教学课程资源库 更多>>

关键词： Engineering management   Entrepreneurship   Bibliometrics   Technological innovation   Research and development   Creativity   Computer science   Bibliometric   engineering management   entre-preneurial mindset   entrepreneurship  

摘要： Technological change and competitiveness in the global industrial sector require new engineers' management skills. Engineering management is a concept in evolution. This work aims to describe an overview of the concept. We made a bibliometric study from the Web of Science (WoS) database in April 2022. The most influential journal is IEEE Transactions on Engineering Management with 37 731 citations and 1286 publications. The most influential author was T.R. Browning with eight publications and 1612 citations mainly in the Business WoS category. The thirty most cited papers are described and categorized into research issues. None of those papers are related to entrepreneurship. Entrepreneurship could be an important gap in engineering management research. We propose the entrepreneurial mindset as a potentially valuable research issue.

关键词： Computer science   Quantum information  

摘要： Machine learning is considered to be one of the most promising applications of quantum computing. Therefore, the search for quantum advantage of the quantum analogues of machine learning models is a key research goal. Here, we show that variational quantum classifiers and support vector machines with quantum kernels can solve a classification problembased on the k-FORRELATION problem, which is known to be PROMISEBQP-complete. Because the PROMISEBQP complexity class includes all Bounded-Error Quantum Polynomial-Time (BQP) decision problems, our results imply that there exists a feature map and a quantum kernel thatmake variational quantum classifiers and quantum kernel support vector machines efficient solvers for any BQP problem. Hence, this work implies that their feature map and quantum kernel, respectively, can be designed to have a quantum advantage for any classification problem that cannot be classically solved in polynomial time but contrariwise by a quantum computer.

关键词： 增强现实   脑机接口   科学探究   心流理论  

摘要： 本研究在心流理论的指导下,开发了一款基于轻量级脑机接口的增强现实科学探究工具,对两组参与基于脑机接口和无脑机接口的AR科学探究活动的41名小学生开展实验。结果显示,基于脑机接口的AR探究工具提高了学生科学探究成绩和参与自我效能,而对认知负荷无显著影响。访谈结果也表明,学生对脑机接口的AR探究工具持正面态度。本研究的发现可为未来虚实融合学习环境的学习监测系统构建提供参考。

关键词： Protocols   Radiofrequency identification   Encoding   Estimation   Testing   Mobile computing   Computer science   Radio frequency identification (RFID) systems   category identification   time efficiency   manchester coding  

摘要： RFID systems have been widely deployed for various applications such as inventory control, retail management, and object tracking. In many scenarios, coarse-grained system management of tag categories is more common than traditional system management of individual tags. Existing works on tag category management require knowing category IDs in advance, however, few protocols have been specially designed to obtain category IDs in multi-category RFID systems. In this paper, we propose CIP, a time-efficient protocol specially designed to collect category IDs in multi-category RFID systems. The challenge in solving this problem is to avoid repeatedly collecting category IDs from multiple tags belonging to the same category. We address this challenge by squeezing tags in the same category into the same time slot and letting them reply to the reader simultaneously. Considering that the reader may receive signals from different tags in a slot, we exploit the Manchester coding to decode the aggregated signals. With this design, CIP collects each category ID only once and thus achieves high time efficiency by avoiding redundant identification of the same category ID. We further enhance CIP by utilizing the lightweight vectors to specify the order of tags' replying and avoid useless slots. Rigorous theoretical analysis is presented to optimize the performance of CIP and guidelines on how to set optimal parameters to minimize the overall execution time are given. We conduct extensive experiments to evaluate the performance of our protocols. The experimental results show that our protocols can significantly improve time efficiency by 67 percent when compared with the state-of-the-art solutions.

关键词： 新农科   线上线下教学   线下线上景观营造   景观提升  

摘要： 为解决传统教学模式下计算机辅助园林设计课程实践项目存在的纯命令操作练习、项目虚拟化、与行业脱节等问题,有必要将新型实践项目与新农科建设项目挂钩,以实现多元融合的专业创新型人才培养目标。基于此,以杨靖宇雕塑模型的制作及改造设计项目为例,提出一个教学实践项目的建设方案,并对该方案的实施效果进行调查分析。结果表明,该实践项目的教学效果良好,能有效激发学生的学习兴趣,在一定程度上提高学生的创新能力及行业发展的适应能力。

关键词： Frequency modulation   Feature extraction   Deep learning   Computer architecture   Adaptation models   Computer science   Computational modeling   Factorization machines   finance product recommendation system   graph-convolved factorization machines   multi-feature interaction  

摘要： Factorization machines (FMs) and their neural network variants (neural FMs) for modeling second-order feature interactions are effective in building modern recommendation systems. However, feature interactions are based upon pairs of features, whereas multi-features correlations commonly arise in real-world financial product recommendation scenarios. We propose an effective neural recommender system, graph-convolved factorization machine (GCFM), with the spirit of the symbolic graph reasoning principle that provides lightweight and interpretable recommendation suggestions. Given a sample for the recommendation, GCFM constructs the corresponding dense feature embeddings and computes the sample-specific feature relationship graph. Then, a multi-filter graph-convolved feature crossing (GCFC) layer for feature embeddings establishes cross features with their neighboring embeddings. GCFM thus extends the feature interactions from pairs to neighbors to capture more comprehensive and explainable information while simultaneously reaping the advantages of representation learning. To exploit these capabilities, we apply a Graph Bayesian Optimization (GBO). During training, our GBO automatically optimizes our GCFM, including training hyperparameters and architecture hyperparameters. Besides, we conduct extensive experiments on two public financial applications benchmarks, USCFC and OTC, and two real-world datasets that we collect offline. Our GCFM significantly outperforms state-of-the-art algorithms and shows its interpretability in recommendation tasks. We further extend our model to online real-world applications, showing an appealing human-level decision intelligence in real scenarios.

关键词： 计算机科学与技术   现代化   应用  

摘要： 不断发展的计算机科学与技术为用户提供了有效的操作方式,使人们日常工作中的各种任务相互协调、相互配合,尤其是数据处理和分析,为用户带来巨大的便利和精确度。同时,利用计算机技术,使现代数据的储存、阅读更加有效、顺畅,大大提高了工作质量和效率。文章阐述了计算机科学与技术现代化应用的重要意义,并结合其应用存在的问题,提出优化策略,以期为相关研究人员提供参考。

关键词： Information entropy   Task analysis   Entropy   Scalability   Training   Computer science   Big Data   Graph embedding   efficiency   scalability   entropy-driven mechanism  

摘要： Graph embedding is becoming widely adopted as an efficient way to learn graph representations required to solve graph analytics problems. However, most existing graph embedding methods, owing to computation-efficiency challenges for large-scale graphs, generally employ a one-size-fits-all strategy to extract information, resulting in a large amount of redundant or inaccurate representations. In this work, we propose HuGE+, an efficient and scalable graph embedding method enabled by an entropy-driven mechanism. Specifically, HuGE+ leverages hybrid-property heuristic random walk to capture node features, which considers both information content of nodes and the number of common neighbors in each walking step. More importantly, to guarantee information effectiveness of sampling, HuGE+ adopts two heuristic methods to decide the random walk length and the number of walks per node, respectively. Extensive experiments on real-world graphs demonstrate that HuGE+ achieves both efficiency and performance advantages over recent popular graph embedding approaches. For three downstream graph tasks, our approach not only offers > 10% average gains, but also exhibits 23x-127x speedup over existing sampling-based methods. In addition, HuGE+ significantly reduces memory footprint by an average of 68.9%, facilitating training for billion-node-scale graph embeddings.