Semantic Intricacies Research Articles

Sketch Video Synthesis

AbstractUnderstanding semantic intricacies and high‐level concepts is essential in image sketch generation, and this challenge becomes even more formidable when applied to the domain of videos. To address this, we propose a novel optimization‐based framework for sketching videos represented by the frame‐wise Bézier Curves. In detail, we first propose a cross‐frame stroke initialization approach to warm up the location and the width of each curve. Then, we optimize the locations of these curves by utilizing a semantic loss based on CLIP features and a newly designed consistency loss using the self‐decomposed 2D atlas network. Built upon these design elements, the resulting sketch video showcases notable visual abstraction and temporal coherence. Furthermore, by transforming a video into vector lines through the sketching process, our method unlocks applications in sketch‐based video editing and video doodling, enabled through video composition.

Exact Bayesian Inference for Loopy Probabilistic Programs using Generating Functions

We present an exact Bayesian inference method for inferring posterior distributions encoded by probabilistic programs featuring possibly unbounded loops . Our method is built on a denotational semantics represented by probability generating functions , which resolves semantic intricacies induced by intertwining discrete probabilistic loops with conditioning (for encoding posterior observations). We implement our method in a tool called Prodigy; it augments existing computer algebra systems with the theory of generating functions for the (semi-)automatic inference and quantitative verification of conditioned probabilistic programs. Experimental results show that Prodigy can handle various infinite-state loopy programs and exhibits comparable performance to state-of-the-art exact inference tools over loop-free benchmarks.

Multilingual topic evolution and comparative analysis for electronic commerce research: A combination of BERTopic and SBERT

Electronic commerce (EC) has become the most critical business activity in the world. China has become the world’s largest market for EC. Over the past three decades, numerous researches have examined the current status of the development of monolingual EC research in specific scenarios. However, the paradigm shift in EC development through the analysis of the dynamic evolution of semantic information has not yet been examined, and the distinctions and connections between multilingual EC studies have not yet been established. This study analyzed 16,207 English and 17,850 Chinese EC-related articles from the Web of Science database and CNKI by combining the BERTopic topic model and SBERT sentence embedding-based similarity computations. The results reveal the distributions of global and local topics in the English and Chinese EC literature, analyze the semantic intricacies of topic convergence and evolution across continuous time, as well as the distinctions and connections between English and Chinese topics. Finally, the evolutionary patterns and life cycle of three crucial English and Chinese topics are explored respectively, including their emergence, development, maturity, and decline. Overall, this study provides a comprehensive overview of EC studies from a topic perspective.

Learner-Centered Analysis in Educational Metaverse Environments: Exploring Value Exchange Systems through Natural Interaction and Text Mining

Amid Education 4.0 and the Fourth Industrial Revolution, we explore the integration of self-directed learning within the metaverse. This study envisions empowered learners, merging the metaverse’s immersive potential with self-directed learning. Using text mining and co-occurrence analysis for student responses prompts questions about their preference over traditional methods. Text mining extracts nuanced insights from open-ended responses, surpassing structured data. Co-occurrence analysis reveals hidden concept relationships, enhancing student engagement and understanding. Beyond XR, the metaverse encompasses avatars, virtual experiences, and value systems. Educators navigate this landscape with text mining, shaping value exchange through engaging content. Integrating real-world experiences in the metaverse merges immersion and personalized learning. Challenges include data precision and semantic intricacies in co-occurrence graphs. Future solutions involve real- time adaptability and sentiment analysis for holistic insights into learner emotions. This study envisions a synergy of self- directed learning and the metaverse, bridging digital and physical realms. Learners navigate interconnected experiences, fostering autonomy. Uncovering the metaverse’s potential contributes to education for digitally adept learners.

New media art design based on fast visual segmentation and 3D image processing

Acquiring innovative styles and compositions from intricate and heterogeneous artistic imagery has emerged as a pivotal research quandry within contemporary new media art image conception. In a concerted effort to adeptly distill the quintessence of artistic styles and elements embedded within these visuals, an innovative methodology is posited herein, underpinned by an enhanced U-net segmentation framework and harmoniously fused with the surface extraction image reconstruction algorithm. This meticulous amalgamation endeavors to attain accurate segmentation and tridimensional reconstruction of the artistry encapsulated in these images. Primarily, the imagery is meticulously partitioned, culminating in an output that artfully encapsulates the inherent artistic attributes. Subsequently, this segmentation outcome is adeptly reconstituted, bestowing form to a three-dimensional artistry model. Empirical validation substantiates the efficacy of this approach, with the method’s Mean Intersection over the Union (MIoU) parameter yielding an impressive score of 0.939 in segmentation performance. Moreover, the peak signal-to-noise ratio and structural similarity attain commendable zeniths of 38.16 and 0.9808, respectively, underscoring the excellence of the reconstruction process. The proposed methodology demonstrates its prowess in exacting segmentation and comprehensive reconstruction of semantic intricacies and nuanced features pervading the realm of artistic imagery. Consequently, this novel methodology augments artists’ capacity to discern diverse artistic paradigms and fabricate superlative new media art compositions of heightened caliber.

An Addition to the Prefixes of Japanese Language: Morpheme <i>do-</i>

This research unveils a morphological entity/unit of modern Japanese with a clear affix nature – morpheme do- – that lacks analysis in the works of Russian and English-speaking scientific audience and that should – according to its morphotactical characteristics (the way it behaves in morphological surroundings) – be assessed as a prefix. The prefix’s etymology, sociolinguistical implications and semantic intricacies are outlined by the author (in particular, the difference between «super-, very» and «too much» use cases) based on the research of Japanese authors. Another important clarification is made in regard to the vernacular, obscenity-related element of the prefix, with a strong accent on the fact that the prefix has been widely used even on national television and is gradually becoming a part of common language with a high non-obscene application range. In terms of functional performance, this emphatic affix fills in the gap that 超 chou- had a hard time filling due to various limitations. Do- is highly compatible with various parts of speech, including nouns, adjectives, adverbs and even – to a lesser extent – verbs. Although there’s some overlap between the two, the relationship is clearly tandemic. A small listing of words (dictionary) compiled out of different sources (including everyday conversations) is provided at the end of the work for future reference. Certain misconceptions of prefix o- are outlined and corrected with presentation of a slightly different perspective. Other prefixes of rare listing are also mentioned briefly and reflected upon in the context of modern language.

Review of Szawerna (2007): A Corpus-Based Study of Nominalizations Predicated by English Deverbal Nouns In -tion

In A Corpus-Based Study of Nominalizations Predicated by English Deverbal Nouns in -tion, Michal Szawerna presents the results of his PhD research on -tion nominalizations. Szawerna’s approach is couched in the Cognitive Grammar framework as developed primarily by Langacker (1987 and 1991) and fits in with the traditional approach to nominalization in that it is essentially word-based (i.e. it focuses on the word level rather than on the nominalized NP as a whole or the discourse context in which it functions). As in most traditional analyses of nominalization, Szawerna takes the lexical-descriptive and aspectual properties of the base verb as starting point (described in cognitive terms) and then attempts to capture the distinct ‘profiles’ of the derivations in -tion in a schematic network of interrelated meanings. Since there are relatively few publications that have nominalizations as their sole focus (other monographs on nominalization are Marchand 1969; Heyvaert 2003; Nordrum 2007) and even fewer that consider derived nouns in -ion, Szawerna’s book is a welcome addition to the literature. Unfortunately, the carefulness with which the author tackles the issue is obscured by the book’s heavy-going style, long-winded explanations, repetitions and extensive use of abbreviations and cognitive terminology. The overall structure of the book, with only three chapters, two of which take up more than 100 pages, moreover makes for hard reading. Those willing to work their way through it, however, will find a rich overview of subtypes of -ion nominalizations and a serious attempt to map out the semantic intricacies of the -ion nominalization system.

A Heterogeneous Geospatial Data Retrieval Method Using Knowledge Graph

Information resources have increased rapidly in the big data era. Geospatial data plays an indispensable role in spatially informed analyses, while data in different areas are relatively isolated. Therefore, it is inadequate to use relational data in handling many semantic intricacies and retrieving geospatial data. In light of this, a heterogeneous retrieval method based on knowledge graph is proposed in this paper. There are three advantages of this method: (1) the semantic knowledge of geospatial data is considered; (2) more information required by users could be obtained; (3) data retrieval speed can be improved. Firstly, implicit semantic knowledge is studied and applied to construct a knowledge graph, integrating semantics in multi-source heterogeneous geospatial data. Then, the query expansion rules and the mappings between knowledge and database are designed to construct retrieval statements and obtain related spatial entities. Finally, the effectiveness and efficiency are verified through comparative analysis and practices. The experiment indicates that the method could automatically construct database retrieval statements and retrieve more relevant data. Additionally, users could reduce the dependence on data storage mode and database Structured Query Language syntax. This paper would facilitate the sharing and outreach of geospatial knowledge for various spatial studies.

Towards Knowledge-Based Geospatial Data Integration and Visualization: A Case of Visualizing Urban Bicycling Suitability

Geospatial information plays an indispensable role in various interdisciplinary and spatially informed analyses. However, the use of geospatial information often entails many semantic intricacies relating to, among other issues, data integration and visualization. For the integration of data from different domains, merely using ontologies is inadequate for handling subtle and complex semantic relations raised by the multiple representations of geospatial data, as the domains have different conceptual views for modelling the geographic space. In addition, for geospatial data visualization - one of the most predominant ways of utilizing geospatial information - semantic intricacies arise as the visualization knowledge is difficult to interpret and utilize by non-geospatial experts. In this paper, we propose a knowledge-based approach using semantic technologies (coupling ontologies, semantic constraints, and semantic rules) to facilitate geospatial data integration and visualization. A traffic spatially informed study is developed as a case study: visualizing urban bicycling suitability. In the case study, we complement ontologies with semantic constraints for cross-domain data integration. In addition, we utilize ontologies and semantic rules to formalize geospatial data analysis and visualization knowledge at different abstraction levels, which enables machines to infer visualization means for geospatial data. The results demonstrate that the proposed framework can effectively handle subtle cross-domain semantic relations for data integration, and empower machines to derive satisfactory visualization results. The approach can facilitate the sharing and outreach of geospatial data and knowledge for various spatially informed studies. (Less)

Conditioning in Probabilistic Programming

This article investigates the semantic intricacies of conditioning, a main feature in probabilistic programming. Our study is based on an extension of the imperative probabilistic guarded command language pGCL with conditioning. We provide a weakest precondition (wp) semantics and an operational semantics. To deal with possibly diverging program behavior, we consider liberal preconditions. We show that diverging program behavior plays a key role when defining conditioning. We establish that weakest preconditions coincide with conditional expected rewards in Markov chains—the operational semantics—and that the wp-semantics conservatively extends the existing semantics of pGCL (without conditioning). An extension of these results with nondeterminism turns out to be problematic: although an operational semantics using Markov decision processes is rather straightforward, we show that providing an inductive wp-semantics in this setting is impossible. Finally, we present two program transformations that eliminate conditioning from any program. The first transformation hoists conditioning while updating the probabilistic choices in the program, while the second transformation replaces conditioning—in the same vein as rejection sampling—by a program with loops. In addition, we present a last program transformation that replaces an independent identically distributed loop with conditioning.

Nrityabodha: Towards understanding Indian classical dance using a deep learning approach

Indian classical dance has existed since over 5000 years and is widely practised and performed all over the world. However, the semantic meaning of the dance gestures and body postures as well as the intricate steps accompanied by music and recital of poems is only understood fully by the connoisseur. The common masses who watch a concert rarely appreciate or understand the ideas conveyed by the dancer. Can machine learning algorithms aid a novice to understand the semantic intricacies being expertly conveyed by the dancer? In this work, we aim to address this highly challenging problem and propose deep learning based algorithms to identify body postures and hand gestures in order to comprehend the intended meaning of the dance performance. Specifically, we propose a convolutional neural network and validate its performance on standard datasets for poses and hand gestures as well as on constrained and real-world datasets of classical dance. We use transfer learning to show that the pre-trained deep networks can reduce the time taken during training and also improve accuracy. Interestingly, we show with experiments performed using Kinect in constrained laboratory settings and data from Youtube that it is possible to identify body poses and hand gestures of the performer to understand the semantic meaning of the enacted dance piece.

Conditioning in Probabilistic Programming

In this paper, we investigate the semantic intricacies of conditioning in probabilistic programming, a major feature, e.g., in machine learning. We provide a quantitative weakest pre–condition semantics. In contrast to all other approaches, non–termination is taken into account by our semantics. We also present an operational semantics in terms of Markov models and show that expected rewards coincide with quantitative pre–conditions. A program transformation that entirely eliminates conditioning from programs is given; the correctness is shown using our semantics. Finally, we show that an inductive semantics for conditioning in non–deterministic probabilistic programs cannot exist.

PhiloLogic4: An Abstract TEI Query System

A common problem for TEI software development is that projects develop their own custom software stack to address the semantic intricacies present in a deeply-encoded TEI corpus. This article describes the design of version 4 of the PhiloLogic corpus query engine, which is designed to handle heterogeneous TEI encoding through its redesigned abstract data model. We show that such an architecture has substantial benefits for software reuse, allowing for powerful TEI applications to be adapted to new corpora with a minimum of custom programming, and we discuss the more general and theoretical implications of abstraction as a TEI processing technique.