Combination of graphics, uncertainty, and semantics: A survey

Yuan, Gao; Rafi, Muhammad

doi:10.1002/cpe.6711

Cited by 4 publications

(6 citation statements)

References 39 publications

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“…The cluster stability analysis involves computation of two different parameters, which are respectively: the average similarity of the features within a cluster (I, see lines 1-6) and average dissimilarity between the clusters (J , see lines [7][8][9][10][11][12][13][14][15][16][17]. In other words, metrics I and J correspond to, respectively, the average distance between all data points within a cluster and the smallest average distance between a data point belonging to a cluster and all other points corresponding to different clusters.…”

Section: Require: the Clustered Left Low-rank Factorsmentioning

confidence: 99%

“…Examples of worldly known knowledge graph projects are YAGO [5], DBpedia [6], and the Google Knowledge Graph (containing ∼ 70 billion relational facts) [7]. The development of knowledge graphs led to increasing activities in statistical relational learning that integrates semantics and probabilistic graphical models in semantic networks [8].…”

Section: Introductionmentioning

confidence: 99%

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Distributed non-negative RESCAL with Automatic Model Selection for Exascale Data

Bhattarai¹,

Kharat²,

Skau³

et al. 2022

Preprint

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With the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been an exponential growth in the volume of data produced around the world. Among these data, relational datasets are growing in popularity as they provide unique insights regarding the evolution of communities and their interactions. Relational datasets are naturally non-negative, sparse, and extra-large. Relational data usually contain triples, (subject, relation, object), and are represented as graphs/multigraphs, called knowledge graphs, which need to be embedded into a low-dimensional dense vector space. Among various embedding models, RESCAL allows learning of relational data to extract the posterior distributions over the latent variables and to make predictions of missing relations. However, RESCAL is computationally demanding and requires a fast and distributed implementation to analyze extra-large real-world datasets. Here we introduce a distributed non-negative RESCAL algorithm for heterogeneous CPU/GPU architectures with automatic selection of the number of latent communities (model selection), called pyDRESCALk. We demonstrate the correctness of pyDRESCALk with real-world and large synthetic tensors, and the efficacy showing near-linear scaling that concurs with the theoretical complexities. Finally, pyDRESCALk determines the number of latent communities in an 11-terabyte dense and 9-exabyte sparse synthetic tensor.

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Section: Require: the Clustered Left Low-rank Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed non-negative RESCAL with Automatic Model Selection for Exascale Data

Bhattarai¹,

Kharat²,

Skau³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As a result, with the development of the times, figurative expressions are no longer sufficient to meet the creative needs of artists and the aesthetic demands of people. e combination of digital art and abstract graphics has created a different visual and aesthetic experience than before [17,18]. e use of abstract graphics in digital art has played a crucial role in the development of the arts.…”

Section: Introductionmentioning

confidence: 99%

“…e essence of abstract graphics is the simplification and generalisation of figurative objects, and the simplicity of expression is the mode of thinking and expression that abstract graphics have always followed. Abstract graphics were born out of the desire to use symbolic metaphors to broaden the viewer's imagination [17]. To a certain extent, the abstract form of representation can be free from the constraints of the abstracted object.…”

Section: Introductionmentioning

confidence: 99%

Digital Art Design Effectiveness Model System Based on K-Medoids Algorithm

Luo

2022

Advances in Multimedia

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With the development of the times, figurative expressions no longer meet the creative needs of artists and the aesthetic demands of the people. In order to express art in a more profound way, the perfect use of abstract graphics plays a crucial role in the success of the work. In recent years, there has been a surge in the creation of digital art, but there is relatively little theoretical literature on the combination of abstract graphics as a visual language and digital art. In addition, research on the theoretical aspects of digital art design is also relatively weak, so it is essential to analyse the formal aesthetics and innovative applications. In fact, digital art design has a very important role to play in promoting the development of creative cultural industries. In other words, the healthy development of digital art design can influence the future prospects of a country’s creative and cultural industries. Digital art design is an integrated and complex production process and labour outcome. In addition to its human, aesthetic, and social value, digital art also has an economic value. Digital art is a new art form that combines digital technology and artistic aesthetics. As such, digital art is characterised by high technology, diverse forms, popularised art, and the advantages of high communication, interactivity, and influence, which can provide more assistance for the innovation and application of abstract graphics. Digital art is multifaceted and has an artistic expression that cannot be matched by other forms of technology. Abstract graphics, driven by digital art, are full of novelty and interest and can greatly enrich people’s emotions and senses. Abstract graphics bring the experience of digital art to its fullest potential. The combination of digital art and abstract graphics offers more innovation and possibilities for the development of art and will bring great prosperity to art communication. With the widespread use of computer and network technology, the Internet has developed rapidly. In this context, digital art, as art created in a digital way and concept, has gained widespread attention. As a result, how to integrate existing computer resources in the new environment to build a model of digital art design effectiveness will cause a direct influence on the quality of digital art design with digital content innovation as the core. At the same time, as digital art becomes more and more popular, the demand for digital talents becomes very urgent. As a result, the cultivation of high-quality digital talents has become a major concern for society. Therefore, in order to explore the success of digital art design and the cultivation of digital art talents, and to better serve the innovation of digital art, this paper proposes a digital art effectiveness model based on the K-medoids algorithm. This model can provide a deeper and more comprehensive understanding of digital art and abstract graphics and provide theoretical support for professional design creation.

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“…The models and methods involved in these efforts including their expressiveness, pros, and cons are discussed. 3 This survey helps establish links between different research directions.…”

mentioning

confidence: 99%

Semantics, knowledge and advanced cyber‐infrastructure for intelligent applications

Sun

Zhuge

2022

Concurrency and Computation

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Semantics, knowledge and advanced cyber-infrastructure represent three communities of practice that have significantly influenced the development of computing technologies and systems. The rapid development of Artificial Intelligence and Data Science challenges the existing research on semantics, knowledge, and cyber-infrastructure. Related research concerns the following topics: (1) semantics, specifically semantics modeling and semantic link networks including construction, discovery, analysis and management; (2) knowledge, specifically knowledge discovery in big data, methods for transforming big data into knowledge, Knowledge modeling and management, Knowledge Graph, and advanced computing infrastructures; (3) advanced cyber-infrastructure, specifically new data models, software tools and methodologies as well as advanced computing infrastructures for big data management; and, (4) applications based on semantics, knowledge and advanced computing infrastructure, including smart cities, healthcare, industries and advanced information services such as question answering, recommendation and summarization on multimedia integrating texts, pictures, and videos. This special issue is to report research development of the above areas, promote cross-area research and accelerate cross-area research.

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Combination of graphics, uncertainty, and semantics: A survey

Cited by 4 publications

References 39 publications

Distributed non-negative RESCAL with Automatic Model Selection for Exascale Data

Distributed non-negative RESCAL with Automatic Model Selection for Exascale Data

Digital Art Design Effectiveness Model System Based on K-Medoids Algorithm

Semantics, knowledge and advanced cyber‐infrastructure for intelligent applications

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