Point-of-Interest Recommendation Algorithm Based on User Similarity in Location-Based Social Networks

Location-based social network is rising recent years with the development of mobile internet, and point-of-interest (POI) recommendation is a hot topic of this field. Because the factors that affect the behavior of users are very complex, most of the research focuses on the context of the recommendation. But overall context data acquisition in practice is often difficult to obtain. In this paper, we have considered the most common collaborative recommendation algorithm based on user similarity, and discussed several methods of user similarity definition. Comparing the effect of different methods in the actual dataset, experimental results show among the factors including that social relation, check-in and geographical location the check-in is extremely important, so this work is of certain guiding significance to the actual applications

Yu zhi quan shan yao yan / [Shunzhi huang di zhu] 御製勸善要言 / [順治皇帝著

In Manchu and Chinese

Discrimination Analysis for Predicting Defect-Prone Software Modules

Software defect prediction studies usually build models without analyzing the data used in the procedure. As a result, the same approach has different performances on different data sets. In this paper, we introduce discrimination analysis for providing a good method to give insight into the inherent property of the software data. Based on the analysis, we find that the data sets used in this field have nonlinearly separable and class-imbalanced problems. Unlike the prior works, we try to exploit the kernel method to nonlinearly map the data into a high-dimensional feature space. By combating these two problems, we propose an algorithm based on kernel discrimination analysis called KDC to build more effective prediction model. Experimental results on the data sets from different organizations indicate that KDC is more accurate in terms of F-measure than the state-of-the-art methods. We are optimistic that our discrimination analysis method can guide more studies on data structure, which may derive useful knowledge from data science for building more accurate prediction models

Spatiotemporal Influence of Urban Environment on Taxi Ridership Using Geographically and Temporally Weighted Regression

Taxicabs play an important role in urban transit systems, and their ridership is significantly influenced by the urban built environment. The intricate relationship between taxi ridership and the urban environment has been explored using either conventional ordinary least squares (OLS) regression or geographically weighted regression (GWR). However, time constitutes a significant dimension, particularly when analyzing spatiotemporal hourly taxi ridership, which is not effectively incorporated into conventional models. In this study, the geographically and temporally weighted regression (GTWR) model was applied to model the spatiotemporal heterogeneity of hourly taxi ridership, and visualize the spatial and temporal coefficient variations. To test the performance of the GTWR model, an empirical study was implemented for Xiamen city in China using a set of weekday taxi pickup point data. Using point-of-interest (POI) data, hourly taxi ridership was analyzed by incorporating it to various spatially urban environment variables based on a 500 × 500 m grid unit. Compared to the OLS and GWR, the GTWR model obtained the best performance, both in terms of model fit and explanatory accuracy. Moreover, the urban environment was revealed to have a significant impact on taxi ridership. Road density was found to decrease the number of taxi trips in particular places, and the density of bus stops competed with taxi ridership over time. The GTWR modelling provides valuable insights for investigating taxi ridership variation as a function of spatiotemporal urban environment variables, thereby facilitating an optimal allocation of taxi resources and transportation planning

Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network

Metro systems play a key role in meeting urban transport demands in large cities. The close relationship between historical weather conditions and the corresponding passenger flow has been widely analyzed by researchers. However, few studies have explored the issue of how to use historical weather data to make passenger flow forecasting more accurate. To this end, an hourly metro passenger flow forecasting model using a deep long short-term memory neural network (LSTM_NN) was developed. The optimized traditional input variables, including the different temporal data and historical passenger flow data, were combined with weather variables for data modeling. A comprehensive analysis of the weather impacts on short-term metro passenger flow forecasting is discussed in this paper. The experimental results confirm that weather variables have a significant effect on passenger flow forecasting. It is interesting to find out that the previous variables of one-hour temperature and wind speed are the two most important weather variables to obtain more accurate forecasting results on rainy days at Taipei Main Station, which is a primary interchange station in Taipei. Compared to the four widely used algorithms, the deep LSTM_NN is an extremely powerful method, which has the capability of making more accurate forecasts when suitable weather variables are included

A disease forecast and early warning system based on electronic health records

Conference Name:8th International Conference on Computer Science and Education, ICCSE 2013. Conference Address: Colombo, Sri lanka. Time:August 26, 2013 - August 28, 2013.Disease forecast and early warning have been always important but difficult tasks. Because of the drawbacks of traditional records, the electronic health records, which bring in the ICD-10, are used in our system. Input information are firstly de-duplicated to remove redundancy. After that, the system are used for disease early warning and forecast. The results show that the proposed system has great help for the health sector to prevent and control the diseases. ? 2013 IEEE

Skeleton extraction via structure-adaptive anisotropic filtering

Conference Name:6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014. Conference Address: Xiamen, China. Time:July 10, 2014 - July 12, 2014.National Natural Foundation of China; SIGMM China Chapter; Xiamen UniversitySkeletonization of gray-scale images is a challenging problem in computer vision due to the non-uniform width of shape and the clutter background. This paper presents a novel approach of skeletonization for gray-scale images directly from original image based on anisotropic Gaussian filter. To deal with the non-uniform width of natural object parts, we adapt the shape of filter kernel to local gradient feature. The orientation of filter is firstly estimated based on local structure tensor, and then the scale is calculated based on gradient vector flux. After that, the anisotropic Gaussian filter is performed on the image. The skeleton strength map is defined by the gradient vector flux measure. Finally, thin and binary skeleton is obtained by non- maximum suppression the skeleton strength map. Our method performs well on both binary and gray image in skeleton extraction even for clutter image. Copyright 2014 ACM

Wireless Localization Based on RSSI Fingerprint Feature Vector

RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large distance error when RSSI is fitted to distance. But experimental data showed that, being affected by the combined factors of the environment, the received RSSI feature vector which is formed by lots of RSSI values from different APs is a certain stability. Therefore, the paper proposed RSSI-based fingerprint feature vector algorithm which divides location area into grids, and mobile devices are localized through the similarity matching between the real-time RSSI feature vector and RSSI fingerprint database feature vectors. Test shows that the algorithm can achieve positioning accuracy up to 2–4 meters in a typical indoor environment