一种高效的电子政务系统的设计与应用

计算机网络技术的出现,全球信息化改变着人们的生活方式,世界各国高度重视发展电子政务,它是一个国家及城市竞争力水平的标志之一.在这样严峻的社会形势下,建立一个高效的电子政务系统是十分必要的.首先,分析了建立电子政务系统的必要性、特点、功能;然后,提出了电子政务系统的体系模型,设计出了功能结构图和网络结构图,详细地描述了方案思路;最后,把此模型应用到商洛市旅游行业中,经过调查数据研究分析,政务系统的出现给旅游业带来巨大的变化,整个城市的经济、影响力大幅度上升.     

大数据时代下计算机专业教学的探索

大数据时代的到来引起了教育领域人士的关注,各高校逐步开始重视相关教学及研究.根据大数据概念及计算机学科联系紧密的特点,能够帮助学习者整理和分析信息,结合实际教学经验,引入计算机专业教学的教育模式、创新教学方法、与时俱进的学习意识等内容来探索大数据时代对计算机专业教学作用及方法,为该专业教学研究人员提供一定参考,也为培养大数据研究人才奠定基础.     

对我国维纶和腈纶建设模式的回顾

通过对我国维纶和腈纶建设模式（统一工艺技术、分散布点、同步建设）的回顾，认为化纤工业技术密集、投资风险大，国家应进行强有力的宏观调控，优先发展性能优良、节省资源、能源，并对环境污染较轻、有发展潜力的品种。选用引进技术设备要可靠、先进，并适合国情。对没有十足把握的技术设备，应试点后，再进行大规模建设。为减少损失，对国产化设备要通过实践考核后再推广使用。     

Evolutionary digital twin: A new approach for intelligent industrial product development

To fulfill increasingly difficult and demanding tasks in the ever-changing complex world, intelligent industrial products are to be developed with higher flexibility and adaptability. Digital twin (DT) brings about a possible means, due to its ability to provide candidate behavior adjustments based on received “feedbacks” from its physical part. However, such candidate adjustments are deterministic, and thus lack of flexibility and adaptability. To address such problem, in this paper an extended concept – evolutionary digital twin (EDT) and an EDT-based new mode for intelligent industrial product development has been proposed. With our proposed EDT, a more precise approximated model of the physical world could be established through supervised learning, based on which the collaborative exploration for optimal policies via parallel simulation in multiple cyberspaces could be performed through reinforcement learning. Hence, more flexibility and adaptability could be brought to industrial products through machine learning (such as supervised learning and reinforcement learning) based self-evolution. As a primary verification of the effectiveness of our proposed approach, a case study has been carried out. The experimental results have well confirmed the effectiveness of our EDT based development mode.     

A novel deep learning prediction model for concrete dam displacements using interpretable mixed attention mechanism

Dam displacements can effectively reflect its operational status, and thus establishing a reliable displacement prediction model is important for dam health monitoring. The majority of the existing data-driven models, however, focus on static regression relationships, which cannot capture the long-term temporal dependencies and adaptively select the most relevant influencing factors to perform predictions. Moreover, the emerging modeling tools such as machine learning (ML) and deep learning (DL) are mostly black-box models, which makes their physical interpretation challenging and greatly limits their practical engineering applications. To address these issues, this paper proposes an interpretable mixed attention mechanism long short-term memory (MAM-LSTM) model based on an encoder-decoder architecture, which is formulated in two stages. In the encoder stage, a factor attention mechanism is developed to adaptively select the highly influential factors at each time step by referring to the previous hidden state. In the decoder stage, a temporal attention mechanism is introduced to properly extract the key time segments by identifying the relevant hidden states across all the time steps. For interpretation purpose, our emphasis is placed on the quantification and visualization of factor and temporal attention weights. Finally, the effectiveness of the proposed model is verified using monitoring data collected from a real-world dam, where its accuracy is compared to a classical statistical model, conventional ML models, and homogeneous DL models. The comparison demonstrates that the MAM-LSTM model outperforms the other models in most cases. Furthermore, the interpretation of global attention weights confirms the physical rationality of our attention-based model. This work addresses the research gap in interpretable artificial intelligence for dam displacement prediction and delivers a model with both high-accuracy and interpretability.     

基于跨语言神经主题模型的汉越新闻话题发现方法

针对汉越跨语言新闻话题发现任务中汉越平行语料稀缺,训练高质量的双语词嵌入较为困难,而且新闻文本一般较长导致双语词嵌入的方法难以很好地表征文本的问题,提出一种基于跨语言神经主题模型（CL-NTM）的汉越新闻话题发现方法,利用新闻的主题信息对新闻文本进行表征,将双语语义对齐转化为双语主题对齐任务。首先,针对汉语和越南语分别训练基于变分自编码器的神经主题模型,从而得到单语的主题抽象表征;然后,利用小规模的平行语料将双语主题映射到同一语义空间;最后,使用K-means方法对双语主题表征进行聚类,从而发现新闻事件簇的话题。实验结果表明,所提方法相较于面向中英文的隐狄利克雷分配主题改进模型（ICE-LDA）在Macro-F1值与主题一致性上分别提升了4个百分点与7个百分点,可见所提方法可有效提升新闻话题的聚类效果与话题可解释性。     

改进在线词对主题模型的微博热点话题演化

话题演化分析是舆情监控的研究热点之一,面向微博热点话题进行演化分析,对于网络用户以及网络监管部门都有很重要的现实意义。针对在线词对主题模型（On-line Biterm Topic Model,OBTM）新旧主题混合、冗余词概率相对较高的问题,对OBTM进行改进,提出基于话题标签和先验参数的OBTM模型（Topic Labels and Prior Parameters OBTM,LPOBTM）。根据微博热点话题的话题标签,将微博文本集区分为含话题标签和不含话题标签的两类数据集,并设置不同的文档-主题先验参数;在前一时间片文档-主题概率分布的基础上,借鉴Sigmod函数对所有主题进行强度排名,从而优化当前时间片上主题-词分布的先验参数计算方法。实验结果表明,LPOBTM能够更准确地描述话题的内容演化情况,并且有更低的模型困惑度。     

基于二分内包围盒的玉米光照逆光线跟踪算法

为了在玉米仿真中构建逼真有效的光环境,提出一种基于二分内包围盒的玉米光照逆光线跟踪算法。首先,在玉米表面建立二分内包围盒进行预处理,减少光线与玉米相交运算量;其次,引入遮挡因子简化光能计算的复杂度;最后,通过调整光能阈值达到光环境逼真度和算法效率的统一。为了验证算法的快速有效性,建立玉米模型,对比不同包围盒求交运算的速率,得出二分内包围盒的求交速度最快,逆光线跟踪算法模拟出的玉米生长效果较为逼真。     

Model checking response times in Networked Automation Systems using jitter bounds

Response time (RT) of Networked Automation Systems (NAS) is affected by timing imperfections induced due to the network, computing and hardware components. Guaranteeing RT in the presence of such timing imperfections is essential for building dependable NAS, and to avoid costly upgrades after deployment in industries.This investigation proposes a methodology and work-flow that combines modelling, simulation, verification, experiments, and software tools to verify the RT of the NAS during the design, rather than after deployment. The RT evaluation work-flow has three phases: model building, modelling and verification. During the model building phase component reaction times are specified and their timing performance is measured by combining experiments with simulation. During the modelling phase, component based mathematical models that capture the network architecture and inter-connection are proposed. Composition of the component models gives the NAS model required for studying the RT performance on system level. Finally, in the verification step, the NAS formal models are abstracted as UPPAAL timed automata with their timing interfaces. To model timing interfaces, the action patterns, and their timing wrapper are proposed. The formal model of high level of abstraction is used to verify the total response time of the NAS where the reactions to be verified are specified using a subset of timed computation tree logic (TCTL) in UPPAAL model checker. The proposed approach is illustrated on an industrial steam boiler deployment.