Visual e-commerce values filtering framework with spatial database metric

Kopecký, Michal; Vojtáš, Peter

doi:10.2298/csis200115030k

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…The increase in expertise of domestic blockchain R & D personnel and the internalization of technology development will also create jobs for R & D personnel. Opportunities for technological innovation on the blockchain and high potential for use in other fields are also provided [28]. As a future task, based on this design, we will develop a zero-knowledge proof system capable of general operation verification.…”

Section: Discussionmentioning

confidence: 99%

A study of universal zero-knowledge proof circuit-based virtual machines that validate general operations & reduce transaction validation

Jeong¹,

Ahn

2021

COMSIS J

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Recently, blockchain technology accumulates and stores all transactions. Therefore, in order to verify the contents of all transactions, the data itself is compressed, but the scalability is limited. In addition, since a separate verification algorithm is used for each type of transaction, the verification burden increases as the size of the transaction increases. Existing blockchain cannot participate in the network because it does not become a block sink by using a server with a low specification. Due to this problem, as the time passes, the data size of the blockchain network becomes larger and it becomes impossible to participate in the network except for users with abundant resources. Therefore, in this paper, we studied the zero knowledge proof algorithm for general operation verification. In this system, the design of zero-knowledge circuit generator capable of general operation verification and optimization of verifier and prover were also conducted. Also, we developed an algorithm for optimizing key generation. Based on all of these, the zero-knowledge proof algorithm was applied to and tested on the virtual machine so that it can be used universally on all blockchains.

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Section: Discussionmentioning

confidence: 99%

A study of universal zero-knowledge proof circuit-based virtual machines that validate general operations & reduce transaction validation

Jeong¹,

Ahn

2021

COMSIS J

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“…We briefly describe how knowledge extracted from UAV aerial images can be sent to a decision support system. Start with a flat general ontology (e.g., DBPedia 10 , Schema 11 ), then we extend it with a Spatio-temporal model [16], and finally with a domain ontology (e.g., here [15] ). The connection between OWL and UML can be made by [1] (or by owl2uml, which is a Protege plugin 12 )…”

Section: Decision Support System Enriched By Knowledge Extracted From...mentioning

confidence: 99%

“…Violet is a position of an ideal point for a user that wants all criteria of maximal benefit. This is a clear multicriterial situation, and we use our learning of aggregation function (to have an FLN-class preference model), see [11]. Many architectures, backbones, and other hyperparameters allow us to move almost continuously along with coordinates within a reasonable range.…”

Section: Decision Support System Enriched By Knowledge Extracted From...mentioning

confidence: 99%

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Deep Learning for Knowledge Extraction from UAV Images1

Brezani¹,

Hrasko²,

Vančo³

et al. 2022

Frontiers in Artificial Intelligence and Applications

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We study possibilities and ways to increase automation, efficiency, and digitization of industrial processes by integrating knowledge gained from UAV (unmanned aerial vehicle) images with systems to support managerial decision-making. Here we present our results in the secondary wood processing industry. First, we present a deployed solution for repeated area and volume estimated calculations of wood stock areas from our UAV images in the customer’s warehouse. Processing with the commercial software we use is time-consuming and requires annotation by humans (each time aerial images are processed). Second, we present a partial solution where for computing areas of woodpiles, the only human activity is annotating training images for deep neural networks’ supervised learning (only once in a while). Third, we discuss a multicriterial evaluation of possible improvements concerning the precision, frequency, and processing time. The method uses UAVs to take images of woodpiles, deep neural networks for semantic segmentation, and an algorithm to improve results. (semantic segmentation as image classification at a pixel level). Our experiments compare several architectures, backbones, and hyperparameters on real-world data. To calculate also volumes, the feasibility of our approach and to verify it will function as envisioned is verified by a proof of concept. The exchange of knowledge with industrial processes is mediated by ontological comparison and translation of OWL into UML. Furthermore, it shows the possibility of establishing communication between knowledge extractors from images taken by UAVs and managerial decision systems.

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Aggregation for Flexible Challenge Response

Brezani¹,

Vojtáš

2021

Flexible Query Answering Systems

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Visual e-commerce values filtering framework with spatial database metric

Cited by 3 publications

References 15 publications

A study of universal zero-knowledge proof circuit-based virtual machines that validate general operations & reduce transaction validation

A study of universal zero-knowledge proof circuit-based virtual machines that validate general operations & reduce transaction validation

Deep Learning for Knowledge Extraction from UAV Images1

Aggregation for Flexible Challenge Response

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