Generating semantically similar permutations of questions by clustering by Famili, Kurniawan Aryanto

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Generating semantic descriptions from drawing of scenes with shadows. by Waltz, David L.

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Generating Semantic Description from Drawings of Scenes with Shadows by Waltz, David L.

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Generating Semantic Descriptions From Drawings of Scenes With Shadows by Waltz, David L.

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Concept graph based semantic matching of articles by Lin, Yanwen

“…In this project, Concept Interaction Graph (CIG), a model generating semantic graphs from articles, was studied.…”
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Scene understanding for autonomous vehicles with deep learning by Wang, Dongying

“…This project aimed to carry out algorithm which is able to generate semantic images and then fuse the semantic images with point cloud to obtain point cloud with semantic information for the scene understanding tasks. …”
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Automated generation of semantic descriptions for data services by Wang, Mengran.

“…As an effort to accelerate user adoption, a software tool “OWL-S Creator” is developed in this project, which enables the users to automatically generate semantic description for data web services. The OWL-S Creator bridges the gap for the automated creation of semantic service description with its streamlined Graphical User Interface, lightweight-ness and OWL-S-specificity.…”
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Semantic map for indoor positioning system (floorplan enhancement with semantic SLAM) by Lim, Han Quan

“…This project thus forms part of a system used to display a user’s position on a floorplan, as well as populate additional detections onto the floorplan using semantic SLAM by focusing on the frame matching problem between the machine generated semantically labelled octomap frame, and the floorplan frame.…”
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Graph-based privacy preference expression for the semantic web by Wagner, Ryan Randall, 1980-

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Similarity measure for retrieval of question items with multi-variable data sets by Che Hassan, Siti Hasrinafasya

“…In designing test question items assessment, similarity measures have a great influence in determining whether the test question items generated semantically match to the learning outcomes and the instructional objectives. …”
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Software agents : an application to the airline MRO industry by Poon, Arthur Wai Keong, 1979-

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Pose estimation using overhead imagery and semantics by Rouben, Yousif Farid Dawood.

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Applications of deep learning for physical layer of wireless communication systems by Xie, Yihang

“…The fourth research work introduces a novel communication system called the diffusion model-based generative semantic communication (DM-GSC) system. The system utilizes the diffusion model and semantic embedding to enable efficient and robust communication in wireless networks. …”
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Automation pipeline and its implementation from point cloud to building information modeling by Liu, Tianrui

“…One of the key innovative contributions of this work is the use of BIMNet, a neural network that can generate semantic and instance information for each point cloud data point. …”
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Semantic driven vulnerability detection and patch analysis by Xu, Zhengzi

“…We develop Vulmet, the prototype to generate semantic preserving patches. The experimental results show that 55 real-world vulnerabilities from the Android kernels have been successfully converted into hot patches, which incur little performance overhead after being applied to the system. …”
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Synthesizing photorealistic images with deep generative learning by Zheng, Chuanxia

“…Part 2 presents approaches that generate semantically reasonable content for masked regions. …”
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Fast and robust visual SLAM for dynamic environments by Singh Gaurav

“…The focus of SLAM has recently shifted towards vision-based approaches, which can provide higher robustness as well as the ability to generate semantically-rich maps. However, visual SLAM (vSLAM) systems suffer from high computational complexity and are unable to deal with dynamic objects in the scene and changing scene conditions. …”
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A methodology to support UML-B model development by Jasser, Muhammed Basheer

“…The method is formalised and verified via a generic proof on a theoretical level to show how the generated semantic implicit invariants and the shared variable formalism are preserved, and to prove that any recomposed machine is indeed correct and a refinement of the original decomposable machine when the method phases conditions and rules are followed. …”
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Automatic program analysis and verification and their applications in smart contracts by Jiao, Jiao

“…The general semantic model is validated with its interpretation in Solidity using the Solidity compiler test set and evaluation results show that it is complete and correct. Lastly, the generated semantics facilitates the formal verification of smart contracts written in a particular high-level programming language as a formal specification of the corresponding language. …”
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