With the extensive application of software collaborative development technology, the processing of code data generated in programming scenes has become a research hotspot. In the collaborative programming process, different users can submit code in a distributed way. The consistency of code grammar can be achieved by syntax constraints. However, when different users work on the same code in semantic development programming practices, the development factors of different users will inevitably lead to the problem of data semantic conflict. In this paper, the characteristics of code segment data in a programming scene are considered. The code sequence can be obtained by disassembling the code segment using lexical analysis technology. Combined with a traditional solution of a data conflict problem, the code sequence can be taken as the declared value object in the data conflict resolution problem. Through the similarity analysis of code sequence objects, the concept of the deviation degree between the declared value object and the truth value object is proposed. A multi-truth discovery algorithm, called the multiple truth discovery algorithm based on deviation (MTDD), is proposed. The basic methods, such as Conflict Resolution on Heterogeneous Data, Voting-K, and MTRuths_Greedy, are compared to verify the performance and precision of the proposed MTDD algorithm.
Combing with graph-based searching technique and the advantadges of the traditional partition and hierarchical clustering methods,SHILL(a new speedy hybrid clustering algorithm) is proposed.SHILL contains three steps: first,crush the whole data set into a number of atom-class clusters;second,deal with isolated point;then,adopt graph-based searching technique to create clusterings.SHILL only requires one parameter,and can discover arbitrary shapes and sizes of cluster,and the time complexity of SHILL is nn~(1/2) under the worst condition.Experimental results show that the algorithm is effective.
Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph. However, most methods require a labelled source graph to provide supervision signals, which might not be accessible in the real-world settings due to regulations and privacy concerns. In this paper, we explore the scenario of source-free unsupervised graph domain adaptation, which tries to address the domain adaptation problem without accessing the labelled source graph. Specifically, we present a novel paradigm called GraphCTA, which performs model adaptation and graph adaptation collaboratively through a series of procedures: (1) conduct model adaptation based on node's neighborhood predictions in target graph considering both local and global information; (2) perform graph adaptation by updating graph structure and node attributes via neighborhood contrastive learning; and (3) the updated graph serves as an input to facilitate the subsequent iteration of model adaptation, thereby establishing a collaborative loop between model adaptation and graph adaptation. Comprehensive experiments are conducted on various public datasets. The experimental results demonstrate that our proposed model outperforms recent source-free baselines by large margins.
The daily passage of vehicles generates a huge amount of location-aware social data, which provides a rich source of data for analyzing vehicle travel behavior. Being able to accurately predict the future destinations of vehicle travel has great economic value and social impact. The presence of larger sparsity, fewer features and error information in the real dataset led to difficulties in convergence of previous models. Therefore, we propose a Novel Vehicle Destination Prediction Model with Expandable Features Using Attention Mechanism and Variational Autoencoder (EFAMVA). The EFAMVA model combines the autoencoder model and the attention mechanism has overcome the above mentioned problems. The variational autoencoder model obtains the hidden features conforming to the characteristics of the data from the structured vehicle driving data. And the attention mechanism can learn the appropriate combination of weight parameters. The comprehensive experimental results with other comparison models show that the EFAMVA model achieved the best index score, with the MSE value of 0.750, the RMSE value of 1.215, and the MAE value of 0.955. Therefore, it can be shown that the EFAMVA model has a better predictive effect on the future destination of the vehicle.
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