), from 20 participants. We retrieved the tempo and its sub-harmonics from our stimuli (songs), and further used this information to predict the beats in the brain response using Machine Learning techniques. We observed a hierarchy of beats in each of the songs, with a specific beat frequency to be dominant (i.e. higher in magnitude) than others. This led us to form three groups of songs and their brain responses, with each of the groups indicating the frequency of a beat that dominated in the hierarchy of beat structure of that song. We used small segments of 1, 3 and 5 seconds of brain responses, rather than the entire song duration. We further created two sets for classification of the three groups of brain responses and utilized two spatial filtering techniques: Mean across electrodes (ME) and first principal component (PC1), and a Dense method using data from all electrodes. This was followed by feature extraction using band power. We developed univariate and multivariate models for classification to demonstrate the significance of each frequency band which represent beat frequencies. The dense method outperformed ME and PC1. Features related to eighth note generated maximum discrimination between classes. We also observed a positive correlation between window length and rate of correct prediction. Accuracy from one second to five seconds window improved significantly in both the sets. We achieved maximum accuracy of 70% and 56% accuracies for binary and ternary classification respectively, which is 20% above chance-level accuracy. Random Forest and kNN performed better than SVM. This work contributes to the growing body of knowledge to understand the underlying neural mechanism of rhythm processing in the brain. © 202

关键词： Electrodes

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Highly Flexible Piezoelectric Pressure Sensor at Extremely High-Temperature Range Using Ultrawide-Bandgap III-N Thin Films

Highly Flexible Piezoelectric Pressure Sensor at Extremely H...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Nam-In Kim Miad Yarali Muhammad Aqib Jae-Hyun Ryou Department of Mechanical Engineering Texas Center for Superconductivity at UH Advanced Manufacturing Institute University of Houston USA Materials Science and Engineering Program University of Houston USA Department of Electrical and Computer Engineering University of Houston USA

Pressure monitoring in extreme conditions, such as in industrial, aerospace, and radioactive atmosphere, is crucial. Our focus is on developing and researching piezoelectric sensors that are highly flexible for detecting gas pressures at extremely high temperatures. We develop the flexible device using epitaxial III -nitride film, which is single-crystalline, by layer-transfer method. The sensor shows a sensitive and stable operation for extended periods. We use software simulation and relevant formulas to support our experimental findings.

关键词： Temperature sensors Pressure sensors X-ray scattering Sensor phenomena and characterization Software Semiconductor process modeling III-V semiconductor materials

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Flexible context-driven sensory processing in dynamical vision models 24

Flexible context-driven sensory processing in dynamical visi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Lakshmi Narasimhan Govindarajan Abhiram Iyer Valmiki Kothare Ila Fiete Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA Department of Electrical Engineering and Computer Science and McGovern Institute for Brain Research and K. Lisa Yang Integrative Computational Neuroscience (ICoN) MIT Cambridge MA Department of Brain and Cognitive Sciences MIT Cambridge MA

ISBN: (纸本)9798331314385

Visual representations become progressively more abstract along the cortical hierarchy. These abstract representations define notions like objects and shapes, but at the cost of spatial specificity. By contrast, low-level regions represent spatially local but simple input features. How do spatially non-specific representations of abstract concepts in high-level areas flexibly modulate the low-level sensory representations in appropriate ways to guide context-driven and goal-directed behaviors across a range of tasks? We build a biologically motivated and trainable neural network model of dynamics in the visual pathway, incorporating local, lateral, and feedforward synaptic connections, excitatory and inhibitory neurons, and longrange top-down inputs conceptualized as low-rank modulations of the input-driven sensory responses by high-level areas. We study this Dynamical Cortical network (DCnet) in a visual cue-delay-search task and show that the model uses its own cue representations to adaptively modulate its perceptual responses to solve the task, outperforming state-of-the-art DNN vision and LLM models. The model's population states over time shed light on the nature of contextual modulatory dynamics, generating predictions for experiments. We fine-tune the same model on classic psychophysics attention tasks, and find that the model closely replicates known reaction time results. This work represents a promising new foundation for understanding and making predictions about perturbations to visual processing in the brain.

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Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

arXiv

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arXiv 2025年

作者： Wu, Weiqi Huang, Shen Jiang, Yong Xie, Pengjun Huang, Fei Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongyi Lab Alibaba Group China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization. Copyright © 2025, The Authors. All rights reserved.

关键词： Modeling languages

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Cutting corners to suppress high-order modes in Mie resonator arrays

arXiv

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arXiv 2023年

作者： Haddadin, Zaid Khan, Shahrose Poulikakos, Lisa V. Department of Electrical & Computer Engineering UC San Diego United States Department of Mechanical & Aerospace Engineering UC San Diego United States Materials Science & Engineering Program UC San Diego United States

Mie resonators as lattice resonant metasurfaces have the capability to produce structural colour. However, design criteria for these metasurfaces are still being investigated. In this work, we numerically examine how the two-dimensional nanostructure shape in a lattice array affects the colorimetric response of the metasurface under linearly polarised light excitation. First, the transformation from a square-shaped to rectangle-shaped nanostructure array resulted in polarisation-sensitive metasurfaces with colorimetric outputs bound along a line on the CIE 1931 2-degree Standard Observer colour space. The bounds of the colorimetry line were tuneable to any desired chromatic range. Second, the removal of the corners in square- or rectangle-shaped nanostructures to create t-shaped nanostructure arrays displayed a dampening effect on the high-order resonance. Finally, we analytically determined that the colour saturation could increase when moving from rectangle-shaped to t-shaped nanostructure arrays. From these results, we present two design guidelines for lattice resonant metasurfaces: (1) Constructing the nanostructure to support fundamental resonances at different wavelengths enables two-colour-bound movement when excited by successive angles of linearly polarised light;(2) Removing portions of the nanostructure that only support high-order resonances dampens these modes while maintaining support for fundamental resonances. These results present first-principles guidelines for engineering nanoparticles in lattice resonant metasurfaces, offering a new toolbox for polarised-light sensing and colorimetric applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Nanostructures

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MODELING THE HUMAN VISUAL SYSTEM: COMPARATIVE INSIGHTS FROM RESPONSE-OPTIMIZED AND TASK-OPTIMIZED VISION MODELS, LANGUAGE MODELS, AND DIFFERENT READOUT MECHANISMS

arXiv

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arXiv 2024年

作者： Saha, Shreya Chadha, Ishaan Khosla, Meenakshi Department of Electrical and Computer Engineering University of California La Jolla San DiegoCA92093 United States Halıcıoglu Data Science Institute University of California La Jolla San DiegoCA92093 United States Department of Cognitive Science University of California La Jolla San DiegoCA92093 United States

Over the past decade, predictive modeling of neural responses in the primate visual system has advanced significantly, largely driven by various deep neural network approaches. These include models optimized directly for visual recognition, cross-modal alignment through contrastive objectives, neural response prediction from scratch, and large language model embeddings. Likewise, different readout mechanisms—ranging from fully linear to spatial-feature factorized methods—have been explored for mapping network activations to neural responses. Despite the diversity of these approaches, it remains unclear which method performs best across different visual regions. In this study, we systematically compare these approaches for modeling the human visual system and investigate alternative strategies to improve response predictions. Our findings reveal that for early to mid-level visual areas, response-optimized models with visual inputs offer superior prediction accuracy, while for higher visual regions, embeddings from Large Language Models (LLMs) based on detailed contextual descriptions of images and task-optimized models pretrained on large vision datasets provide the best fit. Through comparative analysis of these modeling approaches, we identified three distinct regions in the visual cortex: one sensitive primarily to perceptual features of the input that are not captured by linguistic descriptions, another attuned to fine-grained visual details representing semantic information, and a third responsive to abstract, global meanings aligned with linguistic content. We also highlight the critical role of readout mechanisms, proposing a novel scheme that modulates receptive fields and feature maps based on semantic content, resulting in an accuracy boost of 3-23% over existing SOTAs for all models and brain regions. Together, these findings offer key insights into building more precise models of the visual system. © 2024, CC BY-NC-ND.

关键词： Prediction models

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Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering

arXiv

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arXiv 2024年

作者： Wu, Yexin Zhang, Zhuosheng Zhao, Hai College of Zhiyuan Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions through step-by-step reasoning chains. Despite its success, the efficacy of such reasoning is inherently contingent upon the quality of CoT. However, flawless CoT reasoning cannot be guaranteed due to the presence of indecomposable questions and the potential for erroneous reasoning chains, particularly in the case of small-scale language models. To tackle this challenge, we propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. Then, we proceed with CoT reasoning when the reasoning chain demonstrates confidence;otherwise, we opt to predict the answer directly. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the scienceQA, ECQA, and LastLetter tasks. Code is available at https://***/LibroWu/SelF-Reasoner. © 2024, CC BY-SA.

关键词： Computational linguistics

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