In an example, an event prediction system is described which receives variables for a proposed event. The system accesses learnt statistics describing belief about weights associated with the variables and uses the weights to determine probability information that the proposed event will have a ...
detection method for malicious traffic based on coarser-grained data labels, achieving comparable performance to other supervised learning methods. This paper investigates the temporal continuity characteristics of network flow data under cybersecurity event labels, developing a novel multi-instance network f...
FutureEventQueue::remove(event ) this.current_events[ 1].invalid; Procedure 11 Remove It runs in O(1) time, for the same reason as "insert()", so R( ) = O(1). 4.4.3. find_invalid The implementation of find_invalid with our array-based queue is simply a linear search for any ...
G因此是一个异质图,假如O和R的type数量都是1,那么这个异质图神经网络就退化成了一个同质图神经网络了。 定义2:PreView Conversion Prediction,给定一个在T时刻的事件 $P_T = (p,o,d,T)$,也就是从o到d这个地点然后p是这个乘客,预测它接下来事件(Request,Cancel,Finish)的这些概率是多少。 整个模型可以表示...
1使用FAUC进行评价的时候,除了Cancer数据集上MTLR表现最好,TSNN优于其他模型。RSNN基本是次优的,除了在Cancer数据集上FAUC。 2.生存网络与基于KM的生存网络相比,公式2中的删失KL散度可以有效处理删失数据。 3.通过比较TSNN与SNN,KM-TSNN与KM-SNN,TSNN性能更好,说明评估潜在的风险很有效。 4.RSNN具有较高的...
给定一个事件ei={p(a0,a1,a2)}ei={p(a0,a1,a2)},其动词和参数的词嵌入为vp,va0,va1,va2∈Rdvp,va0,va1,va2∈Rd,其中dd是词嵌入的维度。通过映射函数vei=f(vp,v0,va1,va2)vei=f(vp,v0,va1,va2),又多种方式获得整个事件veivei的表示。这里,我们介绍三种广泛使用的语义成分的方法 ...
J.M. Wiebe, R.F. Bruce, T.P. O’Hara Development and use of a gold-standard data set for subjectivity classifications, in: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics Association for Computational Linguistics (1999), pp. ...
(A) full sample withcontrastive loss (CL); (B) full sample with cross-entropy loss (CEL); (C) restrcited sample with CL; and (D) restrcited sample with CEL. The heat maps display similar importance scores in terms of key features and their magnitudes. We then generated feature importa...
Lu, Z.; Yu, W.; Zhang, R.; Li, J.; Wei, H. Discovering event evolution chain in microblog. In Proceedings of the 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE ...
,M), 𝑇𝑃𝑟𝑜𝑏𝜏×𝑉𝑚∈ℛ𝜏×𝑉TProbmτ×V∈Rτ×V. It can be seen that the data dimension increases with the increase of the history length τ and the number of states V, but in the 𝜏×𝑉τ×V features, there is information redundancy. If all the pattern...