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关键词： Software   Computer science   Information technology   Research data  

摘要： Data sharing can save important scientific work from extinction, but only if researchers take care to ensure that resources are easy to find and reuse. Data sharing can save important scientific work from extinction, but only if researchers take care to ensure that resources are easy to find and reuse.

关键词： Biomedical engineering   Computer science   Radiography  

摘要： Here the authors develop a method for accurate auto-labelling of CXR images from large public datasets based on quantitative probability-of similarity to an explainable AI model. The labels can be used to fine-tune the original model through iterative re-training. The inability to accurately, efficiently label large, open-access medical imaging datasets limits the widespread implementation of artificial intelligence models in healthcare. There have been few attempts, however, to automate the annotation of such public databases;one approach, for example, focused on labor-intensive, manual labeling of subsets of these datasets to be used to train new models. In this study, we describe a method for standardized, automated labeling based on similarity to a previously validated, explainable AI (xAI) model-derived-atlas, for which the user can specify a quantitative threshold for a desired level of accuracy (the probability-of-similarity, pSim metric). We show that our xAI model, by calculating the pSim values for each clinical output label based on comparison to its training-set derived reference atlas, can automatically label the external datasets to a user-selected, high level of accuracy, equaling or exceeding that of human experts. We additionally show that, by fine-tuning the original model using the automatically labelled exams for retraining, performance can be preserved or improved, resulting in a highly accurate, more generalized model.

关键词： 计算机   材料科学   课程改革   应用型人才  

摘要： 对计算机在材料科学中的应用课程改革与应用型人才教学实践进行探讨。分析了计算机在材料科学中的应用课程特点,发现了课程教学过程中存在的问题,提出了基于应用型人才培养的教学过程改革措施,总结了解决的问题及具体效果:逐步完善传统教学模式,适当超前化教学,执行严格的课程考核制度,依托群平台形成实时反馈,使用现代化的信息手段不断改革创新并学习新知识,改革考核的方式方法。计算机在材料科学中的应用这门课程通过改革创新打造了计算机科学和材料科学学科深度交叉融合同步发展的新局面,理论与实践相结合的课程属性也使得学生们体会到了学以致用的乐趣和意义。

关键词： Computational biology and bioinformatics   Computer science   Machine learning  

摘要： Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences. Deep learning has enabled advances in understanding biology. In this review, the authors outline advances, and limitations of deep learning in five broad areas and the future challenges for the biosciences.

关键词： Computer architecture   Hardware   Codes   Reduced instruction set computing   Organizations   Software   Integrated circuits   Computer architecture   computer science   Raspberry Pi   remote learning   RISC OS   teaching   UCDebug  

摘要： Understanding the architecture of a processor can be uninteresting and deterring for computer science students, since low-level details of computer architecture are often perceived to lack real-world impact. These courses typically have a strong practical component where students learn the fundamentals of the computer architecture and the handling of input/output operations through the development of simple programs in a low-level assembly programming language. Since these practical sessions require a strong involvement, student attendance and withdrawal rates are poor, lowering academic results and introducing a negative feedback loop that preconditions students to dislike them. This article introduces a new methodology for the practical sessions of Computer Organization and Design courses. This methodology disavows the use of simulators and focuses on actual hardware to promote a feeling of proximity to the execution and outcome of the programs. The proposed setup uses Raspberry Pi devices to encourage students to work autonomously, due to their low cost, capability of running an OS, and rich ecosystem of simple hardware devices. The setup is completed with RISC OS, which combines a simple window-based graphical interface with a low-level management of the hardware without requiring software abstraction layers. The article presents the methodology and the UCDebug tool, developed to help students debug their codes in RISC OS. After the introduction of the new setup at the University of Cantabria, academic results and student satisfaction have improved. The setup has also allowed to sustain a similar organization of the courses throughout the COVID-19 pandemic.

关键词： 黑衣壮   干栏民居题材   水墨文化   文化特色   教育价值  

摘要： 黑衣壮干栏民居题材水墨文化是以黑衣壮干栏民居为所选题材的水墨文化艺术,展示出黑衣壮人的物质观、精神观与价值观,构成一幅民族性与艺术性有机融合的文化艺术形态,是人与自然和谐共生的体现。以黑衣壮干栏民居为题材的水墨文化具有民族性、艺术性、本土性、创新性等特色,其教育价值体现在民族、美术、文化、建筑方面。

关键词： Generators   Predictive models   Electronic mail   Training   Computational modeling   Neurons   Computer science   Convolutional neural networks   image classification   interpretability   discrete masks   reinforcement learning  

摘要： Deep models are commonly treated as black-boxes and lack interpretability. Here, we propose a novel approach to interpret deep image classifiers by generating discrete masks. Our method follows the generative adversarial network formalism. The deep model to be interpreted is the discriminator while we train a generator to explain it. The generator is trained to capture discriminative image regions that should convey the same or similar meaning as the original image from the model's perspective. It produces a probability map from which a discrete mask can be sampled. Then the discriminator is used to measure the quality of the sampled mask and provide feedbacks for updating. Due to the sampling operations, the generator cannot be trained directly by back-propagation. We propose to update it using policy gradient. Furthermore, we propose to incorporate gradients as auxiliary information to reduce the search space and facilitate training. We conduct both quantitative and qualitative experiments on the ILSVRC dataset. Experimental results indicate that our method can provide reasonable explanations for predictions and outperform existing approaches. In addition, our method can pass the model randomization test, indicating that it is reasoning the attribution of network predictions.

关键词： Visualization   Indexes   Human computer interaction   Visual databases   Usability   Computer science   Subgraph query   query autocompletion   database usability  

摘要： Graph query autocompletion (gQAC) generates a small list of ranked query suggestions during the graph query formulation process in a visual environment. The current state-of-the-art of gQAC provides suggestions that are formed by adding subgraph increments to arbitrary places of an existing (partial) user query. However, according to the research results on human-computer interaction (HCI), humans can only interact with a small number of recent software artifacts in hand. Hence, many of such suggestions could be irrelevant. In this paper, we present the GFocus framework that exploits a novel notion of user focus of graph query formulation (or simply focus). Intuitively, the focus is the subgraph that a user is working on. We formulate locality principles inspired by the HCI research to automatically identify and maintain the focus. We propose novel monotone submodular ranking functions for generating popular and comprehensive query suggestions only at the focus. In particular, the query suggestions of GFocus have high result counts (when they are used as queries) and maximally cover the possible suggestions at the focus. We propose efficient algorithms and an index for ranking the suggestions. Our results show that GFocus saves 12-32 percent more mouse clicks and is 35x more efficient than the state-of-the-art competitor.