<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aonty, Shuhena Salam</style></author><author><style face="normal" font="default" size="100%">Deb, Kaushik</style></author><author><style face="normal" font="default" size="100%">Sarma, Moumita Sen</style></author><author><style face="normal" font="default" size="100%">Dhar, Pranab Kumar</style></author><author><style face="normal" font="default" size="100%">Shimamura, Tetsuya</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Biological system modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">bottom-up parsing</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Feature extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Location awareness</style></keyword><keyword><style  face="normal" font="default" size="100%">occlusion</style></keyword><keyword><style  face="normal" font="default" size="100%">Pose estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">skeletal keypoint</style></keyword><keyword><style  face="normal" font="default" size="100%">Solid modeling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">42343-42360</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uddin, Asif Mahbub</style></author><author><style face="normal" font="default" size="100%">Al Miraj, Abdullah</style></author><author><style face="normal" font="default" size="100%">Sen Sarma, Moumita</style></author><author><style face="normal" font="default" size="100%">Das, Avishek</style></author><author><style face="normal" font="default" size="100%">Gani, Md. Manjurul</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Traditional Bengali Food Classification Using Convolutional Neural Network</style></title><secondary-title><style face="normal" font="default" size="100%">2021 IEEE Region 10 Symposium (TENSYMP)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional Neural Network</style></keyword><keyword><style  face="normal" font="default" size="100%">Convolutional neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Data models</style></keyword><keyword><style  face="normal" font="default" size="100%">fine tuning</style></keyword><keyword><style  face="normal" font="default" size="100%">image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Image recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural networks</style></keyword><keyword><style  face="normal" font="default" size="100%">traditional Bengali foods</style></keyword><keyword><style  face="normal" font="default" size="100%">Transfer learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Turning</style></keyword><keyword><style  face="normal" font="default" size="100%">VGG16</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pages><style face="normal" font="default" size="100%">1-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anthony K. Seda</style></author><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Generalized Distance Functions in the Theory of Computation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">denotational semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">fixed-point theorems</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized distance functions</style></keyword><keyword><style  face="normal" font="default" size="100%">Logic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">stable model</style></keyword><keyword><style  face="normal" font="default" size="100%">supported model</style></keyword><keyword><style  face="normal" font="default" size="100%">topology</style></keyword><keyword><style  face="normal" font="default" size="100%">ultra-metrics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1093/comjnl/bxm108</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">443–464</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;We discuss a number of distance functions encountered in the theory of computation, including metrics, ultra-metrics, quasi-metrics, generalized ultra-metrics, partial metrics, d-ultra-metrics and generalized metrics. We consider their properties, associated fixed-point theorems and some general applications they have within the theory of computation. We consider in detail the applications of generalized distance functions in giving a uniform treatment of several important semantics for logic programs, including acceptable programs and natural generalizations of them, and also the supported model and the stable model in the context of locally stratified extended disjunctive logic programs and databases.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pascal Hitzler</style></author><author><style face="normal" font="default" size="100%">Frank van Harmelen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Reasonable Semantic Web</style></title><secondary-title><style face="normal" font="default" size="100%">Semantic Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Automated Reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Formal Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge representation</style></keyword><keyword><style  face="normal" font="default" size="100%">Linked Open Data</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.3233/SW-2010-0010</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">39–44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p class=&quot;rtejustify&quot;&gt;The realization of Semantic Web reasoning is central to substantiating the Semantic Web vision. However, current mainstream research on this topic faces serious challenges, which forces us to question established lines of research and to rethink the underlying approaches. We argue that reasoning for the Semantic Web should be understood as &quot;shared inference,&quot; which is not necessarily based on deductive methods. Model-theoretic semantics (and sound and complete reasoning based on it) functions as a gold standard, but applications dealing with large-scale and noisy data usually cannot afford the required runtimes. Approximate methods, including deductive ones, but also approaches based on entirely different methods like machine learning or natureinspired computing need to be investigated, while quality assurance needs to be done in terms of precision and recall values (as in information retrieval) and not necessarily in terms of soundness and completeness of the underlying algorithms.&lt;/p&gt;
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