With the development of multi-access edge computing (MEC), the cloudlet at the edge of the network can provide nearby high-performance computing services, thus reducing the computational consumption of user equipments (UEs). To provide more real-time computing services to UEs, service providers face the challenge of optimizing the assignment of requests and the allocation of cloudlets’ computing resources to achieve low latency while dealing with the large number of offloaded requests from UEs. Therefore, in this paper, we study the problem of minimizing the total latency to complete the requests in the MEC network by jointly optimizing request assignment and computing resource allocation. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem which is NP-hard. To solve the problem, we decompose the problem into two subproblems which respectively optimize the request assignment and the computing resource allocation. We first deal with the computing resource allocation problem by utilizing the Lagrangian multiplier method, and the resulting solution is applied for the request assignment problem. Then a novel primal-dual based approximation algorithm is devised to address the request assignment problem. Finally, to verify the efficiency of the proposed algorithm, we provide an upper bound on the approximation ratio. The experiment results show that the proposed algorithm outperforms baseline algorithms in terms of total latency, loading balancing, and computational speed.

In recent years, researchers attempt to utilize online social information to alleviate data sparsity for collaborative filtering, based on the rationale that social networks offers the insights to understand the behavioral patterns. However, due to the overlook of inter-dependent knowledge across items (e.g., knowledge graph dependencies between products), existing social recommender systems are insufficient to distill the heterogeneous collaborative signals from both user and item side. In this work, we propose Self- Supervised Metagraph Informax Network (SMIN) which investigates the potential of jointly incorporating social- and knowledge-aware relational structures into the user preference representation framework. To model relation heterogeneity, we design a metapath-guided heterogeneous graph neural network to aggregate feature embeddings from different types of meta-relations across users and items, empowering SMIN to maintain dedicated representations for multifaceted user- and item-wise dependencies. Additionally, to inject high-order collaborative signals into recommendation, we generalize the mutual information learning paradigm from vector space to a self-supervised graph-based collaborative filtering. This endows the expressive modeling of user-item interactive patterns, by exploring global-level collaborative relations and underlying isomorphic transformation property of graph topology. Experimental results on several real-world datasets demonstrate the effectiveness of our model over various state-of-the-art recommendation methods. Further analysis provides insights into the performance superiority of our new recommendation framework. We release our source code at https://github.com/SocialRecsys/SMIN.

We propose a novel pose estimation method for geometric vision of omni-directional cameras. On the basis of the regularity of the pixel movement after camera pose changes, we formulate and prove the sinusoidal relationship between pixels movement and camera motion. We use the improved Fourier-Mellin invariant (iFMI) algorithm to find the motion of pixels, which was shown to be more accurate and robust than the feature-based methods. While iFMI works only on pin-hole model images and estimates 4 parameters (x, y, yaw, scaling), our method works on panoramic images and estimates the full 6 DoF3D transform, up to an unknown scale factor. For that we fit the motion of the pixels in the panoramic images, as determined by iFMI, to two sinusoidal functions. The offsets, amplitudes and phase-shifts of the two functions then represent the 3D rotation and translation of the camera between the two images. We perform experiments for 3D rotation, which show that our algorithm outperforms the feature-based methods in accuracy and robustness. We leave the more complex 3D translation experiments for future work.