Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET ( HAshtag recommendation using Sentence-to-Hashtag Embedding Translation ), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT ( Bidirectional Encoder Representation from Transformer ) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F -score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature ( generative models , unsupervised models, and attention-based supervised models ) by achieving up to 15% improvement in F -score for the hashtag recommendation task and 9% for the topic discovery task.

IFC TO CITYGML CONVERSION ALGORITHM BASED ON GEOMETRY AND SEMANTIC MAPPING

Geographic information system (GIS) is known traditionally for the modelling of two-dimensional (2D) geospatial analysis and therefore present information about the extensive spatial framework. On the other hand, building information modelling (BIM) is digital representation of building life cycle. The increasing use of both BIM and GIS simultaneously because of their mutual relationship, as well as their similarities, has resulted in more relationships between both worlds, therefore the need for their integration. A significant purpose of these similarities is importing BIM data into GIS to significantly assist in different design-related issues. However, currently this is challenging due to the diversity between the two worlds which includes diversity in coordinate systems, three-dimensional (3D) geometry representation, and semantic mismatch. This paper describes an algorithm for the conversion of IFC data to CityGML in order to achieve the set goal of sharing information between BIM and GIS domains. The implementation of the programme developed using python was validated using an IFC model (block HO2) of a student’s hostel, Kolej Tun Fatima (KTF). The conversion is based on geometric and semantic information mapping and the use of 3D affine transformation of IFC data from local coordinate system (LCS) to CityGML world coordinate system (WCS) (EPSG:4236). In order to bridge the gap between the two data exchange formats of BIM and GIS, we conducted geometry and semantic mapping. In this paper, we limited the conversion of the IFC model on level of details 2 (LOD2). The conversion will serve as a bridge toward the development of a software that will perform the conversion to create a strong synergy between the two domains for purpose of sharing information.

Computational reconstruction of mental representations using human behavior

Revealing the contents of mental representations is a longstanding goal of cognitive science. However, there is currently no general framework for providing direct access to representations of high-level visual concepts. We asked participants to indicate what they perceived in images synthesized from random visual features in a deep neural network. We then inferred a mapping between the semantic features of their responses and the visual features of the images. This allowed us to reconstruct the mental representation of virtually any common visual concept, both those reported and others extrapolated from the same semantic space. We successfully validated 270 of these reconstructions as containing the target concept in a separate group of participants. The visual-semantic mapping uncovered with our method further generalized to new stimuli, participants, and tasks. Finally, it allowed us to reveal how the representations of individual observers differ from each other and from those of neural networks.

Teaching Vocabulary Using Semantic Mapping Technique

The aim of this research is to find out the effectiveness of Semantic Mapping technique in teaching vocabulary. Therefore, the hypothesis in this research is the student who are taught by Semantic Mapping technique achieve better than before. The design used in this research is the Pre-Experiment Design. It also has several forms such as a One-Shot Case Research, One-Group Pretest-Posttest Design, One-Group Pretest and Posttest Design also Intact-Group Comparison. One-group pretest and posttest for this research, it means there will be a pretest before giving treatment to the sample and posttest after ending the treatment. In collecting data, Pre-test and Post-test are employed. The tests are multiple choices and complete the sentences. It consists of twenty questions. The right answer will be get 1 point and for the wrong answer is zero (0). The result of this research in pretest that the highest score was 16, the lower score was 9, the median was 13,00, the mode was 14 mean was 12,90, standard deviation was 1,863, range was 7 and total score of pre-test is 387. The posttest was the highest score was 20, the lower score was 14, the median was 17,00, the mode was 16 mean was 17,07, standard deviation was 1,388, range was 6 and total score of post-test is 512. The conclusion is drawn by analyzing the average scores of pretest and posttest by using t-test formula; Sig value is 0,000 < 0,05 it means that alternative hypothesis (HA) is accepted.

Congratulations ! JACIII Best Paper and Young Researcher Awards 2021

We are pleased to announce that the JACIII Awards of 2021 have been decided by the JACIII editorial boards. This year, the award winning papers were severely and fairly selected among 362 papers published in JACIII Vols. 22 (2018) to 24 (2020) and there was no entries that deserved the Best Review Paper award. The award ceremony was held online in order to prevent spreading of COVID-19. JACIII BEST PAPER AWARD 2021 Sotetsu Suzugamine, Takeru Aoki, Keiki Takadama, and Hiroyuki Sato Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cells JACIII Vol.24 No.2, pp. 185-198, 2020. JACIII YOUNG RESEARCHER AWARD 2021 JACIII YOUNG RESEARCHER AWARD 2021 Xiaobo Liu Jinxin Chi Emotion Recognition Based on Multi-Composition Deep Forest and Transferred Convolutional Neural Network Object-Oriented 3D Semantic Mapping Based on Instance Segmentation By Xiaobo Liu, Xu Yin, Min Wang, Yaoming Cai, and Guang Qi By Jinxin Chi, Hao Wu, and Guohui Tian JACIII Vol.23 No.5, pp. 883-890, 2019. JACIII Vol.23 No.4, pp. 695-704, 2019.