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关键词： Cluster computing   Computer science   HPC infrastructure   Parallel workload archive  

摘要： The data of this research describes the logged usage of the Euler cluster located at CIEMAT (Centre for Energy, Environment, and Technology Research), spanning the period between November 2008 and March 2018. The Euler database is open access in Parallel Workload Archive format, available from the PWA repository [1] and Mendeley Data [2], allowing in this way a whole new bunch of research possibilities on computer science. (C) 2019 The Author(s). Published by Elsevier Inc.

关键词： self-efficacy   STEM  

摘要： The change in the English computing curriculum and the shift towards computer science (CS) has been closely observed by other countries. Female participation remains a concern in most jurisdictions, but female attainment in CS is relatively unstudied. Using the English national pupil database, we analyzed all exam results (n = 5,370,064) for students taking secondary school exams in 2016, focusing on those students taking GCSE CS (n = 60,736), contrasting this against ICT (n = 67,359). Combining gender with ethnicity and the IDACI poverty indicator, we find that females from the poorest areas were more likely to take CS than those from the richest areas and that CS was more popular among ethnic minority females than white females. ICT was far more equitable for females and poorer students than CS. CS females typically got better grades than their male peers. However, when controlling for average attainment in other subjects, males got 0.31 of a grade higher. Female relative underperformance in CS was most acute among large female cohorts and with girls studying in mixed-gender schools. Girls did significantly better than boys in English when controlling for CS scores, supporting theories around female relative strengths lying outside STEM subjects. The move to introduce CS into the English curriculum and the removal of the ICT qualifications look to be having a negative impact on female participation and attainment in computing. Using the theory of self-efficacy, we argue that the shift towards CS might decrease the number of girls choosing further computing qualifications or pursuing computing as a career. Computing curriculum designers and teachers need to carefully consider the inclusive nature of their computing courses.

关键词： Computer science   Psychology   Society  

摘要： Although AI companies market software for recognizing emotions in faces, psychologists debate whether expressions can be read so easily.

关键词： citizen science   computer vision   convolutional neural network   ladybird   machine learning   metadata   naturalists   species identification  

摘要： The accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording. Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer-led mass participation recording scheme. Each image is associated with metadata;a date, location and recorder ID, which can be cross-referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi-input neural network models that synthesize metadata and images to identify records to species level. We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image-only baseline of 48.2%, we observe a 9.1 percentage-point improvement in top-1 accuracy with a multi-input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data are being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilizing similar pieces of evidence as human naturalists to make identifications. Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualization offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be pr

关键词： Image retrieval   Semantics   Hash functions   Binary codes   Computer science   Neural networks   Kernel   Image retrieval   convolutional neural network   semantic label   pairwise similarity   deep hashing  

摘要： Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is "1" if they share no less than one class label and "0" if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, an improved deep hashing method is proposed to enhance the ability of multi-label image retrieval. We introduce a pairwise quantified similarity calculated on the normalized semantic labels. Based on this, we divide the pairwise similarity into two situations-"hard similarity" and "soft similarity," where cross-entropy loss and mean square error loss are adapted respectively for more robust feature learning and hash coding. Experiments on four popular datasets demonstrate that the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.

关键词： Privacy   Online social networks   Privacy controls   Social capital   Behavior analysis   Boundary regulation theory   Facebook   Computer privacy   Cognitive aspect of human-computer system   Interaction design   User interface   Software engineering   Computer science   Psychology  

摘要： People's posting behaviors in social networks was perceived as ambiguous, with concerns misaligned with people's public postings. To address this gap, we suggest a model that offers new insights into the relationship between perceptions and actual behaviors. We define a quantitative marker for agility, the frequency in which people update their audience selection when posting information in online social networks, and evaluate the factors that contribute to the variability of agility between different users. We analyzed the posting behavior of Facebook 181 participants, as well as their answers to open and close questions. We find that frequent changes in privacy settings are correlated with high social privacy and with institutional privacy concerns, whereas social concerns were found to be more prominent. Agility was negatively correlated with low public sharing. Our findings show that users use privacy settings to effectively mitigate privacy concerns and desires for creating and strengthening social connections. We discuss how agility can be used to design and to evaluate new user interfaces for managing privacy in social settings.

关键词： Computer science   Wood pith detection   Parawood pith location   Deep neural networks object detection   SSD MobileNet   You-only-look-once  

摘要： The location of pith in a cross-sectional surface of wood can be used to either evaluate its quality or guide the removal of soft wood from the wood stem. There have been many attempts to automate pith detection in images taken by a normal camera. The objective of this study is to comparatively study the effectiveness of two popular deep neural network (DNN) object detection algorithms for parawood pith detection in cross-sectional wood images. In the experiment, a database of 345 cross-sectional images of parawood, taken by a normal camera within a sawmill environment, was quadrupled in size via image augmentation. The images were then manually annotated to label the pith regions. The dataset was used to train two DNN object detection algorithms, an SSD (single shot detector) MobileNet and you-only-look-once (YOLO), via transfer learning. The inference results, utilizing pretrained models obtained by minimizing a loss function in both algorithms, were obtained on a separate dataset of 215 images and compared. The detection rate and average location error with respect to the ground truth were used to evaluate the effectiveness of detection. Additionally, the average distance error results were compared with the results of a state-of-the-art non-DNN algorithm. SSD MobileNet obtained the best detection rate of 87.7% with a ratio of training to test data of 80:20 and 152,000 training iterations. The average distance error of SSD MobileNet is comparable to that of YOLO and six times better than that of the non-DNN algorithm. Hence, SSD MobileNet is an effective approach to automating parawood pith detection in cross-sectional images.

摘要： The Communications Web site, http://***, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications, we'll publish selected posts or *** us on Twitter at http://***/blogCACMhttp://***/blogs/blog-cacmMark Guzdial considers how teaching computing to undergraduates requires better education of the teachers as well.

关键词： capital punishment   crime   death penalty   general news   legal action   manslaughter   murder   political  

关键词： capital punishment   crime   death penalty   general news   legal action   manslaughter   murder   political