目錄
序言
第1章 軟體基礎理論
1.1 An operational semantics of multitasking and exception handling in Ada
1.1.1 Introduction
1.1.2 An outline of multitasking and exception
1.1.3 An operational semantics for multitasking
1.1.4 An operational semantics for exceptions in Ada
1.1.5 Raising a failure exception in another task
1.1.6 Conclusion
References
1.2 A formal semantics for program debugging
1.2.1 Introduction
1.2.2 The language C* and basic notations
1.2.3 A structural operational semantics for tracing
1.2.4 A structural operational semantics for locating
1.2.5 Solving fixing equations
1.2.6 Conclusion
References
1.3 一個開放的邏輯系統
1.3.1 引言
1.3.2 新假設與事實反駁
1.3.3 認識進程
1.3.4 極限及經驗模型
1.3.5 簡單事實反駁
1.3.6 關於預設推理的語義
1.3.7 結論
References
1.4 R-calculus: an inference system for belief revision
1.4.1 Motivation
1.4.2 A formal language of beliefs
1.4.3 A logical inference system for beliefs
1.4.4 The necessary antecedents
1.4.5 The refutation by facts
1.4.6 The R-calculus
1.4.7 Some examples
1.4.8 The reachability, soundness, and completeness
1.4.9 Conclusion
References
1.5 R-calculus without the cut rule
1.5.1 Introduction
1.5.2 R-calculus
1.5.3 Case study of the R-cut rule
1.5.4 R-calculus without the cut rule
1.5.5 Examples
1.5.6 The reachability, soundness and completeness
1.5.7 Conclusion
References
1.6 一種求解合取範式可滿足性問題的數學物理方法
1.6.1 引言
1.6.2 關於可滿足性問題的近似演算法
1.6.3 梯度演算法及計算結果分析
1.6.4 CNF 可滿足問題與覆蓋問題
1.6.5 目標函數$U_2$的物理意義
References
1.7 Many hard examples in exact phase transitions
1.7.1 Introduction
1.7.2 Preliminaries
1.7.3 Model RB and Model RD
1.7.4 Main results
1.7.5 Proof of Theorem 7.
1.7.6 Conclusion
References
1.7.7 Appendix
1.8 A knowledge base management system on relation model and term rewriting
1.8.1 Introduction
1.8.2 KBMS status
1.8.3 The KBMS/BUAA model
1.8.4 The principle
1.8.5 The architecture
1.8.6 Application
1.8.7 Conclusion
References
第2章 海量信息方法
2.1 互聯網海量信息系統的基本問題
2.1.1 背景簡介
2.1.2 兩個基本的科學問題
2.1.3 海量數據的協同和可生存性研究
2.1.4 軟體計算的協同和可生存性研究
2.1.5 系統網路的協同和可生存性研究
2.1.6 結論
2.2 A tetrahedral data model for unstructured data management
2.2.1 Related work
2.2.2 The structure of the tetrahedral data model
2.2.3 The implementation structure of the tetrahedral data model
2.2.4 Unstructured data query language (UDQL)
2.2.5 A complete example
2.2.6 Conclusion
2.2.7 Acknowledgements
References
2.3 Capturing topology in graph pattern matching
2.3.1 Introduction
2.3.2 Strong simulation
2.3.3 Properties of strong simulation
2.3.4 An algorithm for strong simulation
2.3.5 Experimental study
2.3.6 Conclusion
References
2.4 First result from the alpha magnetic spectrometer on the international space station: precision measurement of the positron
fraction in primary cosmic rays of 0.5 -350GeV
2.4.1 Introduction
2.4.2 AMS detector
2.4.3 Results and conclusions
2.4.4 Acknowledgments
2.5 Collaborative hashing
2.5.1 Introduction
2.5.2 Collaborative hashing
2.5.3 Experiments
2.5.4 Conclusion
References
2.6 Large-scale 3D model repository: ShapeNet
2.6.1 Introduction
2.6.2 Background and related work
2.6.3 ShapeNet: an information-rich 3D model repository
2.6.4 Annotation acquisition and validation
2.6.5 Current statistics
2.6.6 Discussion and future work
2.6.7 Conclusion
References
第3章 群體智能與慕課
3.1 Crowdsourcing: Cloud-Based Software Development
3.1.1 Foreword
3.1.2 Summary of the book
3.2 An evaluation framework for software crowdsourcing
3.2.1 Abstract
3.2.2 Introduction
3.2.3 Characterizing software crowdsourcing processes
3.2.4 Evaluation framework for software crowdsourcing
3.2.5 Game theory interpretations
3.2.6 Illustration
3.2.7 Related work
3.2.8 Conclusion
3.2.9 Acknowledgments
References
3.3 Crowd intelligence in AI 2.0 era
3.3.1 Abstract
3.3.2 Introduction
3.3.3 Definition of crowd intelligence
3.3.4 Typical crowd intelligence platforms
3.3.5 Research problems in crowd intelligence
3.3.6 Conclusion
References
3.4 複雜軟體系統的成長性構造與適應性演化
3.4.1 引言
3.4.2 複雜軟體系統的特點和性質
3.4.3 基於還原論的軟體開發方法所面臨的挑戰
3.4.4 複雜軟體系統構造與演化基本法則
3.4.5 複雜軟體系統成長性構造技術體系
3.4.6 複雜軟體系統適應性演化技術體系
3.4.7 適應性演化實現機制
3.4.8 結論
References
3.5 群體智能系統的動力學模型與群體熵度量
3.5.1 引言
3.5.2 群體智能的動力學模型
3.5.3 群體熵的基本性質
3.5.4 群體智能的度量：以圖搜索為例
3.5.5 結論
References
3.6 抓住MOOC發展機遇全面提高高等教育質量
3.6.1 我國高等教育的形勢和任務
3.6.2 全面提高高等教育質量
3.7 面向智慧教育的學習大數據分析技術
3.7.1 智慧教育和大數據分析
3.7.2 面向教育的大數據分析實例
3.7.3 面向教育的大數據分析方法
3.7.4 課程推薦和學習規劃
3.7.5 基於社區的學習分析
3.7.6 總結
References
3.8 升級大學軟體教育
3.8.1 為什麼要升級大學軟體教育
3.8.2 如何升級大學軟體教育
3.8.3 升級軟體教育的探索與實踐
References
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