In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-view consistency in paired complete observations, further imputing and aligning the similarity relationships inherently shared across views. Nevertheless, existing methods are constrained by two-tiered limitations: (1) Neither instance- nor cluster-level consistency learning construct a semantic space shared across views to learn consensus semantics. The former enforces cross-view instances alignment, and wrongly regards unpaired observations with semantic consistency as negative pairs; the latter focuses on cross-view cluster counterparts while coarsely handling fine-grained intra-cluster relationships within views. (2) Excessive reliance on consistency results in unreliable imputation and alignment without incorporating view-specific cluster information. Thus, we propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL). To bridge semantic gaps across all observations, we learn consensus prototypes from available data to discover a shared space, where semantically similar observations are pulled closer for consensus semantics learning. To capture semantic relationships within specific views, we design a heuristic graph clustering based on modularity to recover cluster structure with intra-cluster compactness and inter-cluster separation for cluster semantics enhancement. Extensive experiments demonstrate, compared to state-of-the-art competitors, FreeCSL achieves more confident and robust assignments on IMVC task.

The prevalence of location-based services has made positional information indispensable to everyday life, which has sparked growing concerns about the security of location privacy. In this paper, we propose to protect location privacy in wireless lo-calization from the perspective of power allocation. A closed-form expression of location secrecy metric (LSM) is first established to quantify the degree of risk that the location can be inferred by the eavesdropper. Then, a transmit power optimization problem constrained by the LSM lower bound is formulated. Through problem transformation and fractional programming, the optimal power allocation is ultimately obtained. Simulation results show that compared with the other existing strategies, the proposed power allocation strategy can effectively protect location privacy at the cost of smaller nosltioning accuracy loss.

The symbiotic relationship between gut microbes and their hosts has been widely investigated by scientists. Currently, there exists lots of useful data dispersed in different gut microbiota databases. However, it is not easy for biologists to obtain valuable global information directly from these multi-sources and heterogeneous data for further research. Therefore, an ontology-mediated Gut Microbiota Federated Query Platform (GMFQP) is presented in this paper to solve this problem. Firstly, a Gut Microbiota Ontology (GMO) is built to provide a unified domain view for biologists, hiding the heterogeneity of the underlying data sources. Secondly, a three-phased federated query approach including query reasoning, rewriting, and execution based on Datalog+ rules is proposed to transform users’ queries expressed over the GMO to concrete queries executed over the actual data sources. Thirdly, a federated query prototype is implemented based on the approach mentioned above, providing a user-friendly query interface and returning results in both tabular and graphical formats. Several typical biological query examples with performance analysis are illustrated to demonstrate the utility of our approach and the platform.

Recommender System (RS) is an important online application that affects billions of users every day. The mainstream RS ranking framework is composed of two parts: a Multi-Task Learning model (MTL) that predicts various user feedback, i.e., clicks, likes, sharings, and a Multi-Task Fusion model (MTF) that combines the multi-task outputs into one final ranking score with respect to user satisfaction. There has not been much research on the fusion model while it has great impact on the final recommendation as the last crucial process of the ranking. To optimize long-term user satisfaction rather than obtain instant returns greedily, we formulate MTF task as Markov Decision Process (MDP) within a recommendation session and propose a Batch Reinforcement Learning (RL) based Multi-Task Fusion framework (BatchRL-MTF) that includes a Batch RL framework and an online exploration. The former exploits Batch RL to learn an optimal recommendation policy from the fixed batch data offline for long-term user satisfaction, while the latter explores potential high-value actions online to break through the local optimal dilemma. With a comprehensive investigation on user behaviors, we model the user satisfaction reward with subtle heuristics from two aspects of user stickiness and user activeness. Finally, we conduct extensive experiments on a billion-sample level real-world dataset to show the effectiveness of our model. We propose a conservative offline policy estimator (Conservative-OPEstimator) to test our model offline. Furthermore, we take online experiments in a real recommendation environment to compare performance of different models. As one of few Batch RL researches applied in MTF task successfully, our model has also been deployed on a large-scale industrial short video platform, serving hundreds of millions of users.