As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate human controllers' workload by viewing air traffic optimization as a continuous/online streaming pr...
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ISBN:
(纸本)9781509024537
As we are facing ever increasing air traffic demand, it is critical to enhance air traffic capacity and alleviate human controllers' workload by viewing air traffic optimization as a continuous/online streaming problem. Air traffic optimization is commonly formulated as an integer linear programming (ILP) problem. Since ILP is NP-hard, it is computationally intractable. Moreover, a fluctuating number of flights changes computational demand dynamically. In this paper, we present an elastic middleware framework that is specifically designed to solve ILP problems generated from continuous air traffic streams. Experiments show that our VM scheduling algorithm with time-series prediction can achieve similar performance to a static schedule while using 49% fewer VM hours for a realistic air traffic pattern.
Intrusion Detection System (IDS) is an essential component of the network security infrastructure. It detects malicious activities by monitoring network traffic. There are two main classes of IDS: the anomaly-based ID...
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ISBN:
(纸本)9781479987849
Intrusion Detection System (IDS) is an essential component of the network security infrastructure. It detects malicious activities by monitoring network traffic. There are two main classes of IDS: the anomaly-based IDS and signature-based IDS. An important challenge, for signature-based IDS, is automating attack signature writing from traffic logs, which can be very hard to be established for human administrator. In this paper, we propose a solution addressing this challenge. We propose cloud-based signature learning service using inductive logic programming (ILP). Learning service generates rule describing properties shared by packets labelled as malicious and that do not cover normal packets. The system uses a background knowledge composed of predicates used to describe network attack signature. The cloud architecture of our IDS enables it to have specialized nodes. Preliminary experimentations show that the proposed system is able to reproduce automatically SNORT signature.
This software reuse system helps a user build programs by reusing modules stored in an existing library. The system, dubbed caesar (Case-basEd SoftwAre Reuse), is conceived in the case-based reasoning framework, where...
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Road curb detection and tracking is essential for the autonomous driving of intelligent vehicles on highways and urban roads. In this paper, we present a fast and robust road curb detection algorithm using 3D lidar da...
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ISBN:
(纸本)9781467327435;9781467327428
Road curb detection and tracking is essential for the autonomous driving of intelligent vehicles on highways and urban roads. In this paper, we present a fast and robust road curb detection algorithm using 3D lidar data and Integral Laser Points (ILP) features. Range and intensity data of the 3D lidar is decomposed into elevation data and data projected on the ground plane. First, left and right road curbs are detected for each scan line using the ground projected range and intensity data and line segment features. Then, curb points of each scan line are determined using elevation data. The ILP features are proposed to speed up the both detection procedures. Finally, parabola model and RANSAC algorithm is used to fit the left and right curb points and generate vehicle controlling parameters. The proposed method and feature provide fast and reliable road curb detection speed and performance. Experiments show good results on various highways and urban roads under different situations.
The interest of introducing fuzzy predicates when learning rules is twofold. When dealing with numerical data, it enables us to avoid arbitrary discretization. Moreover, it enlarges the expressive power of what is lea...
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ISBN:
(纸本)3540200851
The interest of introducing fuzzy predicates when learning rules is twofold. When dealing with numerical data, it enables us to avoid arbitrary discretization. Moreover, it enlarges the expressive power of what is learned by considering different types of fuzzy rules, which may describe gradual behaviors of related attributes or uncertainty pervading conclusions. This paper describes different types of first-order fuzzy rules and a method for learning each type. Finally, we discuss the interest of each type of rules on a benchmark example.
The theta-subsumption test is known to be a bottleneck in inductive logic programming. The state-of-the-art learning systems in this field are hardly scalable. Last year, we have created a distributed theta-subsumptio...
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ISBN:
(纸本)9781538638767
The theta-subsumption test is known to be a bottleneck in inductive logic programming. The state-of-the-art learning systems in this field are hardly scalable. Last year, we have created a distributed theta-subsumption process based on an Actor Model, with the aim of being able to decide subsumption on very large clauses. This model was correct and complete, but was also very slow. This is why we introduce ANTS (Actor Network based Theta-Subsumption), a new model also based on an actor network, which is significantly faster than the previous one.
Many domains in the field of inductive logic programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. The ...
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Many domains in the field of inductive logic programming (ILP) involve highly unbalanced data. A common way to measure performance in these domains is to use precision and recall instead of simply using accuracy. The goal of our research is to find new approaches within ILP particularly suited for large, highly-skewed domains. We propose Gleaner, a randomized search method that collects good clauses from a broad spectrum of points along the recall dimension in recall-precision curves and employs an "at least L of these K clauses" thresholding method to combine sets of selected clauses. Our research focuses on Multi-Slot Information Extraction (IE), a task that typically involves many more negative examples than positive examples. We formulate this problem into a relational domain, using two large testbeds involving the extraction of important relations from the abstracts of biomedical journal articles. We compare Gleaner to ensembles of standard theories learned by Aleph, finding that Gleaner produces comparable testset results in a fraction of the training time.
We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequen...
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We describe an algorithm for constructing a set of tree-like conjunctive relational features by combining smaller conjunctive blocks. Unlike traditional level-wise approaches which preserve the monotonicity of frequency, our block-wise approach preserves monotonicity of feature reducibility and redundancy, which are important in propositionalization employed in the context of classification learning. With pruning based on these properties, our block-wise approach efficiently scales to features including tens of first-order atoms, far beyond the reach of state-of-the art propositionalization or inductive logic programming systems.
This paper introduces a proof procedure that integrates Abductive logicprogramming (ALP) and inductive logic programming (ILP) to automate the learning of first order Horn clause theories from examples and background...
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This paper introduces a proof procedure that integrates Abductive logicprogramming (ALP) and inductive logic programming (ILP) to automate the learning of first order Horn clause theories from examples and background knowledge. The work builds upon a recent approach called Hybrid Abductive inductive Learning (HAIL) by showing how language bias can be practically and usefully incorporated into the learning process. A proof procedure for HAIL is proposed that utilises a set of user-specified mode declarations to learn hypotheses that satisfy a given language bias. A semantics is presented that accurately characterises the intended hypothesis space and includes the hypotheses derivable by the proof procedure. An implementation is described that combines an extension of the Kakas-Mancarella ALP procedure within an ILP procedure that generalises the Progol system of Muggleton. The explicit integration of abduction and induction is shown to allow the derivation of multiple clause hypotheses in response to a single seed example and to enable the inference of missing type information in a way not previously possible.
In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a si...
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In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a single predicate learning module CORE. A fast failure mechanism is also proposed which contributes learning efficiency and learnability to the algorithm. MPL-CORE employs background knowledge that can be represented in intensional (Horn clauses) or extensional (ground atoms) forms during its learning process. With the fast failure mechanism, MPL-CORE outperforms previous multiple predicate learning systems in both the computational complexity and learnability.
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