Procedia Computer Science最新文献

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Circular Supply Chains and Industry 4.0: An Analysis of Interfaces in Brazilian Foodtechs 循环供应链与工业 4.0：巴西食品技术公司的界面分析

Procedia Computer Science

Pub Date : 2024-04-23 DOI: 10.1016/j.procs.2024.01.134

Tiago Hilário Hennemann da Silva, Simone Sehnem

引用次数: 0

Potentials of the Metaverse for Robotized Applications in Industry 4.0 and Industry 5.0 元宇宙在工业 4.0 和工业 5.0 中的机器人应用潜力

Procedia Computer Science

Pub Date : 2024-03-31 DOI: 10.1016/j.procs.2024.02.005

E. Kaigom

引用次数: 0

Context-biased vs. structure-biased disambiguation of relative clauses in large language models 大型语言模型中基于上下文与基于结构的相对从句消歧

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.217

Elsayed Issa , Noureddine Atouf

This work investigates the processing behavior of large language models (LLMs) in sentences involving ambiguous relative clauses (RCs). We are particularly interested in unravelling attachment preferences of LLMs in disambiguating RCs (complementizer phrases CPs modifying a genitival phrase), which are either semantically (context-biased) or syntactically (structure-biased) associated to one of the preceding NP referents. A low interpretation of the RC occurs when it is joined to the local NP (low attachment). A high interpretation is provided when the RC modifies the distant NP (high attachment). We create a small dataset of parallel low- and high-attachment sentences. We use zero-shot prompting to evaluate a set of LLMs based on insights from psycholinguistic experiments. Our results show variability in the performance of some models that favor low attachment (semantically-related meanings in the CP) while other models can resolve ambiguity by choosing high-attachment (structure-biased CPs). The findings are discussed in light of directing future experimental studies to consider a comparative paradigm encompassing both multi-modal LLMs and human subjects.

这项研究调查了大型语言模型（LLMs）在涉及含混相对从句（RCs）的句子中的处理行为。我们特别感兴趣的是揭示 LLM 在消歧 RC（修饰属格短语的补语 CP）时的依附偏好，这些 RC 要么在语义上（基于上下文）要么在句法上（基于结构）与前面的一个 NP 指代相关联。当 RC 与本地 NP 相连（低附着）时，RC 会出现低解释。当 RC 修饰远处的 NP 时（高依附），则会出现高解释。我们创建了一个低依附性和高依附性平行句子的小型数据集。我们根据心理语言学实验的见解，使用零点提示来评估一组 LLM。我们的结果表明，一些模型偏向于低依附性（CP 中语义相关的含义），而另一些模型则可以通过选择高依附性（结构偏向的 CP）来解决歧义问题。讨论这些发现是为了指导未来的实验研究，以考虑一种包括多模态 LLM 和人类受试者的比较范式。

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引用次数: 0

Arabic Historical Documents Layout Analysis using Mask RCNN 使用掩码 RCNN 进行阿拉伯语历史文献布局分析

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.10.220

Latifa Aljiffry , Hassanin Al-Barhamtoshy , Felwa Abukhodair , Amani Jamal

In recent times, there has been a notable surge in the interest of researchers in the realm of document analysis and optical character recognition (OCR). Significant advancements have been made in OCR engines across various languages, encompassing both printed and handwritten documents. However, there has been a comparatively lower focus on processing documents written in Arabic when juxtaposed with languages like English. This discrepancy arises from several factors, including the inherent challenges posed by the Arabic language and the limited availability of Arabic document datasets. To implement any OCR engine, the initial step involves analyzing the layout of images before subjecting them to the OCR process. This thesis specifically delves into the realm of layout analysis for historical Arabic documents, employing a deep learning (DL) approach. The chosen methodology utilizes the Mask Region-based Convolutional Neural Network (RCNN). The dataset employed consists of historical Arabic documents, particularly early printed ones, each characterized by unique sizes, structures, and processing prerequisites. Processing historical documents is inherently more challenging due to factors such as the document's layout structure, distinctive handwriting styles of the authors, paper aging, historical timeframe, ink properties, and more. The achieved accuracy result is 51.14%. When juxtaposed with other existing models, it becomes evident that this work attains a state-of-the-art status, showcasing an impressive outcome.

近来，研究人员对文档分析和光学字符识别（OCR）领域的兴趣明显增加。各种语言的光学字符识别引擎都取得了长足的进步，包括印刷文件和手写文件。然而，与英语等语言相比，人们对阿拉伯语文档处理的关注度相对较低。造成这种差异的因素有很多，包括阿拉伯语本身带来的挑战和阿拉伯语文档数据集的有限性。要实现任何 OCR 引擎，第一步都要先分析图像的布局，然后再对其进行 OCR 处理。本论文采用深度学习（DL）方法，专门研究阿拉伯语历史文献的布局分析。所选方法利用了基于掩码区域的卷积神经网络（RCNN）。所使用的数据集由阿拉伯语历史文献组成，尤其是早期印刷文献，每种文献都有独特的尺寸、结构和处理前提。由于文件的版面结构、作者独特的手写风格、纸张老化、历史时限、油墨属性等因素，处理历史文件本身就更具挑战性。所达到的准确率为 51.14%。与其他现有模型相比，这项工作显然达到了最先进的水平，展示了令人印象深刻的成果。

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引用次数: 0

Design and Implementation of Foreign Language Recognition and Translation APP Based on Artificial Intelligence 基于人工智能的外语识别与翻译 APP 的设计与实现

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.09.078

Jie Tang

Globalization has created the demand for cross-language communication, which has promoted the development of machine translation applications (APPs) based on artificial intelligence (AI) for foreign language recognition and translation. The purpose of this study is to develop and implement a mobile application program that can accurately identify and translate foreign languages. APP adopts advanced artificial intelligence technology, uses optical character recognition (OCR) technology to identify the text in images, and uses neural network architecture based on transformer for machine translation. In the data preprocessing stage, text cleaning, word segmentation and other preprocessing steps are performed to create accurate translation input. A translation model is trained on a large bilingual mapping data set to learn the mapping knowledge relationship between the source language and the target language. After a series of tests and experiments, the application of foreign language identification and translation shows the high accuracy and speed of multilingual input and output. The accuracy of this application is 0.9, and the maximum delay is only 180 milliseconds. Although there is still room for improvement in the background of professional fields or complex scenes, this application is an effective tool for cross-language communication. Future work should focus on further optimizing translation models, improving the accuracy of translation for specific domain terms, and enhancing the personalized service capabilities of the APP.

全球化带来了跨语言交流的需求，促进了基于人工智能（AI）的外语识别和翻译机器翻译应用程序（APP）的发展。本研究的目的是开发和实现一种能够准确识别和翻译外语的移动应用程序。APP 采用先进的人工智能技术，使用光学字符识别（OCR）技术识别图像中的文字，并使用基于变压器的神经网络架构进行机器翻译。在数据预处理阶段，将进行文本清理、单词分割和其他预处理步骤，以创建准确的翻译输入。翻译模型在大型双语映射数据集上进行训练，以学习源语言和目标语言之间的映射知识关系。经过一系列测试和实验，外语识别和翻译应用显示了多语言输入和输出的高准确性和快速性。该应用的准确率为 0.9，最大延迟仅为 180 毫秒。虽然在专业领域或复杂场景背景下仍有改进的余地，但这一应用是跨语言交流的有效工具。今后的工作重点应是进一步优化翻译模型，提高特定领域术语的翻译准确性，以及增强 APP 的个性化服务能力。

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引用次数: 0

Design of Translation Error Correction System Based on Improved Seq2Seq 基于改进型 Seq2Seq 的翻译纠错系统设计

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.09.080

Ting Chen

Speech translation technology is an important booster for promoting social communication and advancing human civilization. With the solid advancement of theories and technologies such as speech processing and machine translation, as well as the continuous deepening and development of computer science, the computing power and storage capacity have been further improved. Speech translation systems widely used in English, French, English and Chinese have successively reached a commercial level. However, the development of speech translation systems is limited by corpus resources and a lack of bilingual language research. The experiments and applications of speech translation in some languages are still in its early stages. In addition, existing research mostly adopts a cascading voice translation system as the basis, breaking through the key issues individually, and less adopts direct voice translation methods without relying on intermediate text representation, which brings problems such as cumbersome research content, complex translation models, and long translation delays. This article mainly focuses on the design of a translation error correction system based on improved Seq2Seq. Firstly, relevant research is summarized, relevant application methods are proposed, and the results are discussed. I hope to bring some inspiration to relevant researchers and provide assistance for the development of related fields.

语音翻译技术是促进社会交流、推动人类文明进步的重要助推器。随着语音处理、机器翻译等理论和技术的扎实推进，以及计算机科学的不断深入和发展，计算能力和存储能力进一步提高。广泛应用于英语、法语、英语和汉语的语音翻译系统已相继达到商用水平。然而，语料库资源和双语语言研究的缺乏限制了语音翻译系统的发展。语音翻译在一些语言中的实验和应用还处于初级阶段。此外，现有研究多采用级联式语音翻译系统为基础，逐个突破关键问题，较少采用不依赖中间文本表示的直接语音翻译方法，带来了研究内容繁琐、翻译模型复杂、翻译延迟时间长等问题。本文主要研究基于改进的 Seq2Seq 的翻译纠错系统的设计。首先总结了相关研究，提出了相关应用方法，并对结果进行了讨论。希望能给相关研究人员带来一些启发，为相关领域的发展提供帮助。

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引用次数: 0

Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying 消费者感知特征对水果销售的影响：社区团购的证据

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.08.133

Hailiang Jiang , Xin Tian , Ye Tao , Lulu Wang

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers; fresh produce stores outperform regular stores in fruit sales of CGB; Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels.

零售商通过社区团购（CGB）渠道销售水果的趋势日益明显。基于实际运营数据，我们采用计量经济学模型实证分析了消费者在社区团购中的感知特征如何影响水果销售。我们的研究结果表明，在产品描述中加入主观体验会对零售商的水果销售产生积极影响；生鲜店在 CGB 水果销售中的表现优于普通商店；深色水果在 CGB 渠道中的销量高于浅色水果。我们的研究为通过 CGB 渠道销售水果的零售商提供了有价值的见解。

引用次数: 0

Brand Subsidy Strategies for Mitigating Agricultural Product Supply Risks Under Capital Limitation 资本限制下化解农产品供应风险的品牌补贴策略

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.08.141

Jiyao Feng , Chunbing Bao , Qingchun Meng

This study investigates the application of brand subsidies to capitalize on limited capital in the context of branded agricultural products facing supply risks, aiming to identify a win-win subsidy decision-making model. We consider a supply chain for branded agricultural products comprising financially constrained farmers and financially robust retailers. Financially constrained farmers, who can produce without loans under normal conditions, resort to bank loans to supply branded agricultural products when faced with supply risks. We find that under capital constraints, brand subsidies consistently yield higher returns in terms of brand strength and market demand. Moreover, even after adjusting for supply risks through posterior probability, brand subsidies continue to deliver superior returns.

本研究探讨了在品牌农产品面临供应风险的情况下，如何应用品牌补贴来利用有限的资本，旨在找出一种双赢的补贴决策模式。我们考虑了一个由资金紧张的农民和资金雄厚的零售商组成的品牌农产品供应链。资金紧张的农户在正常情况下无需贷款即可生产，但在面临供应风险时，他们会求助于银行贷款来供应品牌农产品。我们发现，在资本约束条件下，品牌补贴始终能在品牌强度和市场需求方面产生更高的回报。此外，即使通过后验概率调整了供应风险，品牌补贴仍能带来更高的收益。

引用次数: 0

BEVEFNet: A Multiple Object Tracking Model Based on LiDAR-Camera Fusion BEVEFNet：基于激光雷达与相机融合的多目标跟踪模型

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.08.106

Yi Yuan , Ying Liu

As a crucial task in the field of computer vision, object tracking models are widely used in various application domains, such as autonomous driving. However, existing multiple object tracking methods still face challenges in accurately and efficiently tracking moving multi-targets in real time. This paper presents BEVEFNet, a camera-LiDAR multi-target tracking model based on multistage fusion, which effectively utilizes the semantic information from optical images and the spatial and geometric information from LiDAR data to unify multi-modal features in a shared Bird’s Eye View(BEV) representation space. By leveraging LiDAR data to complement optical images, multi-level fusion is achieved at both the feature and decision levels. The proposed efficient sparse 3D feature extraction network significantly enhances the speed of multiple object tracking by incorporating sparse convolution. Experiments conducted on the nuSences dataset demonstrate that BEVEFNet achieves an AMOTA of 69.7, improving the accuracy of multiple object tracking.

作为计算机视觉领域的一项重要任务，物体跟踪模型被广泛应用于自动驾驶等多个应用领域。然而，现有的多目标跟踪方法在准确、高效地实时跟踪移动的多目标方面仍面临挑战。本文提出的 BEVEFNet 是一种基于多级融合的相机-激光雷达多目标跟踪模型，它有效地利用了光学图像的语义信息和激光雷达数据的空间与几何信息，将多模态特征统一在一个共享的鸟瞰图（BEV）表示空间中。通过利用激光雷达数据对光学图像进行补充，在特征和决策层面实现了多级融合。通过结合稀疏卷积，所提出的高效稀疏三维特征提取网络大大提高了多目标跟踪的速度。在 nuSences 数据集上进行的实验表明，BEVEFNet 的 AMOTA 值达到了 69.7，提高了多目标跟踪的准确性。

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引用次数: 0

Network analysis of economic sectors in the world economy 世界经济部门网络分析

Procedia Computer Science

Pub Date : 2024-01-01 DOI: 10.1016/j.procs.2024.08.165

Fuad Aleskerov , Yetkin Cinar , Ivan Deseatnicov , Elena Sergeeva , Daniil Tkachev , Vyacheslav Yakuba

We consider a network of intermediate inputs trade between sectors of OECD Countries’ in 2020. Centrality indices are used to identify most vulnerable sectors in the network of intermediate inputs trade between 45 manufacturing and non-manufacturing sectors of 76 countries. The network is based on the official data of inter-country input-output tables published in 2023 by OECD. We apply new centrality indices to identify sectors, which might be under the risk in case of an economic shock.

我们考虑了 2020 年经合组织国家各部门之间的中间投入品贸易网络。在 76 个国家 45 个制造业和非制造业部门之间的中间投入贸易网络中，我们使用中心性指数来识别最脆弱的部门。该网络基于经合组织 2023 年发布的国家间投入产出表的官方数据。我们采用新的中心性指数来识别在经济冲击情况下可能面临风险的部门。

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全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

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