Curriculum Adaptation in Special Schools for Students with Intellectual Disabilities(SID):A Case Study of Project Learning in One SID School in Hong Kong

Using a qualitative case study approach,the authors analyzed the curriculum adaptation process for one project learning activity in School K,which is a SID school in the context of school-university collaboration.Multiple sources of data were collected for triangulation,including interviews,documents and observations.Curriculum adaptation strategies in this study were analyzed from five perspectives:instructional goals,instructional content,instructional strategies,instructional settings,and student behavioral needs.It was found that curriculum adaptation efforts could help students with ID develop potential at their own level through project learning activates and teachers could also gain professional development during the university-school collaboration process.As for future studies,enlarging the sample size,involving teachers’past orientations and motivations in the project learning process,and collecting quantitative data could all be taken into account.

Generalized projective synchronization of chaotic systems via adaptive learning control

In this paper, a learning control approach is applied to the generalized projective synchronisation （GPS） of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov--Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme.

An Equivalent Form of S-L Projection Learning

Learning a mapping between an input space and an output.space from training data canbe viewed as a problem of function approximation,which means that different criteria result in learningapproaches of different abilities.Among them,projection criterion proposed by one of the authors aimsdirectly at optimal generalization.The projection idea leads to three specific learning approaches,projection learning,partial projection learning,and averaged projection learning,and a framework of afamily of projection learning called S-L projection learning is established to discuss infinite kinds oflearning.S-L projection learning has a different form from the three methods,and it is not easy to analyzetheir relationship.This paper focuses on an equivalent form of S-L projection learning,which shows thatit is a transformed version of partial projection learning.

Applying Project Based Learning to Flipped Bloom Taxonomy for Deep Understanding in Control Systems

The peculiar nature of control theory as a course that cut across a lot of major engineering disciplines calls for a look into how its learning can best be done without students feeling like they are wasting their time.This paper takes a look at control theory as subject cut across various engineering field and has a wide background that students must really be comfortable with.Its wide application and background pose a huge challenge to the teaching of control.It goes further to look into traditional method of teaching,Project-Based Learning Blooms Taxonomy.It then proposes applying Flipped Bloom Taxonomy to Project-based learning for a deep understanding of control systems.

Partial Oblique Projection Learning for Optimal Generalization

In practice, it is necessary to implement an incremental and active learning for a learning method. In terms of such implementation, this paper shows that the previously discussed S-L projection learning is inappropriate to constructing a family of projection learning, and proposes a new version called partial oblique projection (POP) learning. In POP learning, a function space is decomposed into two complementary subspaces, so that functions belonging to one of the subspaces can be completely estimated in noiseless case; while in noisy case, the dispersions are set to be the smallest. In addition, a general form of POP learning is presented and the results of a simulation are given.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Phase unwrapping based on deep learning in light field fringe projection 3D measurement

Phase unwrapping is one of the key roles in fringe projection three-dimensional(3D)measurement technology.We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measurement based on deep learning.A multi-stream convolutional neural network(CNN)is proposed to learn the mapping relationship between camera array light filed wrapped phases and fringe orders of the expected central view,and is used to predict the fringe order to achieve the phase unwrapping.Experiments are performed on the light field fringe projection data generated by the simulated camera array fringe projection measurement system in Blender and by the experimental 3×3 camera array light field fringe projection system.The performance of the proposed network with light field wrapped phases using multiple directions as network input data is studied,and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated.

The Interdisciplinary Education for The Emerging Engineering Majors:Taking Cyberspace Security Major as An Example

The new generation of world information technology revolution has promoted the vigorous development and rapid transform of the new economy.The in-depth implementation of a series of Chinese important national strategies such as“Made in China 2025”and“Internet+”is urgently needed the support of innovative and outstanding emerging engineering talents with cross-border integration abilities.Therefore,interdisciplinary education plays an important role in the training of the needed emerging engineering talents.Taking cybersecurity as an example,this paper summarizes the professional’s requirements and proposes the educational objectives of Harbin Institute of Technology.Then the objective oriented curriculum system and the teaching model emphasizing project-based learning are introduced.The practice and effect of interdisciplinary education are discussed and analyzed in four aspects including curriculum system,faculty,students and academy education.Finally,suggestions are made on the individualized education and sustainable competitiveness cultivation of the emerging engineering talents.

Experience report:investigating bug fixes in machine learning frameworks/libraries

Machine learning(ML)techniques and algorithms have been successfully and widely used in various areas including software engineering tasks.Like other software projects,bugs are also common in ML projects and libraries.In order to more deeply understand the features related to bug fixing in ML projects,we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories,fixing patterns,fixing scale,fixing duration,and types of maintenance.The results show that(1)there are commonly seven types of bugs in ML programs;(2)twelve fixing patterns are typically used to fix the bugs in ML programs;(3)68.80%of the patches belong to micro-scale-fix and small-scale-fix;(4)66.77%of the bugs in ML programs can be fixed within one month;(5)45.90%of the bug fixes belong to corrective activity from the perspective of software maintenance.Moreover,we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical study.The results of our empirical study are basically consistent with the feedback from developers.The findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects.