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检索条件"任意字段=3rd International Conference on Object-Oriented Databases"

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Subject-object Aspect-Based Sentiment Analysis Model Based on News Texts 3

Subject-Object Aspect-Based Sentiment Analysis Model Based o...

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3rd international conference on Computing, Networks and Internet of Things, CNIOT 2022

作者： Wang, Biao Xin, Xin Yang, Jing Li, Shun Shao, Yan University of International Relations Beijing China The Open University of China Beijing China

ISBN: (纸本)9781665469104

News as an important part of the open source intelligence has always played an important role in international relations and national security fields. However, the fine-grained sentiment analysis work such as Aspect-Based Sentiment Analysis (ABSA) tasks focused on the service industry and e-commerce comments and the Target Aspect-Based Sentiment Analysis (T-ABSA) tasks focused on the Twitter datasets, these two types of tasks usually limit the context to the fixed subjects or objects, simplifying the model by limiting several aspects simultaneously. In addition, the sentiment analysis work based on the news texts most focused on the chapters level. Therefore, based on the characteristics of news texts that it contains multiple subjects and objects, this paper proposed the Subject-object Aspect-Based Sentiment Analysis (SO-ABSA) model, which can do fine-grained emotional element extraction in the context of indefinite subjects, objects and aspects. More emotional elements can be mined through SO-ABSA model. The proposed model can extract the uncertain entities efficiently and improve the accuracy of subjects and objects extraction. Moreover, the uncertain aspects also can be extracted flexibly and the sentiment analysis result can represent the subjects' emotional tendency towards specific aspects. To evaluate our method, we built a subject-object oriented dataset (SOOD) with data sourced from 30,000 news articles. We propose a subject-object aspect emotion analysis model and evaluate the model on the SOOD dataset. The experimental results show the effectiveness of our model. © 2022 IEEE.

关键词： Extraction

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Proceedings - 2024 3rd international conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2024

Proceedings - 2024 3rd International Conference on Artificia...

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3rd international conference on Artificial Intelligence, Human-Computer Interaction and Robotics, AIHCIR 2024

ISBN: (纸本)9798331534035

The proceedings contain 65 papers. The topics discussed include: human computer interaction design of distributed energy storage system;research on human-computer interaction assistance technology based on the fusion of eye movement and mouse;multi-level decoupling and interaction network for oriented object detection in aerial imagery;anomaly detection via reconstructed discriminant networks leveraging generative adversarial frameworks;efficient tensorial multi-view clustering based on matrix factorization;integrated approach for cross-modal brain activity classification through manual feature extraction;facial emotion detection research based on an improved multi-modal LLM;and a novel vortex based non-contact mobile inspection tool design for ROV in low visibility water.

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3rd international Workshop on Advanced Virtual Environments and Education, WAVE 2021

3rd International Workshop on Advanced Virtual Environments ...

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3rd international Workshop on Advanced Virtual Environments and Education, WAVE 2021

ISBN: (纸本)9783031070174

The proceedings contain 11 papers. The special focus in this conference is on Advanced Virtual Environments and Education. The topics include: An Educational Digital Game for the Development of Phonological Awareness;emergency Remote Learning One Year Later - What Changed?;preface;Case Study: A CSCL Approach for object-oriented Programming Learning;are the Mobile Applications of Portuguese Higher Education Institutions Accessible?;HCI 2020 Master’s Degree Study: A Review;assessment Methods Determining the Higher Education Students’ Academic Success;HProgramming: a Helping Tool for Teach and Learn Programming;Scratch + CNC: Mixing Physical and Digital Worlds.

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2022 3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

2022 3rd International Conference on Geology, Mapping and Re...

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3rd international conference on Geology, Mapping and Remote Sensing, ICGMRS 2022

ISBN: (纸本)9781665485951

The proceedings contain 175 papers. The topics discussed include: a tunnel crack extraction method based on point cloud;the use of SAR offset tracking for detecting mountain glacier movement;CFD analysis of jet fan layout optimization at intersection of branch tunnel and main tunnel;multi-element quantitative extraction of mining area of laterite nickel mine combined deep learning with object-oriented method;numerical simulation of typhoon waves in Sanmen Bay;comparative study on extraction of banded water and surface water in urban area based on MNDWI;comprehensive evaluation of soil moisture in farmland based on ensemble empirical mode decomposition and partial least squares regression;research on multi-level point cloud classification method of underground tunnel;coordinated variation of precipitable water vapor and rainfall during typhoon;impacts of land reclamation on hydrodynamics in Zhoushan Archipelago;analysis of the effect of mud-water balance shield on soil through water-rich sand layer;research on the influence and control measures of excavation of deep foundation pit adjacent to rail transit;and design and implementation of geological data management platform based on WebGIS.

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Smart extensions to regular cameras in the industrial environment 3

Smart extensions to regular cameras in the industrial enviro...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Brezani, S. Hrasko, R. Vojtas, P. Globesy Ltd Framborska 58 Zilina Slovakia Charles Univ Prague Dept Software Engn Malostranke Nam 25 Prague Czech Republic

Data mining from unstructured data can be skillfully employed to improve the performance of manufacturing or industrial processes. The main goal of this paper is to create a fast emergency aid system for object detection in SME industrial premises. The basic assumption is that SMEs do not have any IT-trained personnel, and the solution has to be unsupervised edge computing. We use several off-the-shelf models of deep neural networks pre-trained for smart city applications, ready for online object recognition and edge computing. Our system works without any retraining, additional annotation, or human intervention. Specifically, we present heuristics for the automated creation of PGT (Pseudo-Ground Truth). Based on PGT, we can automatically decide which model is the best in the specific environment. We present an application of fully automated enhancing image capture camera outputs to smarter ones. We evaluate our system in a controlled experiment. Low-resolution cameras and large areas cause problems for our method. We present a proof-of-concept for improving our system even in these challenging situations. The benefit is a knowledge extraction in a simple and inexpensive way to expand the organizations' databases with information from unstructured data from CCTV/IP cameras. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Unsupervised learning performance improvement of industrial processes object recognition pseudo-ground truth. AI machine learning in Industry

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Enhanced object Detection with Deep Convolutional Neural Networks for Vehicle Detection 3

Enhanced Object Detection with Deep Convolutional Neural Net...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Mishra, Prasoon Bharat Malik, Abdul Safa, M. Saranaya, G. Arun, D. SRM Institute of Science and Technology Department of Networking and Communication Chennai603203 India VMware United States

ISBN: (纸本)9781665462754

Recent years have seen a significant increase in scholarly interest in object detection because of its strong connection to video analysis and picture interpretation. Shallow trainable structures and handcrafted features facilitate conventional object recognition techniques. Intricate ensembles that integrate poor visual elements with high-level data from object detectors and scene classifiers reach an effectiveness threshold relatively quickly. In order to help with problems with conventional architectures, more powerful tools that can learn deeper, higher-level, and more semantic characteristics are becoming available as deep learning develops quickly. For instance, these models behave differently when it comes to network architecture, training strategy, and optimization methodology. In this paper, we evaluate studies on object recognition using deep learning. The authors of the study begin with a primer on deep learning and its principal methods, the convolutional neural network (CNN). The subject will then shift to a number of widely used generic object detection methods, as well as various improvements and practical strategies for improving detection generally. The topic of different common genealogical patterns for object recognition will next be covered, along with some tweaks and practical methods for enhancing detection. even more performance It also briefly explores a number of particular detection tasks, such as pedestrian identification, face detection, and recognition of remarkable items because they display a range of properties. Experimental analyses are also available to compare alternate strategies and get some useful results. The recommendations for further research in both the object and object-oriented fields cover a wide range of prospective directions and objectives. © 2022 IEEE.

关键词： object recognition

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Scale Normalization and Lightweight FPN for Small object Detection in Aerial Images 3

Scale Normalization and Lightweight FPN for Small Object Det...

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3rd international conference on Computer Graphics, Image, and Virtualization, ICCGIV 2023

作者： Wei, Haokun Liu, Jingyi Chen, Jinyong Sun, Yuxin Chu, Boce Zhu, Jin Key Laboratory of Aerospace Information Applications of CETC Shijiazhaung China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

ISBN: (纸本)9781510671720

The detection of small objects by oriented bounding box in aerial images is a recent hot topic. However, since the aerial images are not collected at the same height, the Ground Sample Distance (GSD) is different for each image, so that small objects are easily overlooked. Existing algorithms are designed for multi-scale object detection, and feature fusion is time-consuming, resulting in a large amount of model parameters that is not easy to deploy on embedded devices. We propose three methods to address the above problems. First, we scale the collected aerial images to the same scale according to the GSD value. Second, we change the structure of Feature Pyramid Network (FPN) and only keep the necessary low-level feature maps. Finally, we rescale the anchor for the specific scene. We validate our proposed method on the DOTA dataset. The results show that the modified model using our method can identify more small-scale objects, and the maximum number of model parameters can be reduced by 2.7%, the inference speed can be increased by 13.24%, and the model size was reduced by up to 28% when the detection accuracy is the same as the original algorithm. © 2023 SPIE.

关键词： object detection

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Optimizing Software Development through Data Access Speed using object-Relational Mapping (ORM) on Credit Risk Application

Optimizing Software Development through Data Access Speed us...

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international conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS)

作者： Evelyn Sierra Umi Lali Yuhana Informatics Engineering Sepuluh Nopember Institute of Technology Surabaya Indonesia

This study presents an examination of the implications of integrating object-Relational Mapping (ORM) in credit risk software development. ORM is a software layer that allows developers to work with databases using object-oriented code and concepts, rather than writing raw SQL queries. It provides a set of classes and methods that enable the creation, retrieval, update, and deletion of data in a database using familiar programming constructs like classes, objects, and methods. For a case study, we use credit risk software development with Indonesia macroeconomic data. Two distinct experiments were conducted to assess the advantages and trade-offs associated with ORM implementation, both during mid-project and at the project’s inception. The findings reveal intriguing insights into the impact of ORM on code execution time, security, and code maintainability. The study concludes that the decision to implement ORM hinges on project-specific needs and the state of the database structure. While ORM may pose minor performance trade-offs, its long-term advantages in code quality, security, and scalability underscore its value. Thus, the timing of ORM integration should align with project objectives to strike a balance between performance optimization and code quality.

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Methodology for Studying a Learning object Using Models of Various Typesaper

Methodology for Studying a Learning Object Using Models of V...

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Technology Enhanced Learning in Higher Education (TELE), international conference on

作者： Liubov Yazykova Inna Muzyleva Automation and computer science faculty Lipetsk State Technical University Lipetsk Russian

The authors consider the issues of development of interdisciplinary universal scenarios of behavior among students of electrical engineering areas in the implementation of full-scale and computer educational experiment in order to form professional competencies. Based on the use of information technology, a practice-oriented scenario of the educational process is proposed, as close as possible to scientific activity. The possibilities of different types of computer models of the same object are considered. It is shown that the proposed methodology for the integrated study of one object using several types of models provides students with the necessary information and professional competencies by increasing the awareness of the perception of educational material.

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A Comprehensive Metric for Evaluating object-oriented Software Complexity

A Comprehensive Metric for Evaluating Object-Oriented Softwa...

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Advancement in Technology (ICONAT), international conference for

作者： H. A. N. N. Thathsarani J. H. Aluthwaththage Hatch Works Building iVedha Inc Colombo 1 Sri Lanka IT Department Aventure IT Solution Horana Sri Lanka

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

This paper introduces the Aggregated Complexity Index (ACI) metric, a novel metric designed to address the challenge of accurately measuring complexity in object-oriented programs. The ACI metric integrates six key factors—nesting level of control structures, type of control structures, try-catch blocks, pointers, threads, and dynamic memory allocation—each critical to object-oriented programming and directly impacting code maintainability and readability. The method involves a structured weighting system that quantifies the cognitive load imposed by these factors. The metric was validated through practical application in a sample program, demonstrating its effectiveness in identifying complex code segments. The results highlight the ACI metric’s utility in enhancing software quality by guiding refactoring efforts and optimizing code management. The study concludes that the ACI metric is a valuable addition to the software engineering toolkit, offering a comprehensive approach to managing software complexity in modern development environment.

关键词： Measurement Codes Software quality Cognitive load Software systems Complexity theory Software reliability Software measurement Standards Software development management

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