Knowledge graph embedding is an effective way to represent knowledge graph, which greatly enhance the performances on knowledge graph completion tasks, e.g., entity or relation prediction. For knowledge graph embedding models, designing a powerful loss framework is crucial to the discrimination between correct and incorrect triplets. Margin-based ranking loss is a commonly used negative sampling framework to make a suitable margin between the scores of positive and negative triples. However, this loss can not ensure ideal low scores for the positive triplets and high scores for the negative triplets, which is not beneficial for knowledge completion tasks. In this paper, we present a double limit scoring loss to separately set upper bound for correct triplets and lower bound for incorrect triplets, which provides more effective and flexible optimization for knowledge graph embedding. Upon the presented loss framework, we present several knowledge graph embedding models including TransE-SS, TransH-SS, TransD-SS, ProjE-SS and ComplEx-SS. The experimental results on link prediction and triplet classification show that our proposed models have the significant improvement compared to state-of-the-art baselines.

In knowledge graph embedding models, the margin-based ranking loss as the common loss function is usually used to encourage discrimination between golden triplets and incorrect triplets, which has proved effective in many translation-based models for knowledge graph embedding. However, we find that the loss function cannot ensure the fact that the scoring of correct triplets must be low enough to fulfill the translation. In this paper, we present a limit-based scoring loss to provide lower scoring of a golden triplet, and then to extend two basic translation models TransE and TransH, separately to TransE-RS and TransH-RS by combining limit-based scoring loss with margin-based ranking loss. Both the presented models have low complexities of parameters benefiting for application on large scale graphs. In experiments, we evaluate our models on two typical tasks including triplet classification and link prediction, and also analyze the scoring distributions of positive and negative triplets by different models. Experimental results show that the introduced limit-based scoring loss is effective to improve the capacities of knowledge graph embedding.

Multiple string matching plays a fundamental role in network intrusion detection systems. Automata-based multiple string matching algorithms like AC, SBDM and SBOM are widely used in practice, but the huge memory usage of automata prevents them from being applied to a large-scale pattern set. Meanwhile, poor cache locality of huge automata degrades the matching speed of algorithms. Here we propose a space-efficient multiple string matching algorithm BVM, which makes use of bit-vector and succinct hash table to replace the automata used in factor-searching-based algorithms. Space complexity of the proposed algorithm is O(rm2+ ΣpϵP|p|), that is more space-efficient than the classic automata-based algorithms. Experiments on datasets including Snort, ClamAV, URL blacklist and synthetic rules show that the proposed algorithm significantly reduces memory usage and still runs at a fast matching speed. Above all, BVM costs less than 0.75% of the memory usage of AC, and is capable of matching millions of patterns efficiently.

Behavior targeting (BT) is a promising tool for online advertising. The state-of-the-art BT methods, which are mainly based on regression models, have two limitations. First, learning regression models for behavior targeting is difficult since user clicks are typically several orders of magnitude fewer than views. Second, the user interests are not fixed, but often transient and influenced by media and pop culture. In this paper, we propose to formulate behavior targeting as a classification problem. Specifically, we propose to use an SVM ensemble for behavior prediction. The challenge of using ensemble SVM for BT stems from the computational complexity (it takes 53 minutes in our experiments to predict behavior for 32 million users, which is inadequate for online application). To this end, we propose a fast ensemble SVM prediction framework, which builds an indexing structure for SVM ensemble to achieve sub-linear prediction time complexity. Experimental results on real-world large scale behavior targeting data demonstrate that the proposed method is efficient and outperforms existing linear regression based BT models.

Cloud computing represents one of the most important research directions for modern computing systems. Existing research efforts on Cloud computing were all focused on designing advanced storage and query techniques for static data. None of them consider the problem that data in a Cloud may appear as continuous and rapid data streams. To address this problem, in this paper we propose a new LCN-Index framework to handle continuous data stream queries in the Cloud. LCN-Index uses the Map-Reduce computing paradigm to process all the queries. In the Mapping stage, it divides all the queries into a batch of predicate sets which are then deployed onto mapping nodes using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multi attribute hash index. In so doing, a data stream can be efficiently evaluated by traversing through the LCN-Index framework. Experiments demonstrate the utility of the proposed method.

Regular expression matching is widely used in many network applications to analyze suspicious traffic against predefined signatures, and to discover anomalous events. Deterministic Finite Automaton (DFA), which recognizes a set of regular expressions, is the basic data structure to scan input traffic byte by byte. Though DFA meets the requirement of real-time processing of network traffic, constructing a combined DFA for a set of regular expression signatures is very time-consuming, especially when the signature set is large. To attack this problem, we propose new strategies to accelerate DFA construction. The basic idea of our method is to construct the combined DFA by hierarchical merging of the DFAs of each single regular expression. Our method runs in $O(|Q| |\Sigma|\ln n)$ time, which is substantially superior to the time complexity $O(|Q| |\Sigma|(\overset{n}{\underset{i=1}{\sum}}|Q_i|)^2)$of classical subset construction algorithm\cite{Aho1986}. Experiment on real signatures from open-source systems, such as L7-filter, BRO and SNORT, demonstrates that our method performs 45 times faster than the subset construction algorithm on average.

String matching plays a fundamental role in many network security applications such as NIDS, virus detection and information filtering. In this paper, we proposed cache-efficient methods to accelerate classical multiple string matching algorithms. We observed that most classical algorithms perform poorly as pattern set grows due to their high memory requirement and the poor cache behavior. Based on this observation, we proposed efficient methods employing cache-efficient strategies, i.e., to accelerate string matching by minimizing memory usage and maximizing cache locality. Experimental results on random datasets demonstrated that our new methods are substantially faster than classical methods.