The opinions of local experts in the location‐based social network are of great significance to the collection and dissemination of local information. In this paper, we investigated in‐depth how the user comments can be used to identify the local expert over social networks. We first illustrate the existences of potential local experts in a social network using a scored model by considering the personal profiles, comments, friend relationship, and location preferences. Then, a multidimensional model is proposed to evaluate the local expert candidates and a local expert discovery algorithm is proposed to identify local experts. Meanwhile, a scoring algorithm is proposed to train the weights in the model. Finally, an expert recommendation list can be given based on the score ranks of the candidates. Experimental results demonstrate the effectiveness of proposed model and algorithms.
In Location-Based Services (LBSs) platforms, such as Foursquare and Swarm, the submitted position for a share or search leads to the exposure of users’ activities. Additionally, the cross-platform account linkage could aggravate this exposure, as the fusion of users’ information can enhance inference attacks on users’ next submitted location. Hence, in this paper, we propose GLPP, a personalized and continuous location privacy-preserving framework in account linked platforms with different LBSs (i.e., search-based LBSs and share-based LBSs). The key point of GLPP is to obfuscate every location submitted in search-based LBSs so as to defend dynamic inference attacks. Specifically, first, possible inference attacks are listed through user behavioral analysis. Second, for each specific attack, an obfuscation model is proposed to minimize location privacy leakage under a given location distortion, which ensures submitted locations’ utility for search-based LBSs. Third, for dynamic attacks, a framework based on zero-sum game is adopted to joint specific obfuscation above and minimize the location privacy leakage to a balanced point. Experiments on real dataset prove the effectiveness of our proposed attacks in Accuracy, Certainty, and Correctness and, meanwhile, also show the performance of our preserving solution in defense of attacks and guarantee of location utility.
Objective: Representation learning in the context of biological concepts involves acquiring their numerical representations through various sources of biological information, such as sequences, interactions, and literature. This study has conducted a comprehensive systematic review by analyzing both quantitative and qualitative data to provide an overview of this field. Methods: Our systematic review involved searching for articles on the representation learning of biological concepts in PubMed and EMBASE databases. Among the 507 articles published between 2015 and 2022, we carefully screened and selected 65 papers for inclusion. We then developed a structured workflow that involved identifying relevant biological concepts and data types, reviewing various representation learning techniques, and evaluating downstream applications for assessing the quality of the learned representations. Results: The primary focus of this review was on the development of numerical representations for gene/DNA/RNA entities. We have found Word2Vec to be the most commonly used method for biological representation learning. Moreover, several studies are increasingly utilizing state-of-the-art large language models to learn numerical representations of biological concepts. We also observed that representations learned from specific sources were typically used for single downstream applications that were relevant to the source. Conclusion: Existing methods for biological representation learning are primarily focused on learning representations from a single data type, with the output being fed into predictive models for downstream applications. Although there have been some studies that have explored the use of multiple data types to improve the performance of learned representations, such research is still relatively scarce. In this systematic review, we have provided a summary of the data types, models, and downstream applications used in this task.
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