Future Event Prediction Based on Temporal Knowledge Graph Embedding

Accurate prediction of future events brings great benefits and reduces losses for society in many domains,such as civil unrest,pandemics,and crimes.Knowledge graph is a general language for describing and modeling complex systems.Different types of events continually occur,which are often related to historical and concurrent events.In this paper,we formalize the future event prediction as a temporal knowledge graph reasoning problem.Most existing studies either conduct reasoning on static knowledge graphs or assume knowledges graphs of all timestamps are available during the training process.As a result,they cannot effectively reason over temporal knowledge graphs and predict events happening in the future.To address this problem,some recent works learn to infer future events based on historical eventbased temporal knowledge graphs.However,these methods do not comprehensively consider the latent patterns and influences behind historical events and concurrent events simultaneously.This paper proposes a new graph representation learning model,namely Recurrent Event Graph ATtention Network(RE-GAT),based on a novel historical and concurrent events attention-aware mechanism by modeling the event knowledge graph sequence recurrently.More specifically,our RE-GAT uses an attention-based historical events embedding module to encode past events,and employs an attention-based concurrent events embedding module to model the associations of events at the same timestamp.A translation-based decoder module and a learning objective are developed to optimize the embeddings of entities and relations.We evaluate our proposed method on four benchmark datasets.Extensive experimental results demonstrate the superiority of our RE-GAT model comparing to various base-lines,which proves that our method can more accurately predict what events are going to happen.

基于参数特征偏移的神经网络隐写检测方法

目前,许多深度学习的预训练模型被发布出来用以帮助工程师和研究人员开发基于深度学习的系统或进行研究,从而减少了他们的工作量。过去的工作表明,秘密信息可以被嵌入到神经网络参数中且不影响模型的准确性,恶意开发者可以借此将恶意软件或其他有害信息隐藏到预先训练的模型中。因此,对这些隐写后的预训练模型进行可靠的检测非常重要。我们分析了现有的神经网络隐写方法,发现它们不可避免地会导致参数统计的偏差。针对LSB隐写,实验发现其会造成参数位平面随机性上的偏差;针对COR隐写和SGN隐写,实验发现其会造成参数分布上的偏差。基于这些偏差,我们提出神经网络隐写的隐写分析方法,即从良性和恶性模型中提取特征建立分类器。据我们所知,这是第一个检测神经网络隐写术的工作。实验结果表明,本文提出的检测算法能够可靠地检测出含有嵌入式信息的模型。值得注意的是,我们的检测方法即使在低负载下仍然有效。

Building a Large-Scale and Wide-Area Quantum Internet Based on an OSI-Alike Model

The theory and experiment of quantum information have been studied extensively in recent years,and the feasibility of quantum communication has been proved.Although the fundamental technology is not yet mature,research on quantum internet should be conducted.To implement quantum internet,an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently.In this paper,we present a novel design of a clusterbased structure to describe how quantum nodes are interconnected,and how the structure can improve the performance of qubit transmission and reduce the network complexity.The idea of the quantum local area network(QLAN)is proposed as an essential component of the quantum internet.Besides,each quantum repeater links to neighboring repeaters to form a core network,and multiple QLANs are connected through the core network.The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements.For the sake of interoperability and fast prototyping,we adopt the idea of OSI layering model of the current Internet in the design of quantum internet.Finally,we elaborate on the composition of quantum nodes and the realization of end-to-end communication.

Detecting conflict of heterogeneous access control policies

Policy conflicts may cause substantial economic losses.Although a large amount of effort has been spent on detecting intra-domain policy conflict,it can not detect conflicts of heterogeneous policies.In this paper,considering background knowledge,we propose a conflict detection mechanism to search and locate conflicts of heterogeneous policies.First,we propose a general access control model to describe authorization mechanisms of cloud service and a translation scheme designed to translate a cloud service policy to an Extensible Access Control Markup Language(XACML)policy.Then the scheme based on Multi-terminal Multi-data-type Interval Decision Diagram(MTMIDD)and Extended MTMIDD(X-MTMIDD)is designed to represent XACML policy and search the conflict among heterogeneous policies.To reduce the rate of false positives,the description logic is used to represent XACML policy and eliminate false conflicts.Experimental results show the efficiency of our scheme.

Deep 3D mesh watermarking with self-adaptive robustness

Robust 3D mesh watermarking is a traditional research topic in computer graphics,which provides an efficient solution to the copyright protection for 3D meshes.Traditionally,researchers need manually design watermarking algorithms to achieve suffcient robustness for the actual application scenarios.In this paper,we propose the first deep learning-based 3D mesh watermarking network,which can provide a more general framework for this problem.In detail,we propose an end-to-end network,consisting of a watermark embedding sub-network,a watermark extracting sub-network and attack layers.We employ the topology-agnostic graph convolutional network(GCN)as the basic convolution operation,therefore our network is not limited by registered meshes(which share a fixed topology).For the specific application scenario,we can integrate the corresponding attack layers to guarantee adaptive robustness against possible attacks.To ensure the visual quality of watermarked 3D meshes,we design the curvature consistency loss function to constrain the local geometry smoothness of watermarked meshes.Experimental results show that the proposed method can achieve more universal robustness while guaranteeing comparable visual quality.

Research on Online Leakage Assessment

Leakage assessment is the most common approach applied for assessing side-channel information leakage and validating the effectiveness of side-channel countermeasures.Established evaluation approaches are usually based on Test Vector Leakage Assessment(TVLA)that deployed in a divide and conquer flow with offline computations,which causes two apparent shortcomings in required memory and time.In this paper,a lightweight framework of online leakage assessment is proposed.The problems were analyzed and the evaluation approach was further validated with a Field Programmable Gate Array(FPGA).The experimental results show that it can implement online processing on newly collected data,and instantly stop to give the result when detecting credible leakage.The online leakage assessment can significantly economize on memory and time.It has good performance when there is limited memory or real-time evaluations are needed.