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ON THE RELATION BETWEEN A graph code AND A graph STATE

QUANTUM INFORMATION & COMPUTATION 2016年第3-4期16卷 237-250页

作者： Hwang, Yongsoo Heo, Jun Korea Univ Sch Elect Engn Seoul 136713 South Korea

A graph state and a graph code respectively are defined based on a mathematical simple graph. In this work, we examine a relation between a graph state and a graph code both obtained from the same graph, and show that a graph state is a superposition of logical qubits of the related graph code. By using the relation, we first discuss that a local complementation which has been used for a graph state can be useful for searching locally equivalent stabilizer codes, and second provide a method to find a stabilizer group of a graph code.

关键词： graph code graph state local complementation stabilizer formalism

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Parallelization Strategies for graph-code-Based Similarity Search

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BIG DATA AND COGNITIVE COMPUTING 2023年第2期7卷 70页

作者： Steinert, Patrick Wagenpfeil, Stefan Mc Kevitt, Paul Frommholz, Ingo Hemmje, Matthias Univ Hagen Fac Math & Comp Sci Universitatsstr 1 D-58097 Hagen Germany Acad Int Sci & Res AISR Derry BT48 7JL North Ireland Univ Wolverhampton Sch Engn Comp & Math Sci Wolverhampton WV1 1LY Staffordshire England

The volume of multimedia assets in collections is growing exponentially, and the retrieval of information is becoming more complex. The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. Feature graphs contain semantic information on multimedia assets. Machine learning can produce detailed semantic information on multimedia assets, reflected in a high volume of nodes and edges in the feature graphs. While increasing the effectiveness of the information retrieval results, the high level of detail and also the growing collections increase the processing time. Addressing this problem, Multimedia Feature graphs (MMFGs) and graph codes (GCs) have been proven to be fast and effective structures for information retrieval. However, the huge volume of data requires more processing time. As graph code algorithms were designed to be parallelizable, different paths of parallelization can be employed to prove or evaluate the scalability options of graph code processing. These include horizontal and vertical scaling with the use of graphic Processing Units (GPUs), Multicore Central Processing Units (CPUs), and distributed computing. In this paper, we show how different parallelization strategies based on graph codes can be combined to provide a significant improvement in efficiency. Our modeling work shows excellent scalability with a theoretical speedup of 16,711 on a top-of-the-line Nvidia H100 GPU with 16,896 cores. Our experiments with a mediocre GPU show that a speedup of 225 can be achieved and give credence to the theoretical speedup. Thus, graph codes provide fast and effective multimedia indexing and retrieval, even in billion-scale use cases.

关键词： indexing retrieval explainability semantic multimedia feature graph graph code

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Searching for (near) Optimal codes 9th

Searching for (near) Optimal Codes

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9th Annual International Conference on Combinatorial Optimization and Applications (COCOA)

作者： Li, Xueliang Mao, Yaping Wei, Meiqin Li, Ruihu Nankai Univ Ctr Combinator Tianjin 300071 Peoples R China Nankai Univ LPMC TJKLC Tianjin 300071 Peoples R China Qinghai Normal Univ Dept Math Xining 810008 Qinghai Peoples R China Air Force Engn Univ Inst Sci Xian 710051 Peoples R China

ISBN: (纸本)9783319266268;9783319266251

Formally self-dual (FSD) codes are interesting codes and have received an enormous research effort due to their importance in mathematics and computer science. Danielsen and Parker proved that every self-dual additive code over GF(4) is equivalent to a graph codes in 2006, and hence graph is an important tool for searching (near) optimal codes. In this paper, we introduce a new method of searching (near) optimal binary (formally self-dual) linear codes and additive codes from circulant graphs.

关键词： graph code FSD code Additive code Optimal code Circulant graph

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A graph Representative Structure for Detecting Automorphic graphs 9th

A Graph Representative Structure for Detecting Automorphic G...

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9th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Hlaing, Yu Wai Oo, Kyaw May Univ Comp Studies Yangon Myanmar Univ Informat Technol Yangon Myanmar

ISBN: (纸本)9783319232041;9783319232034

graphs are prevalently used to model the relationships between objects in various domains. Storing the graphs into large databases is a challenging task as it deals with efficient space and time management. Unlike item sets in huge transactional databases, it becomes essential to ensure the consistency of graph databases since relationships among edges of a graph are predominant. One of the necessary procedures required is a mechanism to check whether two graphs are automorphic(duplicated) or not. Difficulty in identifying and eliminating the automorphic graphs is a challenging problem to the research community. In this paper, we propose a graph representative structure that is called graph code. There are three main phases: preprocessing, code generation and code matching. In preprocessing phase, vertex list, edge list and adjacent edge information are generated for input graph. In code generation, edge dictionary plays an important role. The edge dictionary and adjacent edge information are used to generate graph codes. In code matching, the new graph code is compared with those of other graphs in graph dataset to determine whether they are automorphic or not. The experimental results and comparisons offer a positive response to the proposed structure.

关键词： Edge dictionary graph automorphism graph code graph representative structure

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AI-Based Semantic Multimedia Indexing and Retrieval for Social Media on Smartphones

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INFORMATION 2021年第1期12卷 43页

作者： Wagenpfeil, Stefan Engel, Felix Mc Kevitt, Paul Hemmje, Matthias Univ Hagen Fac Math & Comp Sci Univ Str 1 D-58097 Hagen Germany Acad Int Sci & Res Derry BT48 7TG Londonderry North Ireland

To cope with the growing number of multimedia assets on smartphones and social media, an integrated approach for semantic indexing and retrieval is required. Here, we introduce a generic framework to fuse existing image and video analysis tools and algorithms into a unified semantic annotation, indexing and retrieval model resulting in a multimedia feature vector graph representing various levels of media content, media structures and media features. Utilizing artificial intelligence (AI) and machine learning (ML), these feature representations can provide accurate semantic indexing and retrieval. Here, we provide an overview of the generic multimedia analysis framework (GMAF) and the definition of a multimedia feature vector graph framework (MMFVGF). We also introduce AI4MMRA to detect differences, enhance semantics and refine weights in the feature vector graph. To address particular requirements on smartphones, we introduce an algorithm for fast indexing and retrieval of graph structures. Experiments to prove efficiency, effectiveness and quality of the algorithm are included. All in all, we describe a solution for highly flexible semantic indexing and retrieval that offers unique potential for applications such as social media or local applications on smartphones.

关键词： semantic indexing multimedia retrieval framework multimedia feature vector graph MMFVGF MMFVGG AI4MMRA GMAF AI graph encoding graph code

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Fast and Effective Retrieval for Large Multimedia Collections

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BIG DATA AND COGNITIVE COMPUTING 2021年第3期5卷 33页

作者： Wagenpfeil, Stefan Vu, Binh Mc Kevitt, Paul Hemmje, Matthias Univ Hagen Fac Math & Comp Sci Univ Str 1 D-58097 Hagen Germany Acad Int Sci & Res AISR Derry BT48 7JL North Ireland

The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (graph code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the graph code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the graph code, which also enhances the ranked result representations. Thus, graph codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.

关键词： indexing retrieval semantics graph algorithm graph code multimedia

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Explainable Multimedia Feature Fusion for Medical Applications

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JOURNAL OF IMAGING 2022年第4期8卷 104-104页

作者： Wagenpfeil, Stefan Mc Kevitt, Paul Cheddad, Abbas Hemmje, Matthias Univ Hagen Fac Math & Comp Sci Univ Str 1 D-58097 Hagen Germany Acad Int Sci & Res AISR Derry BT48 7JL Londonderry North Ireland Blekinge Inst Technol S-37179 Karlskrona Sweden

Due to the exponential growth of medical information in the form of, e.g., text, images, Electrocardiograms (ECGs), X-ray, and multimedia, the management of a patient's data has become a huge challenge. In particular, the extraction of features from various different formats and their representation in a homogeneous way are areas of interest in medical applications. Multimedia Information Retrieval (MMIR) frameworks, like the Generic Multimedia Analysis Framework (GMAF), can contribute to solving this problem, when adapted to special requirements and modalities of medical applications. In this paper, we demonstrate how typical multimedia processing techniques can be extended and adapted to medical applications and how these applications benefit from employing a Multimedia Feature graph (MMFG) and specialized, efficient indexing indexing structures in the form of graph codes. These graph codes are transformed to feature relevant graph codes by employing a modified Term Frequency Inverse Document Frequency (TFIDF) algorithm, which further supports value ranges and Boolean operations required in the medical context. On this basis, various metrics for the calculation of similarity, recommendations, and automated inferencing and reasoning can be applied supporting the field of diagnostics. Finally, the presentation of these new facilities in the form of explainability is introduced and demonstrated. Thus, in this paper, we show how graph codes contribute new querying options for diagnosis and how Explainable graph codes can help to readily understand medical multimedia formats.

关键词： indexing retrieval explainability semantic multimedia feature graph graph code

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Towards Automated Semantic Explainability of Multimedia Feature graphs

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INFORMATION 2021年第12期12卷 502页

作者： Wagenpfeil, Stefan Mc Kevitt, Paul Hemmje, Matthias Univ Hagen Fac Math & Comp Sci D-58097 Hagen Germany Acad Int Sci & Res AISR Londonderry BT48 7ER England

Multimedia feature graphs are employed to represent features of images, video, audio, or text. Various techniques exist to extract such features from multimedia objects. In this paper, we describe the extension of such a feature graph to represent the meaning of such multimedia features and introduce a formal context-free PS-grammar (Phrase Structure grammar) to automatically generate human-understandable natural language expressions based on such features. To achieve this, we define a semantic extension to syntactic multimedia feature graphs and introduce a set of production rules for phrases of natural language English expressions. This explainability, which is founded on a semantic model provides the opportunity to represent any multimedia feature in a human-readable and human-understandable form, which largely closes the gap between the technical representation of such features and their semantics. We show how this explainability can be formally defined and demonstrate the corresponding implementation based on our generic multimedia analysis framework. Furthermore, we show how this semantic extension can be employed to increase the effectiveness in precision and recall experiments.

关键词： indexing retrieval explainability semantic multimedia feature graph graph code

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