Let Trajectories Speak Out the Traffic Bottlenecks

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification : Given a road network R , a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF , with an approximation ratio of 1-1/ e . To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG . Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

Formal Modelling and Automated Trade-off Analysis of Enforcement Architectures for Cryptographic Access Control in the Cloud

To facilitate the adoption of cloud by organizations, Cryptographic Access Control (CAC) is the obvious solution to control data sharing among users while preventing partially trusted Cloud Service Providers (CSP) from accessing sensitive data. Indeed, several CAC schemes have been proposed in the literature. Despite their differences, available solutions are based on a common set of entities—e.g., a data storage service or a proxy mediating the access of users to encrypted data—that operate in different (security) domains—e.g., on-premise or the CSP. However, the majority of these CAC schemes assumes a fixed assignment of entities to domains; this has security and usability implications that are not made explicit and can make inappropriate the use of a CAC scheme in certain scenarios with specific trust assumptions and requirements. For instance, assuming that the proxy runs at the premises of the organization avoids the vendor lock-in effect but may give rise to other security concerns (e.g., malicious insiders attackers). To the best of our knowledge, no previous work considers how to select the best possible architecture (i.e., the assignment of entities to domains) to deploy a CAC scheme for the trust assumptions and requirements of a given scenario. In this article, we propose a methodology to assist administrators in exploring different architectures for the enforcement of CAC schemes in a given scenario. We do this by identifying the possible architectures underlying the CAC schemes available in the literature and formalizing them in simple set theory. This allows us to reduce the problem of selecting the most suitable architectures satisfying a heterogeneous set of trust assumptions and requirements arising from the considered scenario to a decidable Multi-objective Combinatorial Optimization Problem (MOCOP) for which state-of-the-art solvers can be invoked. Finally, we show how we use the capability of solving the MOCOP to build a prototype tool assisting administrators to preliminarily perform a “What-if” analysis to explore the trade-offs among the various architectures and then use available standards and tools (such as TOSCA and Cloudify) for automated deployment in multiple CSPs.

HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation

Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability.