Chool Of Computer And Information Science

Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of “curse of dimensionality” when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A ne...

The algebraic connectivity of a graph is the second smallest eigenvalue of the associated Laplacian matrix. In this paper, we characterize the extremal tree with the minimal algebraic connectivity among all trees of order n with given matching number.

This paper presents an integrate method, which can provide access to current status and the dynamic visible scanning topography, to enhance the interactive during the terrain roaming and real-time flight simulation. A digital elevation model and digital ortho-photo map data integrated algorithm is proposed as the base algorithm for our approach to build a realistic 3D terrain scene. A new technique with help of render to texture and head of display for generating the navigation pane is used. In the flight simulating, in order to eliminate flying ''jump'', we employs the multidimensional linear interpolation method to adjust the camera parameters dynamically and steadily. Meanwhile, based on the principle of scanning laser imaging, we draw pseudo color figures by scanning topography in different directions according to the real-time flying status. Simulation results demonstrate that the proposed algorithm is prospective for applications and the method can improve the effect and enhance dynamic interaction during the real-time flight.

In recent years, the concept of “open data” has received increasing attention among data providers and publishers. For some data portals in public sectors, such as data.gov, the openness enables public oversight of governmental proceedings. For many other data portals, especially those in academia, open data has shown its potential for driving new scientific discoveries and creating opportunities for multidisciplinary collaboration. While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. To find more theoretical and empirical evidence for that proposition, in this paper, we conduct a systematic literature review of open data, social trust, and recommender systems to explain the fundamental con...

Because waterfowl are sensitive to the environment, the abundance and quantity of waterfowl species are often used to evaluate the ecological status of wetlands. Traditional methods of detecting waterfowl are not only time-consuming but also inaccurate. Some investigations may even be at risk. To solve these problems, we designed an intelligent waterfowl auto monitoring system for supporting the detections of native waterfowl which can reduce unnecessary and unsafe investigations. To implement the framework, we utilized a Model-View-Viewmodel (MVVM) and deployed a waterfowl identi cation model (WIM) of YOLOv5 at a back-end for real-time detecting the species of waterfowl and counting their numbers for different time intervals. It is integrated into a GIS-based platform, which is helpful for analyzing the spatial distributions and variation laws of different species of waterfowl. To manage the related geospatial data, we used the PostgreSQL database and designed the corresponding APIs. In addition, we implemented a tool for uploading and labeling images online which can be used to retrain recognition models on the backend. In our current system, the image database plays a vital role in building recognition models. The image database can be set to auto-update, which means that once the users upload images on the front-end, the pre-trained WIM on the back-end will be updated automatically. Results show that our system performs well although it is still in the testing phase. It meets the basic requirements of detecting the native wetland waterfowl species, and help summarizing the maximum observed number of different species over a certain period of time. The rate of identifying waterfowl exceeds 80% and the recall rate exceeds 90%. These can support some in-depth analysis and decisionmaking for the managers of wetland reserve.

The abundance and quantity of waterbird species are often used to evaluate the ecological status of wetlands because most waterbirds are sensitive to the environment. Traditional methods of detecting waterbirds are not only time-consuming but also inaccurate. Some investigations may even be at risk of the natural environment, E.g., bad weather or wild animal attacks. To address this issue, we designed an intelligent waterbird automatic identification system based on Model-View-Viewmodel (MVVM) framework which can support high effectively, safe and long-time monitoring the native wetland waterbirds. To implement the system, we trained a waterbird identification model (WIM) using the YOLOv5 algorithm and deployed it on a back-end for real-time detections, species identifications, and recording counts at different time intervals. It was integrated into a WebGIS-based application, which can be helpful for user to observe the spatial distributions of waterbirds of different species and a...

The urban heat island (UHI) effect is an important topic for many cities across the globe. Previous studies, however, have mostly focused on UHI changes along either the spatial or temporal dimension. A simultaneous evaluation of the spatial and temporal variations is essential for understanding the long-term impacts of land cover on the UHI. This study presents the first evaluation and application of a newly developed spatiotemporal weighted regression framework (STWR), the performance of which was tested against conventional models including the ordinary least squares (OLS) and the geographically weighted regression (GWR) models. We conducted a series of simulation tests followed by an empirical study over central Phoenix, AZ. The results show that the STWR model achieves better parameter estimation and response prediction results with significantly smaller errors than the OLS and GWR models. This finding holds true when the regression coefficients are constant, spatially heteroge...