Explore the vast range of titles available.

In the past years we have observed enormous adoption and use of digital technologies in almost all domains of human life. In this context, researchers and practitioners have been using the term “digital transformation” (DT) to characterize phenomena related to the changes caused by increased use of digital technologies. The progression and contributions of corresponding discussions significantly depend on a common understanding and conceptualization of DT. Accordingly, the information systems research community has started working toward conceptual clarity. Importantly, the current debate focuses on theoretical contributions by academic researchers. Hence, data on the practitioners’ perspective on DT is missing. Against this background, we investigated general understandings of DT in practice. Our analysis comprises data of N = 529 business decision-makers in the UK. Our results show a significant discrepancy between science (i.e., conceptualizations and definitions of DT in the academic literature) and practice (i.e., business decision-makers’ perceptions and corresponding survey responses). Specifically, for a large proportion of the respondents their decision to classify concrete digitalization examples from the literature as DT or no DT was largely independent of the degree of organizational change caused by the use of digital technologies. This is a surprising result, as high-caliber academic literature proposes this degree to be a critical factor to characterize DT. Another key finding is that a remarkable number of practitioners consider a digitalization initiative as DT based on the mere occurrence of specific buzzwords related to digital technologies in the corresponding descriptions. In particular, the buzzword “digital technology” led the survey participants to classify a definition as instance for DT. We discuss implications of our results and limitations.

We propose a method for describing human activities from video images based on concept hierarchies of actions. Major difficulty in transforming video images into textual descriptions is how to bridge a semantic gap between them, which is also known as inverse Hollywood problem. In general, the concepts of events or actions of human can be classified by semantic primitives. By associating these concepts with the semantic features extracted from video images, appropriate syntactic components such as verbs, objects, etc. are determined and then translated into natural language sentences. We also demonstrate the performance of the proposed method by several experiments.[PUBLICATION ABSTRACT]

In this article, we propose a method for the automatic retrieval of a set of semantic primitive words from an explanatory dictionary and a novel evaluation procedure for the obtained set of primitives. The approach is based on the representation of the dictionary as a directed graph with a single-objective constrained optimization problem via a genetic algorithm with the PageRank scoring model. The problem is defined as a subset selection. The algorithm is fit to search for the sets of words that should fulfil several requirements: the cardinality of the set should not exceed empirically selected limits and the PageRank word importance score is minimized with cycle prevention thresholding. In the experiments, we used the WordNet dictionary for English. The proposed method is an improvement over the previous state-of-the-art solutions.

Based on knowledge representation in product design, we propose a knowledge model of the product design process based on graphical semantic perception. The ontology semantic of the product is analyzed together with the product design process knowledge. Then the basic element model for knowledge representation of the product design process is built. With the concepts of the extension origin point basic element and extension vector thus defined, knowledge representation, consisting of growth, convergence and optimization is realized. On this basis, the model for case base clustering based on graphical semantics is built. Feasibility verification is performed with the case of appearance design of a machine tool. The highlight of the proposed method lies in the combination of the formalized and quantitative approach for product appearance design.

This introduction presents the matter that this Topic/Comment issue addresses—namely, the encoding of aspectual information in the syntax and morphology. The main aim of this issue is to shed light on the identification of the semantic primitives underlying the most prominent viewpoint aspectual forms referred to in the literature (e.g., Imperfect, Perfective, Perfect, and Neutral), and to give a principled explanation of the way in which these semantic primitives are represented in the syntax and mapped onto the morphology. The introduction surveys and compares the different proposals that the authors in the issue defend in this regard and discusses the need of finer grained analyses so that more accurate crosslinguistic correspondences, which are crucial for answering questions in realms such as that of second language acquisition, can be established.

Abstract: The task of providing dictionaries for all the world's languages is prodigious, re-quiring efficient techniques. The text corpus method cannot be used for minority languages lacking texts. To meet the need, the author has constructed a list of 1 600 semantic domains, which he has successfully used to collect words. In a workshop setting, a group of speakers can collect as many as 17 000 words in ten days. This method results in a classified word list that can be efficiently expanded into a full dictionary. The method works because the mental lexicon is a giant web or-ganized around key concepts. A semantic domain can be defined as an important concept together with the words directly related to it by lexical relations. A person can utilize the mental web to quickly jump from word to word within a domain. The author is developing a template for each domain to aid in collecting words and in de-scribing their semantics. Investigating semantics within the context of a domain yields many in-sights. The method permits the production of both alphabetically and semantically organized dic-tionaries. The list of domains is intended to be universal in scope and applicability. Perhaps due to universals of human experience and universals of linguistic competence, there are striking simi-larities in various lists of semantic domains developed for languages around the world. Using a standardized list of domains to classify multiple dictionaries opens up possibilities for cross-lin-guistic research into semantic and lexical universals. Keywords: SEMANTIC DOMAINS, SEMANTIC FIELDS, SEMANTIC CATEGORIES, LEX-ICAL RELATIONS, SEMANTIC PRIMITIVES, DOMAIN TEMPLATES, MENTAL LEXICON, SEMANTIC UNIVERSALS, MINORITY LANGUAGES, LEXICOGRAPHY Opsomming: Samestelling van woordeboeke deur gebruikmaking van se-mantiese domeine. Die taak van die voorsiening van woordeboeke aan al die tale van die wêreld is geweldig en vereis doeltreffende tegnieke. Die tekskorpusmetode kan nie gebruik word vir minderheidstale waarin tekste ontbreek nie. Om in die behoefte te voorsien, het die skrywer 'n lys van 1 600 semantiese domeine opgestel wat hy suksesvol gebruik het om woorde te versamel. In 'n werksessie-omgewing kan 'n groep sprekers tot soveel as 17 000 woorde in tien dae versamel. Hierdie metode lei tot 'n geklassifiseerde woordelys wat doeltreffend uitgebrei kan word tot 'n volledige woordeboek. Die metode werk omdat die mentale leksikon 'n groot web is wat rondom sleutelbegrippe gestruktureer is. 'n Semantiese domein kan gedefinieer word as 'n belangrike kon-sep saam met die woorde wat direk daarmee verband hou vanweë leksikale verwantskappe. 'n Persoon kan die mentale web gebruik om vinnig van woord tot woord binne 'n domein te spring. Die skrywer is besig om vir elke domein 'n profiel te ontwikkel om te help met die versameling van woorde en met die beskrywing van hul semantiek. 'n Ondersoek van semantiek binne die konteks van 'n domein lewer baie insigte. Die metode laat die totstandbrenging van sowel alfabeties as semanties gerangskikte woordeboeke toe. Die lys domeine is bedoel om univer-seel in omvang en toepassing te wees. Moontlik as gevolg van universalia van menslike ervaring en universalia van taalkundige vermoë, is daar treffende ooreenkomste tussen verskillende lyste semantiese domeine wat ontwikkel is vir tale oor die hele wêreld. Die gebruik van 'n gestandaardi-seerde lys domeine om veelsoortige woordeboeke te klassifiseer, skep moontlikhede vir kruislin-guistiese navorsing oor semantiese en leksikale universalia. Sleutelwoorde: SEMANTIESE DOMEINE, SEMANTIESE VELDE, SEMANTIESE KATE-GORIEË, LEKSIKALE VERWANTSKAPPE, SEMANTIESE PRIMITIEWES, DOMEINPROFIELE, MENTALE LEKSIKON, SEMANTIESE UNIVERSALIA, MINDERHEIDSTALE, LEKSIKOGRAFIE

Message ordering is a fundamental abstraction in distributed systems. However, ordering guarantees are usually purely \"syntactic\", that is, message \"semantics\" is not taken into consideration despite the fact that in several cases semantic information about messages could be exploited to avoid ordering messages unnecessarily. In this paper we define the Generic Broadcast problem, which orders messages only if needed, based on the semantics of the messages. The semantic information about messages is introduced by conflict relations. We show that Reliable Broadcast and Atomic Broadcast are special instances of Generic Broadcast. The paper also presents two algorithms that solve Generic Broadcast.

Machine readable dictionaries (MRDs) contain knowledge about language and the world essential for tasks in natural language processing (NLP). However, this knowledge, collected and recorded by lexicographers for human readers, is not presented in a manner for MRDs to be used directly for NLP tasks. What is badly needed are machine tractable dictionaries (MTDs): MRDs transformed into a format usable for NLP. This paper discusses three different but related large-scale computational methods to transform MRDs into MTDs. The MRD used is \"The Longman Dictionary of Contemporary English\" (LDOCE). The three methods differ in the amount of knowledge they start with and the kinds of knowledge they provide. All require some handcoding of initial information but are largely automatic. Method I, a statistical approach, uses the least handcoding. It generates \"relatedness\" networks for words in LDOCE and presents a method for doing partial word sense disambiguation. Method II employs the most handcoding because it develops and builds lexical entries for a very carefully controlled defining vocabulary of 2,000 word senses (1,000 words). The payoff is that the method will provide an MTD containing highly structured semantic information. Method III requires the handcoding of a grammar and the semantic patterns used by its parser, but not the handcoding of any lexical material. This is because the method builds up lexical material from sources wholly within LDOCE. The information extracted is a set of sources of information, individually weak, but which can be combined to give a strong and determinate linguistic data base.

At present, the knowledge of cultural relics in cultural creative design is fragmented and fragmented, and lacks the support of a unified system. In this paper, firstly, with the help of a topological model to characterize the creative process of image thinking, effective reasoning is carried out on the objects, things and relations of cultural and creative products according to the expansion analysis of primitives. Then, a conceptual network of art design for cultural and creative products is constructed based on semantic association, and an identification model of art design elements for cultural and creative products is built based on quantitative theory. Finally, through the experiment of art design for cultural and creative products, the perception of cultural and creative products was analyzed in multiple dimensions. The results show that the maximum factor influencing the value of art design of cultural and creative products is 0.951, among which the cultural characteristics have a strong correlation with the color characteristics of 0.642, and the functional characteristics have a strong correlation with the cultural characteristics of 0.837. The results of this paper provide new ideas and methods for the development and maturation of artistic design of cultural and creative products.

3D point clouds are robust representations of real-world objects and usually contain information about the shape, size, position and radiometry of the scene. However, unstructured point clouds do not directly exploit the full potential of such information and thus, further analysis is commonly required. Especially when dealing with cultural heritage objects which are, typically, described by complex 3D geometries, semantic segmentation is a fundamental step for the automatic identification of shapes, erosions, etc. This paper focuses on the efficient extraction of semantic classes that would support the generation of geometric primitives such as planes, spheres, cylinders, etc. Our semantic segmentation approach relies on supervised learning using a Random Forest algorithm, while the geometric shapes are identified and extracted with the RANSAC model fitting algorithm. In this way the parametric modelling procedure in a HBIM environment is easily enabled. Our experiments show the efficient label transferability of our 3D semantic segmentation approach across different Doric Greek temples, with qualitatively and quantitatively evaluations.