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19th international conference on Mathematical Modeling and Simulation of Systems, MODS 2024

作者： Trunov, Oleksii Dorosh, Mariia Skiter, Igor Trunova, Elena Voitsekhovska, Mariia Chernihiv Polytechnic National University 95 Shevchenko str. Chernihiv14035 Ukraine Institute for Safety Problems of Nuclear Power Plants of the National Academy of Sciences of Ukraine 36a Kirov str. Chernobyl07270 Ukraine

ISBN: (纸本)9783031907340

the article presents an approach to the strategic analysis of information security of transport and logistics centers (TLCs). the proposed method is based on expert assessments of the factors that determine the level of TLC IS risk. Strategic analysis is the basis of a decision support system for ensuring an appropriate level of TLC security. It is based on the tools of fuzzy set theory: Linguistic assessments of the factors influencing the information security risk using triangular membership functions;Fuzzy Analytic Hierarchy Process (AHP) method to determine the fuzzy importance coefficients of the factors and subfactors of the proposed model;Center of Area (CoA) method to defuzzify fuzzy triangular values;Fuzzy Simple Additive Weighting (SAW) method to rank the TLC IS risk factors and select an effective strategic solution. For the strategic analysis of the TLC IS status, an extended and modified informative matrix was used to assets the status of the Wilson system. the developed model for assessing the level of TLC IS risk will ensure the implementation of effective strategies to ensure it. the model is implemented in the Python programming language. Further research is seen in the implementation of the proposed model in the decision support system for the management TLC IS. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Risk assessment

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Data extraction from flash memory and reverse engineering using Xilinx 7 series FPGA boards 19

Data extraction from flash memory and reverse engineering us...

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19th international SoC Design conference (ISOCC) - SoC Technology Towards a New Era of Innovation

作者： Lee, Dongchan Lee, Sanghyun Cho, Mannhee Lee, Hyung-Min Kim, Youngmin Hongik Univ Sch Elect & Elect Eng Seoul South Korea Korea Univ Sch Elect Engn Seoul South Korea

ISBN: (纸本)9781665459716

In this paper, we introduce a general methodology of reverse engineering for Xilinx FPGA devices using flash memory for programming. Based on the structural analysis of the flash memory used for the most recent 7-Series FPGA, data extraction for reverse engineering is carried out targeting the flash memory with the help of a logic analyzer. the accuracy of the extracted bitstream is finally verified using the in-house reverse engineering tool.

关键词： Reverse Engineering FPGA flash memory bitstream logic analyzer

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Synthesis of Failure-Robust Plans for Multi-Robot Systems Under Temporal logic Specifications 19

Synthesis of Failure-Robust Plans for Multi-Robot Systems Un...

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19th IEEE international conference on Automation Science and Engineering, CASE 2023

作者： Huang, Feifei Li, Shaoyuan Yin, Xiang Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China

ISBN: (纸本)9798350320695

In this study, we address the multi-robot path planning problem for tasks specified by linear temporal logic (LTL) formulae. Unlike existing studies, we take into account the possibility of robot failures, where a failed robot can no longer contribute to the completion of the LTL task. Our objective is to find a failure-robust path, which ensures that the LTL task can always be fulfilled, even if a maximum number of robots fail at any point during execution. To achieve this, we extend the mixed-integer linear programming (MILP) approach to the failure-robust setting. To overcome the computational complexity, we identify a fragment of LTL formulae called the free-union-closed LTL, which allows for more scalable synthesis without considering the global combinatorial issue. We present case studies to demonstrate our findings. Our approach provides a novel solution to the problem of multi-robot path planning under robot failures, offering a practical and efficient way to achieve robustness in the face of unforeseen events. © 2023 IEEE.

关键词： Multipurpose robots

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ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser 19

ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser

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19th international conference on Principles of Knowledge Representation and Reasoning, KR 2022

作者： Eiter, thomas Geibinger, Tobias Higuera, Nelson Musliu, Nysret Oetsch, Johannes Stepanova, Daria Institute for Logic and Computation TU Wien Favoritenstraße 9-11 Vienna1040 Austria CD-Lab Artis TU Wien Austria Bosch Center for AI Robert Bosch Campus 1 Renningen71272 Germany

ISBN: (纸本)9781956792010

We present the system ALASPO which implements Adaptive Large-neighbourhood search for Answer Set programming (ASP) Optimisation. Large-neighbourhood search (LNS) is a meta-heuristic where parts of a solution are destroyed and reconstructed in an attempt to improve an overall objective. ALASPO currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon for difference and full integer constraints, and multi-shot solving for an efficient implementation of the LNS loop. Neighbourhoods can be defined in code or declaratively as part of the ASP encoding. While the method underlying ALASPO has been described in previous work, ALASPO also incorporates portfolios for the LNS operators along with self-adaptive selection strategies as a technical novelty. this improves usability considerably at no loss of solution quality, but on the contrary often yields benefits. To demonstrate this, we evaluate ALASPO on different optimisation benchmarks. © 19th international conference on Principles of Knowledge Representation and Reasoning, KR 2022. All rights reserved.

关键词： Optimization

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Selecting User Queries in Interactive Job Scheduling 19thth

Selecting User Queries in Interactive Job Scheduling

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

作者： Varga, Johannes Raidl, Günther R. Rodemann, Tobias Institute of Logic and Computation TU Wien Vienna Austria Honda Research Institute Europe Offenbach Germany

ISBN: (纸本)9783031829512

When solving a job scheduling problem that involves humans, the times in which they are available must be taken into account. For practical acceptance of a scheduling tool, it is further crucial that the interaction with the humans is kept simple and to a minimum. Requiring users to fully specify their availability times is typically not reasonable. We consider and extend a scenario from the literature in which initially users only suggest single starting times for their jobs and an optimized schedule shall then be found within a small number of interaction rounds. In each round a small amount of information can be requested by suggesting alternative time intervals, which are accepted or rejected. We extend the scenario by another form of interaction that allows to request users to indicate alternative time intervals for their jobs. To make the best out of these limited interaction possibilities, we propose a stochastic programming approach that utilizes a Markov model to consider the users’ availabilities. the approach is experimentally evaluated and compared to the approach from the literature. Results show that the stochastic programming approach performs significantly better than the former method from the literature, especially when being able to request alternative time intervals from users. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic programming

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Explainable and Ethical AI: A Perspective on Argumentation and logic programming 1

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19th international conference of the Italian-Association-for-Artificial-Intelligence (AIxIA)

作者： Calegari, Roberta Omicini, Andrea Sartor, Giovanni Alma Mater Studiorum Univ Bologna Alma AI Alma Mater Res Inst Human Ctr Artificial Bologna Italy Alma Mater Studiorum Univ Bologna DISI Cesena Italy

ISBN: (数字)9783030770914

ISBN: (纸本)9783030770914;9783030770907

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming - namely, abduction, inductive logic programming, and probabilistic logic programming - to address the issues of explainable AI as well as some ethical concerns about AI.

关键词： Explainable AI Ethical AI Argumentation logic programming Abduction Probabilistic LP Inductive LP

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19th international Colloquium on theoretical Aspects of Computing, ICTAC 2022

19th International Colloquium on Theoretical Aspects of Comp...

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19th international Colloquium on theoretical Aspects of Computing, ICTAC 2022

ISBN: (纸本)9783031177149

the proceedings contain 29 papers. the special focus in this conference is on theoretical Aspects of Computing. the topics include: Structural Rules and Algebraic Properties of Intersection Types;quantitative Weak Linearisation;on the Formalization and Computational Complexity of Resilience Problems for Cyber-Physical Systems;spatial and Timing Properties in Highway Traffic;preface;veriMon: A Formally Verified Monitoring Tool;Denotational and Algebraic Semantics for the CaIT Calculus;reconciling Communication Delays and Negation;a Combinatorial Study of Async/Await Processes;unsatisfiability of Comparison-Based Non-malleability for Commitments;alternating Automatic Register Machines;functional Choreographic programming;a Model Checking Based Approach to Detect Safety-Critical Adversarial Examples on Autonomous Driving Systems;ground Confluence and Strong Commutation Modulo Alpha-Equivalence in Nominal Rewriting;Local XOR Unification: Definitions, Algorithms and Application to Cryptography;a Matching logic Foundation for Alk;generalized Test Tables: A Domain-Specific Specification Language for Automated Production Systems;a Type System with Subtyping for WebAssembly’s Stack Polymorphism;A Verified Implementation of B + -Trees in Isabelle/HOL;active Learning for Deterministic Bottom-Up Nominal Tree Automata;towards a User Interface Description Language Based on Bigraphs;a Specification logic for Programs in the Probabilistic Guarded Command Language;card-Minimal Protocols for Symmetric Boolean Functions of More than Seven Inputs;runners for Interleaving Algebraic Effects;formal Grammars for Turn-Bounded Deterministic Context-Free Languages;towards a Unifying logical Framework for Neural Networks;type Inference for Rank-2 Intersection Types Using Set Unification.

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Synthesis of Failure-Robust Plans for Multi-Robot Systems Under Temporal logic Specifications

Synthesis of Failure-Robust Plans for Multi-Robot Systems Un...

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IEEE international conference on Automation Science and Engineering (CASE)

作者： Feifei Huang Shaoyuan Li Xiang Yin Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

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Learning to Select Promising Initial Solutions for Large Neighborhood Search-Based Multi-Agent Path Finding 19thth

Learning to Select Promising Initial Solutions for Large N...

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19th international conference on Computer Aided Systems theory, EUROCAST 2024

作者： Huber, Marc Raidl, Günther R. Blum, Christian Institute of Logic and Computation TU Wien Vienna Austria Campus of UAB Spain

ISBN: (纸本)9783031829512

Anytime Multi-Agent Path Finding (MAPF) is a promising paradigm for finding fast and (near-)optimal solutions to large-scale multi-agent systems within a fixed time budget. the currently leading approach builds on Large Neighborhood Search (LNS), which iteratively optimizes a quickly generated initial solution by repeatedly selecting and replanning paths of subsets of agents using randomized destroy heuristics and Prioritized Planning (PP). In this study, we examine the impact of initial solutions on the quality of final solutions in a state-of-the-art LNS-based anytime MAPF algorithm. Our findings demonstrate that its effectiveness is significantly influenced by the choice of the initial solution. Building on this insight, we propose to run PP many times to create a larger pool of potential initial solutions, from which we then select by means of an offline-trained Machine Learning (ML) model a most promising solution to run the LNS on. Empirical results on well-established MAPF benchmark instances show that the ML model successfully selects a most promising solution from the pool of potential initial solutions. this leads to improved performance of the state-of-the-art LNS-based anytime MAPF method in terms of both the final solution quality and the Area Under the Curve when initiated from the selected solution. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Heuristic programming

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Day-Ahead Optimal Power Flow for Efficient Energy Management of Urban Microgrid

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第2期57卷 1285-1293页

作者： Arkhangelski, Jura Abdou-Tankari, Mahamadou Lefebvre, Gilles Univ Paris Est Creteil Environm & Syst Lab Ctr Res & Dev Heat Transfer F-9401 Creteil France

this study deals with the urban microgrid energy management that is dedicated to individual and collective self-consumption by providing flexibility to the distribution grid (DG). the proposed urban community microgrid is interconnected to a DG and it consists of an association of centralized storage units (called community energy storage system), community intermittent renewable generation, and intelligent energy management system (EMS). One of the main advantages of urban microgrid is that, in case of faults in the DG, it can cut existing interconnections and continue to supply the responsible community in the island mode. In this study, the developed urban microgrid EMS is based on the predictive control management through the day-ahead optimal power flow (DA-OPF) strategy. the main contributions of this work can be defined by two points. the first point is related to a development of the DA-OPF strategy for the urban microgrid based on the intelligent deep learning data forecasting and the mixed-integer nonlinear programming optimization methods. the second point concerns a development of an optimization function integrating the concept of ancillary services of DG flexibility. Experimental results and economical evaluation are presented in this article. By using the proposed strategies, it results in an important electricity price reduction for the considered urban microgrid, compared to a conventional distribution system and basic operation schemes.

关键词： Microgrids Energy management Optimization Load flow logic gates Energy storage Smart homes Ancillary services (AS) day-ahead optimal power flow (DA-OPF) deep learning (DL) flexibility mixed-integer nonlinear programming (MINLP) renewable energy self-consumption urban microgrid

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