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关键词： 诺德功能翻译理论   软件工程文本   英汉翻译   翻译方法  

摘要： 计算机网络技术在人类社会中得到广泛的应用,软件几乎充斥着社会的各行各业。为提高软件在市场中的竞争力,软件开发环节至关重要。然而,在现今软件项目的开发建设过程中,工作量估算在项目管理的过程中得不到足够的重视,同时缺乏系统科学方法的指导。因此,软件工作量估算方法的综述对软件测评从业人员在进行测评方法选择方面具有很大的参考价值。本研究报告以软件工程领域学术论文为研究对象,以诺德功能翻译理论为研究方法,具体分析了词汇、句法、语篇层面的语言特征,以期在功能翻译理论指导下总结出适用于不同语言特征的翻译方法,从而提高软件工程类文本的翻译质量。学术论文属于科技英语的范畴,其主要目的在于使译语读者能无障碍地接受源文本所传达的信息。为实现原文的指称功能,在翻译过程中采用了工具型翻译策略来提高译文可读性。研究表明,针对软件工程学术论文翻译而言,在词汇层面,源文本中多使用术语、缩略词和名词化结构,应分别选取相应的翻译方法以确保译文的准确性;句法层面,语言特征主要表现为被动句和复杂句的频繁使用。分析句子结构后,可通过语态转化、顺序重组使译文符合译语表达习惯;语篇层面,各种衔接手段和连贯手段的使用,使源语文本形成了一个有机整体。翻译时,要根据句子、段落之间的逻辑关系,采取相应翻译方法,使译文内各句在语义和主题上紧密衔接。译者在翻译过程中运用了相应策略和方法最大程度地再现源文本功能,因此,诺德功能翻译理论可以指导软件工程学术论文的翻译。

关键词： perseverance   technology   acceptance   COVID-19   under-developed countries  

摘要： A study is conducted with 368 teachers teaching various undergraduate Computer Science and Computer Engineering courses in an under-developed country - Pakistan, during the COVID-19 pandemic. A teaching model is presented in this research, suggesting that the grit had played an important role in technology adoption for online teaching during the COVID-19. It is found that although the absence of technological support such as high speed Internet and consistent, robust and useful LMS, teachers were still adopting and using technologies such as Google Classroom, Zoom and WhatsApp groups for teaching the students in real-time or through recorded videos. This behavior is explained by the teacher's grit that has also motivated the adoption of online teaching related technologies. The lack of technology such as LMS does affect the user satisfaction, however, it didn't affect the usage behavior of the adoption and use of technology. The initial research model had components from non-cognitive learner characteristics, and from various popular technology usage and acceptance models such as an Information System success model, and Unified Theory of Acceptance and Use of Technology. A quantitative questionnaire survey is conducted to assess the proposed research model. The resultant research model had components only from non-cognitive learner characteristics and Unified Theory of Acceptance and Use of Technology model. The data is analyzed using SEM PLS, K-means clustering and Decision Tree classification method.

关键词： Music   Recommender systems   Task analysis   Context modeling   Computer science   Collaboration   Real-time systems   Recommender system   temporal point process   attention mechanism   user profiling   music recommendation  

摘要： Recommender systems, which aim to provide personalized suggestions for users, have proven to be an effective approach to cope with the information overload problem existing in many online applications and services. In this paper, we target two specific sequential recommendation tasks, next music recommendation and next new music recommendation, to predict the next (new) music piece that users would like based on their historical listening records. In current music recommender systems, various kinds of auxiliary/side information, e.g., item contents and users' contexts, have been taken into account to facilitate user/item preference modeling and have yielded comparable performance improvement. Despite the gained benefits, it is still a challenging and important problem to fully exploit sequential music listening records due to the complexity and diversity of interactions and temporal contexts among users and music, as well as the dynamics of users' preferences. To this end, this paper proposes a novel Attentive Temporal Point Process (ATPP) approach for sequential music recommendation, which is mainly composed of a temporal point process model and an attention mechanism. Our ATPP can effectively capture the long- and short-term preferences from the sequential behaviors of users for sequential music recommendation. Specifically, ATPP is able to discover the complex sequential patterns from the interaction between users and music with the temporal point process, as well as model the dynamic impact of historical music listening records on next (new) music pieces adaptively with an attention mechanism. Comprehensive experiments on four real-world music datasets demonstrate that the proposed approach ATPP outperforms state-of-the-art baselines in both next and next new music recommendation tasks.

关键词： Encryption   Privacy   Data privacy   Companies   Cloud computing   Servers   Computer science   Cloud storage   services computing   reverse nearest neighbor query   order-preserving encryption  

摘要： With the advent of cloud computing, it has become more and more popular to outsource various services to the cloud for releasing the burden of local data storage and maintenance. However, it may cause serious privacy problems because the cloud may be untrusted. In this article, we study the privacy-preserving reverse nearest neighbor (PPRNN) query over encrypted spatial data. First, we introduce the concept of reference-locked order-preserving encryption (RL-OPE) with its construction and security proof, which reveals less information than traditional order-preserving encryption (OPE). Then, we present a novel PPRNN scheme in static setting based on structured encryption (SE) and the proposed RL-OPE, called sPPRNN. After that, we design a generic method that extends a PPRNN scheme in static setting to the counterpart in dynamic setting, called dPPRNN. Furthermore, we present a thorough privacy analysis of our proposal. Finally, we demonstrate its efficiency and effectiveness for practical deployment through extensive experiments.

关键词： Fungi   Polymers   Software   Glycoproteins   Integrals   Carbon   Mutation   Carbohydrates   Binding sites   Amino acids   Chromatography   Genomics   Genes   Hydration   Open access   Chemistry   Computer science   Genetics   Mathematics   Polymer chemistry  

摘要： This dissertation summarizes the findings related to the way by which supramolecular architecture of fungal cell wall changes with genetic mutation, dispensing genes responsible for biosynthesis of cell wall polysaccharides. This is necessary because without perfect picture of how supramolecular assembly changes with genetic mutation it is hard to assess new anti-fungal targets. Alongside this we have highlighted how recent advancement into Dynamic Nuclear Polarization (DNP) methods improved characterization of biomolecules both in case of labeled and unlabeled samples. First study utilized Solid-state NMR (SSNMR) which is a non-destructive technique hence enabled us for the first time to deduce how nanoscale packing, hydration, and mobility of inner cell wall polysaccharides: α-1,3-glucan, β-glucan and chitin changes when either α-1,3-glucan or chitin is removed. Although galactomannan (GM) and galactosaminogalactan (GAG) lies in the exterior still GM deficiency brought about changes in the inner cell wall which is mysterious. GAG has an important role is masking β-1,3-glucan from recognition by human immune system hence its identification using SSNMR was very crucial. At the same time, we were surprised how polysaccharide deficiency brought about drastic changes in the protein region. Apart from this we have also treated the control strain with hot alkali and demonstrated that α-1,3-glucan cannot be removed completely which indicates its involvement in forming fibrils in the inner cell wall with β-glucan and chitin. The biggest surprise came in when repeated alkaline treatment could not remove valine. This is a clear indication that valine is involved in forming glycoprotein linkages. This model which depicts architectural change will improve our understanding of the way fungi responds to environmental stress. The recent advancements in magic angle spin dynamic nuclear polarization (MAS-DNP) enabled atomic-level characterization of biomolecules. Main factor contri

关键词： Diseases   Medical diagnostic imaging   Semantics   Medical diagnosis   Encoding   Learning systems   Computer science   Representation learning   information fusion   disease prediction   network embedding  

摘要： Online medical consult and offline medical check-in have generated a large amount of health-related data in medical forums and patient records. However, exploiting the user-generated content for orienting patients online and assisting medical checkup offline is nontrivial due to the sparsity of symptom-disease associations. The serious sparsity is caused by the informal/chatty expressions of symptoms in the data.

关键词： Image reconstruction   Spatial resolution   Optimization   Computer science   Training   Spectral analysis   Sparse matrices   Hyperspectral image super-resolution (SR)   pixel-aware refinement   unsupervised deep learning  

摘要： Unsupervised fusion-based hyperspectral imagery (HSI) super-resolution (SR) is an essential task of HSI processing, which aims to reconstruct a high-resolution (HR) HSI using only an observed low-resolution HSI and a conventional HR image. Although a large number of unsupervised HSI SR methods have been proposed, the heuristic handcrafted image priors adopted by the majority of these methods restrict their capacity to capture specific characteristics of the HSI, as well as their ability to generalize to noisy observation images. In this study, we investigate a fusion-based HSI SR framework with the deep image prior, in which the deep neural network (rather than a heuristic handcrafted image prior) is exploited to capture plenty of image statistics. Within this framework, we further propose an unsupervised recurrence-based HSI SR method using pixel-aware refinement, which utilizes the intermediate reconstruction results to self-supervise unsupervised learning. Due to containing the information of the image-specific characteristic, the proposed method achieves better performance, in terms of both accuracy and robustness to noise, compared with the existing methods. Extensive experiments on four HSI data sets demonstrate the effectiveness of the proposed method.

关键词： STEM   computer science   data science   modelling and integration  

摘要： Currently STEM education evolves rapidly towards a higher integration either among separate constituents (Science, Technology, Engineering and Mathematics) or by adding the new aspects, such as social. This paper aims at extending the science dimension in STEM-driven Computer Science (CS) education by integrating (through modelling) Data Science (DS) concepts into the high school curriculum. Three types of models and modelling (conceptual, feature-based, and physical modelling) incorporated into a coherent methodology form the background of the proposed approach. Models and modelling, as well processes with data, are essential attributes of engineering education too. The core result of this paper is a novel three-layered framework outlining a series of modelling processes to support integration along with the assessment model. The latter includes the Revised Bloom's taxonomy combined with computational and scientific thinking skills. The use of this approach in the real educational setting and provided experiments show that discovered models ensure a seamless integration of the DS component into STEM-driven CS education. This approach contributes to the increased students' motivation to learn due to the interesting real-world task and active learning of engineering aspects through constructing and testing own experimental system and data processing. The approach also enforces the learner's interdisciplinary knowledge by computational, scientific, and designing skills so important for engineering activities.

关键词： Sparsity   Color   Decomposition   Test systems   Design optimization   Numerical analysis   Energy conservation   Engineering   Algorithms   Graphene   Boundary conditions   Visualization   Applied mathematics   Computer science   Energy   Mathematics  

摘要： Optimizing the design and performance of photonic systems has been an active and growing area of research over the past decade with many practical applications such as image sensors, augmented reality and virtual reality, on-chip photonic systems, and more. Moreover, gradient-based methods, such as the adjoint variable method (AVM), have led to very distinctive and complex designs for on-chip multiplexers, tapers, dielectric laser accelerators, and more, while yielding much higher performance metrics which classical design approaches using first principles physics cannot match. However, less research has been dedicated in the photonics community to understanding and improving the underlying numerical methods which are critical for the success of these applications. In this thesis, we will demonstrate four key numerical advancements in the use of gradient-based design methods using frequency domain numerical solvers of Maxwell's equations, particularly finite difference frequency domain (FDFD) solvers. The first is the application of domain decomposition techniques to gradient-based optimization, allowing us to reduce the effective system size for a gain in efficiency. The second exploits the physics of perturbative series expansions to efficiently determine the optimal learning rate essential to gradient-based optimization. The third leverages the fundamental similarities of the previous two methods, allowing us to combine the two to achieve a further multiplicative acceleration. The fourth is exploiting the choice of boundary condition in the context of perfectly matched layers to minimize overhead and optimize the efficiency of the simulations required during the optimization procedure. Furthermore, we will demonstrate one novel practical application in designing a next generation replacement for traditional filter-based image sensors that we term a 'color router'. By using a gradient-based approach, we demonstrate not only can we overcome the traditional limitati