面向小语种机器翻译的平行语料库构建方法

神经机器翻译模型的训练效果在很大程度上取决于平行语料库的规模和质量。除了一些常见语言外,汉语与小语种间高质量平行语料库的建设一直处于滞后状态。现有小语种平行语料库多采用自动句子对齐技术利用网络资源构建而成,在文本质量和领域等方面有诸多局限性。采用人工翻译的方式可以构建高质量平行语料库,但是缺乏相关经验和方法。文中从机器翻译实践者和研究者角度出发,介绍了经济高效的人工构建小语种平行语料库的工作,包括其总体目标、实施过程、流程细节和最后结果。在构建过程中尝试并积累了各种经验,形成了小语种到汉语平行语料库构建方法、建议的总结。最终,成功构建了波斯语到汉语、印地语到汉语、印度尼西亚语到汉语各50万条高质量平行语料。实验结果表明,所构建的平行语料库有较好的质量,提高了小语种神经机器翻译模型的训练效果。     

Korean automatic spacing using pretrained transformer encoder and analysis

Automatic spacing in Korean is used to correct spacing units in a given input sentence. The demand for automatic spacing has been increasing owing to frequent incorrect spacing in recent media, such as the Internet and mobile networks. Therefore, herein, we propose a transformer encoder that reads a sentence bidirectionally and can be pretrained using an out-of-task corpus. Notably, our model exhibited the highest character accuracy (98.42%) among the existing automatic spacing models for Korean. We experimentally validated the effectiveness of bidirectional encoding and pretraining for automatic spacing in Korean. Moreover, we conclude that pretraining is more important than fine-tuning and data size.     

适用于无线体域网的动态口令认证协议

无线体域网中传输的是与生命高度相关的敏感数据，身份认证是信息安全保护的第一道防线。现有的基于人体生物信息的身份认证方案存在信息难提取、偶然性大和误差性大的问题，基于传统密码学的认证方案需较大计算资源和能量消耗，并不适用于无线体域网环境。为此，在动态口令和非对称加密机制基础上，提出一种适用于无线体域网的动态口令双向认证轻量协议，并对其进行形式化分析。通过理论证明、SVO逻辑推理及SPIN模型检测得出：该协议满足双向认证，且能够抵御重放攻击、伪装攻击、拒绝服务器攻击和口令离线攻击，具有较高安全性。     

结合预训练模型和语言知识库的文本匹配方法

针对文本匹配任务,该文提出一种大规模预训练模型融合外部语言知识库的方法。该方法在大规模预训练模型的基础上,通过生成基于WordNet的同义—反义词汇知识学习任务和词组—搭配知识学习任务引入外部语言学知识。进而,与MT-DNN多任务学习模型进行联合训练,以进一步提高模型性能。最后利用文本匹配标注数据进行微调。在MRPC和QQP两个公开数据集的实验结果显示,该方法可以在大规模预训练模型和微调的框架基础上,通过引入外部语言知识进行联合训练有效提升文本匹配性能。     

Co-creation of value and the project context: Towards application on the case of Hinkley Point C Nuclear Power Station

The research analyses decisions as evaluative outcomes regarding project value. The UK-French Hinkley Point C Nuclear Power Station provides the case study. Value is traditionally assessed as inputs and outputs. Here, value is conceptualized as a co-created value proposition at the front-end with implications for realization post-completion. Service-dominant logic (SDL) provides the theoretical lens and contributes to a methodological approach for examining projects. Decision-making outcomes provide the evidence from a range of data sources, including reports and commentaries. The methods are interpretative. The findings show that decision-making extends beyond the time-cost-quality/scope dimensions. The long-term issues regarding value realized are often overlooked. Stakeholders and individual actors have mainly focused upon managing political and financial risks, especially time and cost. The research poses challenges to project management analysis, SDL and research design in assessing evidence. Addressing these issues facilitates a knowledge contribution to SDL theorization and the field of project management.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.     

基于大数据的C语言程序设计

C语言作为一种程序设计语言,在大数据组成的物联网和云计算中应用广泛,具有强大的适应性和兼容性,是大数据时代下,在IT行业中起到关键作用的程序设计语言。文章分析了基于大数据的C语言程序设计的必然性和应用策略,为C语言在大数据上的应用方向提供了参考依据。以汉字的应用为例,提出了解决C语言应用问题的思路。     

All two-input logic gates by a single-stage single electron box

In this paper, the design of all two-input logic gates is presented by only a single-stage single electron box (SEB) for the first time. All gates are constructed based on a same circuit. We have used unique periodic characteristics of SEB to design these gates and present all two-input logic gates (monotonic/non-monotonic, symmetric/non-symmetric) by a single-stage design. In conventional monotonic devices, such as MOSFETs, implementing non-monotonic logic gates such as XOR and XNOR is impossible by only a single-stage design, and a multistage design is required which leads to more complexity, higher power consumption and less speed of the gates. We present qualitative design at first and then detailed designs are investigated and optimised by using our previous works. All designs are verified by a single electron simulator which shows correct operation of the gates.