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摘要： This paper will discuss the way that male and female students rate their abilities in introductory computer science courses. For the past two semesters, students in an introductory computer science course were given a survey at the end of the semester, asking about their experiences in the course. The survey asked students their gender, and also to rate themselves in the course as being either below-average, average, or above-average. The first semester used a traditional lecture-based course delivery. The second semester used team-based learning, in an effort to have students get a better sense of their own and their fellow student's abilities. In both courses however, female students rated themselves significantly lower than male students did, despite the fact that female students actually did better on average (though not statistically significantly better). This paper discusses these results in some detail, and talks about some future work which aims to ameliorate this issue.

摘要： Low Code/No-Code (LCNC) software development has gained its share and popularity in today's software development market, especially in cloud-based application development. In addition to companies specializing in LCNC software development such as Appian, OutSystems and Mendix, major cloud service providers, such as Amazon, Microsoft and Google, have all released their LCNC development platforms.

摘要： Interdisciplinary research is critical to modern-day research. Today researchers should be able to work across disciplinary boundaries seemingly. However, without any practical experience, students graduating from Universities have minimal experience or training. This paper describes one interdisciplinary project that involved several computer science and biological science students and two mentors from each discipline. We description the project, the computer science perspective, the biological science perspective and a conclusion.

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摘要： In general, computer science majors do not consider K-12 education as a career pathway. A potential reason is a lack of introduction to the career path during their undergraduate careers. In this project, supported through a grant from the Maryland Center for Computing Education (***), we developed a pilot computational thinking course blending computer science majors and pre-service teachers. The course focused on describing and integrating computational thinking in a way that would translate to K-12 classrooms while utilizing non-programming based computational tools to demonstrate these ideas. Participants were able to put these ideas into practice in K-8 classrooms during the course and reflect on how the lessons were received. We discuss the implementation of the course, field placement, motivations, and our initial analysis of survey data, including impacts on attitudes on education as a career choice for computer science majors.

关键词： Text categorization   Benchmark testing   Correlation   Bayes methods   Computational modeling   Computer science   Sparse matrices  

摘要： In multilabel text classification, the label information of training instances may be incomplete due to various factors, such as expensively manual labeling, errors, etc. Hence, imposing big challenges to training classifiers. To tackle the problem of the incomplete labels, we proposed to exploit label correlations, and further jointly leverage manifold regularization and label confidence constraint. On one hand, the label manifold regularization preserves the local label structure to handle the high-level incomplete labels. On the other hand, the label confidence constraint, following the likelihood theory, avoids overestimating the weights of negative labels, leading to a safer inference. Upon the ideas, we propose a novel generative model, namely Bayesian Model with Label manifold Regularization and label Confidence constraint (BM-LRC), solved by generalized expectation maximization. Empirical results on popular benchmark data sets show that BM-LRC achieves competitive performance, especially when the available labels are scarce.

关键词： Task analysis   Delays   Real-time systems   Benchmark testing   Estimation   Processor scheduling   Computer science   Cache related pre-emption delay (CRPD)   data cache analysis   real-time systems  

摘要： Timing analysis of real-time systems must consider cache-related pre-emption delay (CRPD) costs when pre-emptive scheduling is used. While most previous work on CRPD analysis only considers instruction caches, the CRPD incurred on data caches is actually more significant. The state-of-the-art CRPD analysis methods are based on useful cache block (UCB) calculation. Unfortunately, as shown in this article, directly extending the existing UCB calculation techniques from instruction caches to data caches will lead to both unsoundness and significant imprecision. To solve these problems, we develop a new UCB calculation technique for data caches, which redefines the analysis unit (to address the unsoundness in the existing method) and precisely captures the dynamic cache access behavior by taking the temporal scopes of memory blocks into consideration. The experimental results show that our new technique yields substantially tighter CRPD estimations comparing with the state-of-the-art.

关键词： Training   Internet of Things   Probes   Task analysis   Computer science   Measurement   Cameras   Convolutional neural networks   hard sample mining   listwise rank learning   multiplet loss   person reidentification (ReID)  

摘要： Person reidentification (ReID) is an important application of Internet of Things (IoT). ReID recognizes pedestrians across camera views at different locations and time, which is usually treated as a ranking task. An essential part of this task is the hard sample mining. Technically, two strategies could be employed, i.e., global hard mining and local hard mining. For the former, hard samples are mined within the entire training set, while for the latter, it is done in mini-batches. In literature, most existing methods operate locally. Examples include batch-hard sample mining and semihard sample mining. The reason for the rare use of global hard mining is the high computational complexity. In this article, we argue that global mining helps to find harder samples that benefit model training. To this end, this article introduces a new system to: 1) efficiently mine hard samples (positive and negative) from the entire training set and 2) effectively use them in training. Specifically, a ranking list network coupled with a multiplet loss is proposed. On the one hand, the multiplet loss makes the ranking list progressively created to avoid the time-consuming initialization. On the other hand, the multiplet loss aims to make effective use of the hard and easy samples during training. In addition, the ranking list makes it possible to globally and effectively mine hard positive and negative samples. In the experiments, we explore the performance of the global and local sample mining methods, and the effects of the semihard, the hardest, and the randomly selected samples. Finally, we demonstrate the validity of our theories using various public data sets and achieve competitive results via a quantitative evaluation.

关键词： Dictionaries   Encoding   Sun   Robustness   Computer science   Training   Learning systems   Image recognition   image representation   locality-adaptive discriminative sparse representation (SR)   robust projective dictionary pair learning (DPL)  

摘要： In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective DPL in four perspectives. First, it applies a sparse l(2,1)-norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the l(2,1)-norm has the potential to minimize the error. Second, it imposes the robust l(2,1)-norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly l(0)/l(1)-norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the efficacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intraclass compactness and interclass separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state of the arts.