The event prediction task learns the dynamic changes of past events and infers the upcoming ones. This task helps people to understand how events evolve in our real world and make rapid, accurate, and efficient reactions in emergencies. The complex relation of events and entities (events participants) changes dynamically over time. The current solutions of event prediction have not fully exploited such event temporal dependency and entity relation dependency. In addition, an event may relate to a considerable number of entities. Among such related entities, only a few of them are the key participants in driving the evolution of the event. Previous works fail to emphasize such key entities and limit the non-related entities. In this paper, we introduce a novel gating and attention mechanism and propose a novel Temporal spAtial dyNamic Graph mOdel (TANGO) that is composed of a graph model (based on Graph Convolutional Network with gated and attention mechanisms) and a sequential model (based on Temporal Convolutional Network). TANGO is able to model the event temporal dependency and entity relation dependency simultaneously; learn the representation of events relations; control the aggregation of relational information among entities with varying degrees of mutual influence; support long effective historical sizes. We demonstrate the validity and effectiveness of our approach on three different datasets (i.e., ICEWS18, GDELT, and ICEWS14). The result shows almost 6.2% MRR improvement and 3.9% Hits@10 improvement over the previous state-of-the-art.@en

This paper introduces DISPERSE, a distributed scalable architecture for delivery of content and services that provides resilience against node failure through location-independent storage and replication of content. Current content delivery networks (CDNs) have, at least to some degree, a centralized structure thus susceptible to a single point of failure. DISPERSE addresses this limitation by implementing a fully de-centralized structure. DISPERSE is a two-layer architecture: The first layer (front-end layer) exposes services (e.g., Web, SFTP) to clients; the second layer (back-end layer) provides reliable distributed storage of content and application state. Content in DISPERSE's back-end layer is stored and exchanged as Named Data Network (NDN) content objects. This allows DISPERSE to implement fine-grained, location-independent, fully decentralized content replication mechanisms. We validate the performance of DISPERSE under two node failure scenarios. In the first scenario, content can be stored in any DISPERSE node, and all nodes are equally likely to fail. In this scenario, we use non-linear optimization techniques to determine the optimal number of content copies under availability and latency constraints. In the second scenario, different nodes fail with different probabilities, and content is stored in nodes according to its value, node failure probability, and resource availability. This scenario is addressed as an instance of the minimum cost flow problem. Our results show that DISPERSE reduces the failure of content retrieval by five orders of magnitude compared to common CDN implementations, without significantly increasing content retrieval delay. Further, numerical results show that DISPERSE improves content availability by a factor of 1.3 imes-2.3 imes when deploying the minimum cost flow algorithm.@en

While the Internet is open by design, it is still the case that users can be subject to censorship by governments or enterprises in accessing Web services and data. In this paper we propose SEnD, a fully-distributed censorship circumvention system built upon an overlay, where users have peer-To-peer virtual private IP tunnels to proxies within their social network. With SEnD, users in an uncensored area can act as proxy servers for their social friends in a censored area, allowing them to bypass the censorship. SEnD is able to outperform the current censorship techniques, such as IP address blocking and active probing attacks. We assessed the effectiveness of SEnD through extensive simulations based on a synthetic dataset, as well as experiments based on a prototype implementation. We built our synthetic dataset based on parameters obtained from questionnaires administered both inside and outside China (we consider China as a case study of censorship area). The results show that SEnD is feasible, efficient and scalable. For example, when the proportion of concurrent active users is less than 60, 99.9 percent of these users are able to find proxy servers. @en