No abstract

Abstract. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. The major problem is to find the best selection strategy function to quickly reach high classification accuracy. Query-by-Committee (QBC) method of active learning is less computation than other active learning approaches, but its classification accuracy can not achieve the same high as passive learning. In this paper, a new selection strategy for the QBC method is presented by combining Vote Entropy with Kullback-Leibler divergence. Experimental results show that the proposed algorithm is better than previous QBC approach in classification accuracy. It can reach the same accuracy as passive learning with few labeled training examples.

Seeking a collaborator is one of the important academic activities of scholars because the right collaborators will help improve the quality of scholars' research and accelerate their research process. Therefore, it is becoming more and more important to recommend scientific collaborators based on big scholarly data. However, previous works mainly consider the research topic as the key academic factor, whereas many scholars' demographic characteristics such as career age, gender, etc are overlooked. It has been studied that scientific collaboration patterns may vary with scholars' career ages. It is not surprising that scholars at different career ages may have different collaboration strategies. To this end, we aim to design a scientific collaboration recommendation model that is sensitive to scholars' career age. For this purpose, we design a career age-aware scientific collaboration model. The model is mainly consisted of three parts, including authorship extraction from the digital libraries, topic extraction based on publication titles/abstract, and career age-aware random walk for measuring scholar similarity. Experimental results on two real-world datasets demonstrate that our proposed model can achieve the best performance by comparison with six baseline methods in terms of precision and recall.

Cognitive wireless sensor networks (CWSNs) can use the idle authorized frequency band to solve the problem of spectrum resource shortage in traditional wireless sensor network. By employing spectrum hole in the authorized frequency band, the spectrum sensing technology can degrade the coexistent interference and enhance the performance of whole sensor network. Due to the characteristics of limited battery energy and low processing capacity with sensor nodes, it is necessary to enhance the energy efficiency while improving spectrum sensing performance. In this paper, a cooperative spectrum sensing strategy for CWSNs based on particle swarm optimization is proposed. Firstly, the system throughput and energy consumption are quantitatively analyzed, and the mathematical model related to energy efficiency is established. Secondly, the particle swarm optimization (PSO) algorithm is used to obtain the optimal selected nodes set under the limited conditions of false alarm probability and detection probability. To avoid local optimization in the process of problem solving, Cauchy mutation method is introduced to optimize the parameter selection of fitness function. The experimental results illustrate that our proposed method can improve the throughput of the system while ensuring the sensing performance, and achieve the energy efficiency effectively.

The artificial bee colony (ABC) algorithm is a biological-inspired optimisation algorithm proposed by Karaboga. Since its solution search equation is good at exploration but poor at exploitation, the ABC algorithm converges slowly and is easy to fall into local optimum. Inspired by opposition-based learning (OBL), the authors propose an improved ABC algorithm called opposition-based learning ABC (OLABC). In OLABC, firstly, the population would be initialised using OBL. Secondly, to ensure the diversity of the population during the iterative process, the solution search equation is employed to bee phase would be improved. Generate the opposite solution when the fitness value of the newly generated solution is smaller than the current solution, and then apply the greedy selection strategy to update the solution. Thirdly, the adaptive weight strategy is used to dynamically adjust the weight, balancing the global exploration and local exploitation capabilities of the algorithm. Experiments on a set of benchmark functions show that OLABC has better convergence speed and optimisation precision than the compared algorithms.