The Development and Influence of RCPS Instructions to Promote Students＇ Scientific Learning Motivation and Scientific Concept Learning

This research aims at developing RCPS （revised creative problem solving） teaching model, besides the authors designed the instructions of chemical reaction to promote eight grade students＇ scientific learning motivation and scientific concept learning. We adopted quasi-experiment study, the experimental group and controlled group all 28 students were chose, go on the parameter is analyzed together compared with textbook instructions, scale of scientific learning motivation and test of scientific conception learning were used for the two groups in prior test and post test, then they used statistical ANCOVA （analysis of covariance） to analyze the differences between the two teaching models. The result of this study finds that RCPS teaching model improved student＇s scientific learning motivation and learning scientific concept was superior to textbook instructions in controlled group, p = 0.001 （〈 0.01）, and all with high experimental treatment effects （〉 0.14）. The study also proposes that when RCPS teaching model was applied to scientific concept teaching, RCPS teaching model should be joined the conception introducing stage, and pay attention to students＇ scientific learning motivation.

Concept Learning in Neuromorphic Vision Systems: What Can We Learn from Insects?

Vision systems that enable collision avoidance, localization and navigation in complex and uncertain environments are common in biology, but are extremely challenging to mimic in artificial electronic systems, in particular when size and power limitations apply. The development of neuromorphic electronic systems implementing models of biological sensory-motor systems in silicon is one promising approach to addressing these challenges. Concept learning is a central part of animal cognition that enables appropriate motor response in novel situations by generalization of former experience, possibly from a few examples. These aspects make concept learning a challenging and important problem. Learning methods in computer vision are typically inspired by mammals, but recent studies of insects motivate an interesting complementary research direction. There are several remarkable results showing that honeybees can learn to master abstract concepts, providing a road map for future work to allow direct comparisons between bio-inspired computing architectures and information processing in miniaturized “real” brains. Considering that the brain of a bee has less than 0.01% as many neurons as a human brain, the task to infer a minimal architecture and mechanism of concept learning from studies of bees appears well motivated. The relatively low complexity of insect sensory-motor systems makes them an interesting model for the further development of bio-inspired computing architectures, in particular for resource-constrained applications such as miniature robots, wireless sensors and handheld or wearable devices. Work in that direction is a natural step towards understanding and making use of prototype circuits for concept learning, which eventually may also help us to understand the more complex learning circuits of the human brain. By adapting concept learning mechanisms to a polymorphic computing framework we could possibly create large-scale decentralized computer vision systems, for example in the form of wireless sensor networks.

Learning of Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teaching

This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning,and discusses several issues related to concept learning,including the assistance of machine learning about concept learning.In terms of individual learning,current evidence shows that the brain tends to process concrete concepts through typical features(shared features);and for abstract concepts,semantic processing is the most important cognitive way.In terms of social learning,interpersonal neural synchrony(INS)is considered the main indicator of efficient knowledge transfer(such as teaching activities between teachers and students),but this phenomenon only broadens the channels for concept sources and does not change the basic mode of individual concept learning.Ultimately,this article argues that the way the human brain processes concepts depends on the concept’s own characteristics,so there are no“better”strategies in teaching,only more“suitable”strategies.

Visual Superordinate Abstraction for Robust Concept Learning

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure to explore the intrinsic semantic hierarchy of visual concepts, e.g., {red, blue,···} ∈“color” subspace yet cube ∈“shape”. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces(i.e., visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5% for reasoning with perturbations and 15.6% for compositional generalization tests.

An Analysis of Conceptions of Learning English Among College Students in China

In the field of second language acquisition, an increasing amount of research has been conducted on learner＇s beliefs. Few empirical studies, however, have been focused on students＇ conceptions of learning English （COLE）. This study aims to assess conceptions of learning particularly in the domain of English. Data were collected through the COLE questionnaire among 284 college students in China. Seven factors of COLE are identified, such as ＂memorizing＂, ＂testing＂, ＂drill and practice＂, ＂increasing knowledge＂, ＂understanding＂, ＂application＂, and ＂seeing in a new way＂. These findings also provide some pedagogical implications for English language instructors and researchers.

A Hybrid Genetic Algorithm for Supervised Inductive Learning

A novel algorithm is presented for supervised inductive learning by integrating a genetic algorithm with hot'tom-up induction process.The hybrid learning algorithm has been implemented in C on a personal computer(386DX/40).The performance of the algorithm has been evaluated by applying it to 11-multiplexer problem and the results show that the algorithm's accuracy is higher than the others[5,12, 13].

Using a Hybrid Genetic Algorithm to Learn Rules From Examples

in this article a novel learning method is proposed,which is a combination of GA and the bottomup induction process. The method has been implemented in a system called KAA,and we evaluate it on a multiplexer problem,which shows the higher predict accuracy even in a noisy environment.

Region-adaptive Concept Aggregation for Few-shot Visual Recognition

Few-shot learning (FSL) aims to learn novel concepts from very limited examples. However, most FSL methods suffer from the issue of lacking robustness in concept learning. Specifically, existing FSL methods usually ignore the diversity of region contents that may contain concept-irrelevant information such as the background, which would introduce bias/noise and degrade the performance of conceptual representation learning. To address the above-mentioned issue, we propose a novel metric-based FSL method termed region-adaptive concept aggregation network or RCA-Net. Specifically, we devise a region-adaptive concept aggregator (RCA) to model the relationships of different regions and capture the conceptual information in different regions, which are then integrated in a weighted average manner to obtain the conceptual representation. Consequently, robust concept learning can be achieved by focusing more on the concept-relevant information and less on the conceptual-irrelevant information. We perform extensive experiments on three popular visual recognition benchmarks to demonstrate the superiority of RCA-Net for robust few-shot learning. In particular, on the Caltech-UCSD Birds-200-2011 (CUB200) dataset, the proposed RCA-Net significantly improves 1-shot accuracy from 74.76% to 78.03% and 5-shot accuracy from 86.84% to 89.83% compared with the most competitive counterpart.

Image retrieval based on multi-concept detector and semantic correlation

With the rapid development of future network, there has been an explosive growth in multimedia data such as web images. Hence, an efficient image retrieval engine is necessary. Previous studies concentrate on the single concept image retrieval, which has limited practical usability. In practice, users always employ an Internet image retrieval system with multi-concept queries, but, the related existing approaches are often ineffective because the only combination of single-concept query techniques is adopted. At present semantic concept based multi-concept image retrieval is becoming an urgent issue to be solved. In this paper, a novel Multi-Concept image Retrieval Model(MCRM) based on the multi-concept detector is proposed, which takes a multi-concept as a whole and directly learns each multi-concept from the rearranged multi-concept training set. After the corresponding retrieval algorithm is presented, and the log-likelihood function of predictions is maximized by the gradient descent approach. Besides, semantic correlations among single-concepts and multiconcepts are employed to improve the retrieval performance, in which the semantic correlation probability is estimated with three correlation measures, and the visual evidence is expressed by Bayes theorem, estimated by Support Vector Machine(SVM). Experimental results on Corel and IAPR data sets show that the approach outperforms the state-of-the-arts. Furthermore, the model is beneficial for multi-concept retrieval and difficult retrieval with few relevant images.

Same or different?Abstract relational concept use in juvenile bamboo sharks and Malawi cichlids

Sorting objects and events into categories and concepts is an important cognitive prerequisite that spares an individual the learning of every object or situation encountered in its daily life.Accordingly,specific items are classified in general groups that allow fast responses to novel situations.The present study assessed whether bamboo sharks Chiloscyllium griseum and Malawi cichlids Pseudotropheus zebra can distinguish sets of stimuli(each stimulus consisting of two abstract,geometric objects)that meet two conceptual preconditions,i.e.,(1)"sameness"versus"difference"and(2)a certain spatial arrangement of both objects.In two alternative forced choice experiments,individuals were first trained to choose two different,vertically arranged objects from two different but horizontally arranged ones.Pair discriminations were followed by extensive transfer test experiments.Transfer tests using stimuli consisting of(a)black and gray circles and(b)squares with novel geometric patterns provided conflicting information with respect to the learnt rule"choose two different,vertically arranged objects",thereby investigating(1)the individuals'ability to transfer previously gained knowledge to novel stimuli and(2)the abstract relational concept(s)or rule(s)applied to categorize these novel objects.Present results suggest that the level of processing and usage of both abstract concepts differed considerably between bamboo sharks and Malawi cichlids.Bamboo sharks seemed to combine both concepts-although not with equal but hierarchical prominence-pointing to advanced cognitive capabilities.Conversely,Malawi cichlids had difficulties in discriminating between symbols and failed to apply the acquired training knowledge on new sets of geometric and,in particular,gray-level transfer stimuli.

Effectively Discriminating Fighting Shots in Action Movies

Fighting shots are the highlights of action movies and an effective approach to discriminating fighting shots is very useful for many applications, such as movie trailer construction, movie content filtering, and movie content retrieval. In this paper, we present a novel method for this task. Our approach first extracts the reliable motion information of local invariant features through a robust keypoint tracking computation; then foreground keypoints are distinguished from background keypoints by a sophisticated voting process; further, the parameters of the camera motion model is computed based on the motion information of background keypoints, and this model is then used as a reference to compute the actual motion of foreground keypoints; finally, the corresponding feature vectors are extracted to characterizing the motions of foreground keypoints, and a support vector machine （SVM） classifier is trained based on the extracted feature vectors to discriminate fighting shots. Experimental results on representative action movies show our approach is very effective.

Editorial

This issue of Science China Physics, Mechanics ＆ Astronomy celebrates the Centenary of Einstein＇s General Theory of Rela- tivity, which changed the way humanity understood the concepts of space, time and matter. Prior to 1915 Einstein had intro- duced his theory of Special Relativity, and Minkowski had introduced the spacetime metric. General Relativity overthrew the Newtonian idea that space, time and matter were independent, replacing it with the idea that space, time and matter are inex- tricably linked. Within a year of the publication of General Relativity came Schwartzchild＇s exact solution of Einstein＇s field equations which describes the spacetime structure of black holes. In 1916 and 1918 Einstein showed that his theory predicted the existence of gravitational waves. Within 7 years, in 1922, Friedmann published a solution for Einstein＇s field equations applied to a homogeneous universe, uncovering the basic physics of Big Bang cosmology.