113 research outputs found
SUNNY-CP and the MiniZinc Challenge
In Constraint Programming (CP) a portfolio solver combines a variety of
different constraint solvers for solving a given problem. This fairly recent
approach enables to significantly boost the performance of single solvers,
especially when multicore architectures are exploited. In this work we give a
brief overview of the portfolio solver sunny-cp, and we discuss its performance
in the MiniZinc Challenge---the annual international competition for CP
solvers---where it won two gold medals in 2015 and 2016. Under consideration in
Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming
(TPLP
The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
Markov networks are models for compactly representing complex probability
distributions. They are composed by a structure and a set of numerical weights.
The structure qualitatively describes independences in the distribution, which
can be exploited to factorize the distribution into a set of compact functions.
A key application for learning structures from data is to automatically
discover knowledge. In practice, structure learning algorithms focused on
"knowledge discovery" present a limitation: they use a coarse-grained
representation of the structure. As a result, this representation cannot
describe context-specific independences. Very recently, an algorithm called
CSPC was designed to overcome this limitation, but it has a high computational
complexity. This work tries to mitigate this downside presenting CSGS, an
algorithm that uses the Grow-Shrink strategy for reducing unnecessary
computations. On an empirical evaluation, the structures learned by CSGS
achieve competitive accuracies and lower computational complexity with respect
to those obtained by CSPC.Comment: 12 pages, and 8 figures. This works was presented in IBERAMIA 201
Propagators and Solvers for the Algebra of Modular Systems
To appear in the proceedings of LPAR 21.
Solving complex problems can involve non-trivial combinations of distinct
knowledge bases and problem solvers. The Algebra of Modular Systems is a
knowledge representation framework that provides a method for formally
specifying such systems in purely semantic terms. Formally, an expression of
the algebra defines a class of structures. Many expressive formalism used in
practice solve the model expansion task, where a structure is given on the
input and an expansion of this structure in the defined class of structures is
searched (this practice overcomes the common undecidability problem for
expressive logics). In this paper, we construct a solver for the model
expansion task for a complex modular systems from an expression in the algebra
and black-box propagators or solvers for the primitive modules. To this end, we
define a general notion of propagators equipped with an explanation mechanism,
an extension of the alge- bra to propagators, and a lazy conflict-driven
learning algorithm. The result is a framework for seamlessly combining solving
technology from different domains to produce a solver for a combined system.Comment: To appear in the proceedings of LPAR 2
NASA SpaceCube Intelligent Multi-Purpose System for Enabling Remote Sensing, Communication, and Navigation in Mission Architectures
New, innovative CubeSat mission concepts demand modern capabilities such as artificial intelligence and autonomy, constellation coordination, fault mitigation, and robotic servicing – all of which require vastly more processing resources than legacy systems are capable of providing. Enabling these domains within a scalable, configurable processing architecture is advantageous because it also allows for the flexibility to address varying mission roles, such as a command and data-handling system, a high-performance application processor extension, a guidance and navigation solution, or an instrument/sensor interface. This paper describes the NASA SpaceCube Intelligent Multi-Purpose System (IMPS), which allows mission developers to mix-and-match 1U (10 cm × 10 cm) CubeSat payloads configured for mission-specific needs. The central enabling component of the system architecture to address these concerns is the SpaceCube v3.0 Mini Processor. This single-board computer features the 20nm Xilinx Kintex UltraScale FPGA combined with a radiation-hardened FPGA monitor, and extensive IO to integrate and interconnect varying cards within the system. To unify the re-usable designs within this architecture, the CubeSat Card Standard was developed to guide design of 1U cards. This standard defines pinout configurations, mechanical, and electrical specifications for 1U CubeSat cards, allowing the backplane and mechanical enclosure to be easily extended. NASA has developed several cards adhering to the standard (System-on-Chip, power card, etc.), which allows the flexibility to configure a payload from a common catalog of cards
Computing Adaptive Feature Weights with PSO to Improve Android Malware Detection
© 2017 Yanping Xu et al. Android malware detection is a complex and crucial issue. In this paper, we propose a malware detection model using a support vector machine (SVM) method based on feature weights that are computed by information gain (IG) and particle swarm optimization (PSO) algorithms. The IG weights are evaluated based on the relevance between features and class labels, and the PSO weights are adaptively calculated to result in the best fitness (the performance of the SVM classification model). Moreover, to overcome the defects of basic PSO, we propose a new adaptive inertia weight method called fitness-based and chaotic adaptive inertia weight-PSO (FCAIW-PSO) that improves on basic PSO and is based on the fitness and a chaotic term. The goal is to assign suitable weights to the features to ensure the best Android malware detection performance. The results of experiments indicate that the IG weights and PSO weights both improve the performance of SVM and that the performance of the PSO weights is better than that of the IG weights
Android Malware Family Classification and Analysis: Current Status and Future Directions
Android receives major attention from security practitioners and researchers due to the influx number of malicious applications. For the past twelve years, Android malicious applications have been grouped into families. In the research community, detecting new malware families is a challenge. As we investigate, most of the literature reviews focus on surveying malware detection. Characterizing the malware families can improve the detection process and understand the malware patterns. For this reason, we conduct a comprehensive survey on the state-of-the-art Android malware familial detection, identification, and categorization techniques. We categorize the literature based on three dimensions: type of analysis, features, and methodologies and techniques. Furthermore, we report the datasets that are commonly used. Finally, we highlight the limitations that we identify in the literature, challenges, and future research directions regarding the Android malware family.https://doi.org/10.3390/electronics906094
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