女生跨栏跑相关素质因素主次的确定

本试验以提高女生100m跨栏教学效果为目的,选择了与100m跨栏相关的三个因素进行教学试验,其结果经方差分析效果显著。     

伦敦奥运会皮划艇1000m项目前三名分段成绩分析与启示

统计了2012年伦敦奥运会皮划艇1 000mK1、K2、K4、C1和C2共5个项目决赛前三名的分段成绩,分析与归纳分段成绩呈现出的特点,对提升中国皮划艇长距离项目训练和比赛水平提出了建议。     

The effects of adult literacy on earnings and employment

This paper provides evidence of the effects of adult literacy on individuals’ income and employability in Brazil based on information obtained from the monthly employment survey (PME). The OLS results indicate that after controlling for observable characteristics, there is a 21.25% increase in wages for individuals who become literate; however, there is no significant impact on employability. Moreover, the findings show an 8.1% increase in the probability of being employed in the formal sector. We also explore the longitudinal structure of the dataset to control for unobservable fixed characteristics of individuals. The fixed-effects estimators show smaller effects compared to the OLS estimators. We find that literacy has a 4.4% effect on wages and a 4.3% impact on the probability of being formally employed. The effects are significantly different from zero.     

Information retrieval in the workplace: A comparison of professional search practices

Legal researchers, recruitment professionals, healthcare information professionals, and patent analysts all undertake work tasks where search forms a core part of their duties. In these instances, the search task is often complex and time-consuming and requires specialist expertise to identify relevant documents and insights within large domain-specific repositories and collections. Several studies have been made investigating the search practices of professionals such as these, but few have attempted to directly compare their professional practices and so it remains unclear to what extent insights and approaches from one domain can be applied to another. In this paper we describe the results of a survey of a purposive sample of 108 legal researchers, 64 recruitment professionals and 107 healthcare information professionals. Their responses are compared with results from a previous survey of 81 patent analysts. The survey investigated their search practices and preferences, the types of functionality they value, and their requirements for future information retrieval systems. The results reveal that these professions share many fundamental needs and face similar challenges. In particular a continuing preference to formulate queries as Boolean expressions, the need to manage, organise and re-use search strategies and results and an ambivalence toward the use of relevance ranking. The results stress the importance of recall and coverage for the healthcare and patent professionals, while precision and recency were more important to the legal and recruitment professionals. The results also highlight the need to ensure that search systems give confidence to the professional searcher and so trust, explainability and accountability remains a significant challenge when developing such systems. The findings suggest that translational research between the different areas could benefit professionals across domains.     

Meta-Circuit machine: Inferencing human collaborative relationships in heterogeneous information networks

Human collaborative relationship inference is a meaningful task for online social networks and is called link prediction in network science. Real-world networks contain multiple types of interacting components and can be modeled naturally as heterogeneous information networks (HINs). The current link prediction algorithms in HINs fail to effectively extract training samples from snapshots of HINs; moreover, they underutilise the differences between nodes and between meta-paths. Therefore, we propose a meta-circuit machine (MCM) that can learn and fuse node and meta-path features efficiently, and we use these features to inference the collaborative relationships in question-and-answer and bibliographic networks. We first utilise meta-circuit random walks to obtain training samples in which the basic idea is to perform biased meta-path random walks on the input and target network successively and then connect them. Then, a meta-circuit recurrent neural network (mcRNN) is designed for link prediction, which represents each node and meta-path by a dense vector and leverages an RNN to fuse the features of node sequences. Experiments on two real-world networks demonstrate the effectiveness of our framework. This study promotes the investigation of potential evolutionary mechanisms for collaborative relationships and offers practical guidance for designing more effective recommendation systems for online social networks.     

新时代00后大学生面临的学习问题及班主任管理对策初探

随着《教育部关于加快建设高水平本科教育全面提高人才培养能力的意见》的发布,我国高校对新时代大学生的学习更加重视。2018年秋季,00后开始进入大学,由于其成长环境和高中与大学教育的差异,出现学习上的不适应。文章主要分析了00后大学生学习面临的主要问题,并提出了作为大学生管理者和引导者的班主任,应该采取哪些对策使其适应大一的学习,为整个大学学习打好基础。     

Measuring patent assessment quality—Analyzing the degree and kind of (in)consistency in patent offices’ decision making

We argue that consistent decision making in judging a patent's validity and basing this on its underlying technological quality are important elements of patent office service (“assessment”) quality. To understand which level of assessment quality patent offices (can) provide, particularly in new technological areas, we study the concordance of the European Patent Office's (EPO) granting and opposition decisions for individual patents. Using the biotechnology industry in the 1980s (an emerging patenting area then) as an example, we find no empirical evidence that the EPO provided maximal or optimal assessment quality as far as can be told from bibliographic indicators. We discuss research limitations and consequences of this first empirical analysis, and suggest ideas for refinements in future work.     

Mixed control of uncertain jumping time-delay systems

In this paper, we investigate a class of linear continuous-time systems with Markovian jump parameters. An integral part of the system dynamics is a delayed state with time-varying and bounded delays. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. Employing norm-bounded parametric uncertainties and utilizing the second-method of Lyapunov, we examine the problem of designing a mixed controller which minimizes a quadratic performance measure while satisfying a prescribed -norm bound on the closed-loop system. It is established that sufficient conditions for the existence of the mixed controller and the associated performance upper bound could be cast in the form of linear matrix inequalities.     

Reliable automatic word spacing using a space insertion and correction model based on neural networks in Korean

Automatic word spacing in Korean remains a significant task in natural language processing owing to the extremely complex word spacing rules involved. Most previous models remove all spaces in input sentences and insert new spaces in the modified input sentences. If input sentences include only a small number of spacing errors, the previous models often return sentences with even more spacing errors than the input sentences because they remove the correct spaces that were typed intentionally by the users. To reduce this problem, we propose an automatic word spacing model based on a neural network that effectively uses word spacing information from input sentences. The proposed model comprises a space insertion layer and a spacing-error correction layer. Using an approach similar to previous models, the space insertion layer inserts word spaces into input sentences from which all spaces have been removed. The spacing error correction layer post-corrects the spacing errors of the space insertion model using word spacing typed by users. Because the two layers are tightly connected in the proposed model, the backpropagation flows are not blocked. As a result, the space insertion and error correction are performed simultaneously. In experiments, the proposed model outperformed all compared models on all measures on the same test data. In addition, it exhibited reliable performance (word-unit F1-measures of 94.17%～97.87%) regardless of how many word spacing errors were present in the input sentences.