开展“五小”活动 发挥评价功效

数学核心素养基于数学知识技能,又高于具体的数学知识技能,凌驾于数学思想与数学方法之上。数学课程标准提出,学习评价的主要目的是为了全面了解学生数学学习的过程和结果,激励学生学习和改进教师教学,应建立目标多元、方法多样的评价体系。既要关注学生学习的结果,也要重视学习的过程;既要关注学生数学学习的水平,也要重视学生在数学活动中所表现出来的情感与态度,帮助学生认识自我,树立信心。笔者认为,在教学中积极鼓励学生当"小老师""小画家""小作家""小玩家",或者发"小视频",将实践与评价有机融合,对提升小学低年级学生的数学核心素养,具有重要意义。下面,笔者谈谈自己的做法和体会。

Perceptual video coding method based on JND and AR model

In order to achieve better perceptual coding quality while using fewer bits, a novel perceptual video coding method based on the just-noticeable-distortion （JND） model and the auto-regressive （AR） model is explored. First, a new texture segmentation method exploiting the JND profile is devised to detect and classify texture regions in video scenes. In this step, a spatial-temporal JND model is proposed and the JND energy of every micro-block unit is computed and compared with the threshold. Secondly, in order to effectively remove temporal redundancies while preserving high visual quality, an AR model is applied to synthesize the texture regions. All the parameters of the AR model are obtained by the least-squares method and each pixel in the texture region is generated as a linear combination of pixels taken from the closest forward and backward reference frames. Finally, the proposed method is compared with the H.264/AVC video coding system to demonstrate the performance. Various sequences with different types of texture regions are used in the experiment and the results show that the proposed method can reduce the bit-rate by 15% to 58% while maintaining good perceptual quality.

Impact of exergames on physical activity and motivation in elementary school students: A follow-up study

The present study was built upon a previous study on the new generation video game, exergame, in elementary school physical education （PE）. The purpose of the study was to examine the effect of exergames on elementary children＇s in-class physical activity （PA） intensity levels and perceived situational interest over time. The results indicated that students＇ situational interest dropped dramatically over two semesters, but their PA intensity increased over time. The results showed that boys and girls were equally active in the exergaming lessons, but boys perceived their gaming experiences more enjoyable than girls did. The findings suggest that exergames may be a possible means to enhance PA in PE. However, whether exergaming is a sustainable way to motivate children in PA is questionable.

Upper oesophageal images and Z-line detection with 2 different small-bowel capsule systems

Transmission of oesophageal images may vary between different small-bowel capsule endoscopy models. A retrospective review of 100 examinations performed with 2 different Small-bowel capsule endoscopy （SBCE） sys- tems （PillCam and MiroCam） was performed. The oral cavity/aero-digestive tract （i.e., tongue, uvula and/or epiglottis） was captured/identified in almost all （99%） of PillCam videos but in none of MiroCam cases, P 〈 0.0001. Furthermore, oesophageal images （i.e., from the upper oesophageal sphincter to the Z-line were cap- tured in 99% of PillCam videos （mean =1= SD, 60.5 ± 334.1 frames, range： 0-3329 frames） and in 66% of Mi- roCam cases （mean ± SD, 11.1 ± 46.5 frames, range： 0-382 frames）, P 〈 0.0001. The Z-line was identified in 42% of PilICam videos and 17% of MiroCam, P = 0.0002. This information might be useful when perform- ing SBCE in patients with high risks for aspiration.

Robust background subtraction in traffic video sequence

For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system.

A novel method for tracking pedestrians from real-time video

This novel method of Pedestrian Tracking using Support Vector (PTSV) proposed for a video surveillance instrument combines the Support Vector Machine (SVM) classifier into an optic-flow based tracker. The traditional method using optical flow tracks objects by minimizing an intensity difference function between successive frames, while PTSV tracks objects by maximizing the SVM classification score. As the SVM classifier for object and non-object is pre-trained, there is need only to classify an image block as object or non-ob-ject without having to compare the pixel region of the tracked object in the previous frame. To account for large motions between successive frames we build pyramids from the support vectors and use a coarse-to-fine scan in the classification stage. To accelerate the training of SVM, a Sequential Minimal Optimization Method (SMO) is adopted. The results of using a kernel-PTSV for pedestrian tracking from real time video are shown at the end. Comparative experimental results showed that PTSV improves the reliability of tracking compared to that of traditional tracking method using optical flow.

Abnormal activity detection for surveillance video synopsis

Video synopsis is an effective and innovative way to produce short video abstraction for huge video archives,while keeping the dynamic characteristic of activities in the original video.Abnormal activity,as the critical event,is always the main concern in video surveillance context.However,in traditional video synopsis,all the normal and abnormal activities are condensed together equally,which can make the synopsis video confused and worthless.In addition,the traditional video synopsis methods always neglect redundancy in the content domain.To solve the above-mentioned issues,a novel video synopsis method is proposed based on abnormal activity detection and key observation selection.In the proposed algorithm,activities are classified into normal and abnormal ones based on the sparse reconstruction cost from an atomically learned activity dictionary.And key observation selection using the minimum description length principle is conducted for eliminating content redundancy in normal activity.Experiments conducted in publicly available datasets demonstrate that the proposed approach can effectively generate satisfying synopsis videos.

A New Peer-to-Peer Topology for Video Streaming Based on Complex Network Theory

A new wave of networks labeled Peer-to-Peer(P2P) networks attracts more researchers and rapidly becomes one of the most popular applications.In order to matching P2 P logical overlay network with physical topology,the position-based topology has been proposed.The proposed topology not only focuses on non-functional characteristics such as scalability,reliability,fault-tolerance,selforganization,decentralization and fairness,but also functional characteristics are addressed as well.The experimental results show that the hybrid complex topology achieves better characteristics than other complex networks' models like small-world and scale-free models;since most of the real-life networks are both scale-free and small-world networks,it may perform well in mimicking the reality.Meanwhile,it reveals that the authors improve average distance,diameter and clustering coefficient versus Chord and CAN topologies.Finally,the authors show that the proposed topology is the most robust model,against failures and attacks for nodes and edges,versus small-world and scale-free networks.