Most existing transformer-based trackers use ViT [1] as the backbone to extract and fuse feature tokens of target templates and search region. Since both the target template and the search region contain background information, their tokens are prone to background interference in interaction that affects tracking performance. We propose a GFATrack that combines spatiotemporal information with prominent target features. The tracker mainly consists of the Dynamic Template Refinement branch and the Search Feature Enhancement branch. The former activates the target features in the dynamic template and provides temporal information. The latter enhances search features by aggregating spatial information from the initial template and temporal information from the dynamic template to achieve precise tracking. Our proposed Feature Activation Module can effectively fuse refined features with reference features and highlight high-similarity features among them. At the same time, we propose a concise and effective dynamic threshold update strategy to capture time context update dynamic templates from historical prediction results. Many experiments have verified the effectiveness and latest performance of the proposed method.

Processing large-scale data sets with limited labels has always been a difficult task in data mining. Facing this difficulty, two local feature selection algorithms, LARD and LRSD, have been proposed based on dependency degree, which can process partially labeled data sets and greatly improve the computational efficiency. However, it is very difficult for these algorithms to calculate large-scale data with millions of samples on a typical personal computer. Although the related family method is a more efficient approach than dependency degree, it cannot be used for partially labeled large-scale data. As a result, a local feature selection method based on related family is proposed to accelerate data processing in the paper. Experiments show that the proposed algorithm can run 405 times faster than LARD on partially labeled data sets and maintain high classification accuracy. In addition, this new algorithm can effectively process partially labeled large-scale data sets with 5,000,000 samples or 20,000 features on a typical personal computer.

In mobile data acquisition, mobile robots usually face challenging tasks when collecting information in an undetermined environment with energy limitation and time-sensitive requirements. We formulate the task of data acquisition as a multiobjective optimization problem under energy and time constraints. In our investigation, three objectives for data acquisition are considered, including collecting the largest amount of information, moving along a path with the smallest probability of encountering obstacles, and traveling with shortest possible overall distance. To resolve the formulated problem which yields the best path for a mobile robot, we propose a mixed cognition particle swarm optimization (MCPSO) algorithm, which adopts the min-max normalization to calculate the fitness, and we transform the multiobjective optimization problem into a single-objective optimization problem by summation after normalization. The efficiency of the MCPSO algorithm is evaluated for mobile data acquisition in several well-known benchmarks by simulation. The simulation results demonstrate that the proposed MCPSO algorithm can achieve higher accuracy and faster convergence compared with other particle swarm optimization algorithms.

The merchandise recommendation is an important part of electronic commerce. In view of the difficulty in obtaining user private information and modeling user interest, this paper is based on the relationship between goods for commodity recommendation. We use fuzzy clustering learning to construct quasi-classes space. Through the intersection of quasi-class and the collection of goods that are being ordered by users, we can know the customers appetites for merchandise, and then recommend the goods. In the construction of quasi-classes space, the value of the threshold Λ must be appropriate, because the threshold Λ determines the size of the quasi-class. It will affect the recommendation of the goods that the size of the quasi-class is too large or too small. The influence of threshold Λ on commodity recommendation is discussed by numerical example, and we finally find the best value of Λ in this paper.