In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious t...
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In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to c
In this paper we show how our approach of extending Language Driven Engineering (LDE) with natural language-based code generation supports system migration: The characteristic decomposition of LDE into tasks that are ...
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This paper presents an approach to no-code development based on the interplay of formally defined (graphical) Domain-Specific Languages and informal, intuitive Natural Language which is enriched with contextual inform...
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Instead of the traditional 'system-environment' pair for objects of the regional economy, it is proposed to consider metasystems - these objects together with all interacting objects of the microenvironment. E...
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We explore a continuous aggregated dynamic model for developing two gas fields. The new borehole commissioning rates are the control parameters. Changes in the average flow rate of producing boreholes and current natu...
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We analyze an aggregated dynamic model for the development of gas fields. The same company consistently drills all fields. We pose and solve the problem of maximizing accumulated gas production over a predetermined pe...
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The principle of multiplicity is introduced for the components of the investment planning process. Some algorithms are given that ensure the process of investment planning at the regional level: the formation of integ...
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We formulate a reverse-mode automatic differentiation (RAD) algorithm for (applied) simply typed lambda calculus in the style of Pearlmutter and Siskind [27], using the graphical formalism of string diagrams. Thanks t...
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In this study, we introduce the Sheffer stroke L-algebra and prove some fundamental theorems, propositions and lemmas of Sheffer Stroke L-algebras. The notions of filter and ultrafilter for Sheffer stroke L-algebra ar...
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Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into com...
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ISBN:
(纸本)9798350351507
Background: In this Innovative Practice Work in Progress, we present our initial efforts to integrate formal methods, with a focus on model-checking specifications written in Temporal Logic of Actions (TLA+), into computerscience education, targeting undergraduate juniors/seniors and graduate students. Many safety-critical systems and services crucially depend on correct and reliable behavior. Formal methods can play a key role in ensuring correct and safe system behavior, yet remain underutilized in educational and industry contexts. Aims: We aim to (1) qualitatively assess the state of formal methods in computerscience programs, (2) construct level-appropriate examples that could be included midway into one's undergraduate studies, (3) demonstrate how to address successive 'failuresy' through progressively stringent safety and liveness requirements, and (4) establish an ongoing framework for assessing interest and relevance among students. Methods: We detail our pedagogical strategy for embedding TLA+ into an intermediate course on formal methods at our institution. After starting with a refresher on mathematical logic, students specify the rules of simple puzzles in TLA+ and use its included model checker (known as TLC) to find a solution. We gradually escalate to more complex, dynamic, event-driven systems, such as the control logic of a microwave oven, where students will study safety and liveness requirements. We subsequently discuss explicit concurrency, along with thread safety and deadlock avoidance, by modeling bounded counters and buffers. Results: Our initial findings suggest that through careful curricular design and choice of examples and tools, it is possible to inspire and cultivate a new generation of software engineers proficient in formal methods. Conclusions: Our initial efforts suggest that 84% of our students had a positive experience in our formal methods course. Our future plans include a longitudinal analysis within our own institution and
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