教学课程资源库 更多>>

关键词： GOST SIBID   library science   computer science   terminology   information   information resource   information function   library   information and library (library and information) terminology   library fund   reference and information fund  

摘要： From the point of view of the development of basic terminology, the state of library science can be described as unsatisfactory. Key library science terms are created with violations of the logical rules of classification of concepts. First of all, this applies to the key terms "library," "library-information," "information-library," which are considered in some cases as synonymous and in others as subordinate. However, they are not such in any approach. This article sets out the logical rules for distinguishing the types of theoretical concepts, and analyzes the listed initial terms of library science on this basis. It is concluded that any combination of the words "library" and "information" contradicts the logical rules and norms of word formation in the Russian language. Arguments are given in favor of a balanced and limited use of the terms "information resource," "information function" and the call is made to build library terminology considering the actual exact meaning of the entered professional terms and their meanings. The author considers the urgent need to rework the main terminological GOST SIBID, which has existed for more than 20 years and has entered into a mismatch with later terminological standards in this system, dedicated to specific issues of librarianship.

关键词： Computer science   Mentoring   Teaching-to-mentoring evaluation   Teaching-to-mentoring framework   Undergraduate research training  

摘要： Mentoring is an essential aspect of scientific research training across multiple academic career levels. In student research training contexts, mentoring is intertwined with expectations of increased autonomy and development of research confidence and independence. An often assumed premise of student research mentoring is that students are cognizant of the expectations of mentee-mentor interactions in research and that they are prepared to step into their new mentee role. This assumption is problematic in undergraduate research training where students may conceptualize their interactions with faculty based on student-teacher interactions they have become familiar with in their coursework. There is also indication that the present lack of structured training elements to help students navigate changing roles may especially impact underrepresented minorities in STEM education. Our contributions include reporting on the design and lessons learned from implementing a Teaching-to-Mentoring Framework comprising six professional development strategies in the context of a cohort-based research experience program. The framework aims to support this transitioning into adopting a new role, making the distinction between mentee-mentor and student-teacher interactions transparent, and enabling students to make the most of their undergraduate research experiences. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction   Optimization   Sociology   Statistics   Evolutionary computation   Measurement   Computer science   Classification   duplication analysis   evolutionary algorithm (EA)   feature selection   multiobjective optimization  

摘要： Feature selection is a complex optimization problem with important real-world applications. Normally, its main target is to reduce the dimensionality of the dataset and increase the effectiveness of the classification. Owing to the population-inspired characteristics, different evolutionary algorithms (EAs) have been proposed to solve feature selection problems over the past decades. However, the majority of them only consider single-objective optimization while many real-world problems have multiple objectives, which creates a genuine demand for designing more suitable and effective EAs to handle multiobjective feature selection. A multiobjective feature selection problem usually consists of two objectives: one is to minimize the number of selected features and the other is to minimize the error of classification. In this article, we propose a duplication analysis-based EA (DAEA) for biobjective feature selection in classification. In the proposed algorithm, we make improvements on the basic dominance-based EA framework in three aspects: first, the reproduction process is modified to improve the quality of offspring;second, a duplication analysis method is proposed to filter out the redundant solutions;and third, a diversity-based selection method is adopted to further select the reserved solutions. In the experiments, we have compared the proposed algorithm with five state-of-the-art multiobjective EAs (MOEAs) and tested them on 20 classification datasets, using two widely used performance metrics. According to the empirical results, DAEA performs the best on most datasets, indicating that DAEA not only gains outstanding optimization performance but also obtains good classification and generalization results.

关键词： game-based learning   learning programming   learning JavaScript   learning of algorithms   educational web application   educational mobile app   sustainable development education  

摘要： With increasing awareness of the advantages of game-based learning, there is a growing number of studies showing its application to both computer science education and sustainable development education. In this paper, we describe, with the example of the Eco JSity application, how both of these areas can be combined into a single tool. The presented evaluation results showed that despite incorporating the sustainable development topics into both the theme and mechanics of the educational game, both the usefulness and ease-of-use of the application were still assessed positively by students. We also identified three clusters of students depending on their attitude towards combining education on computer science and sustainable development. We concluded that game-based learning provided a capable means of incorporating sustainable development topics into teaching of unrelated technical skills.

关键词： Pose estimation   Heating systems   Computational modeling   Task analysis   Detectors   Computer vision   Computer science   Human pose estimation   prediction quality acquisition   end to end learning  

摘要： Making human poses serve high-level computer vision tasks such as action recognition, recognizing the quality of estimated poses is of critical importance. Conventionally, the mean confidence of each keypoint is used as pose quality in most human pose estimation frameworks. However, because different types of keypoint are not identical in visibility and size, they should not contribute equally, which produces biased quality scores. In the paper, we propose end-to-end human pose quality learning, which adds a quality prediction block alongside pose regression. The proposed block learns the object keypoint similarity (OKS) between the estimated pose and its corresponding ground truth by sharing the pose features with heatmap regression. The predicted OKS correlates well with pose quality, making the selection of reliable poses straightforward. Moreover, utilizing the learned quality as pose score improves pose estimation performance during COCO AP evaluation, because it ranks more accurate ones high among all pose detections. We conduct extensive experiments based on the three most popular human pose estimation frameworks, including Hourglass, SimpleBaseline and HRNet. Adding the proposed quality learning block is able to consistently bring nearly 1 percent AP improvement on all the frameworks.

关键词： Computer science   IMS-VDEX   logic   LOM   mathematics   metadata   repositories  

摘要： The number of digital resources that exist in repositories and on the Internet in general is enormous. Recovering resources that fit with the user's specific needs poses a problem. To solve this problem, metainformation is added to the resources. One type of metainformation is the classification of a resource using a classification system that is widely recognised and agreed upon by its users. In this way, each resource is assigned a precise place within the classification system, thus facilitating its location. This article proposes a taxonomy for the classification of the resources (notes, exercises, exams or programmes that could be stored within a digital repository) that are generated within the scope of a Mathematical Logic course for a computer science degree programme. It also describes how to represent the proposed taxonomy using the IMS-VDEX standard and how to integrate it in the LOM and Dublin Core metadata specifications and proposes a set of controlled vocabularies that make it possible to refine the taxonomic metainformation.

关键词： Training   Optimization   Data models   Adaptation models   Uncertainty   Image reconstruction   Computer science   Sensor fusion   visual learning  

摘要： We present a method to infer a dense depth map from a color image and associated sparse depth measurements. Our main contribution lies in the design of an annealing process for determining co-visibility (occlusions, disocclusions) and the degree of regularization to impose on the model. We show that regularization and co-visibility are related via the fitness (residual) of model to data and both can be unified into a single framework to improve the learning process. Our method is an adaptive weighting scheme that guides optimization by measuring the residual at each pixel location over each training step for (i) estimating a soft visibility mask and (ii) determining the amount of regularization. We demonstrate the effectiveness our method by applying it to several recent unsupervised depth completion methods and improving their performance on public benchmark datasets, without incurring additional trainable parameters or increase in inference time.