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检索条件"丛书名=Communications in Computer and Information Science"

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A Two-Stage Fine-Tuning Procedure to Improve the Performance of Language Models in Sensitive Health information Recognition and Normalization Tasks

A Two-Stage Fine-Tuning Procedure to Improve the Performance...

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2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Chiu, Pin-Sen Hou, Bo-Wei Chen, Yen-Ting Huang, Shi-Hao Natl Kaohsiung Univ Sci & Technol Appl Intelligence Lab Dept Elect Engn Kaohsiung 807618 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Intelligent Control & Comp Vis Lab Kaohsiung 807618 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Intelligent Automat Lab Kaohsiung 807618 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Broadband Mobile Wireless Lab Kaohsiung 807618 Taiwan

ISBN: (纸本)9789819779659;9789819779666

In recent years, artificial intelligence (AI) technology has experienced flourishing development, and large-scale language models (LMs) are increasingly recognized as a pivotal direction for the future of AI in the field of intelligent healthcare. In the realm of clinical medical applications, there is an increasing focus on eliminating patient privacy information from textual electronic health records within medical institutions. To address this, we have developed a two-stage fine-tuning procedure which employs the Pythia model for training purposes, enabling the model to effectively extract and normalize patient-related privacy information. Furthermore, ChatGPT is utilized for data augmentation to generate additional training data, enhancing the overall robustness of the system.

关键词： Natural Language Processing Sensitive Health information Pythia ChatGPT Language Model

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Learning and Embodied Decisions in Active Inference 5th

Learning and Embodied Decisions in Active Inference

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5th International Workshop on Active Inference

作者： Priorelli, Matteo Stoianov, Ivilin Peev Pezzulo, Giovanni Natl Res Council Italy Inst Cognit Sci & Technol Rome Italy Natl Res Council Italy Inst Cognit Sci & Technol Padua Italy

ISBN: (纸本)9783031771378;9783031771385

Biological organisms constantly face the necessity to act timely in dynamic environments and balance choice accuracy against the risk of missing valid opportunities. As formalized by embodied decision models, this might require brain architectures wherein decision-making and motor control interact reciprocally, in stark contrast to traditional models that view them as serial processes. Previous studies have assessed that embodied decision dynamics emerge naturally under active inference - a computational paradigm that considers action and perception as subject to the same imperative of free energy minimization. In particular, agents can infer their targets by using their own movements (and not only external sensations) as evidence, i.e., via self-evidencing. Such models have shown that under appropriate conditions, action-generated feedback can stabilize and improve decision processes. However, how adaptation of internal models to environmental contingencies influences embodied decisions is yet to be addressed. To shed light on this challenge, in this study we systematically investigate the learning dynamics of an embodied model of decision-making during a two-alternative forced choice task, using a hybrid (discrete and continuous) active inference framework. Our results show that active inference agents can adapt to embodied contexts by learning various statistical regularities of the task - namely, prior preferences for the correct target, cue validity, and response strategies that prioritize faster or slower (but more accurate) decisions. Crucially, these results illustrate the efficacy of learning discrete preferences and strategies using sensorimotor feedback from continuous dynamics.

关键词： active inference hybrid models embodied decisions motor inference motor learning

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DeepMHT: Moving Object Segmentation in the Point Cloud Based on Deep Learning and Multiple Hypothesis Tracking 5th

DeepMHT: Moving Object Segmentation in the Point Cloud Based...

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5th China Intelligent Robotics Academic Conference

作者： Chen, Baifan Jin, Yuxuan Zhao, Zishuo Wan, Jiadong Peng, Chengyu Cent South Univ Sch Automat Changsha 410073 Peoples R China

ISBN: (纸本)9789819616138;9789819616145

In autonomous driving, moving object segmentation plays an essential role in improving the accuracy of mapping and path planning. However, accurately and consistently differentiating between objects that are actually moving and those that are potentially moving is still challenging. This paper proposes DeepMHT, a novel approach that combines a multiple hypothesis tracking algorithm (MHT) with deep learning to solve the issue of moving object segmentation of LiDAR point clouds. This paper designs a data association network to extract deep features from the object point cloud and 3D box and fuses these two features to obtain affinity weights. We propose an innovative local hypothesis update model that integrates affinity weights with the weights of MHT to make data associations more accurate and stable. Unlike other moving object segmentation algorithms that calculate differences in the point clouds of adjacent frames, DeepMHT directly estimates the motion state of objects and segments moving objects by their position and velocity, which significantly improves the precision of segmentation. This paper compares DeepMHT with several other moving object segmentation algorithms on the KITTI tracking dataset and finds that DeepMHT is highly accurate.

关键词： Moving object segmentation learning-based data association multiple hypothesis tracking

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Application of Active Experiment Planning Methods for Determining the Coefficients of the Regression Equation to Determine the Optimal Composition of the Mixture in the Production of Ceramic Products 2nd

Application of Active Experiment Planning Methods for Determ...

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2nd International Conference on information Technologies and Their Applications

作者： Abasova, Nigar Mammadli, Maryam Azerbaijan Univ Architecture & Construct Dept Informat Technol & Syst Baku Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

The article describes the method of statistical planning of the experiment to determine the optimal composition of the charge in the production of ceramic products based on the Box-Behnken plan of K = 3 dimensions. A plan for conducting an active experiment was proposed, the coefficients of mathematical models were calculated, and the adequacy of the models obtained and the significance of the coefficients of the regression equations were verified. Ceramic products are one of the best quality buildings facing materials used in paving parks and recreation centers. Also, one of the solutions to the actual problem of saving heat consumption in residential buildings is the use of facing ceramic products with high thermal insulation properties. In modern times, as in all fundamental studies, a systematic approach is typical in the study of multicomponent ceramic materials. Such a systematic approach determines the study of the studied materials and objects both separately and in interaction with each other. Conducting such a systematic study of ceramic products is an important issue for more efficient organization of its production. A 2-component mixture of raw materials is usually used in the production of ceramic materials (bricks and tiling, ceramic pans with external coating) in the country. From this point of view, it is very important to systematically study the 3-component ceramic mass composition with modern methods.

关键词： ceramic products mixture active experiment mathematical model adequacy coefficients regression equation

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Digitalising the Supply Chain for Enhanced Efficiency and Customer Satisfaction 5th

Digitalising the Supply Chain for Enhanced Efficiency and Cu...

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5th 5th Olympus - International Conference on Supply Chain

作者： Balaska, Vasiliki Symeonidis, Symeon Antoniou, Sarantis Fotiadis, Thomas Chatzoglou, Prodromos Gasteratos, Antonios Democritus Univ Thrace Dept Prod & Management Engn Xanthi Greece

ISBN: (纸本)9783031693502;9783031693519

In the rapidly evolving landscape of global supply chains, integrating digital technologies is a key factor in enhancing operational efficiency, ensuring product traceability, and capturing consumer sentiment. This research delves into the transformative potential of blockchain, Non-Fungible Tokens (NFTs), and advanced sentiment analysis in redefining supply chain management practices. By harnessing these technologies, the study proposes a novel framework to achieve real-time visibility, end-to-end traceability, and a deeper understanding of consumer preferences. The methodology encompasses the deployment of digital passports and NFTs for securing product information, alongside utilising natural language processing techniques to analyse customer feedback across various platforms. Through case studies in food safety, pharmaceuticals, and retail, the paper shows how integrating digital strategies mitigates risks, boosts transparency, and enhances customer engagement. The findings reveal significant improvements in supply chain resilience, operational transparency, and customer satisfaction. The research outlines challenges such as data security, technological complexity, and workforce upskilling for adopting digital tools. Conclusively, this paper underscores the critical importance of digital innovation in supply chains, advocating for further research and development to overcome existing barriers. The future focuses on digitalised supply chains enhancing efficiency and value creation via customer insights and sustainable methods.

关键词： supply chain digitalisation blockchain traceability NFT digital passports sentiment analysis

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Last-Mile Delivery Optimization Using Mixed Electric Vehicles, UAVs and Full Truck Delivery Based on Artificial Intelligence Algorithms 5th

Last-Mile Delivery Optimization Using Mixed Electric Vehicle...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Hanaa, Bouhia Benhra, Jamal Ed-dehbi, Wissal ESITH Casablanca Morocco Univ Hassan 2 ENSEM Casablanca Morocco

ISBN: (纸本)9783031807749;9783031807756

This paper presents the Electric Vehicle Routing Problem with Drones (EVRPD), a novel strategy aimed at enhancing last-mile delivery through the combined use of Electric Vehicles (EVs) and drones. The primary objective is to reduce energy usage and CO2 emissions while maintaining effective delivery operations. The proposed system employs a diverse fleet where EVs act as mobile bases, and drones handle the final leg of parcel delivery. Both EVs and drones can deliver packages, with drones being utilized for smaller, time-sensitive shipments. The system emphasizes optimal payload distribution to boost energy efficiency and drone endurance. Drones are resupplied on the EVs' rooftops via IoT technology. To address the routing challenge, the solution leverages bio-inspired algorithms, including Ant Colony Optimization (ACO) for EVs and Particle Swarm Optimization (PSO) for drones. This methodology enhances energy efficiency and drone autonomy, ensuring effective parcel delivery. By incorporating these advanced techniques, EVRPD presents a promising approach for a more sustainable and effective urban logistics network.

关键词： EVRPD Last-mile delivery Energy efficiency CO2 reduction EVs Drones Bio-inspired algorithms ACO PSO

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ASM: Adaptive Subgraph Matching via Efficient Compression and Label Filter 7th

ASM: Adaptive Subgraph Matching via Efficient Compression an...

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8th Asia Pacific Web and Web-Age information Management Joint International Conference on Web and Big Data

作者： Chai, Yanfeng Li, Jiashu Zhang, Qiang Ge, Jiake Wang, Xin Taiyuan Univ Sci & Technol Taiyuan 030024 Peoples R China North Univ China Taiyuan 030051 Peoples R China Tianjin Univ Tianjin 300350 Peoples R China

ISBN: (纸本)9789819600540;9789819600557

How to efficiently get subgraphs that match the given query graph on large-scale graphs has become a hot topic in both academia and industry. Subgraph matching, as an important research direction of graph algorithms, realizes the basic operation for efficient queries on graph data, and its essence is the subgraph isomorphism problem, which is proved to be an np-complete problem. The filtering phase in the process of subgraph matching is especially critical because it directly affects the efficiency of the overall algorithm. An excellent filtering mechanism can screen out the eligible candidate nodes in a shorter time, thus saving a lot of computing time for the whole process. By analyzing the existing methods for handling the subgraph matching problem, we find that the following problems mainly exist in the existing methods in the filtering stage: (1) the problem of repeated enumeration of equivalent nodes;(2) Incomplete filtering problems with existing structures. These will lead to a large number of redundant validation issues during the validation phase. Then (3) we propose an Adaptive Subgraph Matching (ASM) mechanism to address the aforementioned shortcomings by efficient Compressed Graph Nodes (CGN) and a Label Count Filter (LCF) algorithm to improve the performance. The experiments show that our approach outperforms state-of-the-art subgraph search and matching algorithms by several orders of magnitude in terms of query processing time.

关键词： Subgraph matching Subgraph isomorphism Reducing verification cost

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Kernel Cutset-Type Possibility C-Means Algorithm for Gaussian Granularity 4th

Kernel Cutset-Type Possibility C-Means Algorithm for Gaussia...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Tang, Yiming Dong, Yajie Chen, Rui Gao, Jianwei Li, Shujie Wu, Xi Hefei Univ Technol Sch Comp & Informat Anhui Prov Key Lab Affect Comp & Adv Intelligent Hefei 230601 Anhui Peoples R China

ISBN: (纸本)9789819603534;9789819603541

With the development of various disciplines, most of the traditional clustering algorithms for numerical data cannot adapt to more complex datasets. The introduction of granular clustering breaks through the bottleneck of this research field. Previous studies mainly focus on interval, triangular and trapezoidal granular data to carry out granular clustering. However, for Gaussian granularity, there is little involvement. Aiming at such problem, in this study, we propose the Kernel Cutset-type Possibility C-Means (KC-PCM) algorithm of granular clustering for Gaussian granularity. Firstly, a new granularity weight is designed for each Gaussian granularity with normalized granularity quality based upon the principle of justifiable granularity. Secondly, the Gaussian kernel distance is employed instead of Euclidean distance to measure the distance between granular data. Thirdly, the KC-PCM algorithm is proposed based on the granularity weight and the Gaussian kernel distance. Lastly, we evaluate and compare the performance of some related granular clustering algorithms on artificial and UCI datasets. It is validated that the KC-PCM algorithm can effectively solve the overlapping problem of cluster centers in the PCM algorithm series. By contrast with the related granular clustering algorithm, the cluster center obtained by KC-PCM is closer to the actual cluster center, and the obtained reconstruction error is smaller, and the polygonal convergence trend of KC-PCM is more significant. As a consequence, the performance of KC-PCM is superior over other algorithms.

关键词： Clustering Granular Computing Fuzzy C-Means Gaussian Granularity Gaussian Kernel Distance

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Dental Caries Segmentation from Orthopantomograms Using Optimized DeepLabV3+ 5th

Dental Caries Segmentation from Orthopantomograms Using Opti...

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5th International Symposium on Intelligent Computing Systems

作者： Neeraja, R. Anbarasi, L. Jani Geetha, S. Revanth, Gadhamsetty Prasad, Prudhvi Raj, Benson Edwin Mohan, Geetha Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India Higher Coll Technol Dept Comp Informat Sci Fujairah U Arab Emirates Inst Appl Technol Comp Sci Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031829307;9783031829314

Orthopantomograms are essential tools for dentists in analyzing dental conditions like dental caries. Typically, dentists diagnose cavities by manually examining these images. However, manual detection can sometimes miss cavities due to factors such as negligence from excessive workloads and lack of expertise. Hence, the demand arises for computer-based smart vision technologies that employ machine learning, image processing approaches and artificial intelligence techniques to tackle these challenges. This study leverages an advanced deep CNN based DeepLabV3+ techniques such as atrous convolutions and encoder-decoder architectures to capture rich contextual information and fine-grained spatial details from orthopantomograms (OPGs). The encoder-decoder architecture enhances the ability to capture minute details in the segmentation of tooth caries, by capturing multi-scale context with multiple parallel filters employing different atrous rates, enabling the model to consider features at various scales simultaneously. The OPGs were obtained from the Nanded Rural Dental College & Research Centre in Maharashtra. A total of 153 OPGs selected and processed by an experienced dentist to identify the caries regions in the original images. The mask dataset is generated by extracting the caries region for the segmentation process. Various augmentation approaches such as zooming, rotation, horizontal and vertical flipping were applied to expand the dataset and achieved improved segmentation results. The results indicate that the model is highly effective in diagnosing dental caries, achieving an accuracy score of 99.2%, IoU score as 99.3% and Dice coefficient as 99.68%, respectively.

关键词： Orthopantomograms Tooth Caries DeepLabV3+ Segmentation

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Calculation of Modes and Electromagnetic Influences of a Long-Distance Ultra-high Voltage Power Transmission to Pipeline Based on Digital Models 12th

Calculation of Modes and Electromagnetic Influences of a Lon...

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2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Kryukov, Andrey Suslov, Konstantin Kryukov, Alexandr Irkutsk Natl Res Tech Univ Dept Power Supply & Elect Engn Irkutsk Russia Irkutsk State Transport Univ Dept Transport Elect Power Irkutsk Russia Natl Res Univ Moscow Power Engn Inst Dept Hydropower & Renewable Energy Moscow Russia

ISBN: (纸本)9783031709654;9783031709661

The task of determining the voltages induced on a pipeline by the electromagnetic field of power lines is significantly complex and requires taking into account a number of factors, such as: operating mode of the influencing line;approach trajectory;the nature of the grounding of the metal structure;the length of the approach area and the conductivity of soils on it. The goal of the research presented in this article was to develop computer models of long-distance ultra-high voltage (UHV) power transmission lines to provide comprehensive modeling of power flows and calculating electromagnetic interference effects on extended steel structures. In developing the models, we employed the methods based on the use of the phase frame of reference and equivalent lattice circuits with a fully connected topology. The simulations were carried out for a 1,150 kV UHV transmission line with a length of 900 km, each phase of which was formed by eight AC-330 wires. Simulations were performed using the software package Fazonord. Along with power flow calculations and determination of the voltages created by the 1,150 kV long-distance transmission line on the pipeline, we simulated electromagnetic fields, taking into account the impact exerted by the grounded steel structure. The following modes were considered: normal with loads at the receiving end of 300 + j200 MVA per phase;emergency, caused by single-phase and two-phase short circuits;single-phase with loads 80+ j50 MVA per phase. The results of modeling a long-distance 1,150 kV transmission line with receiving end loads of 300 + j 200 MVA per phase led to the following conclusions: in the case of a normal power flow with balanced loads at individual points of the structure the levels of induced voltages did not exceed the allowable limit of 60 V;in the case of two-phase and single-phase short-circuit power flows the maximum induced voltages also did not exceed the 1,000 V limit set by the regulatory document. When phase B is di

关键词： Long-distance ultrahigh-voltage transmission lines Comprehensive modeling in the phase frame of reference

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