Hasil untuk "Semantics"

Ellen F. Lau, C. Phillips, D. Poeppel

Y. Liu, Dengsheng Zhang, Guojun Lu et al.

Jorge Pérez, M. Arenas, Claudio Gutiérrez

SPARQL is the standard language for querying RDF data. In this article, we address systematically the formal study of the database aspects of SPARQL, concentrating in its graph pattern matching facility. We provide a compositional semantics for the core part of SPARQL, and study the complexity of the evaluation of several fragments of the language. Among other complexity results, we show that the evaluation of general SPARQL patterns is PSPACE-complete. We identify a large class of SPARQL patterns, defined by imposing a simple and natural syntactic restriction, where the query evaluation problem can be solved more efficiently. This restriction gives rise to the class of well-designed patterns. We show that the evaluation problem is coNP-complete for well-designed patterns. Moreover, we provide several rewriting rules for well-designed patterns whose application may have a considerable impact in the cost of evaluating SPARQL queries.

A. V. Gelder, K. A. Ross, J. Schlipf

G. Berry, Georges Gonthier

Ronald Fagin, Phokion G. Kolaitis, Renée J. Miller et al.

M. Vigneau, V. Beaucousin, P. Hervé et al.

T. Parsons

P. Barnaghi, Wei Wang, C. Henson et al.

M. Kountouris, Nikolaos Pappas

Wireless connectivity has traditionally been regarded as an opaque data pipe carrying messages, whose context-dependent meaning and effectiveness have been ignored. Nevertheless, in emerging cyber-physical and autonomous networked systems, acquiring, processing, and sending excessive amounts of distributed real-time data, which ends up being stale or useless to the end user, will cause communication bottlenecks, increased latency, and safety issues. We envision a communication paradigm shift, which makes the semantics of information (i.e., the significance and usefulness of messages) the foundation of the communication process. This entails a goal-orient-ed unification of information generation, transmission, and reconstruction, by taking into account process dynamics, signal sparsity, data correlation, and semantic information attributes. We apply this structurally new, synergetic approach to a communication scenario where the destination is tasked with real-time source reconstruction for the purpose of remote actuation. Capitalizing on semantics-empowered sampling and communication policies, we show significant reduction in both reconstruction error and cost of actuation error, as well as in the number of uninformative samples generated.

Everett Hildenbrandt, Manasvi Saxena, Nishant Rodrigues et al.

A developing field of interest for the distributed systems and applied cryptography communities is that of smart contracts: self-executing financial instruments that synchronize their state, often through a blockchain. One such smart contract system that has seen widespread practical adoption is Ethereum, which has grown to a market capacity of 100 billion USD and clears an excess of 500,000 daily transactions. Unfortunately, the rise of these technologies has been marred by a series of costly bugs and exploits. Increasingly, the Ethereum community has turned to formal methods and rigorous program analysis tools. This trend holds great promise due to the relative simplicity of smart contracts and bounded-time deterministic execution inherent to the Ethereum Virtual Machine (EVM). Here we present KEVM, an executable formal specification of the EVM's bytecode stack-based language built with the K Framework, designed to serve as a solid foundation for further formal analyses. We empirically evaluate the correctness and performance of KEVM using the official Ethereum test suite. To demonstrate the usability, several extensions of the semantics are presented. and two different-language implementations of the ERC20 Standard Token are verified against the ERC20 specification. These results are encouraging for the executable semantics approach to language prototyping and specification.

Congcong Li, Yucheng Dong, F. Herrera et al.

J. P. Louw

In an introduction

Congcong Li, Yucheng Dong, F. Herrera

In linguistic large-scale group decision making (LSGDM), it is often necessary to achieve a consensus. Particularly, when computing with words and linguistic decision, we must keep in mind that words mean different things to different people. Therefore, to represent the specific semantics of each individual, we need to consider the personalized individual semantics (PIS) model in linguistic LSGDM. In this paper, we propose a consensus model based on PIS for LSGDM. Specifically, a PIS process to obtain the individual semantics of linguistic terms with linguistic preference relations is introduced. A consensus process based on PIS, including the consensus measure and feedback recommendation phases, is proposed to improve the willingness of decision makers who follow the suggestions to revise their preferences in order to achieve a consensus in linguistic LSGDM problems. The consensus measure defines two opposing consensus groups with respective acceptable and unacceptable consensus. In the feedback recommendation phase, a PIS-based clustering method to get decision makers with similar individual semantics is proposed. Recommendation rules design a feedback for decision makers with unacceptable consensus, finding suitable moderators from the decision makers with acceptable consensus based on cluster proximity.

Gemma Boleda

Distributional semantics provides multidimensional, graded, empirically induced word representations that successfully capture many aspects of meaning in natural languages, as shown by a large body of research in computational linguistics; yet, its impact in theoretical linguistics has so far been limited. This review provides a critical discussion of the literature on distributional semantics, with an emphasis on methods and results that are relevant for theoretical linguistics, in three areas: semantic change, polysemy and composition, and the grammar–semantics interface (specifically, the interface of semantics with syntax and with derivational morphology). The goal of this review is to foster greater cross-fertilization of theoretical and computational approaches to language as a means to advance our collective knowledge of how it works.

Long Chen, Hanwang Zhang, Jun Xiao et al.

We propose a novel framework called Semantics-Preserving Adversarial Embedding Network (SP-AEN) for zero-shot visual recognition (ZSL), where test images and their classes are both unseen during training. SP-AEN aims to tackle the inherent problem - semantic loss - in the prevailing family of embedding-based ZSL, where some semantics would be discarded during training if they are non-discriminative for training classes, but could become critical for recognizing test classes. Specifically, SP-AEN prevents the semantic loss by introducing an independent visual-to-semantic space embedder which disentangles the semantic space into two subspaces for the two arguably conflicting objectives: classification and reconstruction. Through adversarial learning of the two subspaces, SP-AEN can transfer the semantics from the reconstructive subspace to the discriminative one, accomplishing the improved zero-shot recognition of unseen classes. Comparing

Sourav Garg, Niko Sunderhauf, Feras Dayoub et al.

For robots to navigate and interact more richly with the world around them, they will likely require a deeper understanding of the world in which they operate. In robotics and related research fields, the study of understanding is often referred to as semantics, which dictates what does the world"mean"to a robot, and is strongly tied to the question of how to represent that meaning. With humans and robots increasingly operating in the same world, the prospects of human-robot interaction also bring semantics and ontology of natural language into the picture. Driven by need, as well as by enablers like increasing availability of training data and computational resources, semantics is a rapidly growing research area in robotics. The field has received significant attention in the research literature to date, but most reviews and surveys have focused on particular aspects of the topic: the technical research issues regarding its use in specific robotic topics like mapping or segmentation, or its relevance to one particular application domain like autonomous driving. A new treatment is therefore required, and is also timely because so much relevant research has occurred since many of the key surveys were published. This survey therefore provides an overarching snapshot of where semantics in robotics stands today. We establish a taxonomy for semantics research in or relevant to robotics, split into four broad categories of activity, in which semantics are extracted, used, or both. Within these broad categories we survey dozens of major topics including fundamentals from the computer vision field and key robotics research areas utilizing semantics, including mapping, navigation and interaction with the world. The survey also covers key practical considerations, including enablers like increased data availability and improved computational hardware, and major application areas where...

Guojun Yin, Bin Liu, Lu Sheng et al.

Synthesizing photo-realistic images from text descriptions is a challenging problem. Previous studies have shown remarkable progresses on visual quality of the generated images. In this paper, we consider semantics from the input text descriptions in helping render photo-realistic images. However, diverse linguistic expressions pose challenges in extracting consistent semantics even they depict the same thing. To this end, we propose a novel photo-realistic text-to-image generation model that implicitly disentangles semantics to both fulfill the high-level semantic consistency and low-level semantic diversity. To be specific, we design (1) a Siamese mechanism in the discriminator to learn consistent high-level semantics, and (2) a visual-semantic embedding strategy by semantic-conditioned batch normalization to find diverse low-level semantics. Extensive experiments and ablation studies on CUB and MS-COCO datasets demonstrate the superiority of the proposed method in comparison to state-of-the-art methods.

Bob Rehder

A desirable property of any theory of causal reasoning is to explain not only why people make causal reasoning errors but also <i>when</i> they make them. The <i>mutation sampler</i> is a rational process model of human causal reasoning that yields normatively correct inferences when sufficient cognitive resources are available but introduces systematic errors when they are not. The mutation sampler has been shown to account for a number of causal reasoning errors, including <i>Markov violations</i>, the phenomenon in which human reasoners treat causally related variables as statistically dependent when they are normatively independent. A Markov violation arises, for example, when an individual reasoning about a causal chain <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mo>→</mo><mi>Y</mi><mo>→</mo><mi>Z</mi></mrow></semantics></math></inline-formula> treats <i>X</i> as informative about the state of <i>Z</i> even when the state of <i>Y</i> is known. Recently, the mutation sampler was used to predict the existence of previously untested experimental conditions in which the <i>sign</i> of Markov violations would switch from positive to negative. Here, it was used to predict the existence of conditions in which Markov violations should <i>disappear</i> entirely. In fact, asking subjects to reason about a novel causal structure with nothing but <i>generative</i> causal relations (a cause makes its effect more likely) resulted in Markov violations in the usual positive direction. But simply describing one of four causal relations as <i>inhibitory</i> (the cause makes its effect less likely) resulted in the elimination of those violations. Theoretical model fitting confirmed how this novel result is predicted by the mutation sampler.

Haibin Chen, Qianli Ma, Zhenxi Lin et al.

Hierarchical text classification is an important yet challenging task due to the complex structure of the label hierarchy. Existing methods ignore the semantic relationship between text and labels, so they cannot make full use of the hierarchical information. To this end, we formulate the text-label semantics relationship as a semantic matching problem and thus propose a hierarchy-aware label semantics matching network (HiMatch). First, we project text semantics and label semantics into a joint embedding space. We then introduce a joint embedding loss and a matching learning loss to model the matching relationship between the text semantics and the label semantics. Our model captures the text-label semantics matching relationship among coarse-grained labels and fine-grained labels in a hierarchy-aware manner. The experimental results on various benchmark datasets verify that our model achieves state-of-the-art results.