A REVIEW OF THE DISTINGUISHING FEATURES OF THE HISTORICAL BUILDINGS IN SAFRANBOLU REGION FOR THE PURPOSE OF CLASSIFICATION FOR SEMANTICALLY ENHANCED 3D BUILDING MODEL

In addition to making our daily life easier with the use of it in different areas of our lives, technology continues to be used increasingly with different applications in many sectors.With the increase of developments in the construction sector, which is the locomotive of many sectors, different applications have been used in the field of modelling. But a building needs many projects such as static, dynamic, electricity, installation, furniture, etc. While these sectors are working with different software specific to them, it has been possible to work on these building projects in a single project by the help of BIM (Building Information Modelling).In addition to its function of projecting new buildings, BIM is an important development and building model in terms of preserving historical buildings, easily creating original building details, and transferring them to future generations without deterioration. The term HBIM (Historic / Heritage Building Information Modelling) has been brought to the literature by using the BIM model in historical buildings.The known history of Safranbolu district of Karabük in Turkey dates back to 3000 years. Safranbolu, which has hosted Roman, Byzantine, Seljuk and Ottoman empires in its history, has buildings that are still preserved with their originality. These structures were built in the pre-Ottoman, the Ottoman and the Republic periods.In our study, historical buildings such as houses, commercial houses, places of worship, inns, baths, fountains, and clock towers will be examined. Building models and distinctive features were examined to classify these structures by modeling them with BIM.While the differentiation of the buildings can be made easily by the building model, the distinguishing features of the houses built in different periods or by different civilizations were also determined in order to distinguish the housing types.While structures such as baths, clock towers or inns are not in a number that can be classified, it has been observed that there are residences, businesses and places of worship that can be classified. It has been determined that it is possible to classify the buildings by their materials, building sizes and shapes, and by their other features that can be used to classify.

European Journal of Taxonomy: a deeper look into a decade of data

The European Journal of Taxonomy (EJT) is a decade-old journal dedicated to the taxonomy of living and fossil eukaryotes. Launched in 2011, the EJT published exactly 900 articles (31 778 pages) from 2011 to 2021. The journal has been processed in its entirety by Plazi, liberating the data therein, depositing it into TreatmentBank, Biodiversity Literature Repository and disseminating it to partners, including the Global Biodiversity Information Facility (GBIF) using a combination of a highly automated workflow, quality control tools, and human curation. The dissemination of original research along with the ability to use and reuse data as freely as possible is the key to innovation, opening the corpus of known published biodiversity knowledge, and furthering advances in science. This paper aims to discuss the advantages and limitations of retro-conversion and to showcase the potential analyses of the data published in EJT and made findable, accessible, interoperable and reusable (FAIR) by Plazi. Among others, taxonomic and geographic coverage, geographical distribution of authors, citation of previous works and treatments, timespan between the publication and treatments with their cited works are discussed. Manually counted data were compared with the automated process, the latter being analysed and discussed. Creating FAIR data from a publication results in an average multiplication factor of 166 for additional access through the taxonomic treatments, figures and material citations citing the original publication in TreatmentBank, the Biodiversity Literature Repository and the Global Biodiversity Information Facility. Despite the advances in processing, liberating data remains cumbersome and has its limitations which lead us to conclude that the future of scientific publishing involves semantically enhanced publications.

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

Knowledge-Based System for Crop Pests and Diseases Recognition

With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%.

Improving Human Happiness Analysis based on Transfer Learning：Algorithm Development and Validation (Preprint)

BACKGROUND Happiness refers to the joyful and pleasant emotions that humans produce subjectively. It is the positive part of emotions, and it affects the quality of human life. Therefore, understanding human happiness is a meaningful task in sentiment analysis. We mainly discuss two facets (Agency/Sociality) of happiness in this study. Through analysis and research on happiness, we can expand on new concepts that define happiness and enrich our understanding of emotions. OBJECTIVE In this paper, we treated each happy moment as a sequence of short sentences, then proposed a short happiness detection model based on transfer learning to analyze the Agency and Sociality aspects of happiness. METHODS Happiness analysis is a novel and challenging research task. However, the current dataset in the field of happiness is small. To solve this problem，we utilized the unlabeled training set and transfer learning to train a semantically enhanced language model in the target domain. Then, the trained language model with domain characteristics was further combined with other deep learning models to obtain various models. Finally, we used the improved voting strategy to further improve the experimental results. RESULTS The proposed approach was evaluated on the public dataset. Experimental results showed that our approach significantly outperforms the baselines. When predicting the Agency aspect of happiness, our approach achieved an accuracy of 0.8574 and an F1 score of 0.90, repectively. When predicting Sociality, our approach achieved an accuracy of 0.928 and an F1 score of 0.9360, respectively. CONCLUSIONS Through the evaluation of the dataset, the comparison results demonstrated the effectiveness of our approach for happiness analysis. Experimental results confirmed that our method achieved state-of-the-art performance and transfer learning effectively improved happiness analysis.

Impact of Deep Learning on Semantic Sentiment Analysis

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.