Successes and lessons from a trial of the three-way university-enterprise cooperation program on data science and big data processing technology in China

Work integrated learning (WIL), most in the form of co-operative (co-op) partnerships or workplace placements/internships, has been incorporated into many undergraduate programs in universities around the world. In this express report, we share a recent trial of a new WIL model for a bachelor's IT degree in data science and big data processing technology experimented at our University (Inner Mongolia Agricultural University, IMAU) in China. This new model involves three entities, an institution as IMAU (Part A), an industry-certification training agency (Part B), and a cloud computing enterprise (Part C). Our experiment was initiated in September 2018 with the first intake of about 120 undergraduate students and completed in July 2022 over four years of full-time study. The initial results show that the three-way WIL initiative produced more than 60 employment-ready and industry-certified professionals for ICT enterprises and service providers specialized in data science and big data processing technology. The industry-standard certification training and the four-month industry placement in a top 500 ICT enterprise in the world significantly improved both the hands-on skills required by the ICT industry and the employment opportunities for the graduates

Satellite road extraction method based on RFDNet neural network

The road network system is the core foundation of a city. Extracting road information from remote sensing images has become an important research direction in the current traffic information industry. The efficient residual factorized convolutional neural network (ERFNet) is a residual convolutional neural network with good application value in the field of biological information, but it has a weak effect on urban road network extraction. To solve this problem, we developed a road network extraction method for remote sensing images by using an improved ERFNet network. First, the design of the network structure is based on an ERFNet; we added the DoubleConv module and increased the number of dilated convolution operations to build the road network extraction model. Second, in the training process, the strategy of dynamically setting the learning rate is adopted and combined with batch normalization and dropout methods to avoid overfitting and enhance the generalization ability of the model. Finally, the morphological filtering method is used to eliminate the image noise, and the ultimate extraction result of the road network is obtained. The experimental results show that the method proposed in this paper has an average F1 score of 93.37% for five test images, which is superior to the ERFNet (91.31%) and U-net (87.34%). The average value of IoU is 77.35%, which is also better than ERFNet (71.08%) and U-net (65.64%)

A cooperative ant colony system and genetic algorithm for TSPs

The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and is unlikely to find an efficient algorithm for solving TSPs directly. In the last two decades, ant colony optimization (ACO) has been successfully used to solve TSPs and their associated applicable problems. Despite the success, ACO algorithms have been facing constantly challenges for improving the slow convergence and avoiding stagnation at the local optima. In this paper, we propose a new hybrid algorithm, cooperative ant colony system and genetic algorithm (CoACSGA) to deal with these problems. Unlike the previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to the subsequent ACO iteration, this new approach combines both GA and ACS together in a cooperative and concurrent fashion to improve the performance of ACO for solving TSPs. The mutual information exchange between ACS and GA at the end of each iteration ensures the selection of the best solution for the next round, which accelerates the convergence. The cooperative approach also creates a better chance for reaching the global optimal solution because the independent running of GA will maintain a high level of diversity in producing next generation of solutions. Compared with the results of other algorithms, our simulation demonstrates that CoACSGA is superior to other ACO related algorithms in terms of convergence, quality of solution, and consistency of achieving the global optimal solution, particularly for small-size TSPs

A K-means Algorithm Based On Feature Weighting

Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects. K-means clustering algorithm has been receiving much attention from scholars because of its excellent speed and good scalability. However, the traditional K-means algorithm does not consider the influence of each attribute on the final clustering result, which makes the accuracy of clustering have a certain impact. In response to the above problems, this paper proposes an improved feature weighting algorithm. The improved algorithm uses the information gain and ReliefF feature selection algorithm to weight the features and correct the distance function between clustering objects, so that the algorithm can achieve more accurate and efficient clustering effect. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm clustering results are stable, and the accuracy of clustering is significantly improved

A K-means Algorithm Based On Feature Weighting

Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects. K-means clustering algorithm has been receiving much attention from scholars because of its excellent speed and good scalability. However, the traditional K-means algorithm does not consider the influence of each attribute on the final clustering result, which makes the accuracy of clustering have a certain impact. In response to the above problems, this paper proposes an improved feature weighting algorithm. The improved algorithm uses the information gain and ReliefF feature selection algorithm to weight the features and correct the distance function between clustering objects, so that the algorithm can achieve more accurate and efficient clustering effect. The simulation results show that compared with the traditional K-means algorithm, the improved algorithm clustering results are stable, and the accuracy of clustering is significantly improved

Solving the traveling salesman problem using cooperative genetic ant systems

The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and has applications in planning, scheduling, and searching in many scientific and engineering fields. Ant colony optimization (ACO) has been successfully used to solve TSPs and many associated applications in the last two decades. However, ACO has problem in regularly reaching the global optimal solutions for TSPs due to enormity of the search space and numerous local optima within the space. In this paper, we propose a new hybrid algorithm, cooperative genetic ant system (CGAS) to deal with this problem. Unlike other previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to subsequent ACO iterations, this new approach combines both GA and ACO together in a cooperative manner to improve the performance of ACO for solving TSPs. The mutual information exchange between ACO and GA in the end of the current iteration ensures the selection of the best solutions for next iteration. This cooperative approach creates a better chance in reaching the global optimal solution because independent running of GA maintains a high level of diversity in next generation of solutions. Compared with results from other GA/ACO algorithms, our simulation shows that CGAS has superior performance over other GA and ACO algorithms for solving TSPs in terms of capability and consistency of achieving the global optimal solution, and quality of average optimal solutions, particularly for small TSPs