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patternGPT :A pattern-Driven Framework for Large Language Model Text Generation 23

PatternGPT :A Pattern-Driven Framework for Large Language Mo...

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Proceedings of the 2023 12th international conference on computing and pattern recognition

作者： Le Xiao Xin Shan Xiaolin Chen Henan University of Technology China

ISBN: (纸本)9798400707988

Large language models(LLMS) have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes patternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.

关键词： LLM framework pattern text generation

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Foliar Diseases Identification in Apple Tree Using Deep Learning

Foliar Diseases Identification in Apple Tree Using Deep Lear...

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international Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Saira Farooq Labiba Gillani Fahad Department of Computing National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9798350308259

ISBN: (纸本)9798350308266

Apple is a popular fruit crop that is susceptible to a variety of foliar diseases. In apple trees detecting foliar diseases is a critical aspect of orchard management, as it can prevent yield losses and maintain food security. Currently, disease detection is carried out through manual inspection and visual assessment, which can be time-consuming, subjective, and prone to errors. Early and precise recognition of these diseases is significant for effective disease management. The proposed methodology involves digital images of apple leaves, preprocessing the images, segmentation, and training a Convolutional Neural Network (CNN) model to recognize the visual patterns associated with different foliar diseases. The model's accuracy, precision, and recall are used to analyze how well it performs. The results indicate that our approach is better able to achieve high accuracy in identifying various foliar diseases in apple trees. The proposed approach can provide a reliable, and accurate tool for apple growers to monitor the health status of their trees and take timely action against diseases.

关键词： Deep learning Training Visualization Plant diseases Pathology Accuracy pattern recognition Convolutional neural networks Object recognition Diseases

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Basic Research on Machine Vision Underpinned by Image Frame Algebra (VFA) and Visual Semantic Algebra (VSA)

Basic Research on Machine Vision Underpinned by Image Frame ...

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international conference on Machine Vision and Information Technology (CMVIT)

作者： Yingxu Wang President International Institute of Cognitive Informatics and Cognitive Computing (I2CICC) Visiting Professor: Stanford Univ. (2008|2016) MIT (2012) UC Berkeley (2008) Oxford Univ. (1995|2018–2022) Distinguished Visiting Professor Tsinghua Univ. (2019–22) Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary 2500 University Drive NW Calgary Alberta Canada

Computer vison [1], [2], [3], [4], [5] studies properties of machine vision, its semantic understanding, and general manipulations by Intelligent Mathematics (IM) [6], [7], [8], [9], [10] [11], [12], [13], [14], [15] [16], [17]. Computer vison has been studies from various aspects such as algorithmic methods, analysis methods, pattern recognitions, and neural-network-regression (AI) technologies [2], [3]. However, there is a lack of fundamental theories for enabling autonomous image recognition and processing by machines. Basic research on contemporary IM has revealed that formal manipulations of visual objects by intelligent machines may be rigorously implemented by Image Frame Algebra (IFA) [8], [18] in the front-end and Visual Semantic Algebra (VSA) [19] in the backend. IFA formally manipulates visual images as general 2D matrixes by a set of algebraic operators such as modeling, analyses, syntheses, feature elicitation, and pattern recognition [4], [5], [18]. Then, its counterpart, VSA, transforms the geographic relations of visual objects to their semantic interpretations by algebraic analyses and compositions. The coherent theory of IFA and VSA provides a formal methodology for machine-enabled image processing and comprehension. This keynote presents a theoretical framework of machine vision underpinned by IFA and VSA for the structural denotations of visual objects and functional manipulations of visual mechanisms [3], [8], [9]. It demonstrates how the persistent challenges to machine vision may be rigorously and efficiently solved by the IFA/VSA methodology. Case studies on applying IFA/VSA for rigorous visual pattern detection, recognition, analysis, and composition in real world will be demonstrated [5], [18], [20]. As two coherent paradigms of IM, among others [21], [22], [23], [24], [25] [26], [27], [28], [29], [30], IFA and VSA have been applied not only in robot visual and spatial reasoning, but also in computational intelligence and AI for rigorously

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Research on the Spatiotemporal pattern of Bus Interchange based on Travel Big Data

Research on the Spatiotemporal Pattern of Bus Interchange ba...

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Cloud computing, Big Data Applications and software Engineering (CBASE), international conference on

作者： Kai Huang Jianqin Zhang Xinyue Cheng Chaonan Hu Xinyi Huang Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

With rapid urbanization, the public transportation system plays a crucial role in alleviating urban traffic congestion and enhancing travel efficiency. Bus interchange, as a link between different bus routes, directly impacts passengers' travel experience and the service coverage of the public transportation system. In this study, we utilized bus card swipe data to explore the current situation and characteristics of bus interchange travel from aspects such as travel time, distance, passenger distribution, interchange points, and interchange routes. The findings are as follows: (1) The distribution of passenger flow in bus interchange travel is generally consistent with overall bus travel. The highest demand for bus interchange travel is observed between Chaoyang District and Dongcheng District; (2) The average duration of the first bus ride in bus interchange travel is approximately 24 minutes, and the second bus ride is around 21 minutes, with an interchange time of about 8 minutes; (3) Hotspot bus interchange stations are mainly located around the Third Ring Road, such as Beijing West Station and Sihui Hub Station. The bus interchange routes with significant passenger volumes include Route 322–1, and Route 321–57, primarily concentrated in Chaoyang District and the central area of the capital. The findings of this study provide valuable insights for guiding the planning and management of the public transportation system.

关键词： Smart cards Roads soft sensors Urban areas Spatiotemporal phenomena Planning Socioeconomics Public transportation Traffic congestion software engineering

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Black Measles Disease Identification in Grape Plant (Vitis vinifera) Using Deep Learning

Black Measles Disease Identification in Grape Plant (Vitis v...

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international conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Kirti Rajpal, Navin Yadav, Jyotsna Guru Gobind Singh Indraprastha Univ Univ Sch Informat Commun & Technol Delhi India

ISBN: (纸本)9781728185293

The most common diseases found in plants are the fungi infections/diseases. One of the common fungal diseases is Esca (Black Measles) which is found in the Grape Plants and can be easily identified as brown streaking lesions on any part of the leaf. The affected leaves can dry off completely and fall off from the plant prematurely which eventually results in death of the plant. In this work, an improved technique based on Deep Learning algorithm for identifying Esca Black measles in GrapeVines is proposed. The proposed method yields better performance and accuracy in detecting the disease, than past Machine Learning based approaches. Grape Plant dataset from PlantVillage Database is used for the work. The dataset contains total 1807 images (healthy and diseased). ResNet 50 architecture of Deep Neaural Network in combination with Transfer Learning and Fine Tuning was used to compute the results. The proposed system provides an accuracy of more than 97% and performed better than the existing approaches which are based on feature extraction methods.

关键词： Crop pattern recognition Disease Identification Deep Learning Agriculture ResNet 50 Grape Plant

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ResAsapp: An Effective Convolution to Distinguish Adjacent Pixels For Scene Text Detection 22

ResAsapp: An Effective Convolution to Distinguish Adjacent P...

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Proceedings of the 2022 11th international conference on computing and pattern recognition

作者： Kangming Weng Xia Du Kunze Chen Dahan Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

The segmentation-based approach is an essential direction of scene text detection, and it can detect arbitrary or curved text, which has attracted the increasing attention of many researchers. However, extensive research has shown that the segmentation-based method will be disturbed by adjoining pixels and cannot effectively identify the text boundaries. To tackle this problem, we proposed a ResAsapp Conv based on the PSE algorithm. This convolution structure can provide different scale visual fields about the object and make it effectively recognize the boundary of texts. The method's effectiveness is validated on three benchmark datasets, CTW1500, Total-Text, and ICDAR2015 datasets. In particular, on the CTW1500 dataset, a dataset full of long curve text in all kinds of scenes, which is hard to distinguish, our network achieves an F-measure of 81.2%.

关键词： Pixel Interference Scene Text Detection Curved Text

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AI Based Adaptive QoS Optimization for IoT Devices in Healthcare Networks

AI Based Adaptive QoS Optimization for IoT Devices in Health...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： S. Karthika K. Juliana Gnanaselvi K. Palanisamy S. Umamaheswari Department of Computer Science& Business Systems Dr. N. G. P Institute of Technology Coimbatore India Department of Computer Science Rathinam College of Arts and Science Coimbatore India Department of CSE Karpagam Academy of Higher Education Coimbatore India Department of ECE Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper proposes an artificial intelligence-based adaptive Quality of Service (QoS) optimization framework for Internet of Things (IoT) devices in healthcare networks. IoT devices, such wearable monitoring and remote diagnostic tools, struggle to maintain optimal performance due to fluctuating network circumstances and hardware limitations. The suggested system uses machine learning and deep learning techniques to dynamically update QoS settings in order to achieve consistent, low-latency, and energy-efficient data transport. The AI model forecasts congestion, distributes resources optimally, and enhances network performance by continuously learning from real-time data and network conditions. This customisable solution improves healthcare delivery by providing timely, continuous services, resulting in better patient outcomes and ensuring the long-term viability of IoT healthcare networks. The framework's adaptive nature addresses scalability and flexibility challenges in healthcare IoT systems by constantly learning and optimising performance as network conditions change. The simulation results demonstrate how well the proposed solution works to improve QoS, ensure seamless healthcare delivery, and improve patient outcomes in dynamic network situations. This study paves the way for AI-powered healthcare IoT networks that are more reliable, responsive, and intelligent.

关键词： Performance evaluation Adaptive systems Medical services Quality of service Throughput Real-time systems pattern recognition Internet of Things Convolutional neural networks Optimization

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Graph Neural Networks for Functional Connectivity Analysis: Uncovering Neural Network Disruptions in Parkinson’s Disease

Graph Neural Networks for Functional Connectivity Analysis: ...

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Advancements in Electrical, Electronics, Communication, computing and Automation (ICAECA), international conference on

作者： R. Ayyappa C. H. Hussaian Basha S. Senthilkumar K. Balasubramanian S Arunkumar S. Venkatesh Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering SR University Telangana India Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam India Department of Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

In the field of Parkinson’s’ disease diagnosis and progression monitoring, functional and structural connection patterns in the brain, which can be observed by modalities such as functional magnetic resonance imaging (MRI) and electroencephalography (EEG) are becoming more recognized as promising bio markers. An innovative use of Graph Neural Network (GNNs) is proposed in this study for analyzing patterns of brain connectivity. By utilizing the graph structure of brain networks, the researchers identify disturbances that are unique to Parkinson’s disease conditions. In the proposed framework, brain regions are represented as nodes, and the edges of the graph correspond to functional or structural connections derived from either fMRI or EEG data, depending on the modality used for analysis. The investigation of the brain regions and the connection that are most pertinent to the diagnosis can be accomplished through the utilization of attention weights in feature importance analysis. In recognizing brain network changes in Parkinson’s disease, the findings indicates that the GNN perform much better than traditional machine learning methods. Additionally, these research offers significant advancements into mechanism of neuronal degeneration leads attention to certain regions of the brain and connected throughout the brain that are impacted by Parkinson’s disease. This work contributes to a more comprehensive knowledge of neural network disruptions in neurodegenerative disease and enhances the potential of GNN as diagnostic tool.

关键词： Accuracy Functional magnetic resonance imaging Motors Graph neural networks Electroencephalography Telecommunication computing Planning pattern recognition Biological neural networks Diseases

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ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring

ScoreHypo: Probabilistic Human Mesh Estimation with Hypothes...

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conference on Computer Vision and pattern recognition (CVPR)

作者： Yuan Xu Xiaoxuan Ma Jiajun Su Wentao Zhu Yu Qiao Yizhou Wang Center on Frontiers of Computing Studies School of Computer Science Peking University International Digital Economy Academy (IDEA) School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Inst. for Artificial Intelligence Peking University Nat'1 Eng. Research Center of Visual Technology Nat'1 Key Lab of General Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Monocular 3D human mesh estimation is an ill-posed problem, characterized by inherent ambiguity and occlusion. While recent probabilistic methods propose generating multiple solutions, little attention is paid to obtaining high-quality estimates from them. To address this limitation, we introduce ScoreHypo, a versatile framework by first leveraging our novel HypoNet to generate multiple hy-potheses, followed by employing a meticulously designed scorer, ScoreNet, to evaluate and select high-quality esti-mates. ScoreHypo formulates the estimation process as a re-verse denoising process, where HypoNet produces a diverse set of plausible estimates that effectively align with the im-age cues. Subsequently, ScoreNet is employed to rigorously evaluate and rank these estimates based on their quality and finally identify superior ones. Experimental results demon-strate that HypoNet outperforms existing state-of-the-art probabilistic methods as a multi-hypothesis mesh estimator. Moreover, the estimates selected by ScoreNet significantly outperform random generation or simple averaging. Notably, the trained ScoreNet exhibits generalizability, as it can effectively score existing methods and significantly reduce their errors by more than 15%. Code and models are available at ht tps: / /xy02- 05. gi thub. io/ScoreHypo.

关键词： Computer vision Three-dimensional displays Accuracy Codes Noise reduction Estimation Benchmark testing

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Conversation Analysis (CA)-Informed Algorithmic Approach to Augmenting Closed Captions with Stressed Syllables to Help Thai EFL Learners Improve Their English Pronunciation

Conversation Analysis (CA)-Informed Algorithmic Approach to ...

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IEEE international Smart Cities conference (ISC2)

作者： Prawit Boonmee Kemtong Sinwongsuwat Kanuengnij Kubola Prajaks Jitngernmadan Ponlawat Chophuk Shehenaz Shaik Sittisak Saechueng Jiranan Kongsomjit Faculty of Informatics Burapha University Chonburi Thailand Faculty of Liberal Arts Prince of Songkla University Songkla Thailand Faculty of Computing East Tennessee State University Johnson City Tennessee USA

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

Closed captions have been proven to be effective resources for English as a Foreign Language (EFL) teaching. Informed by Conversation Analysis (CA) research, this study seeks to enhance closed captioning features to assist EFL learners in recognizing stress and intonation patterns in spoken English. Using an algorithmic approach, CA-informed closed captions (CC) were generated by underlining stressed syllables and capitalizing pitch accents. Our goal is to enable EFL learners, particularly those from non-stress-timed languages such as Thai, to articulate English with greater clarity and speak with higher intelligibility. Evaluation results demonstrate that the proposed approach achieved an accuracy of 63 %, with a precision of 90 % and recall of 61 %. Further work is in progress to evaluate and improve the usability of the application by EFL teachers and learners.

关键词： Smart cities Generative AI Education Closed captioning Oral communication Libraries pattern recognition Multimedia communication Usability Stress

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