104,199 research outputs found
Disjunctive Answer Set Solvers via Templates
Answer set programming is a declarative programming paradigm oriented towards
difficult combinatorial search problems. A fundamental task in answer set
programming is to compute stable models, i.e., solutions of logic programs.
Answer set solvers are the programs that perform this task. The problem of
deciding whether a disjunctive program has a stable model is
-complete. The high complexity of reasoning within disjunctive
logic programming is responsible for few solvers capable of dealing with such
programs, namely DLV, GnT, Cmodels, CLASP and WASP. In this paper we show that
transition systems introduced by Nieuwenhuis, Oliveras, and Tinelli to model
and analyze satisfiability solvers can be adapted for disjunctive answer set
solvers. Transition systems give a unifying perspective and bring clarity in
the description and comparison of solvers. They can be effectively used for
analyzing, comparing and proving correctness of search algorithms as well as
inspiring new ideas in the design of disjunctive answer set solvers. In this
light, we introduce a general template, which accounts for major techniques
implemented in disjunctive solvers. We then illustrate how this general
template captures solvers DLV, GnT and Cmodels. We also show how this framework
provides a convenient tool for designing new solving algorithms by means of
combinations of techniques employed in different solvers.Comment: To appear in Theory and Practice of Logic Programming (TPLP
Side-channel based intrusion detection for industrial control systems
Industrial Control Systems are under increased scrutiny. Their security is
historically sub-par, and although measures are being taken by the
manufacturers to remedy this, the large installed base of legacy systems cannot
easily be updated with state-of-the-art security measures. We propose a system
that uses electromagnetic side-channel measurements to detect behavioural
changes of the software running on industrial control systems. To demonstrate
the feasibility of this method, we show it is possible to profile and
distinguish between even small changes in programs on Siemens S7-317 PLCs,
using methods from cryptographic side-channel analysis.Comment: 12 pages, 7 figures. For associated code, see
https://polvanaubel.com/research/em-ics/code
Recommended from our members
Fewer epistemological challenges for connectionism
Seventeen years ago, John McCarthy wrote the note Epistemological challenges for connectionism as a response to Paul Smolensky’s paper 'On the proper treatment of connectionism'. I will discuss the extent to which the four key challenges put forward by McCarthy have been solved, and what are the new challenges ahead. I argue that there are fewer epistemological challenges for connectionism, but progress has been slow. Nevertheless, there is now strong indication that neural-symbolic integration can provide effective systems of expressive reasoning and robust learning due to the recent developments in the field
The Integration of Connectionism and First-Order Knowledge Representation and Reasoning as a Challenge for Artificial Intelligence
Intelligent systems based on first-order logic on the one hand, and on
artificial neural networks (also called connectionist systems) on the other,
differ substantially. It would be very desirable to combine the robust neural
networking machinery with symbolic knowledge representation and reasoning
paradigms like logic programming in such a way that the strengths of either
paradigm will be retained. Current state-of-the-art research, however, fails by
far to achieve this ultimate goal. As one of the main obstacles to be overcome
we perceive the question how symbolic knowledge can be encoded by means of
connectionist systems: Satisfactory answers to this will naturally lead the way
to knowledge extraction algorithms and to integrated neural-symbolic systems.Comment: In Proceedings of INFORMATION'2004, Tokyo, Japan, to appear. 12 page
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take inspiration from the hierarchical structure of the visual cortex, to form deep layers of convolutional operations, along with fully connected classifiers. Hardware implementations of these deep CNN architectures are challenged with memory bottlenecks that require many convolution and fully-connected layers demanding large amount of communication for parallel computation. Multi-core CPU based solutions have demonstrated their inadequacy for this problem due to the memory wall and low parallelism. Many-core GPU architectures show superior performance but they consume high power and also have memory constraints due to inconsistencies between cache and main memory. FPGA design solutions are also actively being explored, which allow implementing the memory hierarchy using embedded BlockRAM. This boosts the parallel use of shared memory elements between multiple processing units, avoiding data replicability and inconsistencies. This makes FPGAs potentially powerful solutions for real-time classification of CNNs. Both Altera and Xilinx have adopted OpenCL co-design framework from GPU for FPGA designs as a pseudo-automatic development solution. In this paper, a comprehensive evaluation and comparison of Altera and Xilinx OpenCL frameworks for a 5-layer deep CNN is presented. Hardware resources, temporal performance and the OpenCL architecture for CNNs are discussed. Xilinx demonstrates faster synthesis, better FPGA resource utilization and more compact boards. Altera provides multi-platforms tools, mature design community and better execution times
- …