How to comprehensively, clearly, and accurately obtain the electromagnetic characteristics and relations of planning objects is an important issue in realizing efficient and flexible electromagnetic spectrum planning....
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ISBN:
(数字)9798350384437
ISBN:
(纸本)9798350384444
How to comprehensively, clearly, and accurately obtain the electromagnetic characteristics and relations of planning objects is an important issue in realizing efficient and flexible electromagnetic spectrum planning. In this paper, we use knowledge Graph, an intelligent technology in Semantic Web research, to construct the knowledge graph ontology of frequency equipment. We use the reasoning structure based on OWL + SWRL + Jess, combined with the OWL API to construct reasoning rules and procedures to complete the knowledge service required for system-level electromagnetic spectrum planning by setting up specific scenarios.
Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these benchmarks are limited to specific vis...
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This research explores the development of an expert system for legal decision-making, specifically designed for the Indian legal context, utilizing Prolog for case-based reasoning. The Indian legal system, governed by...
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ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
This research explores the development of an expert system for legal decision-making, specifically designed for the Indian legal context, utilizing Prolog for case-based reasoning. The Indian legal system, governed by the Indian Penal Code (IPC), various constitutional laws, and legal precedents, relies heavily on judicial decisions to inform future rulings. Prolog, a logic programming language with rule-based inference capabilities, presents an effective solution for automating such legal reasoning. In this paper, we construct an expert system that processes legal facts, applies relevant sections of Indian laws, and matches current legal cases to relevant prior judgments to deliver case-based legal decisions. The system incorporates key provisions from the IPC, such as sections dealing with theft, criminal offenses, and contract disputes, using legal precedents from landmark Indian cases to demonstrate its applicability. This research highlights the system’s effectiveness in handling rule-based legal reasoning while discussing the challenges posed by ambiguous legal language, scalability, and real-world complexities. We conclude by proposing enhancements, such as integrating machine learning techniques to refine case interpretation and automate more complex aspects of the Indian judicial process.
We report on yet another formalization of the Church-Rosser property in lambda-calculi, carried out with the proof environment BELUGA. After the well-known proofs of confluence for β-reduction in the untyped settings...
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In logic programming, partial evaluation (PE) performs unfolding rules in advance to reduce the cost of inferencing. Recently, PE of logic programs has been implemented in vector spaces by computing the powers of matr...
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ISBN:
(数字)9798331527235
ISBN:
(纸本)9798331527242
In logic programming, partial evaluation (PE) performs unfolding rules in advance to reduce the cost of inferencing. Recently, PE of logic programs has been implemented in vector spaces by computing the powers of matrix representations. It has been reported that linear algebraic PE substantially enhances the practical performance of linear algebraic methods for logic programming. However, most recent research has focused exclusively on And-rules, assuming that their dependency graph is acyclic. In this paper, we introduce cycle-resolving techniques to ensure that linear algebraic PE works effectively even with cycles in the program. Additionally, we demonstrate that linear algebraic PE can also be extended to accommodate Or-rules. Moreover, we propose using eigendecomposition and Jordan normal form to conduct PE in vector spaces. We compare the proposed techniques on a set of acyclic and cyclic logic programs to evaluate their effectiveness. It is shown that the iteration method for PE, especially with sparse format, is the most efficient one in general cases. However, the decomposition method has the potential for future research to leverage eigenvalues and eigenvectors of program matrices for reasoning.
The inexpressive Description logic (DL) FL0, which has conjunction and value restriction as its only concept constructors, had fallen into disrepute when it turned out that reasoning in FL0 w.r.t. general TBoxes is Ex...
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We introduce extensions to Data Spatial programming (DSP) that enable scale-agnostic programming for application development. Building on DSP’s paradigm shift from "data-to-compute" to "compute-to-data...
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Automation plays an important and irreplaceable part in many modern industries, where automated control systems gather, analyze, and respond to information almost autonomously to increase productivity. The goal of thi...
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ISBN:
(数字)9798331542788
ISBN:
(纸本)9798331542795
Automation plays an important and irreplaceable part in many modern industries, where automated control systems gather, analyze, and respond to information almost autonomously to increase productivity. The goal of this project is to use the LogixPro 500 PLC simulator to teach ladder logic programming to students in the sixth term of the Mechatronics Engineering program easily and interactively by using real-life industrial process simulations. Students learn in a gamified environment by designing, implementing, and fixing automation systems. We covered electrical circuit wiring, timers, counters, and shift registers while evaluating the motivation and engagement of students using the MDI-EE and MUSIC instruments.
argumentation is a popular toolkit for modeling, evaluating, and comparing arguments. Relationships between arguments are specified in argumentation frameworks (AFs), and conditions are placed on sets (extensions) of ...
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Deep learning constitutes a fundamental pillar in the field of image recognition within autonomous vehicles (AVs), facilitating precise predictions based on unprocessed data. However, unlike human cognition, deep lear...
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ISBN:
(数字)9798331505929
ISBN:
(纸本)9798331505936
Deep learning constitutes a fundamental pillar in the field of image recognition within autonomous vehicles (AVs), facilitating precise predictions based on unprocessed data. However, unlike human cognition, deep learning models are susceptible to adversarial attacks. This paper proposes a novel approach, termed the Robust logic-infused Deep Learning (RLDL) Approach, designed for traffic sign recognition. RLDL employs Inductive logic programming (ILP) to derive logical rules from a combination of positive and negative examples. These rules are subsequently transformed into a matrix of logical constraints, allowing for the assessment of logical consistency in predictions. Then, this logical consistency is incorporated into the neural network through the loss function. This study explores the impact of integrating logical constraints into deep learning models on the reliability of vision tasks in AVs. Our experiments demonstrate that the proposed method substantially enhances the accuracy of recognising traffic signs under adversarial attacks.
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