The Prospective Threat Vector of a Bounded and Controllable Optimized Computational Approach for Spatio-Temporal Knowledge Graph Completion

There is a presumption by some that the removal of certain materials from online repositories can increase “security through obscurity.” This potentially specious reasoning (as snapshots of these materials may still be available via various digital preservation archives on the surface web as well as the dark web) tends to also be coupled with an underestimation regarding the State-of-the-Art (SOTA) for Spatio-Temporal Knowledge Graph Completion (STKGC) (i.e., the positing of missing nodes, links, etc.) and the possibility of effectively computing the minimum/optimal Control Energy Cost (CEC) for controlling a Large-Scale Complex Network (LSCN). Prospective vulnerable Real World Scenario (RWS) LSCN include power grids and Artificial Intelligence (AI) compute clusters. This paper discusses a prototype Key Node Identification/Assessment (KNIA) module, which successfully undertook STKGC for a power grid and computed the optimal number of Control Signals (CS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) operating on an optimal number of Key Control Driver Nodes (KCDN<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) at an optimal CEC (CEC<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) over an optimal Elongated Temporal Span (ETS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) so as to constitute prospective meaningful control over the involved digital rendition LSCN. A bespoke architectural construct, in the form of a Hypergraph-Induced Infimal Convolutional Manifold Neural Network (H-IICMNN), was utilized to resolve the aforementioned KNIA-related CS-KCDN-CEC-ETS optimality problems. By discerning these key pathways, defensive bulwarks for mitigation against certain prospective threat vectors can be formulated and instantiated.

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Publication Details

Published
2024-08-23
DOI
https://doi.org/10.1109/ibdap62940.2024.10689701
Citations
2
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
0.69
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article

The Prospective Threat Vector of a Bounded and Controllable Optimized Computational Approach for Spatio-Temporal Knowledge Graph Completion

Sheung Chi Chan
2 citations
Semantic Web and Ontologies
0.69
article

The Prospective Threat Vector of a Bounded and Controllable Optimized Computational Approach for Spatio-Temporal Knowledge Graph Completion

Sheung Chi Chan
article en
2 citations

Abstract

There is a presumption by some that the removal of certain materials from online repositories can increase “security through obscurity.” This potentially specious reasoning (as snapshots of these materials may still be available via various digital preservation archives on the surface web as well as the dark web) tends to also be coupled with an underestimation regarding the State-of-the-Art (SOTA) for Spatio-Temporal Knowledge Graph Completion (STKGC) (i.e., the positing of missing nodes, links, etc.) and the possibility of effectively computing the minimum/optimal Control Energy Cost (CEC) for controlling a Large-Scale Complex Network (LSCN). Prospective vulnerable Real World Scenario (RWS) LSCN include power grids and Artificial Intelligence (AI) compute clusters. This paper discusses a prototype Key Node Identification/Assessment (KNIA) module, which successfully undertook STKGC for a power grid and computed the optimal number of Control Signals (CS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) operating on an optimal number of Key Control Driver Nodes (KCDN<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) at an optimal CEC (CEC<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) over an optimal Elongated Temporal Span (ETS<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</inf>) so as to constitute prospective meaningful control over the involved digital rendition LSCN. A bespoke architectural construct, in the form of a Hypergraph-Induced Infimal Convolutional Manifold Neural Network (H-IICMNN), was utilized to resolve the aforementioned KNIA-related CS-KCDN-CEC-ETS optimality problems. By discerning these key pathways, defensive bulwarks for mitigation against certain prospective threat vectors can be formulated and instantiated.

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Semantic Web and Ontologies
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