地方高校计算机科学与技术专业人才培养模式改革与实践

本文分析了目前我国地方高等院校计算机专业人才培养现状,以太原科技大学计算机科学与技术专业为例,针对办学指导思想、专业定位、专业特色、专业培养模式及课程体系进行了研究与探讨,提出了"行业特色明显、专业方向细化、实践技能突出、素质教育鲜明"的专业建设指导思想。在基于知识深度优先和专业模块细化的基础上,详细论述了计算机工程专业方向的人才培养方案及课程体系设置。     

计算机科学与技术专业应用型人才培养模式的探讨

高校应用型人才培养模式是高校为培养应用型人才而探索形成的一种可操作性的经验与理论架构。本文根据湖南工学院计算机科学系计算机科学与技术专业的教学情况,对该专业应用型人才培养模式进行了探讨,具体包括应用型人才的特点、培养目标、课程体系建设、专业方向设置、实践教学等方面。     

基于计算机科学与技术专业嵌入式方向人才培养研究

结合学校的办学定位和办学特色,以社会对计算机人才的市场需求为导向,将我校计算机科学与技术专业培养方向定位于嵌入式方向。本文针对计算机科学与技术专业嵌入式方向,在人才培养模式、课程体系、实践体系等方面提出了一些建议。     

在计算机科学与技术专业中增设嵌入式软件专业方向的探讨

本文基于江苏技术师范学院以培养应用型本科人才的教学体系,讨论研究在计算机科学与技术专业中增设嵌入式软件专业方向的可行性,人才培养的目标与规格,课程体系以及实验室建设等问题。     

计算机科学与技术专业人才培养体系建设浅析

本文阐述了计算机科学与技术专业的培养目标以及业务规格,对如何进行计算机科学与技术专业建设进行了论证,并对三个专业方向的教学实施环节进行了阐述。     

论综合性大学计算机科学与技术师范专业的建设

本文根据计算机科学与技术教师教育专业、方向的特点,对我们的教学改革进行了研究,提出一种教师教育人才培养模式的观点。分析了教学改革中应做的工作和教师教育专业学生的特点,并总结了这种人才培养模式的主要特色。     

计算机科学与技术特色专业建设的思考与实践

计算机科学与技术专业是培养计算机人才的重要阵地,近些年来,该专业的建设出现了下滑的趋势,在人才的培养上面出现了瓶颈.随着社会的不断发展,计算机科学与技术专业也需要进行特色建设,避免千篇一律的人才培养模式,否则难以适应社会的发展要求.高校计算机科学与技术特色专业建设需要加强对专业特色的定位和师资队伍的建设,并且要注重对学生实践能力的培养,使得特色建设真正走上正轨.     

计算机科学与技术专业“卓越工程师”培养模式与实施方案研究

本文对“卓越计划”的内涵和我校计算机科学与技术专业现状进行了分析,然后根据“卓越计划”的相关要求,结合我校办学定位与专业建设实际,对计算机科学与技术专业“卓越工程师”人才培养模式与实施方案进行了阐述.     

行业院校计算机专业人才培养模式探索

根据教育部计算机科学与技术教学指导委员会颁布的《高等学校计算机科学与技术专业发展战略研究报告暨专业规范(试行)》,结合西安邮电学院的办学定位和行业特色,我校在计算机工程方向多元化人才培养模式、多位一体实践教学体系、校企合作模式、IT应用型人才实训中心建设等方面开展了大量探索与实践,取得了一定的经验,对地方行业院校计算机专业人才的培养具有一定的参考作用。本文对我校的探索与实践过程进行了总结。     

面向大数据工程师方向的“嵌入式”人才培养模式探索与实践

"嵌入式"培养在人才培养中发挥了非常重要的作用。南京航空航天大学金城学院计算机科学与技术专业以面向大数据工程师方向人才培养为切入点,对"嵌入式"培养过程中的质量把控、课程体系建设、双师型师资队伍建设三个方面进行了积极地探索与实践。为高校人才培养和教学模式改革提供了思路,具有一定的借鉴意义。     

面向计算机专业研究生的人工智能教育

人工智能是为计算机专业研究生开设的一门重要课程。分析了为计算机专业研究生开设人工智能课程的必要性以及人工智能在研究生培养过程中的重要作用。结合计算机专业研究生的特点,进一步提出了关于研究生人工智能教育的几点建议。     

产学研一体的工程型、复合型、国际化计算机人才培养

总结了浙江大学计算机科学与技术学院基于产学研一体培养工程型、复合型、国际化计算机高级人才的一些基本经验。浙江大学计算机学院通过建立多层次的工程实践能力培养体系、建设全方位的国际交流能力培养体系、创建多学科融合的人才培养知识体系等手段,逐步探索出产学研共同参与的新型高级计算机人才培养模式。     

计算机科学与技术学科教育与教育改革研究所进展通报

一、引言 1.研究项目立项背景介绍 1995年,教育部在广泛调研的基础上,深感国内高等教育特别是高等理科教育存在着不能适应学科和社会发展需要的问题。教育思想与教学观念陈旧,许多教学内容几十年不变,教学管理和教学手段落后,办学经费与教学研究经费投入不足等,严重制约了我国高等教育培养的各类专业技术人才参与下一世纪的国际竞争,必须下大力气改变这种状况。随后,教育部启动了高等理科面向21世纪教学内容与课程体系改革研究计     

xCCL: A Survey of Industry-Led Collective Communication Libraries for Deep Learning

Journal of Computer Science and Technology - Machine learning techniques have become ubiquitous both in industry and academic applications. Increasing model sizes and training data volumes...     

Segmentation of stick text based on sub connected area analysis

A new stick text segmentation method based on the sub connected area analysis is introduced in this paper.The foundation of this method is the sub connected area representation of text image that can represent all connected areas in an image efficiently.This method consists mainly of four steps:sub connected area classification,finding initial boundary following point,finding optimal segmentation point by boundary tracing,and text segmentaton.This method is similar to boundary analysis method but is more efficient than boundary analysis.     

Interactive online learning

This paper describes a system for visual object recognition based on mobile augmented reality gear. The user can train the system to the recognition of objects online using advanced methods of interaction with mobile systems: Hand gestures and speech input control “virtual menus,” which are displayed as overlays within the camera image. Here we focus on the underlying neural recognition system, which implements the key requirement of an online trainable system—fast adaptation to novel object data. The neural three-stage architecture can be adapted in two modes: In a fast training mode (FT), only the last stage is adapted, whereas complete training (CT) rebuilds the system from scratch. Using FT, online acquired views can be added at once to the classifier, the system being operational after a delay of less than a second, though still with reduced classification performance. In parallel, a new classifier is trained (CT) and loaded to the system when ready. The text was submitted by the authors in English. Gunther Heidemann was born in 1966. He studied physics at the Universities of Karlsruhe and Münster and received his PhD (Eng.) from Bielefeld University in 1998. He is currently working within the collaborative research project “Hybrid Knowledge Representation” of the SFB 360 at Bielefeld University. His fields of research are mainly computer vision, robotics, neural networks, data mining, bonification, and hybrid systems. Holger Bekel was born in 1970. He received his BS degree from the University of Bielefeld, Germany, in 1997. In 2002 he received a diploma in Computer Science from the University of Bielefeld. He is currently pursuing a PhD program in Computer Science at the University of Bielefeld, working within the Neuroinformatics Group (AG Neuroinformatik) in the project VAMPIRE (Visual Active Memory Processes and Interactive Retrieval). His fields of research are active vision and data mining. Ingo Bax was born in 1976. He received a diploma in Computer Science from the University of Bielefeld in 2002. He is currently pursuing a PhD program in Computer Science at the Neuroinformatics Group of the University of Bielefeld, working within the VAMPIRE project. His fields of interest are cognitive computer vision and pattern recognition. Helge J. Ritter was born 1958. He studied physics and mathematics at the Universities of Bayreuth, Heidelberg and Munich. After a PhD in physics at Technical University of Munich in 1988, he visited the Laboratory of Computer Science at Helsinki University of Technology and the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Since 1990 he has headed the Neuroinformatics Group at the Faculty of Technology, Bielefeld University. His main interests are principles of neural computation and their application to building intelligent systems. In 1999, she was awarded the SEL Alcatel Research Prize, and in 2001, the Leibniz Prize of the German Research Foundation DFG.     

Proofs of a set of hybrid let-polymorphic type inference algorithms

We present a generalized let-polymorphic type inference algorithm, prove that any of its instances is sound and complete with respect to the Hindley/Milner let-polymorphic type system, and find a condition on two instance algorithms so that one algorithm should find type errors earlier than the other. By instantiating the generalized algorithm with different parameters, we can obtain not only the two opposite algorithms (the bottom-up standard algorithmW and the top-down algorithmM) but also other hybrid algorithms which are used in real compilers. Such instances’ soudness and completeness follow automatically, and their relative earliness in detecting type-errors is determined by checking a simple condition. The set of instances of the generalized algorithm is a superset of those used in the two most popular ML compilers: SML/NJ and OCaml. This work is supported by Creative Research Initiatives of the Korean Ministry of Science and Technology National Creative Research Initiative Center, http://ropas.kaist.ac.kr Work done while the third author was associated with Korea Advanced Institute of Science and Technology Hyunjun Eo: He is a Ph.D. candidate in the Department of Computer Science at KAIST (Korea Advanced Institute of Science and Technology). He recieved his bachelor’s degree and master’s degree in Computer Science from KAIST in 1996 and 1998, respectively. His research interest has been on static program analysis, fixpoint iteration algorithm and higher-order and typed languages. From fall 1998, he has been a research assistant of the National Creative Research Initiative Center for Research on Program Analysis System. He is currently working on developing a tool for automatic generation of program analyzer. Oukseh Lee: He is a Ph.D. candidate in the Department of Computer Science at KAIST (Korea Advanced Institute of Science and Technology). He received his bachelor’s and master’s degree in Computer Science from KAIST in 1995 and 1997, respectively. His research interest has been on static program analysis, type system, program language implementation, higher-order and typed languages, and program verification. From 1998, he has been a research assistant of the National Creative Research Initiative Center for Research on Program Analysis System. He is currently working on compile-time analyses and verification for the memory behavior of programs. Kwangkeun Yi, Ph.D.: His research interest has been on semanticbased program analysis and systems application of language technologies. After his Ph.D. from University of Illinois at Urbana-Champaign he joined the Software Principles Research Department at Bell Laboratories, where he worked on various static analysis approaches for higher-order and typed programming languages. For 1995 to 2003 he was a faculty member in the Department of Computer Science, Korea Advanced Institute of Science and Technology. Since fall 2003, he has been a faculty member in the School of Computer Science and Engineering, Seoul National University.     

A new approach to fuzzy modeling and control for nonlinear dynamic systems: Neuro-fuzzy dynamic characteristic modeling and adaptive control mechanism

The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example. Recommended by Editor Young-Hoon Joo. This work was jointly supported by National Natural Science Foundation of China under Grant 60604010, 90716021, and 90405017 and Foundation of National Laboratory of Space Intelligent Control of China under Grant SIC07010202. Xiong Luo received the Ph.D. degree from Central South University, Changsha, China, in 2004. From 2005 to 2006, he was a Postdoctoral Fellow in the Department of Computer Science and Technology at Tsinghua University. He currently works as an Associate Professor in the Department of Computer Science and Technology, University of Science and Technology Beijing. His research interests include intelligent control for spacecraft, intelligent optimization algorithms, and intelligent robot system. Zengqi Sun received the bachelor degree from Tsinghua University, Beijing, China, in 1966, and the Ph.D. degree from Chalmers University of the Technology, Gothenburg, Sweden, in 1981. He currently works as a Professor in the Department of Computer Science and Technology, Tsinghua University. His research interests include intelligent control of robotics, fuzzy neural networks, and intelligent flight control. Fuchun Sun received the Ph.D. degree from Tsinghua University, Beijing, China, in 1998. From 1998 to 2000, he was a Postdoctoral Fellow in the Department of Automation at Tsinghua University, where he is currently a Professor in the Department of Computer Science and Technology. His research interests include neural-fuzzy systems, variable structure control, networked control systems, and robotics.     

“软件工程”双语教学和案例教学的体会

"软件工程"是计算机科学与技术专业本科必修课程,对培养军队信息化建设的人才有重要作用。本文介绍我们在"软件工程"课程教学中的做法及体会,包括采用英文原版教材、实行双语教学、通过课程大作业对所学理论和技术进行综合实践、贯穿课程的案例教学等方面。通过这些方法,强化了学生的实践应用能力,提高了软件工程师的综合素质。