一种基于项目属性评分的协同过滤推荐算法

协同过滤是电子商务推荐系统中应用最成功的推荐技术之一,但面临着严峻的用户评分数据稀疏性和推荐精度低等问题。针对数据稀疏性高和单一评分导致的推荐精度低等问题,提出一种基于项目属性评分的协同过滤推荐算法。首先通过均值法或缩放法构造用户-项目属性评分矩阵将单一评分转化为多评分;其次基于每个属性评分矩阵,计算用户间的偏好相似度,得到目标用户的偏好最近邻居集;然后针对每个最近邻居集,在用户-项目评分矩阵上完成对目标用户的初步评分预测;最后,将多个初步预测评分加权求和作为综合评分,完成推荐。在Movie Lens扩展数据集上的实验结果表明,该算法能有效提高推荐精度。     

Directional pedestrian counting with a hybrid map-based model

Detection-based pedestrian counting methods produce results of considerable accuracy in non-crowded scenes. However, the detection-based approach is dependent on the camera viewpoint. On the other hand, map-based pedestrian counting methods are performed by measuring features that do not require separate detection of each pedestrian in the scene. Thus, these methods are more effective especially in high crowd density. In this paper, we propose a hybrid map-based model that is a new directional pedestrian counting model. Our proposed model is composed of direction estimation module with classified foreground motion vectors, and pedestrian counting module with principal component analysis. Our contributions in this paper have two aspects. First, we present a directional moving pedestrian counting system that does not depend on object detection or tracking. Second, the number and major directions of pedestrian movements can be detected, by classifying foreground motion vectors. This representation is more powerful than simple features in terms of handling noise, and can count the moving pedestrians in images more accurately.

     

基于Python的网络游戏脚本系统的设计与实现

传统的网络游戏开发采用C/C++实现游戏的逻辑功能,随着游戏内容越来越丰富以及快速变化的市场需要,C/C++已经不能满足开发者对于语言灵活性、易用性、动态编译的需求。为此文中提出了一种基于Python的游戏脚本系统,通过将游戏内容与游戏引擎相分离、避免硬编码,提高了游戏的开发效率和稳定性。     

Research on social relations cognitive model of mobile nodes in Internet of Things

Interaction and communication between humans with smart mobile devices are a new trend of development in Internet of Things (IoT). With the powerful sensing capability of smart device and human mobility, various services could be provided by building a trusted chain between service requesters and suppliers. The cognition of social relations between mobile nodes is the basis of final mobile-aware services. It involves many decision factors, such as time, space and activity patterns. Using social network theory, a new cognitive model for social relations of mobile nodes in IoT is proposed. Firstly, nodes' social relations are reasoned and quantified from multiple perspectives based on the summary of social characteristics of mobile nodes and the definition of different decision factors. Then the location factor, interconnection factor, service evaluation factor and feedback aggregation factor are defined to solve the shortcomings in existing quantitative models. Finally, the weight distribution is set up by information entropy and rough set theory for these decision factors; it can overcome the shortage of traditional methods, in which the weight is set up by subjective ways and hence their dynamic adaptability is poor. We compare our cognitive model to existing models using MIT dataset by defining a variety of test indicators, such as network overall density (NOD), the degree center potential (DCP), the network distribution index (EI), etc. Simulation results show that, the cognitive model has better internal structure and significant validity in network analysis, and thus can provide mobile-aware service effectively in dynamic environment.     

A combined UASB-MBR with shortcut nitrification-denitrification for energy reduction in wastewater reclamation

Shortcut nitrification has been successfully applied in a laboratory scale nitrification-denitrification process consisting of an up-flow anaerobic sludge blanket (UASB) and an aerobic membrane bioreactor (MBR) in treating synthetic and municipal wastewater to simultaneously remove organic carbon and nitrogen. For the treatment of synthetic wastewater, the combined system exhibited a high TOC removal of 98% with a steady ammonia removal efficiency of about 98% in the MBR and a total nitrogen (TN) removal efficiency of 90%. In treating municipal wastewater, due to its low COD concentration, removal efficiencies of TOC, ammonia and TN were 70%, 98% and 60%, respectively. The biogas production was around 76.4 L/m3 wastewater when treating synthetic wastewater. However, little biogas was produced when treating municipal wastewater which was the result of low organic carbon loading to the UASB. Energy analysis has demonstrated that this novel shortcut nitrification process could consume less energy than a conventional process and have the potential of bio-energy generation via biogas production thus helping to achieve a more favorable energy balance.     

Tripartite entanglement of a spin star model with Dzialoshinski–Moriya interaction

The effect of Dzialoshinski–Moriya (DM) interaction on the tripartite thermal entanglement of a spin-star model with four spins has been analyzed by an entanglement measure of the tripartite negativity. Our results imply that the tripartite thermal entanglement can be established among the three surrounding parties which do not interact with each other but interact with the central party independently. From the results, we find that the strong DM interaction can enhance the tripartite thermal entanglement while the high temperature can shrink it. The effect of the inhomogeneous coupling on the tripartite thermal entanglement is also discussed.     

设计转换函数实现保留上下文环境体绘制

摘要为了帮助医生对医学图像进行准确的定位和诊断,在显示感兴趣区域的同时保留其上下文信息,提出一种保留上下文环境体绘制方法.首先记录沿光线方向的首个等值面的空间位置;然后根据采样点到等值面的距离和采样点处的梯度构造衰减函数,将衰减函数作用于不透明度转换函数,以控制光线合成.对头、腹和脚部等CT数据的实验结果表明,该方法可以在保留组织轮廓清晰的前提下增强感兴趣区域显示.