Trevor Londt, Xiaoying Gao, Peter M. Andreae, Yi Mei
{"title":"XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks","authors":"Trevor Londt, Xiaoying Gao, Peter M. Andreae, Yi Mei","doi":"10.1007/978-981-99-8391-9_33","DOIUrl":"https://doi.org/10.1007/978-981-99-8391-9_33","url":null,"abstract":"","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"15 8","pages":"416-428"},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Approximating Solutions to the Knapsack Problem Using the Lagrangian Dual Framework","authors":"Mitchell Keegan, Mahdi Abolghasemi","doi":"10.1007/978-981-99-8388-9_37","DOIUrl":"https://doi.org/10.1007/978-981-99-8388-9_37","url":null,"abstract":"","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"55 15","pages":"455-467"},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improvement of Arc Consistency in Asynchronous Forward Bounding Algorithm","authors":"Rachid Adrdor, L. Koutti","doi":"10.1007/978-3-030-97546-3_47","DOIUrl":"https://doi.org/10.1007/978-3-030-97546-3_47","url":null,"abstract":"","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"1 1","pages":"582-591"},"PeriodicalIF":0.0,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42374380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"SG-Shuffle: Multi-aspect Shuffle Transformer for Scene Graph Generation","authors":"Anh Duc Bui, S. Han, Josiah Poon","doi":"10.48550/arXiv.2211.04773","DOIUrl":"https://doi.org/10.48550/arXiv.2211.04773","url":null,"abstract":". Scene Graph Generation (SGG) serves a comprehensive representation of the images for human understanding as well as visual understanding tasks. Due to the long tail bias problem of the object and predicate labels in the available annotated data, the scene graph generated from current methodologies can be biased toward common, non-informative relationship labels. Relationship can sometimes be non-mutually exclusive, which can be described from multiple perspectives like geometrical relationships or semantic relationships, making it even more challenging to predict the most suitable relationship label. In this work, we proposed the SG-Shuffle pipeline for scene graph generation with 3 components: 1) Parallel Transformer Encoder, which learns to predict object relationships in a more exclusive manner by grouping relationship labels into groups of similar purpose; 2) Shuffle Transformer, which learns to select the final relationship labels from the category-specific feature generated in the previous step; and 3) Weighted CE loss, used to alleviate the training bias caused by the imbalanced dataset.","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"1 1","pages":"87-101"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49414200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Vision Transformer Based Model for Describing a Set of Images as a Story","authors":"Zainy M. Malakan, G. Hassan, A. Mian","doi":"10.1007/978-3-031-22695-3_2","DOIUrl":"https://doi.org/10.1007/978-3-031-22695-3_2","url":null,"abstract":"","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"1 1","pages":"15-28"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47525714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tyche: A library for probabilistic reasoning and belief modelling in Python","authors":"Padraig X. Lamont","doi":"10.48550/arXiv.2208.09838","DOIUrl":"https://doi.org/10.48550/arXiv.2208.09838","url":null,"abstract":"This paper presents Tyche, a Python library to facilitate probabilistic reasoning in uncertain worlds through the construction, querying, and learning of belief models. Tyche uses aleatoric description logic (ADL), which provides computational advantages in its evaluation over other description logics. Tyche belief models can be succinctly created by defining classes of individuals, the probabilistic beliefs about them (concepts), and the probabilistic relationships between them (roles). We also introduce a method of observation propagation to facilitate learning from complex ADL observations. A demonstration of Tyche to predict the author of anonymised messages, and to extract author writing tendencies from anonymised messages, is provided. Tyche has the potential to assist in the development of expert systems, knowledge extraction systems, and agents to play games with incomplete and probabilistic information.","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"1 1","pages":"381-396"},"PeriodicalIF":0.0,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47013311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Hybrid Multiagent-Based Rescheduling Mechanism for Open and Stochastic Environments Concerning the Execution Stage","authors":"Yikun Yang, F. Ren, Minjie Zhang","doi":"10.1007/978-3-030-97546-3_45","DOIUrl":"https://doi.org/10.1007/978-3-030-97546-3_45","url":null,"abstract":"","PeriodicalId":91448,"journal":{"name":"Applied informatics","volume":"70 1","pages":"556-569"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73834248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}