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关键词： Visualization   Rivers   Rendering (computer graphics)   Three-dimensional displays   Streaming media   Computer science   Shape   Flow visualization   stream surfaces   visual transformation   comparative visualization  

摘要： We present SurfRiver, a new visual transformation approach that flattens stream surfaces in 3D to rivers in 2D for comparative visualization. Leveraging the TextFlow-like visual metaphor, SurfRiver untangles the convoluted individual stream surfaces along the flow direction and maps them along the horizontal direction of the abstract river view. It stacks multiple surfaces along the vertical direction of the river view. This visual mapping makes it easy for users to track along the flow direction and align stream surfaces for comparative study. Through brushing and linking, the river view is connected to the spatial surface view for collective reasoning. SurfRiver can be used to examine a single stream surface, investigate seeding sensitivity or variability of a family of surfaces from a group of related seeding curves, or explore a collection of representative surfaces. We describe our optimization solution to achieve the desirable mapping, present SurfRiver interface and interactions, and report results from different flow fields to demonstrate its efficacy. Feedback from a domain expert also indicates the promise of SurfRiver.

关键词： 实践教学模式   校企合作   实践教学基地  

摘要： 针对计算机类专业人才培养中出现的一些具体问题,结合大连民族大学计算机科学与工程学院实践教学的实际情况,在学院“一头一尾两足双翼”人才培养模式指导下,构建“四年八学期一贯制”的实践教学体系,提出“5-8”校企深度合作实践教学模式,三年的实践表明,其对提高学生的实践创新能力和提升毕业生的就业质量效果显著。

关键词： Tensile stress   Big Data   Data analysis   History   Computer science   Tools   Matrix decomposition   Cyber-physical-social systems   big data   ubiquitous computing   tensor decomposition   tensor network   incremental tensor train decomposition   quantized tensor train  

摘要： Cyber-physical-social big data generated from ubiquitous devices and diverse spaces generally are multi-source, heterogeneous, and deeply intertwined. To efficiently analyze and handle the ubiquitous cyber-physical-social big data, tensor is considered as an effective tool, but the curse of dimensionality is still the main bottleneck of tensor-based big data analysis. Tensor networks can considerably alleviate or overcome it through the tensor approximate theory. Therefore, this paper focuses on developing an efficient big data processing framework based on tensor networks and providing an incremental tensor train decomposition approach for the streaming big data. Concretely, this paper first presents a hierarchical cyber-physical-social big data processing framework composed of three planes, namely, data representation and decomposition, data storage and processing, and data analysis and service, in which tensor train (TT) and quantized TT decompositions are particularly introduced to remarkably overcome the curse of dimensionality. Besides, to efficiently handle the continuous streaming big data and avoid the repeated decomposition for the history data, an incremental tensor train decomposition (ITTD) approach is proposed and the complexities are further analyzed in detail. Experimental results demonstrate that ITTD demonstrably outperforms the nonincremental TT decomposition in execution time on the precise of guaranteeing the nearly equal approximation error.

关键词： Layout   Memory management   Optimization   Acceleration   Space exploration   Computer science   Dictionaries   Database   conjunctive predicates   storage layout   CPU  

摘要： Database queries can contain multiple predicates. The optimization of conjunctive predicates is still vital to the overall performance of analytic data processing tasks. Prior work proposes several memory-efficient storage layouts, e.g., BitWeaving and ByteSlice, to significantly accelerate predicate evaluation, as circuit-level intra-cycle parallelism available in modern CPUs can be exploited such that the total number of instructions can be dramatically reduced. However, the performance potential of conjunctive predicates has not been harvested yet under such storage layouts as there is no accurate cost model to provide necessary insights that guide the optimization process. In this paper, we propose a hybrid empirical/analytical cost model (Understanding) to unveil the performance characteristics of such storage layouts when applying to predicate evaluation. Our cost model takes into account effect of non-linear factors, e.g., cache miss and branch misprediction, and easily applies to different CPUs. The main finding from our cost model is to distinguish high-cost instruction (which suffers from cache miss and/or branch misprediction) from low-cost instruction (which enjoys cache hit and correct branch prediction) in the context of predicate evaluation under these storage layouts. Guided by such a finding, we propose a simple execution scheme Hebe (Optimizing), which is order-oblivious while maintaining high performance. Hebe is attractive to the query optimizer (QO), as the QO does not need to go through a sampling process to decide the optimal evaluation order in advance. The intuition behind Hebe is to significantly reduce the number of high-cost instructions while keeping low-cost instructions unchanged. Our finding from Hebe sheds light on the importance of accurate cost model that guide us to derive an efficient execution scheme for query processing on modern CPUs.

关键词： 大数据时代   软件工程   计算机   开发  

摘要： 大数据信息技术应用背景下,充分掌握软件工程方法技术发展方向,并了解软件工程方法在计算机软件开发方面基础特征,可以充分提高软件工程技术应用技术能力,对解决软件工程方法在计算机软件开发方面面临技术问题起到辅助作用。

关键词： 新工科   产教融合   多学科交叉   改革  

摘要： “新工科”是基于国家战略发展新需求、国际竞争新形势、立德树人新要求而提出的工程教育改革方向。在这样的背景下,计算机科学与技术作为信息技术的核心之一,其专业改革势在必行。本文对计算机科学与技术专业,剖析人才培养方面存在的不足,从培养方案的制定、多学科交叉融合、产教融合等方面入手,更新教育理念,优化人才培养标准,为新工科背景下计算机科学与技术专业改革提供新思路。

关键词： 创新能力培养   无线网络   物联网   学-练-赛”模式  

摘要： 学生的创新能力培养是近年来高校本科教育的主要关注点之一,是国家实施创新驱动发展战略、促进经济提质增效升级的迫切需要。为培养符合社会和国家需要的人才,本文提出了"学-练-赛"驱动的计算机类本科生创新能力培养模式,即学好课程基础理论、强化动手实践训练,将所学知识用于解决实际的工程问题,并在竞赛中探索新思路和新方法,并以"无线网络技术"课程为案例,探讨如何将"学-练-赛"模式进行实际运用。实践表明,其在学生创新力培养方面具有较好的效果,能有效提升学生的积极性。

关键词： Allocentric   Coding toys   Computer science   Early childhood   Egocentric   Reference frames   Syntonicity  

摘要： This research explores reference frames and reference frame shifts among young children (ages 5 and 6) as they are learning to program with a commercial coding toy. To date, little is known about reference frames used by young children in toy-based coding. Video recordings of 16 children engaging in two programming tasks were collected. Results from an analysis of 240 min of video data indicate that young children engage in shifts of reference frames when learning to program, however, young children do not have a precise coordination system. The present study proposes two additional reference frames, ProtoEgocentric and ProtoAllocentric, that allow for the consideration of developmental imprecision in children. The most common reference frame shift observed was ProtoAllocentric to ProtoEgocentric, which accounted for 47.4% of all shifts across sites and contexts. Three cases are presented to illustrate reference frame shifts and how children’s developmental imprecision affects their experience.

关键词： Binary codes   Semantics   Computer architecture   Runtime   Computer science   Feature extraction   Internet of Things   Binary code similarity comparison   reverse engineering   program analysis   code clone  

摘要： Binary code similarity comparison is a methodology for identifying similar or identical code fragments in binary programs. It is indispensable in fields of software engineering and security, which has many important applications (e.g., plagiarism detection, bug detection). With the widespread of smart and Internet of Things (IoT) devices, an increasing number of programs are ported to multiple architectures (e.g., ARM, MIPS). It becomes necessary to detect similar binary code across architectures as well. The main challenge of this topic lies in the semantics-equivalent code transformation resulting from different compilation settings, code obfuscation, and varied instruction set architectures. Another challenge is the trade-off between comparison accuracy and coverage. Unfortunately, existing methods still heavily rely on semantics-less code features which are susceptible to the code transformation. Additionally, they perform the comparison merely either in a static or in a dynamic manner, which cannot achieve high accuracy and coverage simultaneously. In this paper, we propose a semantics-based hybrid method to compare binary function similarity. We execute the reference function with test cases, then emulate the execution of every target function with the runtime information migrated from the reference function. Semantic signatures are extracted during the execution as well as the emulation. Lastly, similarity scores are calculated from the signatures to measure the likeness of functions. We have implemented the method in a prototype system designated as BinMatch which performs binary code similarity comparison across architectures of x86, ARM and MIPS on the Linux platform. We evaluate BinMatch with nine real-word projects compiled with different compilation settings, on variant architectures, and with commonly-used obfuscation methods, totally performing over 100 million pairs of function comparison. The experimental results show that BinMatch is resilient to t

关键词： Data models   Analytical models   Machine learning   Decision trees   Machine learning algorithms   Knowledge discovery   Computer science   Missing data imputation   cost-sensitive learning   decision tree   classification   imputation order   C4   5 algorithm   imputation cost  

摘要： Different from previous imputation methods which impute missing values in the incomplete samples by using the information in the complete samples, this paper proposes a Date-drive Incremental imputation Model, DIM for short, which uses all available information in the data set to impute missing values economically, effectively, orderly, and iteratively. To this end, we propose a scoring rule to rank the missing features by taking into account both the economical criterion and the effective imputation information. The economical criterion takes both the imputation cost and the discriminative ability of the feature into account, while the effective imputation information enables to use all observed information in the data set including the imputed missing values to impute the left missing values. During the imputation process, our DIM first detects the neednot-impute samples for reducing the imputation cost and noise, and then selects the missing features with the top rank to impute first. The imputation process orderly imputes the missing features until all missing values are imputed or the imputation cost is exhausted. Experimental results on UCI data sets demonstrated the advantages of our proposed DIM, compared to the comparison methods, in terms of prediction accuracy and classification accuracy.