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检索条件"任意字段=30th International Conference on Logic Programming"

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Bounded Predicates in Description logics with Counting 30

Bounded Predicates in Description Logics with Counting

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30th international Joint conference on Artificial Intelligence, IJCAI 2021

作者： Lukumbuzya, Sanja Šimkus, Mantas Institute of Logic and Computation TU Wien Austria

ISBN: (纸本)9780999241196

Description logics (DLs) support so-called anonymous objects, which significantly contribute to the expressiveness of these KR languages, but also cause substantial computational challenges. this paper investigates reasoning about upper bounds on predicate sizes for ontologies written in the expressive DL ALCHOIQ extended with closed predicates. We describe a procedure based on integer programming that allows us to decide the existence of upper bounds on the cardinality of some predicate in the models of a given ontology in a data-independent way. Our results yield a promising supporting tool for constructing higher quality ontologies, and provide a new way to push the decidability frontiers. To wit, we define a new safety condition for Datalog-based queries over DL ontologies, while retaining decidability of query entailment. © 2021 international Joint conferences on Artificial Intelligence. All rights reserved.

关键词： Ontology

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Towards a framework for analyzing context-oriented programming languages 13

Towards a framework for analyzing context-oriented programmi...

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13th ACM international Workshop on Context-Oriented programming and Advanced Modularity, COP 2021, co-located with the 35th European conference on Object-Oriented programming, ECOOP 2021 and the 30th ACM SIGSOFT international Symposium on Software Testing and Analysis, ISSTA 2021

作者： Elyasaf, Achiya Sturm, Arnon Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9781450385428

Context-aware systems keep on emerging in all of our daily activities. To cope with this new situation, programming languages were extended to support the notion of context. Although context-oriented programming languages exist for over 15 years, they were tested for their suitability in developing context-aware systems only to a limited extent. In this paper, we propose a framework for analyzing context-oriented languages from that viewpoint. In this framework, we refer to context definition and activation, reasoning capabilities, process aspects of how to work with the suggested extension, and pragmatic considerations. To demonstrate the use of the framework, we applied it to a relatively new context-oriented programming paradigm, namely context-oriented behavioral programming. the proposed framework can support users of context-oriented programming languages in deciding on the most fitted language for their purpose. Developers of context-oriented programming languages can also use the framework to improve such languages and their supporting tools. © 2021 ACM.

关键词： Behavioral research

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Translating a large software from c# to c++: An experience report 30th

Translating a large software from c# to c++: An experience r...

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30th international conference on Software Engineering and Data Engineering, SEDE 2021, co-located with the 34th international conference on Computer Applications in Industry and Engineering, CAINE 2021

作者： Gabriel, Edgar Biddle, Nicholas Roos Whitmire, Shane Tran Casey, Allen Parallel Software Technologies Laboratory Department of Computer Science University of Houston HoustonTX77204-3010 United States

Converting source code from one programming language to another is a problem that occurs regularly in real life, but has attracted limited attention and has not been investigated systematically. this paper presents the challenges of translating a large source code from one high-level programming language to another, and the solutions developed to resolve these problems. Furthermore, the paper introduces a systematic classification of the occurring challenges during the translation process and elaborates on the ability to handle them without manual intervention by a programmer. A detailed performance comparison between the two language versions of the code is being performed. © 2021, EasyChair. All rights reserved.

关键词： C++ (programming language)

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Sequential Prediction with logic Constraints for Surgical Robotic Activity Recognition 30

Sequential Prediction with Logic Constraints for Surgical Ro...

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30th IEEE international conference on Robot and Human Interactive Communication (RO-MAN)

作者： Rahman, Md Masudur Voyles, Richard M. Wachs, Juan Xue, Yexiang Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ Sch Engn Technol W Lafayette IN 47907 USA Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA

ISBN: (纸本)9781665404921

Many real-world time-sensitive and high-stake applications (e.g., surgical, rescue, and recovery robotics) exhibit sequential nature;thus, applying Recurrent Neural Network (RNN)-based sequential models is an attractive approach to detect robotic activity. One limitation of such approaches is data scarcity. As a result, limited training samples may lead to over-fitting, producing incorrect predictions during deployment. Nevertheless, abundant domain knowledge may still be available, which may help formulate logic constraints. In this paper, we propose a novel way to integrate domain knowledge into RNN-based sequential prediction. We build a Markov logic Network (MLN)-based classifier that automatically learns constraint weights from data. We propose two methods to incorporate this MLN-based prediction: (i) PriorLayer, in which the values of the hidden layer of the RNN are combined with weights learned from logic constraints in an additional neural network layer, and (ii) Conflation, in which class probabilities from RNN predictions and constraint weights are combined based on the conflation of class probabilities. We evaluate robotic activity classification methods on a simulated OpenAl Gym environment and a real-world DESK dataset for surgical robotics. We observe that our proposed MLN-based approaches boost the performance of LSTM-based networks. In particular, MLN boosts the accuracy of LSTM from 71% to 84% on the Gym dalaset and from 68% to 72% on the Taurus robot dataset. Furthermore, MLN (i.e., PriorLayer) shows regularization capability where it improves accuracy in initial LSTM training while avoiding over-fitting early, thus improves the linal classification accuracy on unseen data.

关键词： Training Recurrent neural networks Systematics Markov processes Activity recognition Robots

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Graph-based Normalizing Flow for Human Motion Generation and Reconstruction 30

Graph-based Normalizing Flow for Human Motion Generation and...

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30th IEEE international conference on Robot and Human Interactive Communication (RO-MAN)

作者： Yin, Wenjie Yin, Hang Kragic, Danica Bjorkman, Marten KTH Royal Inst Technol Robot Percept & Learning Lab Stockholm Sweden

ISBN: (纸本)9781665404921

Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics. Challenges remain in these fields for generating high-fidelity samples and robustly reconstructing motion from imperfect input data, due to e.g. missed marker detection. In this paper, we propose a probabilistic generative model to synthesize and reconstruct long horizon motion sequences conditioned on past information and control signals, such as the path along which an individual is moving. Our method adapts the existing work MoGlow by introducing a new graph-based model. the model leverages the spatial-temporal graph convolutional network (ST-GCN) to effectively capture the spatial structure and temporal correlation of skeletal motion data at multiple scales. We evaluate the models on a mixture of motion capture datasets of human locomotion with foot-step and bone-length analysis. the results demonstrate the advantages of our model in reconstructing missing markers and achieving comparable results on generating realistic future poses. When the inputs are imperfect, our model shows improvements on robustness of generation.

关键词： Adaptation models Analytical models Correlation Convolution Media Probabilistic logic Robustness

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Towards Scalable Complete Verification of ReLU Neural Networks via Dependency-based Branching 30

Towards Scalable Complete Verification of ReLU Neural Networ...

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30th international Joint conference on Artificial Intelligence (IJCAI)

作者： Kouvaros, Panagiotis Lomuscio, Alessio Imperial Coll London Dept Comp London England

ISBN: (纸本)9780999241196

We introduce an efficient method for the complete verification of ReLU-based feed-forward neural networks. the method implements branching on the ReLU states on the basis of a notion of dependency between the nodes. this results in dividing the original verification problem into a set of sub-problems whose MILP formulations require fewer integrality constraints. We evaluate the method on all of the ReLU-based fully connected networks from the first competition for neural network verification. the experimental results obtained show 145% performance gains over the present state-of-the-art in complete verification.

关键词： Integer programming

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Game Semantics for Constructive Modal logic 30th

Game Semantics for Constructive Modal Logic

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30th international conference on Automated Reasoning with Analytic Tableaux and Related Methods (TABLEAUX)

作者： Acclavio, Matteo Catta, Davide Strassburger, Lutz Univ Luxembourg Belval Luxembourg Univ Montpellier LIRMM Montpellier France INRIA Saclay Palaiseau France LIX Ecole Polytech Palaiseau France

ISBN: (纸本)9783030860592;9783030860585

In this paper we provide the first game semantics for the constructive modal logic CK. We first define arenas encoding modal formulas, we then define winning innocent strategies for games on these arenas, and finally we characterize the winning strategies corresponding to proofs in the logic CK. To prove the full-completeness of our semantics, we provide a sequentialization procedure of winning strategies. We conclude the paper by proving their compositionality and showing how our results can be extend to the constructive modal logic CD.

关键词： Semantics

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30th international conference on Automated Reasoning with Analytic Tableaux and Related Methods, TABLEAUX 2021, co-located with the 13th international Symposium on Frontiers of Combining Systems, FroCoS 2021

30th International Conference on Automated Reasoning with An...

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30th international conference on Automated Reasoning with Analytic Tableaux and Related Methods, TABLEAUX 2021, co-located with the 13th international Symposium on Frontiers of Combining Systems, FroCoS 2021

ISBN: (纸本)9783030860585

the proceedings contain 26 papers. the special focus in this conference is on Automated Reasoning with Analytic Tableaux and Related Methods. the topics include: lazyCoP: Lazy Paramodulation Meets Neurally Guided Search;AC Simplifications and Closure Redundancies in the Superposition Calculus;the Role of Entropy in Guiding a Connection Prover;the nanoCoP 2.0 Connection Provers for Classical, Intuitionistic and Modal logics;eliminating Models During Model Elimination;learning theorem Proving Components;a Formally Verified Cut-Elimination Procedure for Linear Nested Sequents for Tense logic;cut-Elimination for Provability logic by Terminating Proof-Search: Formalised and Deconstructed Using Coq;complexity of a Fragment of Infinitary Action logic with Exponential via Non-well-founded Proofs;constraint Tableaux for Two-Dimensional Fuzzy logics;uniform Interpolation from Cyclic Proofs: the Case of Modal Mu-Calculus;cyclic Hypersequent Calculi for Some Modal logics with the Master Modality;a Focus System for the Alternation-Free μ -Calculus;terminating Calculi and Countermodels for Constructive Modal logics;nested Sequents for Intuitionistic Modal logics via Structural Refinement;game Semantics for Constructive Modal logic;the Došen Square Under Construction: A Tale of Four Modalities;analytic Tableaux for Non-deterministic Semantics;tableaux for Free logics with Descriptions;CEGAR-Tableaux: Improved Modal Satisfiability via Modal Clause-Learning and SAT;proof-theory and Semantics for a theory of Definite Descriptions;basing Sequent Systems on Exclusive-Or;proof Search on Bilateralist Judgments over Non-deterministic Semantics;from Input/Output logics to Conditional logics via Sequents – with Provers.

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HyperLDLf: a logic for Checking Properties of Finite Traces Process Logs 30

HyperLDLf: a Logic for Checking Properties of Finite Traces ...

引用

30th international Joint conference on Artificial Intelligence (IJCAI)

作者： De Giacomo, Giuseppe Felli, Paolo Montali, Marco Perelli, Giuseppe Sapienza Univ Rome Rome Italy Free Univ Bozen Bolzano Bolzano Italy

ISBN: (纸本)9780999241196

Temporal logics over finite traces, such as LTLf and its extension LDLf, have been adopted in several areas, including Business Process Management (BPM), to check properties of processes whose executions have an unbounded, but finite, length. these logics express properties of single traces in isolation, however, especially in BPM it is also of interest to express properties over the entire log, i.e., properties that relate multiple traces of the log at once. In the case of infinite-traces, HyperLTL has been proposed to express these "hyper" properties. In this paper, motivated by BPM, we introduce HyperLDL(f), a logic that extends LDL f with the hyper features of HyperLTL. We provide a sound, complete and computationally optimal technique, based on DFAs manipulation, for the model checking problem in the relevant case where the set of traces (i.e., the log) is a regular language. We illustrate how this form of model checking can be used to specify and verify sophisticated properties within BPM and process mining.

关键词： Model checking

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Strain Elastogram Improvement through Dynamic programming Refinement using Multi-objective Optimization Technique

Strain Elastogram Improvement through Dynamic Programming Re...

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Iranian conference of Biomedical Engineering (ICBME)

作者： Mohammadhosein Kalani Ali Fallah Saeid Rashidi Maryam Mehdizadeh Dastjerdi Faculty of Biomedical Engineering Amirkabir University of Technology Tehran Iran Faculty of Medical Sciences and Technologies Science and Research Branch Islamic Azad University Tehran Iran

Elastograms suffer from noise and undesirable artifacts, making it necessary to enhance their signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for accurate detection of tissue deformations, determination of its displacement fields, and representation of tissue characteristics to medical professionals. this research aims to improve the quality of elastograms. Among various displacement estimation methods, the dynamic programming (DP) approach is chosen due to its superior speed, accuracy, and lower computational complexity and cost. the research objectives are defined as enhancing the SNR and CNR metrics and evaluating the resulting improvement in the elastogram quality. To achieve DP refinement, a perturbation is employed. the variables of the DP cost function are evaluated, and finally, the adjustment weight (w) is selected as a suitable criterion for optimization. After assessing different multi-objective optimization algorithms, the Electric Fish optimization algorithm with Non-dominated Sorting (NS-EFO) is chosen. In the designed optimization process, the DP cost function is considered as the objective function, SNR and CNR as objectives, and w as the design variable. To evaluate the improved performance of DP, two RF samples are used, recorded from a CIRS phantom and a Polyvinyl alcohol (PVA) single-layer mammary gland phantom. Following determining the optimal w, the SNR and CNR values are computed for both one-dimensional (ID) and two-dimensional (2D) models under different conditions. the results indicate that the SNR and CNR values for the CIRS phantom, on average, increased respectively by 556.74% and 853.72% in the ID model and by 83.56% and 127.91% in the 2D model, compared to the primitive values. For the single-layer glandular phantom, the SNR and CNR values, on average, increased respectively by 66.98% and 33.03% in the ID model and by 102.67% and 2178.99% in the 2D model, compared to the primary values.

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