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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kovarasan, Rajesh Kambattan Shibu, N.V. Sairam, A. Manivannan, K.K. Ishwarya, M.V. GaneshBabu, T.R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Mechatronics Engineering KCG College of Technology Tamil Nadu Chennai India Artificial Intelligence and Data science Department Agni College of Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9798350374933

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IoT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns. © 2024 IEEE.

关键词： Environmental monitoring

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A NOVEL APPROACH FOR PREDICTING EPIDEMIOLOGICAL FORECASTING PARAMETERS BASED ON REAL-TIME SIGNALS AND data ASSIMILATION

arXiv

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arXiv 2023年

作者： Molinas, Romain Casas, César Quilodrán Arcucci, Rossella Şerban, Ovidiu Department of Computing Imperial College London United Kingdom Data Science Institute Imperial College London United Kingdom Data Science Institute Department of Earth Science & Engineering Imperial College London United Kingdom Data Science Institute Department of Computing Imperial College London United Kingdom

This paper proposes a novel approach to predict epidemiological parameters by integrating new real-time signals from various sources of information, such as novel social media-based population density maps and Air Quality data. We implement an ensemble of Convolutional Neural Networks (CNN) models using various data sources and fusion methodology to build robust predictions and simulate several dynamic parameters that could improve the decision-making process for policymakers. Additionally, we used data assimilation to estimate the state of our system from fused CNN predictions. The combination of meteorological signals and social media-based population density maps improved the performance and flexibility of our prediction of the COVID-19 outbreak in London. While the proposed approach outperforms standard models, such as compartmental models traditionally used in disease forecasting (SEIR), generating robust and consistent predictions allows us to increase the stability of our model while increasing its accuracy. © 2023, CC BY-SA.

关键词： COVID-19

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MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Learning Based Framework for Few-Shot Text Classification 29

MetaSLRCL: A Self-Adaptive Learning Rate and Curriculum Lear...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Zhao, Kailin Jin, Xiaolong Guan, Saiping Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of SciencesSchool of Computer Science and Technology University of Chinese Academy of Sciences

Due to the lack of labeled data in many realistic scenarios, a number of few-shot learning methods for text classification have been proposed, among which the meta learning based ones have recently attracted much attention. Such methods usually consist of a learner as the classifier and a meta learner for specializing the learner to different tasks. For the learner, learning rate is crucial to its performance. However, existing methods treat it as a hyper parameter and adjust it manually, which is time-consuming and laborious. Intuitively, for different tasks and neural network layers, the learning rates should be different and self-adaptive. For the meta learner, it requires a good generalization ability so as to quickly adapt to new tasks. Motivated by these issues, we propose a novel meta learning framework, called MetaSLRCL, for few-shot text classification. Specifically, we present a novel meta learning mechanism to obtain different learning rates for different tasks and neural network layers so as to enable the learner to quickly adapt to new training data. Moreover, we propose a task-oriented curriculum learning mechanism to help the meta learner achieve a better generalization ability by learning from different tasks with increasing difficulties. Extensive experiments on three benchmark datasets demonstrate the effectiveness of MetaSLRCL. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Text processing

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Temporal Knowledge Graph Question Answering: A Survey

arXiv

引用

arXiv 2024年

作者： Su, Miao Li, Zixuan Chen, Zhuo Bai, Long Jin, Xiaolong Guo, Jiafeng CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA), an emerging task to answer temporal questions. However, this field grapples with ambiguities in defining temporal questions and lacks a systematic categorization of existing methods for TKGQA. In response, this paper provides a thorough survey from two perspectives: the taxonomy of temporal questions and the methodological categorization for TKGQA. Specifically, we first establish a detailed taxonomy of temporal questions engaged in prior studies. Subsequently, we provide a comprehensive review of TKGQA techniques of two categories: semantic parsing-based and TKG embedding-based. Building on this review, the paper outlines potential research directions aimed at advancing the field of TKGQA. This work aims to serve as a comprehensive reference for TKGQA and to stimulate further research. © 2024, CC BY.

关键词： Surveying

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SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yuxuan Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University China Department of Computing Science University of Aberdeen United Kingdom

With the rapid development of large language models (LLMs), fully fine-tuning(FT) these models is becoming increasingly infeasible due to high computational demands. Moreover, FT also increases the risk of catastrophic forgetting. As an alternative, Low-Rank Adaptation (LoRA) has been proposed. By fine-tuning only a small subset of parameters, LoRA achieves performance similar to FT while significantly reducing resource requirements. However, since LoRA inherits FT’s design, the issue of catastrophic forgetting still remains. To address these limitations, we propose SECURA: Sigmoid-Enhanced CUR Decomposition LoRA, a novel PEFT variant designed to mitigate catastrophic forgetting while improving fine-tuning performance. Our method introduces a novel normalization technique, Sigmoid based Magnitude Norm(SMagNorm), which enhances parameter retention and improve fine-tuning performance. SECURA has been evaluated on a diverse range of tasks, including mathematical problem-solving (GSM8K), complex question-answering (CNNDM), translation (NewsDE), and complex multiple-choice reasoning (LogiQA). Experimental results demonstrate that it achieves an average fine-tuning improvement of 3.59% across 4 MCQ sphere tasks and a 2.51% improvement in 5 QA sphere tasks across Gemma2 2b, Qwen2 1.5b, Qwen2 7b,Llama3 8b,Llama3.1 8b compared to DoRA. Additionally, SECURA demonstrates superior knowledge retention capabilities, achieves state-of-the-art performance in 16 continual learning tests maintaining more than 70% accuracy on LLMs’ basic knowledge compared with Experience Replay(ER), sequential learning(SEQ),EWC, I-LoRA and CUR-LoRA. © 2025, CC BY.

关键词： Problem solving

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Energy Efficient Probabilistic Semantic Communication over SAGIN

Energy Efficient Probabilistic Semantic Communication over S...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhao, Zhouxiang Yang, Zhaohui Chen, Mingzhe Gan, Xu Huang, Chongwen Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering Hangzhou China Hangzhou China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Southeast University National Mobile Communications Research Laboratory Nanjing China

ISBN: (纸本)9798350351255

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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CeTrivium:A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

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Computer Systems science & Engineering 2023年第12期47卷 2895-2920页

作者： Osama S.Younes Abdulmohsen Alharbi Ali Yasseen Faisal Alshareef Faisal Albalawi Umar A.Albalawi Faculty of Computer and Information Technology University of TabukTabuk71491Saudi Arabia Faculty of Computers and Information Menoufia UniversityMenoufia13829Egypt School of Computing&Data Science Wentworth Institute of TechnologyBoston02115USA

Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia *** based onCellularAutomata(CA)can provide amore effective *** CA have recently gained recognition as a robust cryptographic primitive,being used as pseudorandom number generators in hash functions,block ciphers and stream *** have the ability to perform parallel transformations,resulting in high throughput ***,they exhibit a natural tendency to resist fault *** stream cipher schemes based on CA have been proposed in the ***,their encryption/decryption throughput is relatively low,which makes them unsuitable formultimedia *** and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project,but they have proven to be vulnerable to differential fault *** work introduces a novel and scalable stream cipher named CeTrivium,whose design is based on *** is a 5-neighborhood CA-based streamcipher inspired by the designs of Trivium and *** is constructed using three building blocks:the Trivium(Tr)block,the Nonlinear-CA(NCA)block,and the Nonlinear Mixing(NM)*** NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA,while the Tr block has the same structure as the Trivium stream *** NM block is a nonlinear,balanced,and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a *** of CeTrivium has indicated that it can resist various attacks,including correlation,algebraic,fault,cube,Meier and Staffelbach,and side channel ***,the scheme is evaluated using histogramand spectrogramanalysis,aswell as several differentmeasurements,including the correlation coefficient,number of samples change rate,signal-to-noise ratio,entropy,and peak signal-to-noise *** performance of CeTrivium is evaluated and compared with other state-of-

关键词： Stream ciphers cellular automata securing real-time streaming cryptography CeTrivium

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KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction

arXiv

引用

arXiv 2024年

作者： Li, Zixuan Zeng, Yutao Zuo, Yuxin Ren, Weicheng Liu, Wenxuan Su, Miao Guo, Yucan Liu, Yantao Li, Xiang Hu, Zhilei Bai, Long Li, Wei Liu, Yidan Yang, Pan Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of unified schema representation that LLMs can easily understand and an effective learning framework that encourages LLMs to follow schemas and extract structured knowledge accurately. To achieve these, KnowCoder introduces a code-style schema representation method to uniformly transform different schemas into Python classes, with which complex schema information, such as constraints among tasks in UIE, can be captured in an LLM-friendly manner. We further construct a code-style schema library covering over 30,000 types of knowledge, which is the largest one for UIE, to the best of our knowledge. To ease the learning process of LLMs, KnowCoder contains a two-phase learning framework that enhances its schema understanding ability via code pretraining and its schema following ability via instruction tuning. After code pretraining on around 1.5B automatically constructed data, KnowCoder already attains remarkable generalization ability and achieves relative improvements by 49.8% F1, compared to LLaMA2, under the few-shot setting. After instruction tuning, KnowCoder further exhibits strong generalization ability on unseen schemas and achieves up to 12.5% and 21.9%, compared to sota baselines, under the zero-shot setting and the low resource setting, respectively. Additionally, based on our unified schema representations, various human-annotated datasets can simultaneously be utilized to refine KnowCoder, which achieves significant improvements up to 7.5% under the supervised setting. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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AstroFusion: A GAN-Augmented Approach for Exoplanet Detection

AstroFusion: A GAN-Augmented Approach for Exoplanet Detectio...

引用

Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Aakash Suresh Lakshmi C Gopirajan PV K Dhanasekaran School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Chennai TN India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387070

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe.

关键词： Radio frequency Logistic regression Accuracy Uncertainty Computational modeling Heuristic algorithms Exoplanet data augmentation data models Synthetic data

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Deep Learning for Disease Identification in Cassava Plants Using RGB Imaging 6th

Deep Learning for Disease Identification in Cassava Plants U...

引用

6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Sutar, Shivam Mohite, Snehal Kamble, Tushar Kolhar, Shrikrishna Jagtap, Jayant Shastri, Rajveer Joshi, Shubham Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology Maharashtra Baramati India Maharashtra Pune India Marik Institute of Computing Artificial Intelligence Robotics and Cybernetics NIMS University Rajasthan Jaipur303121 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819778614

Agriculture is the most critical sector in the world economy. Food security and consistent food supply are the key challenges due to rapid growth in the world population and crop diseases. Crop yield gets significantly hampered due to diseases attacking large crop fields. The precise classification of plant diseases is essential to provide appropriate pesticide supplements. This study applies deep learning (DL) models to identify diseases in cassava plants using red–green–blue (RGB) images. Three models, namely Xception-net, MobileNetV2, and EfficientNetB0, are trained and evaluated on the cassava disease dataset publicly available on Kaggle. Since the dataset is imbalanced, five-fold cross-validation is used to check the consistency of the results. Transfer learning and random weight initialization were used for model training. The Xception-net is the clear winner among the three models, with an accuracy of 87.8% using transfer learning, 81.33% using random weight initialization, and 85% average accuracy using five-fold cross-validation. Further, this work is extended to develop web and mobile applications with DL models deployed at the backend to predict plant diseases from input images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Plant diseases

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