Style Of Semantics Research Articles

Mechanization of coherence and adequacy: Being extrinsic extended to subtyping

• Mechanization of Reynolds' bracketing theorem. • Mechanized proof of coherence for an intrinsic semantics. • Adequacy of an operational semantics with respect to the extrinsic denotational semantics by means of biorthogonality. • The adequacy of the operational semantics with respect to the intrinsic semantics follows from the adequacy for the extrinsic semantics and bracketing. Operational and denotational are two common approaches to specify the semantics of programming languages: the former is more suitable for expressing a particular evaluation strategy and the latter is more abstract. If there are both an operational and a denotational semantics for a programming language, one expects that the operational semantics be adequate with respect to the denotational one (this ensures that a program whose denotational semantics is some mathematical function computes that function). Moreover, there are two styles of denotational semantics for typed languages: the extrinsic one gives a denotation for any expression (even if ill-typed), while in the intrinsic only well-typed expressions are meaningful. In this paper we mechanize in Coq a typed, call-by-value language by specifying its operational semantics and giving intrinsic and extrinsic denotational semantics, both using domain theory. We also prove that the denotational semantics are equivalent; this is interesting because it leads to a direct proof of coherence for the intrinsic semantics. Finally, we prove the adequacy of the operational semantics with respect to the denotational semantics. As far as we know, this is the first mechanization of Reynolds' bracketing theorem and also the use of biorthogonality with extrinsic semantics instead of intrinsic semantics. We also show that the methodology is modular by extending those results for a larger language with more types (Booleans and products) and subtyping.

Valency Dictionaries and Chinese Vocabulary Acquisition for Foreign Learners

Valency is a major source of lexical errors in foreign language learning. Accordingly, the research question is how the syntactic and semantic properties of a word can be retrieved from the corpora and represented in a Chinese valency dictionary to facilitate foreign learners' vocabulary acquisition. Within the three aspects of the valency framework — logical-semantic, syntactic and semantic-pragmatic valency — this study examines 60 cases of Chinese lexical misuse extracted from the HSK (Chinese Language Proficiency Test) Dynamic Compositions Corpus. The results suggest that the majority of cases of misuse occur in the dimension of semantic-pragmatic valency and that this semantic-pragmatic misuse can be ascribed to various factors such as semantic collocations, emotive variables, text styles, registers, and other contextual factors. The results are then utilized as syntactic, semantic and pragmatic information to be presented in a Chinese valency dictionary. Specifically, the results obtained from a case study of a misused word by referring to a large-scale native Chinese speaker corpus help retrieve a relatively full list of complementation patterns, based on which the study designs a Chinese valency entry that embodies three basic elements — quantitative valency, qualitative valency and valency patterns.

Visual Reconstruction of Ancient Coins Using Cycle-Consistent Generative Adversarial Networks

In this paper, our goal is to perform a virtual restoration of an ancient coin from its image. The present work is the first one to propose this problem, and it is motivated by two key promising applications. The first of these emerges from the recently recognised dependence of automatic image based coin type matching on the condition of the imaged coins; the algorithm introduced herein could be used as a pre-processing step, aimed at overcoming the aforementioned weakness. The second application concerns the utility both to professional and hobby numismatists of being able to visualise and study an ancient coin in a state closer to its original (minted) appearance. To address the conceptual problem at hand, we introduce a framework which comprises a deep learning based method using Generative Adversarial Networks, capable of learning the range of appearance variation of different semantic elements artistically depicted on coins, and a complementary algorithm used to collect, correctly label, and prepare for processing a large numbers of images (here 100,000) of ancient coins needed to facilitate the training of the aforementioned learning method. Empirical evaluation performed on a withheld subset of the data demonstrates extremely promising performance of the proposed methodology and shows that our algorithm correctly learns the spectra of appearance variation across different semantic elements, and despite the enormous variability present reconstructs the missing (damaged) detail while matching the surrounding semantic content and artistic style.

The Challenges in Translating Indonesian Abstract Text to English

The translation is a tool to communicate for people who have different languages. The translation makes it easier for them to understand what others say and get more information from the other languages, especially on the abstract that will help the researcher so that it is easy to get the desired information. In the abstract, their much information that gains for the researcher. Therefore, the researcher is dedicated to studying the challenges of translating.
 This study aims to know the challenges in translating Indonesian abstract text to English faced by eight-semester students' of the English Department at Widya Gama Mahakam Samarinda. The subject used of the study was 8th-semester students in the English department of Widya Gama Mahakam Samarinda. There are five students as participants. The research design to use is qualitative. To collect the data, the researcher uses interview guidelines. The interview result showed that lexicon, grammar, element semantic, and translation style were the challenges in translating Indonesian abstract text to English.

Uncouple Generative Adversarial Networks for Transferring Stylized Portraits to Realistic Faces

Stylized portraits widely exist in artwork or paintings. It is interesting to restore the original identity of portrait artworks, but consider that the rarity and the style diversity of these artworks, it is difficult to pair and obtain sufficient training data to restore their original identities by existing methods. Therefore, it is challenging to explore a method to restore a single stylized portrait to its original identity. Although CycleGAN can convert paintings into realistic photographs in unpaired datasets, it was not developed specifically for portraits, and photo-realistic faces require more accurate structures, thus the visual results obtained with CycleGAN are not satisfactory. In this paper, we propose Uncouple-Generative Adversarial Networks (UncGANs) for transferring stylized portraits to realistic faces. Our UncGANs framework is inspired by CariGANs to tackle the visual problem in CycleGAN for obtaining realistic faces from stylized portraits. In addition, we introduce three losses, namely, the semantic style consistency loss and the cycle consistency loss to effectively guide the training of generators and discriminators on unpaired datasets, the global and local adversarial loss ensure the consistency of appearance characteristics before and after translation, and the location consistency loss to establish the precise correspondence between the source domain and the target domain as well as assist the discriminators. Extensive experimental results and comparisons with state-of-the-art methods including Style, Deep-Image-Analogy, UNIT, MUNIT, CycleGAN, CP-GAN, and PS2-MAN demonstrate that our framework is better at generating realistic faces from stylized portraits with accurate structures and features.

Automatic semantic style transfer using deep convolutional neural networks and soft masks

This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field model. The results on various images show that our method outperforms the most recent techniques.

A Review on Neural Style Transfer with Auto Text Generation

Neural Style Transfer has been a topic of discussion since the past few years, using which any simple content image can be converted in the style of a particular artist. Several methods have been proposed so far for the implementation of Style Transfer, and we discuss a few of these approaches in this paper, (semantic style transfer and photorealistic style transfer) and compare them accordingly. Text generation using neural networks is another field of interest, which has been explored by various researchers in the past. This paper further discusses various text generation models created in the past; using character based Long-Short term memory (LSTM), Recurrent Neural Network Language Model (RNNLM) as well as Multi Adversarial Training. Both of these technologies (neural style transfer and auto text generation) show potential to further be combined, such that the text is generated in the context of the stylized image.

SUBSTRUCTURAL INQUISITIVE LOGICS

AbstractThis paper shows that any propositional logic that extends a basic substructural logic BSL (a weak, nondistributive, nonassociative, and noncommutative version of Full Lambek logic with a paraconsistent negation) can be enriched with questions in the style of inquisitive semantics and logic. We introduce a relational semantic framework for substructural logics that enables us to define the notion of an inquisitive extension of λ, denoted as ${\lambda ^?}$, for any logic λ that is at least as strong as BSL. A general theory of these “inquisitive extensions” is worked out. In particular, it is shown how to axiomatize ${\lambda ^?}$, given the axiomatization of λ. Furthermore, the general theory is applied to some prominent logical systems in the class: classical logic Cl, intuitionistic logic Int, and t-norm based fuzzy logics, including for example Łukasiewicz fuzzy logic Ł. For the inquisitive extensions of these logics, axiomatization is provided and a suitable semantics found.

СЕМАНТИЧЕСКИЕ ОБРАЗЫ «ПЕРМСКОГО» СТИЛЯ В ГОРОДСКОЙ ОБЩЕСТВЕННОЙ И ЖИЛОЙ СРЕДЕ

The article discusses the features of the formation of Perm symbolic field, which is based on “Perm” text, understood as a semantic structuring and coherence of all the statements about Perm and all the symbolic manifestations of “Perm” character. By the example of the semantics of Perm animal style, its artistic and aesthetic rethinking in the works of masters, we can see that the perception of the cultural landscape of urban space is unthinkable without serious immersion in mythical tales and legends, the true history and worldly wisdom of people’s generations. The city of Perm has acquired its visual and semantic framework, the nodal points of which are the objects of Perm symbolics, stylized in the works of artists and sculptors, as well as in the exterior of the city public buildings.

Questioning speech acts

We investigate the sentence-final particle ho from Cantonese, which can stack on topof other sentence-final particles indicating various types of speech acts. We argue that ho is ahigher level question operator that operates at the level of speech acts. More concretely, it takesa speech act (assertion or question) and returns a new interrogative speech act asking whether theinput speech act can be felicitously performed by the addressee. We take the presence of thiskind of higher level question operator in natural language as novel evidence that a mechanism foroperating on speech acts is needed. Building on Farkas and Bruce (2009), Rawlins (2010), Bledinand Rawlins (2017), we develop a mechanism in the style of Update Semantics for operating onspeech acts.Keywords: speech acts, sentence-final particles, Cantonese, update semantics.

Announcement as effort on topological spaces

We propose a multi-agent logic of knowledge, public announcements and arbitrary announcements, interpreted on topological spaces in the style of subset space semantics. The arbitrary announcement modality functions similarly to the effort modality in subset space logics, however, it comes with intuitive and semantic differences. We provide axiomatizations for three logics based on this setting, with S5 knowledge modality, and demonstrate their completeness. We moreover consider the weaker axiomatizations of three logics with S4 type of knowledge and prove soundness and completeness results for these systems.

Announcement as effort on topological spaces

We propose a multi-agent logic of knowledge, public and arbitrary announcements, that is interpreted on topological spaces in the style of subset space semantics. The arbitrary announcement modality functions similarly to the effort modality in subset space logics, however, it comes with intuitive and semantic differences. We provide axiomatizations for three logics based on this setting, and demonstrate their completeness.

宋元話本小說 ‘篇尾’에 나타난 수사학적 표현예술의 의미 연구

宋元話本小說 ‘篇尾’에 나타난 수사학적 표현예술의 의미 연구

A Montagovian treatment of modal subordination

Epistemic modality involves complex contextual dependencies that linguists have studied (Roberts 1989; Veltman 1996). We show that they have a natural treatment within a continuation style semantics.

Cognitive styles in admission procedures for assessing candidates of architecture

Cognitive style has a strong predictive power in academic and professional success. This study investigated the cognitive profile of candidates studying architecture. Specifically, it explored the relation between visual and verbal cognitive styles, and the performance of candidates in admission procedures. The cognitive styles of candidates who were accepted were compared with those of rejected candidates and first-year architectural students using the Visual/Semantic Cognitive-Style computerised test. Results showed that candidates with visual style achieved higher scores than candidates with semantic style. A significant difference was found for cognitive-style intensity, where visual style strength in first-year students was significantly higher than in rejected candidates, but less significant when compared to the accepted candidates. Moreover, semantic style intensity in both first-year students and accepted candidates was significantly lower than in the group of rejected candidates. The test is proposed as a reliable predictor of success in architectural studies.

Research on Evaluation Method of Product Style Semantics Based on Neural Network

This study sets up the corresponding relation between product modeling elements and style semantics based on neural network. First, establish matrices of office chair modeling elements and style semantics by questionnaire method respectively. Then, build standard Back Propagation Neural Network (BPNN), BPNN on Levenberg-Marquardt (L-M) algorithm, standard Radial Basis Function (RBF) neural network and RBFNN on K-means clustering algorithm by MATLAB software and compare the simulating results on two kinds of BPNN and RBFNN. Finally, choose the RBFNN on Kmeans clustering algorithm as the best model to guide product modeling design. The effectiveness and applicability of this method are demonstrated by experimental results on the office chair design. It is shown that this method not only improves the efficiency of existing products style semantics judgment but also can be used to evaluate the style semantics of each design candidate.

Semantics of programming languages: Using Asf+Sdf

A semantic specification of a programming language can be relevant for programmers to understand software written in the language, as well as for the implementers of a language to understand the intentions of its designers. In the early 1980s, Jan Heering and Paul Klint envisioned complete language specifications as libraries of reusable individual constructs, supported by a generic programming environment. This led to the development of the Asf+Sdf specification language and its Meta-Environment.This paper first recalls how programming languages can be specified in Asf+Sdf. It then analyses the apparent difficulty of reusing the specifications of individual constructs, and considers some alternative styles of semantics that have been supported using Asf+Sdf and its Meta-Environment. It is suggested that these alternative styles could facilitate reuse when specifying programming languages in Rascal, which has superseded Asf+Sdf.

Epistemic and DeonticShould

Probabilistic theories of “should” and “ought” face a predicament. At first blush, it seems that such theories must provide different lexical entries for the epistemic and the deontic interpretations of these modals. I show that there is a new style of premise semantics that can avoid this consequence in an attractively conservative way.

Nominal SOS

Plotkinʼs style of Structural Operational Semantics (SOS) has become a de facto standard in giving operational semantics to formalisms and process calculi. In many such formalisms and calculi, the concepts of names, variables and binders are essential ingredients. In this paper, we propose a formal framework for dealing with names in SOS. The framework is based on the Nominal Logic of Gabbay and Pitts and hence is called Nominal SOS. We define nominal bisimilarity, an adaptation of the notion of bisimilarity that is aware of binding. We provide evidence of the expressiveness of the framework by formulating the early π-calculus and Abramskyʼs lazy λ-calculus within Nominal SOS. For both calculi we establish the operational correspondence with the original calculi. Moreover, in the context of the π-calculus, we prove that nominal bisimilarity coincides with Sangiorgiʼs open bisimilarity and in the context of the λ-calculus we prove that nominal bisimilarity coincides with Abramskyʼs applicative bisimilarity.

Operational semantics for signal handling

Signals are a lightweight form of interprocess communication in Unix. When a process receives a signal, the control flow is interrupted and a previously installed signal handler is run. Signal handling is reminiscent both of exception handling and concurrent interleaving of processes. In this paper, we investigate different approaches to formalizing signal handling in operational semantics, and compare them in a series of examples. We find the big-step style of operational semantics to be well suited to modelling signal handling. We integrate exception handling with our big-step semantics of signal handling, by adopting the exception convention as defined in the Definition of Standard ML. The semantics needs to capture the complex interactions between signal handling and exception handling.