the results of the evidence analysis phase in Digital Forensics (DF) provide objective data which however require further elaboration by the investigators, that have to contextualize analysis results within an investi...
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the results of the evidence analysis phase in Digital Forensics (DF) provide objective data which however require further elaboration by the investigators, that have to contextualize analysis results within an investigative environment so as to provide possible hypotheses that can be proposed as proofs in court, to be evaluated by lawyers and judges. Aim of our research has been that of exploring the applicability of Answer Set programming (ASP) to the automatization of evidence analysis. this offers many advantages, among which that of making different possible investigative hypotheses explicit, while otherwise different human experts often devise and select different solutions in an implicit way. Moreover, ASP provides a potential for verifiability which is crucial in such an application field. Very complex investigations for which human experts can hardly find solutions turn out in fact to be reducible to optimization problems in classes P or NP or not far beyond, that can be thus expressed in ASP. As a proof of concept, in this paper we present the formulation of some real investigative cases via simple ASP programs, and discuss how this leads to the formulation of concrete investigative hypotheses.
the proceedings contain 37 papers. the special focus in this conference is on High Performance Computing. the topics include: Asynchronous Iterative Algorithm for Computing Incomplete Factorizations on GPUs;Matrix Mul...
the proceedings contain 37 papers. the special focus in this conference is on High Performance Computing. the topics include: Asynchronous Iterative Algorithm for Computing Incomplete Factorizations on GPUs;Matrix Multiplication on High-Density Multi-GPU Architectures: theoretical and Experimental Investigations;A Framework for Batched and GPU-Resident Factorization Algorithms Applied to Block Householder Transformations;Parallel Efficient Sparse Matrix-Matrix Multiplication on Multicore Platforms;On the Design, Development, and Analysis of Optimized Matrix-Vector Multiplication Routines for Coprocessors;Large-Scale Neo-Heterogeneous programming and Optimization of SNP Detection on Tianhe-2;ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines;Accelerating LBM and LQCD Application Kernels by In-Memory Processing;On Quantum Chemistry Code Adaptation for RSC PetaStream Architecture;Dtree: Dynamic Task Scheduling at Petascale;Feasibility Study of Porting a Particle Transport Code to FPGA;A Scalable, Linear-Time Dynamic Cutoff Algorithm for Molecular Dynamics;BWTCP: A Parallel Method for Constructing BWT in Large Collection of Genomic Reads;Lattice-CSC: Optimizing and Building an Efficient Supercomputer for Lattice-QCD and to Achieve First Place in Green500;An Efficient Clique-Based Algorithm of Compute Nodes Allocation for In-memory Checkpoint System;A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing;Optimizing Processes Mapping for Tasks with Non-uniform Data Exchange Run on Cluster with Different Interconnects;Striping Layout Aware Data Aggregation for High Performance I/O on a Lustre File System;Hop: Elastic Consistency for Exascale Data Stores;Energy-Efficient Data Processing through Data Sparsing with Artifacts.
this paper proposes a stochastic framework for demand response (DR) aggregator to procure DR from customers and sell it to purchasers in the wholesale electricity market. the aggregator assigns fixed DR contracts with...
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ISBN:
(纸本)9781509027064
this paper proposes a stochastic framework for demand response (DR) aggregator to procure DR from customers and sell it to purchasers in the wholesale electricity market. the aggregator assigns fixed DR contracts with customers based on three different load reduction strategies. In the presented problem the uncertainty of market price is considered and the risk of aggregator participation is managed in stochastic optimization problem with CVaR. the feasibility of this problem is studied on a case of Alberta electricity market.
We study the framework of abductive logicprogramming extended with integrity constraints. For this framework, we introduce a new measure of the simplicity of an explanation based on its degree of arbitrariness: the m...
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We study the framework of abductive logicprogramming extended with integrity constraints. For this framework, we introduce a new measure of the simplicity of an explanation based on its degree of arbitrariness: the more arbitrary the explanation, the less appealing it is, with explanations having no arbitrariness - they are called constrained - being the preferred ones. In the paper, we study basic properties of constrained explanations. For the case when programs in abductive theories are stratified we establish results providing a detailed picture of the complexity of the problem to decide whether constrained explanations exist.
Query answering in Answer Set programming (ASP) is usually solved by computing (a subset of) the cautious consequences of a logic program. this task is computationally very hard, and there are programs for which compu...
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Query answering in Answer Set programming (ASP) is usually solved by computing (a subset of) the cautious consequences of a logic program. this task is computationally very hard, and there are programs for which computing cautious consequences is not viable in reasonable time. However, current ASP solvers produce the (whole) set of cautious consequences only at the end of their computation. this paper reports on strategies for computing cautious consequences, also introducing anytime algorithms able to produce sound answers during the computation.
this book constitutes revised selected papers of the 19thinternationalconference on Applications of Declarative programming and Knowledge Management, INAP 2011, and the 25th Workshop on logicprogramming, WLP 2011, ...
ISBN:
(数字)9783642415241
ISBN:
(纸本)9783642415234;9783642415241
this book constitutes revised selected papers of the 19thinternationalconference on Applications of Declarative programming and Knowledge Management, INAP 2011, and the 25th Workshop on logicprogramming, WLP 2011, held in Vienna, Austria, in September 2011. the 19 papers presented in this volume were carefully reviewed and selected from 27 papers presented at the conference and initially a total of 35 submissions. the book also contains the papers of two invited talks. the papers are organized in topical sections on languages; answer-set programming and abductive reasoning; constraints and logicprogramming; answer-set programming and model expansion; application papers; and system descriptions.
In a deregulated electricity market, the most important purpose of each generating company (GENCO) is to find its optimal bid at each trading period. this paper proposes a new algorithm to determine optimal prices and...
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ISBN:
(纸本)9781509027064
In a deregulated electricity market, the most important purpose of each generating company (GENCO) is to find its optimal bid at each trading period. this paper proposes a new algorithm to determine optimal prices and quantities for the GENCO using a new context named dominating demand. the proposed method considers transmission constraints as a determining factor to construct optimal bids. the method is appropriate to be used for a GENCO in the complex system due to its simplicity. Finally, dominating demand and the optimal bid is calculated for modified IEEE 30-bus system in order to approve method's transparent application.
the paper presents a knowledge representation language Alog which extends ASP with aggregates. the goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some propert...
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the paper presents a knowledge representation language Alog which extends ASP with aggregates. the goal is to have a language based on simple syntax and clear intuitive and mathematical semantics. We give some properties of Alog, an algorithm for computing its answer sets, and comparison with other approaches.
SNP detection is a fundamental procedure in genome analysis. A popular SNP detection tool SOAPsnp can take more than one week to analyze one human genome with a 20-fold coverage. To improve the efficiency, we develope...
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ISBN:
(数字)9783319201191
ISBN:
(纸本)9783319201191;9783319201184
SNP detection is a fundamental procedure in genome analysis. A popular SNP detection tool SOAPsnp can take more than one week to analyze one human genome with a 20-fold coverage. To improve the efficiency, we developed mSNP, a parallel version of SOAPsnp. mSNP utilizes CPU cooperated with Intel (R) Xeon Phi (TM) for large-scale SNP detection. Firstly, we redesigned the key data structure of SOAPsnp, which significantly reduces the overhead of memory operations. Secondly, we devised a coordinated parallel framework, in which CPU collaborates with Xeon Phi for higher hardware utilization. thirdly, we proposed a read-based window division strategy to improve throughput and parallel scale on multiple nodes. To the best of our knowledge, mSNP is the first SNP detection tool empowered by Xeon Phi. We achieved a 45x speedup on a single node of Tianhe-2, without any loss in precision. Moreover, mSNP showed promising scalability on 4,096 nodes on Tianhe-2.
Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the ...
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Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logicprogramming (PLP), lifted inference has been applied up to now only to relational languages outside of logicprogramming. In this paper we adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the problem of computing the probability of queries to probabilistic logic programs under the distribution semantics. In particular, we extend the Prolog Factor Language (PFL) to include two new types of factors that are needed for representing ProbLog programs. these factors take into account the existing causal independence relationships among random variables and are managed by the extension to variable elimination proposed by Zhang and Poole for dealing with convergent variables and heterogeneous factors. Two new operators are added to GC-FOVE for treating heterogeneous factors. the resulting algorithm, called LP2 for Lifted Probabilistic logicprogramming, has been implemented by modifying the PFL implementation of GC-FOVE and tested on three benchmarks for lifted inference. A comparison with PITA and ProbLog2 shows the potential of the approach.
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