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关键词： 科技翻译   编程语言   目的论   翻译策略  

摘要： Python是一种优秀的计算机编程语言,与其他编程语言相比,Python能够将复杂且具有挑战性的工作简化为简洁的代码,因此在各行各业中得到了广泛的应用,比如人工智能、大数据、自动化办公。Intro to Python for Computer Science and Data Science是一本教科书,主要内容是Python的运用,帮助读者从零基础开始学习这门编程语言。对这本书进行翻译可以帮助了解Python语言的应用前景,为目标读者学习和应用该编程语言提供指引和参考。 本文是基于Intro to Python for Computer Science and Data Science的英汉翻译实践报告,原文属于科技英语文本,结构紧凑、语言严谨、多长句、多被动句等,且专业术语及缩略词多。本报告在汉斯·弗米尔的目的论指导下,从词汇和句子两个层面,通过具体的案例分析,研究并探讨了翻译计算机编程类科技英语文本所涉及的一些翻译策略。在词汇层面,主要针对相关的专业术语采用了词义选择和零翻译策略;而在句子层面,针对文本中存在的被动句、长难句、无灵主语句,主要采用语态对等、语态转换、顺句驱动、语序调整、语句拆分、主语增补和主语转换的翻译策略。 希望本文可以为国内广大Python语言学习者提供参考,对计算机相关领域理论文本的翻译也可以提供一些借鉴。

关键词： Combinatorics   Computational Complexity, Mathematics   Computer Science  

摘要： A matroid M is an ordered pair (E, I), where E is a finite set called the ground set and a collection I ⊂ 2E called the independent sets which satisfy the conditions: (i) ∅ ∈ I, (ii) I′ ⊂ I ∈ I implies I′ ∈ I, and (iii) I1, I2 ∈ I and |I1| 2| implies that there is an e ∈ I2 such that [Formula presented]. The rank rk(M) of a matroid M is the maximum size of an independent set. We say that a matroid M = (E, I) is representable over the reals if there is a map [Formula presented] such that [Formula presented] if and only if φ(I) forms a linearly independent set. We study the problem of Matroid [Formula presented]-Representability over the reals. Given a matroid M, we ask whether there is a set of points in the Euclidean space representing M. We show that Matroid [Formula presented]-Representability is [Formula presented]-complete, already for matroids of rank 3. The complexity class [Formula presented] can be defined as the family of algorithmic problems that is polynomial-time equivalent to determining if a multivariate polynomial with integer coefficients has a real root. Our methods are similar to previous methods from the literature. Yet, the result itself was never pointed out and there is no proof readily available in the language of computer science. © 2024 by the author(s)

关键词： Welding   Ultraviolet (UV) radiation   occupational noise   safety   health   machine learning   industry  

摘要： BACKGROUND: Quantitatively analyzing the impact of UV radiation and noise during welding operations is essential to assess the exposure, identify potential hazards, and develop targeted safety protocols to ensure worker safety and adherence to safety regulations, especially in developing countries with inadequate adherence to safety standards and resources. OBJECTIVES: This study employs machine learning for predicting ultraviolet radiation and noise levels during welding, emphasizing worker safety. The focus is on the Indian foundry sector to gauge actual exposure vis-a-vis safety standards. MATERIALS AND METHODS: Ultraviolet radiation and noise emitted during the welding of a ferrous alloy were collected from three foundries in Agra, India. Five machine learning (ML) algorithms were applied for data analysis and prediction of UV radiation and noise levels, and a relative performance comparison was carried out on the compiled data against safety standards. RESULTS: Out of all the ML algorithms applied, the Support Vector Machine regression algorithm (RMSE = 356.93) obtained the best performance on UV radiation data, and the Random Forest algorithm (RMSE =11.4) was found to give the best results for the noise level prediction task. CONCLUSIONS: This work represents the first known application of machine learning techniques for predicting UV radiation and noise levels in arc welding processes. The results show the efficacy of algorithms such as SVM regression and Random Forest for the problem. Further, the datasets and ML algorithms implemented in the work will be made openly available to support further research endeavors in this and related areas.

关键词： Tensors   Standards   Data models   Computer science   Technological innovation   Indexes   Vectors   Inter-class separability   stranger information  

摘要： Multi-view clustering has attracted extensive attention in recent years, which aims at integrating data from different views to improve the clustering performance. In this letter, we propose a novel approach for multi-view clustering. We propose to leverage high-order stranger information of the samples with the aid of Markov random walks to enhance inter-class separability of representation matrix in each view. Then, we seek a direct and intuitive clustering interpretation through view-specific spectral embeddings and cross-view spectral rotation fusion with auto-adjusted weights. Extensive experimental results confirm the effectiveness of our method.

关键词： Convolutional neural networks   Accuracy   Computational modeling   Biological system modeling   Computer science   Artificial intelligence   Classification algorithms   Taxonomy   Organisms   Animals   Ecology   Convolutional neural network (CNN)   specie recognition   Res-Net-50   artificial intelligence  

摘要： Species identification is critical for biological studies, ecological monitoring, and conservation efforts. A comprehensive comprehension of the evolutionary mechanisms that lead to biological variety is necessary while species are distinct categories of living organisms;however, naming, identifying, and differentiating between species is more complex than it may seem. Traditional methods, relying on dichotomous keys and manual observation, are time-consuming and error-prone. Precise species identification is crucial for all taxonomic investigations and biological procedures. Numerous experts are currently engaged in the task of identifying a solitary species. To address these challenges, we present a robust artificial intelligence framework for species identification using deep learning techniques, specifically leveraging the ResNet-50 Convolutional Neural Network (CNN). Our approach utilizes a ResNet-50-based CNN to accurately classify 15 species, including humans, plants, and animals, from images taken at unique locations and angles. The dataset was pre-processed and augmented to enhance training, ensuring robustness against variations in lighting, occlusion, and background clutter. Featuring 4 million trainable parameters, our modified ResNet-50 model demonstrated superior computational efficiency and accuracy. The proposed model achieved an overall accuracy of 96.5%, with class-specific accuracies of 98.25% for humans, 97.81% for animals, and 96.90% for plants. These results surpass those of existing models such as GoogleNet, VGG, SegNet, and DeepLab v3+, highlighting the efficacy of our approach. Performance was evaluated using metrics such as sensitivity, specificity, and error rate, further validating its reliability. Our findings suggest that the ResNet-50-based CNN model is highly effective for automatic species identification, offering significant improvements in accuracy and computational efficiency.

关键词： Generative adversarial networks   Generators   Image synthesis   Training   Tokenization   Computer science   Text processing   Image processing   Text-to-image synthesis   generative adversarial networks   C-GAN   attention mechanism   contrastive learning technique   consistency  

摘要： The generation of high-quality images from textual descriptions is a challenging task in computer vision and natural language processing. The goal of text-to-image synthesis, a current topic of research, is to produce excellent images from written descriptions. This study proposes a hybrid approach to evaluating a dataset consisting of various text-image pairs by efficiently combining conditional generative adversarial networks (C-GAN), attention mechanisms, and contrastive learning (C-GAN+ATT+CL). We suggest a two-step method to improve image quality that starts by utilizing generative adversarial networks (GANs) with attention mechanisms to create low-resolution images and then contrastive learning to improve. Contrastive learning modules train on a separate dataset of high-resolution pictures;GANs learn on datasets of low-resolution text and image pairs. The Conditional GAN with Attention Mechanism and Contrastive Learning Method provides state-of-the-art performance in terms of image quality, diversity, and visual realism, among the several methods. The results of this study demonstrate that the proposed approach works better than all other methods, achieving an Inception Score (IS) of 35.23, a Frechet Inception Distance (FID) of 18.2, and an R-Precision of 89.14. Our findings demonstrate that our "C-GAN+ATT+CL" approach significantly improves image quality and diversity and offers exciting paths for further study.

关键词： 37B30   55-06   55P99   55U10   68-06   68Q60   68Q85  

关键词： Engineering education   Engineering profession   Databases   Software engineering   Computer science   Industries   Diversity methods   Gender equity   Educational institutions   Career development   diversity   education   engineering   gender gap   literature review   mapping study   software engineering  

摘要： Software engineering (SE) and computer science (CS) programs in universities worldwide are marked by a gender gap, which subsequently translates into a gender gap at the industry level. However, there are positive activities that can help attract more women to these male-dominant professions. This study maps the literature related to the achievement of gender balance in SE and CS university-level education and identifies future research directions. More specifically, this article reports on a systematic mapping study of female-inclusive SE and CS tertiary education programs. The authors collected 882 publications between 2015 and 2022 from five databases (ACM, IEEE, Scopus, Web of Science, and Science Direct), selecting 143 peer-reviewed papers for further analysis. The results showed that the main academic contributors were researchers from the USA. The majority of the publications contained observations and explanations regarding the gender gap in computing education. However, an important part of the literature considered proposals and practical activities for achieving gender balance in SE and CS programs. Finally, the authors classified the literature related to female-inclusive SE and CS tertiary education programs, identified the main research focuses and regional distribution, and considered ideas for future research.

关键词： Clustering algorithms   Vehicular ad hoc networks   Routing protocols   Routing   Smart cities   Electronic mail   Computer science   Safety   Throughput   AODV   Clustering   emergency-message dissemination   OMNET plus plus   packet delivery ratio (PDR)   routing protocols   throughput   vehicular Ad hoc network (VANET)  

摘要： The future of smart cities depends on Vehicular Ad hoc Networks that offer services from safety applications to infotainment. However, significant challenges in urban scenarios include connectivity issues, routing, obstacles, and path loss. This paper proposes a comprehensive framework for emergency message dissemination in urban Vehicular Adhoc Network scenarios, focusing on a comparative analysis of clustering-based routing protocols. The RSU Assisted Multi-Hop Weighted Mobility (RA-MHWM) clustering algorithm is proposed as a promising solution, consisting of an RSU Assisted Hybrid Multi-Hop Weighted Mobility for Cluster Head selection algorithm and a Sub-Cluster Head algorithm. The routing is implemented using Cross Layer Weighted Position Based Routing Protocol (CLWPR), Ad hoc On-Demand Distance Vector, and Connected Junction Based Routing (CJBR) protocols, which are compared with each other. A forwarder gateway node selection mechanism is proposed to maximize information coverage. The analysis evaluates that RA-MHWM with CLWPR and CJBR possess significantly higher average throughput, increased Packet Delivery Ratio, and decreased average end-to-end delivery compared to RA-MHWM with AODV and other protocols. Notably, RA-MHWM with CJBR outperforms in terms of information coverage by 85% to deliver emergency messages compared to other protocols for intra-cluster communication.

关键词： Computer Science   Formal Languages and Automata Theory, Computer Science   Logic in Computer Science  

摘要： We study the expressiveness and succinctness of history-deterministic pushdown automata (HD-PDA) over finite words, that is, pushdown automata whose nondeterminism can be resolved based on the run constructed so far, but independently of the remainder of the input word. These are also known as good-for-games pushdown *** prove that HD-PDA recognise more languages than deterministic PDA (DPDA) but not all context-free languages (CFL). This class is orthogonal to unambiguous CFL. We further show that HD-PDA can be exponentially more succinct than DPDA, while PDA can be double-exponentially more succinct than HD-PDA. We also study HDness in visibly pushdown automata (VPA), which enjoy better closure properties than PDA, and for which we show that deciding HDness is ExPTimE-complete. HD-VPA can be exponentially more succinct than deterministic VPA, while VPA can be exponentially more succinct than HD-VPA. Both of these lower bounds are *** then compare HD-PDA with PDA for which composition with games is well-behaved, i.e. good-for-games automata. We show that these two notions coincide, but only if we consider potentially infinitely branching ***, we study the complexity of resolving nondeterminism in HD-PDA. Every HDPDA has a positional resolver, a function that resolves nondeterminism and that is only dependent on the current configuration. Pushdown transducers are sufficient to implement the resolvers of HD-VPA, but not those of HD-PDA. HD-PDA with finite-state resolvers are determinisable.