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关键词： anniversary   return   mother  

摘要： Chinese President Xi Jinping,also general secretary of the Communist Party of China Central Committee and chairman of the Central Military Commission,on July 1 made an address at the meeting celebrating the 25th anniversary of Hong Kong’s return to the motherland and the inaugural ceremony of the sixth-term government of the Hong Kong Special Administrative Region.

关键词： anniversary   return   mother  

摘要： Chinese President Xi Jinping,also General Secretary of the C ommunist Party of China Central Committee and Chairman of the Central Military C ommission,on July I made an address at the meeting celebrating the 25th anniversary of Hong Kong's return to the motherland and the inaugural ceremony of the sixth-term government of the Hong Kong Special Administrative *** following is the full text of the address.

关键词： 混合式教学   碎片化教学   计算机综合能力  

摘要： 基于案例分析,阐述利用“云班课”移动平台和线下课堂教学相结合的混合式教学形式,课程的碎片化教学,从而满足对计算机综合能力的应用型人才的需求。

关键词： Computational modeling   Mathematical model   Data models   Numerical models   Social network services   Eigenvalues and eigenfunctions   Computer science   Social network   signed network   scale-free   power-law   balanced indices   network reconstruction  

摘要： We propose modeling signed networks by considering two layers in a social network for generation of positive and negative links where both the layers comprise of identical set of nodes. The growth process is modeled based on preferential attachment, formation of links probabilistically asserting structural balance of local groups, and internal growth which happens without addition of new nodes. We prove that the degree distribution of a generated network follows a power-law whose exponent depends on the largest eigenvalue of a matrix which governs the dynamics of growth of degrees of nodes with respect to positive and negative links. A computable formula for average degree and lower-bounds for the number of balanced and unbalanced triads of modelled networks are also obtained. A method for structural reconstruction of real signed networks is formulated through estimation the values of the model parameters to generate the network that can inherit different structural properties of the corresponding real network. Experimental results show that our model which we term as 2L-SNM can replicate properties of several real world signed networks much more robustly than competitive state-of-the-art techniques.

关键词： 师范院校   计算机研究生   实践能力   科普服务   信息科学  

摘要： 从学科优势、毕业去向、生源质量及学习需求等方面分析师范院校计算机研究生实践能力培养面临的问题,发现计算机研究生具有师范技能提升与计算机专业实践的双重需求。针对该需求,提出借助优质的信息科学科普服务,设计趣味化课程教学,建立助教制度,促使师范院校计算机研究生师范技能及计算机专业实践能力提升。实践证明,深度参与科普服务的计算机研究生,在其师范技能得到充分锻炼的同时,专业创新实践能力也得以有效提升。

关键词： Training   Task analysis   Benchmark testing   Perturbation methods   Knowledge engineering   Cybernetics   Computer science   Fuzzy rules   generalization capability   interpretability   knowledge adversarial training   Takagi-Sugeno-Kang (TSK) fuzzy classifiers  

摘要： While input or output-perturbation-based adversarial training techniques have been exploited to enhance the generalization capability of a variety of nonfuzzy and fuzzy classifiers by means of dynamic regularization, their performance may perhaps be very sensitive to some inappropriate adversarial samples. In order to avoid this weakness and simultaneously ensure enhanced generalization capability, this work attempts to explore a novel knowledge adversarial attack model for the zero-order Tagaki-Sugeno-Kang (TSK) fuzzy classifiers. The proposed model is motivated by exploiting the existence of special knowledge adversarial attacks from the perspective of the human-like thinking process when training an interpretable zero-order TSK fuzzy classifier. Without any direct use of adversarial samples, which is different from input or output perturbation-based adversarial attacks, the proposed model considers adversarial perturbations of interpretable zero-order fuzzy rules in a knowledge-oblivion and/or knowledge-bias or their ensemble to mimic the robust use of knowledge in the human thinking process. Through dynamic regularization, the proposed model is theoretically justified for its strong generalization capability. Accordingly, a novel knowledge adversarial training method called KAT is devised to achieve promising generalization performance, interpretability, and fast training for zero-order TSK fuzzy classifiers. The effectiveness of KAT is manifested by the experimental results on 15 benchmarking UCI and KEEL datasets.

关键词： Semantics   Adversarial machine learning   Correlation   Visualization   Generators   Generative adversarial networks   Computer science  

摘要： The main challenge of cross-modal retrieval is how to efficiently realize cross-modal semantic alignment and reduce the heterogeneity gap. However, existing approaches either ignore the multigrained semantic knowledge learning from different modalities, or fail to learn consistent relation distributions of semantic details in multimodal instances. To this end, this article proposes a novel end-to-end cross-modal representation method, termed as deep multigraph-based hierarchical enhanced semantic representation (MG-HESR). This method is an integration of MG-HESR with cross-modal adversarial learning, which captures multigrained semantic knowledge from cross-modal samples and realizes fine-grained semantic relation distribution alignment, and then generates modalities-invariant representations in a common subspace. To evaluate the performance, extensive experiments are conducted on four benchmarks. The experimental results show that our method is superior than the state-of-the-art methods.

关键词： 办公自动化   ban gong zi dong hua   应用软件   高等学校   教材  

ISBN: (纸本)9787115585462

摘要： 本书是《计算机科学概论与计算思维》的配套实践用书，用于补充和拓展大学计算机基础教学中的实践教学部分。全书共6章，内容包括微机硬件系统与中文输入、Windows10操作系统、文字处理软件Word2016、电子表格软件Excel2016、演示文稿软件PowerPoint2016、计算机网络配置与应用，每章最后都设置了精心设计的上机实践环节。作为以应用为核心的教材，本书可以独立使用。书中案例丰富，讲解翔实，具有很强的指导性。本书适合作为高等学校计算机基础课程的实践指导教材，也可作为全国计算机等级考试Office实践部分的培训教材。

关键词： Optimization   Convergence   Search problems   Acceleration   Computer science   Signal restoration   Prediction algorithms   Change response   dynamic optimization   evolutionary algorithm (EA)   learning   multiobjective optimization  

摘要： Dynamic multiobjective optimization problems are challenging due to their fast convergence and diversity maintenance requirements. Prediction-based evolutionary algorithms currently gain much attention for meeting these requirements. However, it is not always the case that an elaborate predictor is suitable for different problems and the quality of historical solutions is sufficient to support prediction, which limits the availability of prediction-based methods over various problems. Faced with these issues, this article proposes a knowledge learning strategy for change response in the dynamic multiobjective optimization. Unlike prediction approaches that estimate the future optima from previously obtained solutions, in the proposed strategy, we react to changes via learning from the historical search process. We introduce a method to extract the knowledge within the previous search experience. The extracted knowledge can accelerate convergence as well as introduce diversity for the optimization of the future environment. We conduct a comprehensive experiment on comparing the proposed strategy with the state-of-the-art algorithms. Results demonstrate the better performance of the proposed strategy in terms of solution quality and computational efficiency.