Learning a novel phonological contrast depends on interactions between individual differences and training paradigm design

Studies evaluating phonological contrast learning typically investigate either the predictiveness of specific pretraining aptitude measures or the efficacy of different instructional paradigms. However, little research considers how these factors interact--whether different students learn better from different types of instruction--and what the psychological basis for any interaction might be. The present study demonstrates that successfully learning a foreign-language phonological contrast for pitch depends on an interaction between individual differences in perceptual abilities and the design of the training paradigm. Training from stimuli with high acoustic-phonetic variability is generally thought to improve learning; however, we found high-variability training enhanced learning only for individuals with strong perceptual abilities. Learners with weaker perceptual abilities were actually impaired by high-variability training relative to a low-variability condition. A second experiment assessing variations on the high-variability training design determined that the property of this learning environment most detrimental to perceptually weak learners is the amount of trial-by-trial variability. Learners' perceptual limitations can thus override the benefits of high-variability training where trial-by-trial variability in other irrelevant acoustic-phonetic features obfuscates access to the target feature. These results demonstrate the importance of considering individual differences in pretraining aptitudes when evaluating the efficacy of any speech training paradigm.     

基于Android的大学物理移动学习平台设计及应用研究

移动学习作为一种新型的学习模式使学习者摆脱时间和空间的束缚,实现真正意义上的自主学习.本文基于对大学物理课程教学现状和学生学习需求的分析,设计并开发了基于Android系统的大学物理移动学习平台,并应用于课程开展教学实证研究,通过测试和问卷调查评估平台的应用效果.     

Mie theory 1908, on the mobile phone 2008

Hundred years ago Gustav Mie published his famous paper on the theory of light scattering by spherical particles. It has been programmed in many programming languages since invention of the computer. Nowadays with the mobile phone becoming an educational tool it would be of interest to port a Mie scattering application to a Java enabled hand set. The objective of this paper is to investigate the feasibility of porting existing Mie software to a hand held device. We will show that the mobile phone can be an advanced programming environment for the Mie theory and we will present some exemplary computational results.     

多重表征与学习进阶整合的“库仑定律”教学探索

多重表征能够为学习者提供多样化、弹性的信息呈现方式.多重表征教学有助于学生学习进阶的顺利进行.以"库仑定律"的教学为例,探索了将多重表征与学习进阶整合的教学在物理规律知识形成中的具体应用.     

KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.     

基于Emu8086仿真的微机原理及接口技术教学整合的实践与探索简

探讨了将Emu8086仿真技术与微机原理、接口技术课程教学有机结合。运用“主体一主导”的教学设计理念,合理利用EmuS086仿真软件进行设计教学,使学生直观、形象地理解微机结构及接口的工作原理和过程,提高学生的学习兴趣,激发学生的创造性和能动性,培养学生整体设计和开发能力,实现教学方式的转变。     

Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.     

大学物理教学中的合作学习实践

作者基于电子科技大学的实际情况,开展针对大班低年级本科生的大学物理教学中的合作学习（cooperative learning）实践,通过课堂教学与课外学习相结合、理论学习与实践锻炼相结合、共性培养与个性培养相结合,深入研究与课程密切相关的探究性课题,加强师生之间、同学之间的合作与互动,充分培养学生对科学研究的一般性思维,激发他们对科学探索的兴趣.     

波尔共振仪实验的网络化教学

本文介绍网络化的智能型波尔共振仪实验系统的基本结构，信息控制与实验软件，网络化教学管理的实现，及其在实验教学中的应用．     

例谈物理核心素养视角下培养学生策略性学习的实践研究

文章结合自身教学实践,针对在物理核心素养背景下,从"借助项目设计,培养学习习惯,促进学生‘策略性学习’反应程序化""借助过程优化,积累学习方法,促进学生‘策略性学习’研究深度化""借助物理与生活互动,提高学生对学习理解,促进学生‘策略性学习’运用实践化"3个途径来提升学生思维水平,发展学生思维品质,进而提高学生物理核心素养.     

工科大学物理混合式教学实践与探索

以大学物理课程为例,从前期准备、教学实践、课程考核3方面进行混合式教学的探索与实践.实践表明混合式教学突破了传统课堂的时空局限,极大地扩展了传统课堂的教学内容和深度,完善了网络教学,使学生实践了真正意义上的自主学习,实现了理想的师生互动关系.     

Transportation Mode Detection Using an Optimized Long Short-Term Memory Model on Multimodal Sensor Data

The advancement of sensing technologies coupled with the rapid progress in big data analysis has ushered in a new era in intelligent transport and smart city applications. In this context, transportation mode detection (TMD) of mobile users is a field that has gained significant traction in recent years. In this paper, we present a deep learning approach for transportation mode detection using multimodal sensor data elicited from user smartphones. The approach is based on long short-term Memory networks and Bayesian optimization of their parameters. We conducted an extensive experimental evaluation of the proposed approach, which attains very high recognition rates, against a multitude of machine learning approaches, including state-of-the-art methods. We also discuss issues regarding feature correlation and the impact of dimensionality reduction.     

Direct regressions for underwater acoustic source localization in fluctuating oceans

In this paper, we show the potential of machine learning regarding the task of underwater source localization through a fluctuating ocean. Underwater source localization is classically addressed under the angle of inversion techniques. However, because an inversion scheme is necessarily based on the knowledge of the environmental parameters, it may be not well adapted to a random and fluctuating underwater channel. Conversely, machine learning only requires using a training database, the environmental characteristics underlying the regression models. This makes machine learning adapted to fluctuating channels. In this paper, we propose to use non linear regressions for source localization in fluctuating oceans. The kernel regression as well as the local linear regression are compared to typical inversion techniques, namely Matched Field Beamforming and the algorithm MUSIC. Our experiments use both real tank-based and simulated data, introduced in the works of Real et al. Based on Monte Carlo iterations, we show that the machine learning approaches may outperform the inversion techniques.     

多元智力理论在大学物理实验学习评价中的应用

霍华德·加德纳的多元智力理论是影响美国和西方国家教育改革指导思想的一种重要理论．根据多元智力理论分析了学生在大学物理实验学习评价中的智力构成的表现,并据此表现设计实施评价的可行性量表,这将有助于提高实验教学效果．     

物理教育的衔接现状及教师策略研究

近年来全国多省份陆续进行高考改革,在学生从高中过渡到大学的过程中,高中选科制度衍生出了在心理、学习方式、学习内容等方面的一系列问题,也成为教师必须面对的问题.基于以上出现的新情况,设计了关于物理衔接的调查问卷,以中央民族大学物理专业大一学生为研究对象,探究了大学物理与高中物理衔接在教师方面的影响因素,并辅以大学生学习动机的调查问卷,进一步探索了如何提升物理衔接的教学效果,并结合教育心理学关于内部动机与外部动机的研究理论,从教师的角度提出了大中衔接过程中的实施建议及策略.     

基于注意力曲线的近代物理专题智慧课堂的设计

近代物理专题作为基础通识必修课程大学物理的延续课程,担当着帮助学生了解近代物理学的发展概貌、提高大学生的科学素养的教学目标.文章工作以固体物理基础与硅信息时代专题为例,结合注意力曲线特点,使用混合式教学方法和手段,理论与应用相结合,进行了固体物理基础和硅材料应用的智慧课堂设计.经多次教学实践,取得了良好的教学效果.     

Co-Training for Visual Object Recognition Based on Self-Supervised Models Using a Cross-Entropy Regularization

Automatic recognition of visual objects using a deep learning approach has been successfully applied to multiple areas. However, deep learning techniques require a large amount of labeled data, which is usually expensive to obtain. An alternative is to use semi-supervised models, such as co-training, where multiple complementary views are combined using a small amount of labeled data. A simple way to associate views to visual objects is through the application of a degree of rotation or a type of filter. In this work, we propose a co-training model for visual object recognition using deep neural networks by adding layers of self-supervised neural networks as intermediate inputs to the views, where the views are diversified through the cross-entropy regularization of their outputs. Since the model merges the concepts of co-training and self-supervised learning by considering the differentiation of outputs, we called it Differential Self-Supervised Co-Training (DSSCo-Training). This paper presents some experiments using the DSSCo-Training model to well-known image datasets such as MNIST, CIFAR-100, and SVHN. The results indicate that the proposed model is competitive with the state-of-art models and shows an average relative improvement of 5% in accuracy for several datasets, despite its greater simplicity with respect to more recent approaches.     

优化设计性实验教学培养学生的科学素质

通过论证设计性实验与科学素质之间的关系，提出体现培养科学素质的问题式学习模式，并在大学物理实验教学中进行了教学改革试验。     

A comparative study of two learning rules for associative memory

This paper addresses itself to a practical problem encountered in using iterative learning rules for associative memory models. The performance of a learning rule based on linear programming which overcomes this problem is compared with that of a representative iterative rule by numerical simulation. Results indicate superior performance by the linear programming rule. An algorithm for computing radii of maximal hyperspheres around patterns in the state space of a model is presented. Fractional volumes of basins of attractions are computed for the representative iterative rule as well as the linear programming rule. With the radii of maximal hyperspheres as weight factors for corresponding patterns to be stored, the linear programming rule gives rise to the maximal utilisation of the state space.     

Local Integral Regression Network for Cell Nuclei Detection

Nuclei detection is a fundamental task in the field of histopathology image analysis and remains challenging due to cellular heterogeneity. Recent studies explore convolutional neural networks to either isolate them with sophisticated boundaries (segmentation-based methods) or locate the centroids of the nuclei (counting-based approaches). Although these two methods have demonstrated superior success, their fully supervised training demands considerable and laborious pixel-wise annotations manually labeled by pathology experts. To alleviate such tedious effort and reduce the annotation cost, we propose a novel local integral regression network (LIRNet) that allows both fully and weakly supervised learning (FSL/WSL) frameworks for nuclei detection. Furthermore, the LIRNet can output an exquisite density map of nuclei, in which the localization of each nucleus is barely affected by the post-processing algorithms. The quantitative experimental results demonstrate that the FSL version of the LIRNet achieves a state-of-the-art performance compared to other counterparts. In addition, the WSL version has exhibited a competitive detection performance and an effortless data annotation that requires only 17.5% of the annotation effort.