网络自习室中青年的陪伴行动与空间权力实践——基于行动者网络理论视角

在社会竞争加剧和现实时空压缩的背景下,青年基于数字媒介开展陪伴学习行动,旨在缓解孤独和焦虑,通过联结空间中新的陪伴和监督机制提升行动效果。文章以D平台“陪伴学习”直播为例进行了14个月的网络民族志,发现青年在自习室中利用“全景敞视”和“独景窥视”两种模式开展了创新性的空间权力实践。“全景敞视”下,青年通过部署非人行动者来装潢行动空间、传达学习行动志向,实现“睹物思人”和“托物言志”,建构了多元的、流动的、参与式的空间权力关系网络。在“独景窥视”下,青年通过对现实世界中的权力实践方式进行盗猎式的挪用,将权力关系建立在了行动逻辑、行动规则以及契约精神上,提升学习效率,其基于数字媒介的缓冲带也可以良好地应对空间中出现的越轨现象。

结伴独学:青少年网络自习实践与空间生产

在“云”生活成为后疫情时代生活常态的背景下,面临考试、升学等压力的青少年利用网络进行“云”自习。文章从传播社会学视角切入,基于对不同学龄阶段青少年的参与观察与深度访谈,探讨青少年网络自习实践、生成逻辑、空间生产及学习生活方式的重构问题。研究发现,青少年通过音视频直播、游戏式学习、网络社交等方式进行网络自习。学习倦怠、以学会友的需求以及大加速社会的背景是网络自习的主要生成逻辑。在青少年网络自习过程中,自习场景与多元现实空间的互嵌实现学习空间的生产,并以“结伴独学”共同体的形式重构了当代青少年的学习生活方式。

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

Identification of dynamic systems using support vector regression neural networks

A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method.

A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings

Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data.

Coal mine safety production forewarning based on improved BP neural network

Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.

Fast Learning in Spiking Neural Networks by Learning Rate Adaptation

For accelerating the supervised learning by the SpikeProp algorithm with the temporal coding paradigm in spiking neural networks (SNNs), three learning rate adaptation methods (heuristic rule, delta-delta rule, and delta-bar-delta rule), which are used to speed up training in artificial neural networks, are used to develop the training algorithms for feedforward SNN. The performance of these algorithms is investigated by four experiments: classical XOR (exclusive or) problem, Iris dataset, fault diagnosis in the Tennessee Eastman process, and Poisson trains of discrete spikes. The results demonstrate that all the three learning rate adaptation methods are able to speed up convergence of SNN compared with the original SpikeProp algorithm. Furthermore, if the adaptive learning rate is used in combination with the momentum term, the two modifications will balance each other in a beneficial way to accomplish rapid and steady convergence. In the three learning rate adaptation methods, delta-bar-delta rule performs the best. The delta-bar-delta method with momentum has the fastest convergence rate, the greatest stability of training process, and the maximum accuracy of network learning. The proposed algorithms in this paper are simple and efficient, and consequently valuable for practical applications of SNN.

Fuzzy adaptive learning control network with sigmoid membership function

To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.

Research on College English Teaching Model Based on Multimedia and Network

Based on multimedia and network environment, college English teaching mode can effectively improve the teaching environment of traditional English teaching and students, and expand the capacity of English classroom information, and enrich the students’ English culture background. Therefore, as a teacher, we should first of all make efforts to learn new knowledge and teaching methods to improve our English professional quality. Strengthening the multimedia, network technology and educational psychology research, and put themselves in the teaching position. In addition, the college English teaching model based on multimedia and network environment can cultivate students’ independent thinking, autonomous learning, analysis and problem - solving ability, and promote the high efficiency of college English teaching.

Times Series Prediction to Basis of a Neural Network Conceived by a Real Genetic Algorithm

Neural network and genetic algorithms are complementary technologies in the design of adaptive intelligent system. Neural network learns from scratch by adjusting the interconnections betweens layers. Genetic algorithms are a popular computing framework that uses principals from natural population genetics to evolve solutions to problems. Various forecasting methods have been developed on the basis of neural network, but accuracy has been matter of concern in these forecasts. In neural network methods forecasted values depend to the choose of neural predictor structure, the number of the input, the lag. To remedy to these problem, in this paper, the authors are investing the applicability of an automatic design of a neural predictor realized by real Genetic Algorithms to predict the future value of a time series. The prediction method is tested by using meteorology time series that are daily and weekly mean temperatures in Melbourne, Australia, 1980-1990.

Effective Learner-Lecturer Interaction Working With a Virtual Learning Environment

Using the Internet to learn a language creates wide opportunities to enhance learning （Association of teachers of English in Catalonia （APAC）, 2010）. The Internet activities promote learners＇ self-monitoring ability, encourage the use of multimedia and network technology, and develop students＇ cooperation and participation. During the latest years, there have been many changes in education as these new technologies, including VLEs （Virtual Learning Environments）, which have become an important part in the teaching/learning process. According to Tech Terms Computer Dictionary （2012）, VLE is a virtual classroom where teachers and students communicate. VLEs have evolved as at an early stage, they were only ways of transmitting information： Teachers uploaded the multimedia resources and students read this information. At a higher stage, VLEs have become interactive. This means that students become active. We have designed a virtual environment where students, weekly, must contribute their opinions and comments in response to a required activity uploaded by the teacher. In this paper, we describe this weekly task and analyze students＇ opinion about this planned activity. The students become an active subject in this field. In this paper, we show how VLEs are no longer a means of transmitting information but a means of interaction as well as a way of motivating our students to be involved in their learning process