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关键词： 计算机系统   信息技术   工程建设   优化资源配置  

摘要： 阐述计算机科学与技术在工程建设项目中的特点、应用实践、应用策略,探讨工程建设项目的高质量管理与企业和国家的发展相关,它需要先进的计算机科学和技术来提升质量管理效率,优化资源配置,从而在工程建设项目在现代化、信息化的进程中给予足够的技术支撑。

关键词： 科学阐释学   三角测量   毕达哥拉斯主义   计算主义   系统科学   新目的论自然观  

摘要： 从分析哲学家戴维森的“三角测量”理论出发,多视角地透视阐释学中文本、原作者、解读者三方关系,就能克服主观片面性,在主体间达成共识,从而实现“视域融合”。对自然文本(世界)进行多视角的透视,则每一种视角都通过各自独特的语言来表述,不同语言之间,可以互为文本和解读者,相互翻译,相互转换。毕达哥拉斯主义的基本精神,是追求宇宙的数学和谐性的思想,它能成功地引导科学家使用数学语言来刻画物理世界的内在奥秘。当今流行的“计算主义”可以同它互为文本与解读者。按照系统科学模式重新理解的目的论自然观,能提供生命世界、人工生命乃至自组织的宇宙的合理解释。毕达哥拉斯主义、计算主义与包含“程序自动化”的新目的论自然观的视域融合,将实现自然科学与人文科学的“阐释学统一性”。

关键词： restaurant   computer   Mike  

摘要： 听下面一段独白,完成表格。独白读两遍。根据首字母及句意补全单词。*** works as a c__ in a Chinese restaurant.

关键词： 听障大学生   就创业能力   计算机专业  

摘要： 在当前国内外环境和疫情变化存在不确定性的情况下,国家将高校毕业生就业作为就业工作重中之重,而听障大学生作为一个特殊群体,就业创业更加困难,即使是计算机类的热门专业也难逃就业难的问题,高校亟需采取措施应对。文章以计算机专业为例,分析高校在听障大学生就创业教育方面的实际现状,积极探索提升路径,旨在帮助高校做好计算机专业听障大学生就创业能力的提升工作,促进听障大学生高质量就业。

关键词： Testing   Color   Software   Complexity theory   Combinatorial testing   Licenses   Computer science   Pending schema   minimal failure-causing schema   combinatorial testing   software testing  

摘要： Combinatorial Testing (CT) is an effective testing technique for detecting failures which are triggered by the interactions of various factors that influence the behaviour of a system. Although many studies in CT have designed elaborate test suites (called covering arrays) to systemically check each possible factor interaction, they provide weak support to locate the concrete failure-inducing interactions, i.e., the Minimal Failure-causing Schemas (MFS). To this end, a variety of MFS identification approaches have been proposed. However, as this study reveals, these approaches suffer from various issues such as cannot identify multiple overlapping MFSs, cannot handle MFSs with high degrees, cannot be applied to systems with large number of parameters, etc. These issues are essentially caused by the exponential computing complexity of checking every interaction in the test cases. Therefore, they can only focus on a subset of all the possible interactions, resulting in many interactions unnoticed. Ignoring these unnoticed interactions could potentially cause failures that have never been systematically checked. Hence, it is beneficial for MFS identification approaches to identify these interactions. In order to account for these unnoticed interactions in CT, this study introduces the notion of pending schema, based on which a theoretical framework of CT schemas is established. In particular, we formally define the determinability of a schema in CT with respect to given information;as such, the yet-to-be determined schemas are exactly the pending schemas. The relationships between the different schemas (faulty, healthy, and pending) and test cases are also theoretically analyzed. Based on which, we further propose three formulas, along with three corresponding algorithms, for the identification of the pending schemas in failing test cases, and formally prove their correctness. As a result, we reduce the complexity of obtaining pending schemas with respect to the number

关键词： Hash functions   Optimization   Codes   Semantics   Estimation   Computer science   Annotations   Cross-modal retrieval   unsupervised deep hashing   similarity estimation   optimization strategy  

摘要： Cross-modal hashing that leverages hash functions to project high-dimensional data from different modalities into the compact common hamming space, has shown immeasurable potential in cross-modal retrieval. To ease labor costs, unsupervised cross-modal hashing methods are proposed. However, existing unsupervised methods still suffer from two factors in the optimization of hash functions: 1) similarity guidance, they barely give a clear definition of whether is similar or not between data points, leading to the residual of the redundant information;2) optimization strategy, they ignore the fact that the similarity learning abilities of different hash functions are different, which makes the hash function of one modality weaker than the hash function of the other modality. To alleviate such limitations, this paper proposes an unsupervised cross-modal hashing method to train hash functions with discriminative similarity guidance and adaptively-enhanced optimization strategy, termed Deep Adaptively-Enhanced Hashing (DAEH). Specifically, to estimate the similarity relations with discriminability, Information Mixed Similarity Estimation (IMSE) is designed by integrating information from distance distributions and the similarity ratio. Moreover, Adaptive Teacher Guided Enhancement (ATGE) optimization strategy is also designed, which employs information theory to discover the weaker hash function and utilizes an extra teacher network to enhance it. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed DAEH against the state-of-the-arts.

关键词： Euclidean distance   Training   Task analysis   Feature extraction   Testing   Computer science   Object detection   Center loss   deep learning   Euclidean distance center   Pearson correlation coefficient   vehicle re-identification  

摘要： Center loss is widely used as a supervision tool in deep learning method. However, the center loss also has some shortcomings, the most important of which is that it must be combined with softmax loss to run well. In this article, we sum up five shortcomings of center loss and solve all of them by proposing a dual distance center loss (DDCL). Compared with center loss, DDCL can run without the combination of softmax to supervise training the model. In addition, we verify the inconsistency between the proposed DDCL and softmax loss in the feature space. To be specifically, we add the Pearson distance on the basis of the Euclidean distance to the same center, which makes all features of the same class be confined to the intersection of a hypersphere and a hypercone in the feature space, strengthens the intraclass compactness of the center loss, and enhances the generalization ability of center loss. Moreover, by designing a Euclidean distance threshold between all center pairs, we not only strengthen the interclass separability of center loss, but also make the center loss (or DDCL) works well without the combination of softmax loss. We verify the effectiveness of DDCL in four datasets, two of which are widely used in the field of vehicle re-identification named VeRi-776 dataset and VehicleID dataset, and two other datasets are widely used in the field of person re-identification named Market1501 dataset and MSMT17 dataset. The experimental results of the proposed DDCL exceed that of the softmax loss in all the four datasets, indicating that our proposed method not only can run without the combination of softmax, but also has a high accuracy.

关键词： Planning   Search problems   Reinforcement learning   Urban areas   Space exploration   Probabilistic logic   Computer science   Automatic itinerary planning   deep reinforcement learning   multiobjective optimization  

摘要： Automatic itinerary planning that provides an epic journey for each traveler is a fundamental yet inefficient task. Most existing planning methods apply heuristic guidelines for certain objective, and thereby favor popular preferred point of interests (POIs) with high probability, which ignore the intrinsic correlation between the POIs exploration, traveler's preferences, and distinctive attractions. To tackle the itinerary planning problem, this paper explores the connections of these three objectives in probabilistic manner based on a Bayesian model and proposes a triple-agent deep reinforcement learning approach, which generates 4-way direction, 4-way distance, and 3-way selection strategy for iteratively determining next POI to visit in the itinerary. Experiments on five real-world cities demonstrate that our triple-agent deep reinforcement learning approach can provide better planning results in comparison with state-of-the-art multiobjective optimization methods.