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关键词： 计算机   技术   系统能力  

摘要： 产业界对计算机系统人才的需求日益增加,对系统能力培养的要求不断提高。在专业课时的限制下,以课程群的方式组合与系统能力培养有关的各门课程,使多门课程相互协作,产生合力,对提升学生系统能力大有益处。具体培养内容有:建设系统能力课程群,细化培养目标并分解到多门不同课程,细化能力考核指标和方案,以问题和项目为契机展开实践教学。实施以上的相应措施后,不仅提高了学生应对问题的能力,也为专业培养模式和能力考核机制提供了参考。

关键词： Signal processing algorithms   Phase measurement   Time measurement   Decoding   Approximation algorithms   Compressed sensing   Computer science   Phase retrieval   compressive sensing   sparse recovery   sublinear-time algorithms   combinatorial algorithms  

摘要： In the problem of compressed phase retrieval, the goal is to reconstruct a sparse or approximately k-sparse vector x epsilon C-n given access to y = | phi x|, where |v| denotes the vector obtained from taking the absolute value of v epsilon Cn coordinate-wise. In this paper we present sublinear-time algorithms for a few for-each variants of the compressive phase retrieval problem which are akin to the variants considered for the classical compressive sensing problem in theoretical computer science. Our algorithms use pure combinatorial techniques and near-optimal number of measurements.

关键词： Graph theory  

摘要： Researchers discuss some of the most notorious research topics in theoretical computer science (TCS) in Latin America. Their perspective is inspired by the research scope of LATIN, LAGOS, and Latincrypt;we have grouped them into nine topics. One of the main instigators of interest and research in TCS in Brazil has been Imre Simon, whose work in automata theory has been influential Owing to Simon’s research, a semiring and some of its variants were dubbed tropical semirings, and the name has endured as they became fashionable in algebraic geometry. In 1992, he launched the first Latin American theory conference (LATIN), fostering the emergence of a vibrant regional community. In Chile, Eric Goles mentored several of the first Chilean TCS researchers, who, together with his recent students, are active throughout several institutions in Chile, working in graph theory, distributed computing, Boolean networks, and other areas.

关键词： Middle school youth   near-peer mentor   high school youth   self-efficacy   interest   access   diversity  

摘要： In response to the need to broaden participation in computer science, we designed a summer camp to teach middle-school-aged youth to code apps with MIT App Inventor. For the past four summers, we have observed significant gains in youth's interest and self-efficacy in computer science, after attending our camps:Me majority of these youth, however, were youth from our local community. To provide equal access across the state and secure more diversity, we were interested in examining the effect of the camp on a broader population of youth. Thus, we partnered with an outreach program to reach and test our camps on youth from low-income high-poverty areas in the Intermountain West. During the summer of 2019, we conducted two sets of camps: locally advertised app camps that attracted youth from our local community and a second set of camps as part of a larger outreach program for youth from low-income high-poverty areas. The camps for both populations followed the same design of personnel, camp activities, structure, and curriculum. However, the background of the participants was slightly different. Using survey data, we found that the local sample experienced significant gains in both self-efficacy and interest, while the outreach group only reported significant gains in self-efficacy after attending the camp. However, the qualitative data collected from the outreach participants indicated that they had a positive experience both with the camp and their mentors. In this article, we discuss the camp design and findings in relation to strategies for broadening participation in Computer Science education.

关键词： Computer science   PM2.5 prediction   Regression   Atmospheric pollution   Time series analysis   Machine learning  

摘要： During the last many years, the air quality of the capital city of India, Delhi had been hazardous. A large number of people have been diagnosed with Asthma and other breathing-related problems. The basic reason behind this has been the high concentration of life-threatening PM2.5 particles dissolved in its atmosphere. A good model, to forecast the concentration level of these dissolved particles, may help to prepare the residents with better prevention and safety strategies in order to save them from many health-related diseases. This work aims to forecast the PM2.5 concentration levels in various regions of Delhi on an hourly basis, by applying time series analysis and regression, based on various atmospheric and surface factors such as wind speed, atmospheric temperature, pressure, etc. The data for the analysis is obtained from various weather monitoring sites, set-up in the city, by the Indian Meteorological Department (IMD). A regression model is proposed, which uses Extra-Trees regression and AdaBoost, for further boosting. Experimentation for comparative study with the recent works is done and results indicate the efficacy of the proposed model.

关键词： Computational and artificial intelligence   computer science   computers and information processing   data mining   formal languages   learning automata   learning systems   pattern recognition  

摘要： Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyberphysical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential curves, and sinusoids as atomic symbols with symbolic constraints on the shape parameters. SEs enable a natural and intuitive specification of complex temporal patterns over possibly noisy data. In this article, we propose a novel method for mining a broad and interesting fragment of SEs from time-series data using a combination of techniques from linear regression, unsupervised clustering, and learning finite automata from positive examples. The learned SE for a given dataset provides an explainable and intuitive model of the observed system behavior. We demonstrate the applicability of our approach on two case studies from different application domains and experimentally evaluate the implemented specification mining procedure.

关键词： Learning-by-modeling   Stem plus CT   Synergistic learning   Evidence-centered design   Domain-specific modeling language  

摘要： Driven by our technologically advanced workplaces and the surge in demand for proficiency in the computing disciplines, it is becoming imperative to provide computational thinking (CT) opportunities to all students. One approach for making computing accessible and relevant to learning and problem-solving in K-12 environments is to integrate it with existing Science, Technology, Engineering, and Math (STEM) curricula. However, novice student learners may face several difficulties in trying to learn STEM and computing concepts simultaneously. To address some of these difficulties, we present a systematic approach to learning STEM and CT by designing and developingdomain-specific modeling languages(DSMLs) to aid students in their model building and problem-solving processes. The paper discusses a theoretical framework and the design principles for developing DSMLs, which is implemented as a four-step process. We apply the four-step process in three domains: Physics, Marine Biology, and Earth Science to demonstrate its generality, and then perform case studies to show how the DSMLs impact student learning and model building. We conclude with a discussion of our findings and then present directions for future work.

关键词： Fuzzy sets   Robustness   Area measurement   Computer science   Fuzzy systems   Cognition   Distance measure (DM)   interval-valued data   similarity measure (SM)   subsethood  

摘要： With a growing number of areas leveraging intervalvalued data-including in the context of modeling human uncertainty (e.g., in cybersecurity), the capacity to accurately and systematically compare intervals for reasoning and computation is increasingly important. In practice, well established set-theoretic similarity measures, such as the Jaccard and Sorensen-Dice measures, are commonly used, whereas axiomatically, a wide breadth of possible measures have been theoretically explored. This article identifies, articulates, and addresses an inherent and so far not discussed limitation of popular measures-their tendency to be subject to aliasing-where they return the same similarity value for very different sets of intervals. The latter risks counter-intuitive results and poor-automated reasoning in real-world applications dependent on systematically comparing interval-valued system variables or states. Given this, we introduce new axioms establishing desirable properties for robust similarity measures, followed by putting forward a novel set-theoretic similarity measure based on the concept of bidirectional subsethood, which satisfies both traditional and new axioms. The proposed measure is designed to be sensitive to the variation in the size of intervals, thus avoiding aliasing. This article provides a detailed theoretical exploration of the new proposed measure, and systematically demonstrates its behavior using an extensive set of synthetic and real-world data. Specifically, the measure is shown to return robust outputs that follow intuition-essential for real-world applications. For example, we showthat it is bounded above and belowby the Jaccard and Sorensen-Dice similarity measures (when the minimum t-norm is used). Finally, we show that a dissimilarity or distance measure, which satisfies the properties of a metric, can easily be derived from the proposed similarity measure.