以学生为中心的物理化学远程教学初探

During the fight against the new coronavirus epidemic, the "Physical Chemistry" team of Beijing Normal University used various network platforms and tools, such as MOOC of China University, Rain Classroom and WeChat Group in the teaching process. The exploration and practice of online learning of "Physical Chemistry" is carried out based on MOOC teaching, with student learning as the center and teacher guidance as the support. Taking the most difficult chapter of "fundamentals for statistical thermodynamic" as an example, we designed a complex teaching program that includes previews, watching MOOC videos in class, real-time online exercises, teachers' answers, and after-class reflection and questioning. The students were encouraged to learn independently through the reserved information, designed activities and assignments. Practice shows that the online course has cultivated the students' self-management ability, trained their critical thinking skills, and achieved good teaching results.     

Learning hierarchical spatio-temporal pattern for human activity prediction

Human activity prediction has become increasingly valuable in many applications. This paper, initially from the perspective of cognition science, presents a novel approach to learning a hierarchical spatio-temporal pattern of human activities to predict ongoing activities from videos that contain only the onsets of the activities. Spatio-temporal pattern can be learned by a Hierarchical Self-Organizing Map (HSOM), which consists of two self-organizing maps (i.e., action map and actionlet map) connected via associative links trained by Hebbian learning. Ongoing activities can be predicted by Variable order Markov Model (VMM), which provides the means for capturing both large and small order Markov dependencies based on the training actionlet sequences. Experiments of the proposed method on four challenging 3D action datasets captured by commodity depth cameras show promising results.     

Student enrichment in mathematics: a case study with first year university students

ABSTRACT

This paper presents an enrichment case study to showcase a possible avenue for attending to the needs of academically strong mathematics students. We report on a group of university students who were presented with the opportunity of exploring a specific first year mathematics topic deeper, using an inquiry-based learning approach as part of an enrichment programme. Following the intervention, students completed a questionnaire and a few were interviewed to establish their experiences of the enrichment programme. We discuss the successes and pitfalls of the intervention and report on the impact it had on the participants.     

Content-aware image resizing based on random-carving with probability

To improve the running speed of image resizing,a fast content-aware image resizing algorithm was proposed based on the threshold learning and random-carving with probability.Firstly the important map was calculated by combining the graph-based visual saliency map and gradient map.Then the image threshold value was obtained by radial basis function (RBF) neural network learning.And by the threshold,the original image was separated into the protected part and the unprotected part which was corresponding to the important part and the unimportant part of the original image individually.Finally,the two parts were allocated different resizing scales and the random-carving with probability was applied to them respectively.Experiments results show that the proposed algorithm has lower time cost comparing to the state-of-arts algorithms in MSRA image database,and has a better visual perception on image resizing.     

‘New mathematics’ and teaching

Inside the scientific world it is not always understood that the mood of mathematics, which is a product and a part of culture, can change with time. This is partly why many have been surprised by the coming of the so‐called new mathematics.

In the truly creative mathematical mind two opposite tendencies coexist: the logical and the imaginative. Apparently it seems that new mathematics can be reduced to a purely logical machinery. In fact it contains as much imaginative contributions as classical mathematics. But it is difficult to show simultaneously the logical sequence of propositions and the clumsy progression of research itself. Mathematical exposition does not always follow the ‘ most natural slopes’ of the mind. Unfamiliar presentations often give an impression of ‘ abstraction ‘, more familiar ones an impression of concreteness ‘.

So it appears that difficulties with new mathematics are mostly of psychological origin. Misuses of it can easily raise up intolerance reactions and emotional blocks. Perhaps insisting upon the fact that, here as elsewhere, it is important to be able to guess, to realize that intuition and imagination are essential, could help to make new mathematics better understood, more useful and more able to be considered as a unifing element among sciences.     

Survey on differential privacy and its progress

With the arrival of the era of big data sharing,data privacy protection issues will be highlighted.Since its introduction in 2006,differential privacy technology has been widely researched in data mining and data publishing.In recent years,Google,Apple and other companies have introduced differential privacy technology into the latest products,and differential privacy technology has become the focus of academia and industry again.Firstly,the traditional centralized model of differential privacy was summarized,from the perspective of analysis of data mining and data released in the differential privacy way.Then the latest local differential privacy regarding data collection and data analysis based on the local model was described,involving crowdsourcing with random response technology,BloomFilter,statistical inference techniques.Finally,the main problems and solutions of differential privacy technology were summarized.     

计算机应用设计3DMAX的学习感受

3DMAX软件是一款专业服务艺术设计的三维设计软件，由美国Autdesk传媒娱乐部开发，运行在Win32和Win64平台的计算机图形软件。当前3DMAX软件运用在多个领域，并由于高性价比、易上手、用户多易交流等优势得到广泛认可，业界甚至可以通过对3DMAX软件能力，去衡量个人或企业的水准。鉴于此，研究3DMAX的运用和学习能够深刻读者对该软件的认识，进一步提升相关学者的软件学习能力。     

Chaos in social learning with multiple true states

Most existing social learning models assume that there is only one underlying true state. In this work, we consider a social learning model with multiple true states, in which agents in different groups receive different signal sequences generated by their corresponding underlying true states. Each agent updates his belief by combining his rational self-adjustment based on the external signals he received and the influence of his neighbors according to their communication. We observe chaotic oscillation in the belief evolution, which implies that neither true state could be learnt correctly by calculating the largest Lyapunov exponents and Hurst exponents.     

Adaptive visual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker

Multiple visual target tracking is a challenging problem due to various uncertainties including occlusion, miss-detection and noisy measurement. Most tracking approaches utilize an object-specific detector, pre-trained on many labeled images, to provide suitable measurements for their tracking system. In this paper, we use a simple background subtraction detector which only needs the background image to localize targets independent of their shape or type. In order to cope with the uncertainties resulted by the detector, we propose an adaptive appearance model and develop an incremental appearance learning algorithm to learn the target appearances in time. The proposed method employs the background information and our defined keypoints’ miss-matched history to adapt the target appearances within different frames. Furthermore, we combine Refined Gaussian Mixture Probability Hypothesis Density (RGM-PHD) tracker with the detectors to keep target trajectories and handle uncertainties. The experiments conducted on several video datasets show the effectiveness of our proposed method.