A complementary view for better understanding the term computational thinking

The term Computational Thinking is closely related to efforts connected to teach a systematic and well-structured way of problem solving that includes a set of tools and techniques used in Computer Science. While substantial research in this field has shown promising outcomes concerning distinct intervention programs and teaching initiatives, the term Computational Thinking itself requires to be revised in order to get a wider consensus about its meaning and purpose. This paper contributes to the ongoing quest concerning the definition of the term by starting with a fundamental perspective on computational theory and corresponding concepts in order to describe the theoretical building blocks of a systematic view to further elaborate on an approach for teaching and learning about Computational Thinking. Additionally, based on this foundational effort, more advanced concepts are presented and discussed in order to better understand this domain. Finally, the paper identifies and discusses a set of relevant challenges taking a cognitive psychology perspective on Computational Thinking

Programming China: the Communist Party’s autonomic approach to managing state security

Programming China: The Communist Party’s Autonomic Approach to Managing State Security, introduces the new analytical framework called China's “Autonomic Nervous System” (ANS). The ANS framework applies complex systems management theory to explain the process the Chinese Communist Party calls “social management”. Through the social management process, the Party-state leadership interacts with both the Party masses and non-Party masses. The process involves shaping, managing and responding and is aimed at ensuring the People’s Republic of China’s systemic stability and legitimacy—i.e. (Party-) state security. Using the ANS framework, this thesis brings cohesion to a complex set of concepts such as “holistic” state security, grid management, social credit and national defence mobilisation. Research carried out for the thesis included integrated archival research and the author’s database of nearly 10,000 social unrest events. Through ANS, the author demonstrates that in the case of the People’s Republic of China we may be witnessing a sideways development, where authoritarianism is stabilised, largely through a way of thinking that both embodies and applies complex systems management and attempts to “automate” that process through technology designed based on the same concepts. The party's rule of China, thus, evolves away from traditional political scales like reform versus retrenchment or hard versus soft authoritarianism. The ANS framework should be seen not as an incremental improvement to current research of China’s political system but as a fundamentally different approach to researching and analysing the nature of Chinese politics

Programming China: the Communist Party’s autonomic approach to managing state security

Programming China: The Communist Party’s Autonomic Approach to Managing State Security, introduces the new analytical framework called China's “Autonomic Nervous System” (ANS). The ANS framework applies complex systems management theory to explain the process the Chinese Communist Party calls “social management”. Through the social management process, the Party-state leadership interacts with both the Party masses and non-Party masses. The process involves shaping, managing and responding and is aimed at ensuring the People’s Republic of China’s systemic stability and legitimacy—i.e. (Party-) state security. Using the ANS framework, this thesis brings cohesion to a complex set of concepts such as “holistic” state security, grid management, social credit and national defence mobilisation. Research carried out for the thesis included integrated archival research and the author’s database of nearly 10,000 social unrest events. Through ANS, the author demonstrates that in the case of the People’s Republic of China we may be witnessing a sideways development, where authoritarianism is stabilised, largely through a way of thinking that both embodies and applies complex systems management and attempts to “automate” that process through technology designed based on the same concepts. The party's rule of China, thus, evolves away from traditional political scales like reform versus retrenchment or hard versus soft authoritarianism. The ANS framework should be seen not as an incremental improvement to current research of China’s political system but as a fundamentally different approach to researching and analysing the nature of Chinese politics

Creating a Favorable International Public Opinion Environment: External Propaganda (Duiwai Xuanchuan) as a Global Concept with Chinese Characteristics

This thesis offers a political history of PRC propaganda targeted at foreigners in the 20th and 21st century. It seeks to give an answer to the seeming contradiction that particularly over the last decade, China has repeatedly pledged to invest more resources into improving its image and influencing international public opinion, yet at the same time, there continue to be blunders of the most basic type, particularly in areas where China wants to influence foreigners’ opinions most. The thesis examines how and to what extent China has been able to adapt its external propaganda apparatus, initially set up on the basis of the Soviet propaganda model that depended on the ability of the Party to regulate the flow of information into and out of China, to the current global media environment marked by porous national borders and fast-paced flows of information across the globe. Drawing on internal publications, archival documents, openly available materials, and interviews, it combines a bird’s-eye perspective on the development of external propaganda in China over the course of the 20th and 21st century with in-depth reading and analysis of key texts. Two propositions are tested: First, that external factors, including foreign models that China learns from, have had significant impact on how Chinese external propaganda policy has developed and second, that previous choices the PRC has made for its external propaganda sector substantially restrict the options available to the CPC today. Arguing that external propaganda has been path dependent at various levels, this study explains the difficulties China’s external propaganda apparatus continues to face as well as what strategies people pushing for reforms have used to overcome historical, ideological, and bureaucratic baggage

Task-based Learning Strategies for Developing Students’ Textual Understanding and Critical Thinking Abilities in An English Intensive Reading Class

The focus of global competition among countries has been transferred to the cultivation of talents in various fields. Innovative spirit and ability are the key characteristic of talents, while critical thinking is the precondition and basis of innovative thinking. Therefore, the cultivation of critical thinking is urgent and most significant. In China, existing research focuses more on the theory introduction and scale design of critical thinking, less on empirical and systematic study. This study is to combine Task-Based Learning teaching approach and cooperative learning to form a new teaching mode---Teaching Trilogy Mode and to study the potential problems and teaching effects of this mode in cultivating students' critical thinking ability and improving students' reading comprehension ability. Through a semester of researching, it is found that: Teaching Trilogy mode can help students form the habit of critical thinking. Task-based Learning approach used in groups can help students overcome the pressure from the classroom teaching and promote more active attendance in classroom discussion. With critical thinking skills, students can have a further understanding of the reading materials. The implications of this study for teaching are as follows: Critical thinking habit can be trained through Teaching Trilogy. Critical thinking skills can make students shift from passive learning to autonomous learning. Suitable tasks in groups can stimulate individual participation and bring significant achievements. By showing sincere welcome to questions, teachers can encourage students asking questions. The innovation and significance of this study lies in: The introduction of Teaching Trilogy mode and action research into the teaching and research of college English intensive reading class cannot only expand the research method and perspective, but also enrich the teaching theory. The Teaching Trilogy mode and teaching design are constructed and optimized through this study, and it is more targeted and conductive to teaching. Task-based approach combined with group cooperative learning brought vigor and vitality to each student as well as the college English intensive reading class. The Teaching Trilogy mode provides a clue for the similar study in other subjects

Journeys towards Masters’ literacies: Chinese students’ transitions from undergraduate study in China to postgraduate study in the UK

This research explored Chinese students‟ experiences of acquiring and practising academic literacies as required in their Master‟s programmes. To date, academic literacy studies in common with wider research on higher education students‟ learning have tended to focus on the experiences of undergraduate students, particularly in western universities. The current study addresses this gap in the literature by investigating the learning journeys of students who had gained a first degree in China and were undertaking postgraduate study in the UK. Data were collected from three-phases of semi-structured interview: at the beginning, at the halfway and the end of the teaching component prior to the Master‟s dissertation phrase. Each of the participants was drawn from one of three contrasting Master‟s programmes at the University of Edinburgh (Education, Finance and Investment, and Signal Processing and Communications) and participated in all three phases of interview. All eighteen participants‟ experiences are presented as case studies to bring their voices to the fore and acknowledge the complexity and individuality of their learning journeys. The research shows that five dimensions of transitions are significant and relevant to all the participants – transitions in language, pedagogical culture, subject, level of study, and living and learning abroad. The language barrier is particularly important both in itself as well as through its influence on other transitions, although all five transitions are in various respects interwoven. The extent to which the transitions are challenging differs across participants and programmes. The perspective of transitions does not therefore suffice to capture the richness of the Masters‟ students‟ journeys. Accordingly, the perspective of Masters‟ literacies is introduced as a powerful lens through which to explore the Chinese participants‟ learning experiences and challenges and how these are linked to their confidence in themselves as Master‟s students. Four academic literacy practices are viewed in this study as key components of Masters‟ literacies: autonomy in learning, subject discourses, critical and analytical thinking, and interaction with teachers and students. Finally, the conceptual, methodological and practical implications of these findings are explored

Late assignment of syntax theory: evidence from Chinese and English

The attraction of the well-structured arguments of the mental syntactic processing device (parser) in Chomsky’s theory has led to an overemphasis on syntactic processing to the exclusion of semantic and other processing in the initial sentence processing stage (Frazier & Clifton, 1996; Gibson & Hickok, 1993; Pickering & van Gompel, 2006). The current thesis joins some others (Green & Mitchell, 2006; MacDonald et al., 1994; Townsend & Bever, 2001, etc.), investigating the timecourse of the information processing of sentences. The first interest centres on ambiguous sentence resolution. Crosslinguistic studies have shown different resolutions in processing the relative clause (RC) attachment as in “the servant of the actress who was on the balcony” (Cuetos & Mitchell, 1988). Three studies confirmed that there is an NP-low preference in Chinese; however, this effect was delayed in comparison to its English counterparts. The NP-low preference can be explained by syntax-first, syntax parallel, and syntax later theories. However, the delay effect questions the traditional syntax-first theories. This leads to the second investigation of direct comparison of the timecourse of syntactic and semantic processing using anomalous materials in English and Chinese. Two experiments have confirmed that the syntactic anomaly is recognised later than semantic anomaly in both languages. The empirical investigation in the current thesis used various methodologies, including self-paced reading, a questionnaire, and eye-tracking studies, where the design of materials strictly followed linguistic principles. All the results support the late assignment of syntax theory (LAST) (Townsend & Bever, 2001). In fact, LAST can explain most of the evidence for syntax-first and syntax-parallel theories, and it is in line with the latest development of the linguistic UG theories (the Minimalist Programme)

Fault-tolerant satellite computing with modern semiconductors

Miniaturized satellites enable a variety space missions which were in the past infeasible, impractical or uneconomical with traditionally-designed heavier spacecraft. Especially CubeSats can be launched and manufactured rapidly at low cost from commercial components, even in academic environments. However, due to their low reliability and brief lifetime, they are usually not considered suitable for life- and safety-critical services, complex multi-phased solar-system-exploration missions, and missions with a longer duration. Commercial electronics are key to satellite miniaturization, but also responsible for their low reliability: Until 2019, there existed no reliable or fault-tolerant computer architectures suitable for very small satellites. To overcome this deficit, a novel on-board-computer architecture is described in this thesis.Robustness is assured without resorting to radiation hardening, but through software measures implemented within a robust-by-design multiprocessor-system-on-chip. This fault-tolerant architecture is component-wise simple and can dynamically adapt to changing performance requirements throughout a mission. It can support graceful aging by exploiting FPGA-reconfiguration and mixed-criticality.  Experimentally, we achieve 1.94W power consumption at 300Mhz with a Xilinx Kintex Ultrascale+ proof-of-concept, which is well within the powerbudget range of current 2U CubeSats. To our knowledge, this is the first COTS-based, reproducible on-board-computer architecture that can offer strong fault coverage even for small CubeSats.European Space AgencyComputer Systems, Imagery and Medi

Discriminant feature pursuit: from statistical learning to informative learning.

Lin Dahua.Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.Includes bibliographical references (leaves 233-250).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- The Problem We are Facing --- p.1Chapter 1.2 --- Generative vs. Discriminative Models --- p.2Chapter 1.3 --- Statistical Feature Extraction: Success and Challenge --- p.3Chapter 1.4 --- Overview of Our Works --- p.5Chapter 1.4.1 --- New Linear Discriminant Methods: Generalized LDA Formulation and Performance-Driven Sub space Learning --- p.5Chapter 1.4.2 --- Coupled Learning Models: Coupled Space Learning and Inter Modality Recognition --- p.6Chapter 1.4.3 --- Informative Learning Approaches: Conditional Infomax Learning and Information Chan- nel Model --- p.6Chapter 1.5 --- Organization of the Thesis --- p.8Chapter I --- History and Background --- p.10Chapter 2 --- Statistical Pattern Recognition --- p.11Chapter 2.1 --- Patterns and Classifiers --- p.11Chapter 2.2 --- Bayes Theory --- p.12Chapter 2.3 --- Statistical Modeling --- p.14Chapter 2.3.1 --- Maximum Likelihood Estimation --- p.14Chapter 2.3.2 --- Gaussian Model --- p.15Chapter 2.3.3 --- Expectation-Maximization --- p.17Chapter 2.3.4 --- Finite Mixture Model --- p.18Chapter 2.3.5 --- A Nonparametric Technique: Parzen Windows --- p.21Chapter 3 --- Statistical Learning Theory --- p.24Chapter 3.1 --- Formulation of Learning Model --- p.24Chapter 3.1.1 --- Learning: Functional Estimation Model --- p.24Chapter 3.1.2 --- Representative Learning Problems --- p.25Chapter 3.1.3 --- Empirical Risk Minimization --- p.26Chapter 3.2 --- Consistency and Convergence of Learning --- p.27Chapter 3.2.1 --- Concept of Consistency --- p.27Chapter 3.2.2 --- The Key Theorem of Learning Theory --- p.28Chapter 3.2.3 --- VC Entropy --- p.29Chapter 3.2.4 --- Bounds on Convergence --- p.30Chapter 3.2.5 --- VC Dimension --- p.35Chapter 4 --- History of Statistical Feature Extraction --- p.38Chapter 4.1 --- Linear Feature Extraction --- p.38Chapter 4.1.1 --- Principal Component Analysis (PCA) --- p.38Chapter 4.1.2 --- Linear Discriminant Analysis (LDA) --- p.41Chapter 4.1.3 --- Other Linear Feature Extraction Methods --- p.46Chapter 4.1.4 --- Comparison of Different Methods --- p.48Chapter 4.2 --- Enhanced Models --- p.49Chapter 4.2.1 --- Stochastic Discrimination and Random Subspace --- p.49Chapter 4.2.2 --- Hierarchical Feature Extraction --- p.51Chapter 4.2.3 --- Multilinear Analysis and Tensor-based Representation --- p.52Chapter 4.3 --- Nonlinear Feature Extraction --- p.54Chapter 4.3.1 --- Kernelization --- p.54Chapter 4.3.2 --- Dimension reduction by Manifold Embedding --- p.56Chapter 5 --- Related Works in Feature Extraction --- p.59Chapter 5.1 --- Dimension Reduction --- p.59Chapter 5.1.1 --- Feature Selection --- p.60Chapter 5.1.2 --- Feature Extraction --- p.60Chapter 5.2 --- Kernel Learning --- p.61Chapter 5.2.1 --- Basic Concepts of Kernel --- p.61Chapter 5.2.2 --- The Reproducing Kernel Map --- p.62Chapter 5.2.3 --- The Mercer Kernel Map --- p.64Chapter 5.2.4 --- The Empirical Kernel Map --- p.65Chapter 5.2.5 --- Kernel Trick and Kernelized Feature Extraction --- p.66Chapter 5.3 --- Subspace Analysis --- p.68Chapter 5.3.1 --- Basis and Subspace --- p.68Chapter 5.3.2 --- Orthogonal Projection --- p.69Chapter 5.3.3 --- Orthonormal Basis --- p.70Chapter 5.3.4 --- Subspace Decomposition --- p.70Chapter 5.4 --- Principal Component Analysis --- p.73Chapter 5.4.1 --- PCA Formulation --- p.73Chapter 5.4.2 --- Solution to PCA --- p.75Chapter 5.4.3 --- Energy Structure of PCA --- p.76Chapter 5.4.4 --- Probabilistic Principal Component Analysis --- p.78Chapter 5.4.5 --- Kernel Principal Component Analysis --- p.81Chapter 5.5 --- Independent Component Analysis --- p.83Chapter 5.5.1 --- ICA Formulation --- p.83Chapter 5.5.2 --- Measurement of Statistical Independence --- p.84Chapter 5.6 --- Linear Discriminant Analysis --- p.85Chapter 5.6.1 --- Fisher's Linear Discriminant Analysis --- p.85Chapter 5.6.2 --- Improved Algorithms for Small Sample Size Problem . --- p.89Chapter 5.6.3 --- Kernel Discriminant Analysis --- p.92Chapter II --- Improvement in Linear Discriminant Analysis --- p.100Chapter 6 --- Generalized LDA --- p.101Chapter 6.1 --- Regularized LDA --- p.101Chapter 6.1.1 --- Generalized LDA Implementation Procedure --- p.101Chapter 6.1.2 --- Optimal Nonsingular Approximation --- p.103Chapter 6.1.3 --- Regularized LDA algorithm --- p.104Chapter 6.2 --- A Statistical View: When is LDA optimal? --- p.105Chapter 6.2.1 --- Two-class Gaussian Case --- p.106Chapter 6.2.2 --- Multi-class Cases --- p.107Chapter 6.3 --- Generalized LDA Formulation --- p.108Chapter 6.3.1 --- Mathematical Preparation --- p.108Chapter 6.3.2 --- Generalized Formulation --- p.110Chapter 7 --- Dynamic Feedback Generalized LDA --- p.112Chapter 7.1 --- Basic Principle --- p.112Chapter 7.2 --- Dynamic Feedback Framework --- p.113Chapter 7.2.1 --- Initialization: K-Nearest Construction --- p.113Chapter 7.2.2 --- Dynamic Procedure --- p.115Chapter 7.3 --- Experiments --- p.115Chapter 7.3.1 --- Performance in Training Stage --- p.116Chapter 7.3.2 --- Performance on Testing set --- p.118Chapter 8 --- Performance-Driven Subspace Learning --- p.119Chapter 8.1 --- Motivation and Principle --- p.119Chapter 8.2 --- Performance-Based Criteria --- p.121Chapter 8.2.1 --- The Verification Problem and Generalized Average Margin --- p.122Chapter 8.2.2 --- Performance Driven Criteria based on Generalized Average Margin --- p.123Chapter 8.3 --- Optimal Subspace Pursuit --- p.125Chapter 8.3.1 --- Optimal threshold --- p.125Chapter 8.3.2 --- Optimal projection matrix --- p.125Chapter 8.3.3 --- Overall procedure --- p.129Chapter 8.3.4 --- Discussion of the Algorithm --- p.129Chapter 8.4 --- Optimal Classifier Fusion --- p.130Chapter 8.5 --- Experiments --- p.131Chapter 8.5.1 --- Performance Measurement --- p.131Chapter 8.5.2 --- Experiment Setting --- p.131Chapter 8.5.3 --- Experiment Results --- p.133Chapter 8.5.4 --- Discussion --- p.139Chapter III --- Coupled Learning of Feature Transforms --- p.140Chapter 9 --- Coupled Space Learning --- p.141Chapter 9.1 --- Introduction --- p.142Chapter 9.1.1 --- What is Image Style Transform --- p.142Chapter 9.1.2 --- Overview of our Framework --- p.143Chapter 9.2 --- Coupled Space Learning --- p.143Chapter 9.2.1 --- Framework of Coupled Modelling --- p.143Chapter 9.2.2 --- Correlative Component Analysis --- p.145Chapter 9.2.3 --- Coupled Bidirectional Transform --- p.148Chapter 9.2.4 --- Procedure of Coupled Space Learning --- p.151Chapter 9.3 --- Generalization to Mixture Model --- p.152Chapter 9.3.1 --- Coupled Gaussian Mixture Model --- p.152Chapter 9.3.2 --- Optimization by EM Algorithm --- p.152Chapter 9.4 --- Integrated Framework for Image Style Transform --- p.154Chapter 9.5 --- Experiments --- p.156Chapter 9.5.1 --- Face Super-resolution --- p.156Chapter 9.5.2 --- Portrait Style Transforms --- p.157Chapter 10 --- Inter-Modality Recognition --- p.162Chapter 10.1 --- Introduction to the Inter-Modality Recognition Problem . . . --- p.163Chapter 10.1.1 --- What is Inter-Modality Recognition --- p.163Chapter 10.1.2 --- Overview of Our Feature Extraction Framework . . . . --- p.163Chapter 10.2 --- Common Discriminant Feature Extraction --- p.165Chapter 10.2.1 --- Formulation of the Learning Problem --- p.165Chapter 10.2.2 --- Matrix-Form of the Objective --- p.168Chapter 10.2.3 --- Solving the Linear Transforms --- p.169Chapter 10.3 --- Kernelized Common Discriminant Feature Extraction --- p.170Chapter 10.4 --- Multi-Mode Framework --- p.172Chapter 10.4.1 --- Multi-Mode Formulation --- p.172Chapter 10.4.2 --- Optimization Scheme --- p.174Chapter 10.5 --- Experiments --- p.176Chapter 10.5.1 --- Experiment Settings --- p.176Chapter 10.5.2 --- Experiment Results --- p.177Chapter IV --- A New Perspective: Informative Learning --- p.180Chapter 11 --- Toward Information Theory --- p.181Chapter 11.1 --- Entropy and Mutual Information --- p.181Chapter 11.1.1 --- Entropy --- p.182Chapter 11.1.2 --- Relative Entropy (Kullback Leibler Divergence) --- p.184Chapter 11.2 --- Mutual Information --- p.184Chapter 11.2.1 --- Definition of Mutual Information --- p.184Chapter 11.2.2 --- Chain rules --- p.186Chapter 11.2.3 --- Information in Data Processing --- p.188Chapter 11.3 --- Differential Entropy --- p.189Chapter 11.3.1 --- Differential Entropy of Continuous Random Variable . --- p.189Chapter 11.3.2 --- Mutual Information of Continuous Random Variable . --- p.190Chapter 12 --- Conditional Infomax Learning --- p.191Chapter 12.1 --- An Overview --- p.192Chapter 12.2 --- Conditional Informative Feature Extraction --- p.193Chapter 12.2.1 --- Problem Formulation and Features --- p.193Chapter 12.2.2 --- The Information Maximization Principle --- p.194Chapter 12.2.3 --- The Information Decomposition and the Conditional Objective --- p.195Chapter 12.3 --- The Efficient Optimization --- p.197Chapter 12.3.1 --- Discrete Approximation Based on AEP --- p.197Chapter 12.3.2 --- Analysis of Terms and Their Derivatives --- p.198Chapter 12.3.3 --- Local Active Region Method --- p.200Chapter 12.4 --- Bayesian Feature Fusion with Sparse Prior --- p.201Chapter 12.5 --- The Integrated Framework for Feature Learning --- p.202Chapter 12.6 --- Experiments --- p.203Chapter 12.6.1 --- A Toy Problem --- p.203Chapter 12.6.2 --- Face Recognition --- p.204Chapter 13 --- Channel-based Maximum Effective Information --- p.209Chapter 13.1 --- Motivation and Overview --- p.209Chapter 13.2 --- Maximizing Effective Information --- p.211Chapter 13.2.1 --- Relation between Mutual Information and Classification --- p.211Chapter 13.2.2 --- Linear Projection and Metric --- p.212Chapter 13.2.3 --- Channel Model and Effective Information --- p.213Chapter 13.2.4 --- Parzen Window Approximation --- p.216Chapter 13.3 --- Parameter Optimization on Grassmann Manifold --- p.217Chapter 13.3.1 --- Grassmann Manifold --- p.217Chapter 13.3.2 --- Conjugate Gradient Optimization on Grassmann Manifold --- p.219Chapter 13.3.3 --- Computation of Gradient --- p.221Chapter 13.4 --- Experiments --- p.222Chapter 13.4.1 --- A Toy Problem --- p.222Chapter 13.4.2 --- Face Recognition --- p.223Chapter 14 --- Conclusion --- p.23