Teaching Practice and Research on BIM-Based Assembled Building Measurement and Valuation

As the construction industry and information technology are being deeply integrated, it is of great practical significance to rapidly develop the practical training course Measurement and Valuation of Prefabricated Construction based on building information modeling (BIM). In this paper, we comprehensively analyze the status quo of teaching of the course Measurement and Valuation of Prefabricated Construction in China as well as the existing problems about the BIM-integrated teaching. Further, we take into account four aspects as per the talent training program: course system setup, syllabus development, teacher team building, and construction of a BIM-based construction cost practice base. Then, we put forward a teaching reform mode of the course Measurement and Valuation of Prefabricated Construction, namely a new "five-in-one" teaching mode, which matches new technology and adapts to the transformation and upgrading of China's construction industry. We perform a teaching reform in the BIM-based practical training course Measurement and Valuation of Prefabricated Construction for construction cost majors in School of Construction and Engineering at a Guangxi university. This reform is fruitful and provides a theoretical reference for carrying out the similar reform across China. Keywords: prefabricated construction; measurement and valuation course; BIM; teaching practice and research DOI: 10.7176/JEP/10-27-01 Publication date:September 30th 201

Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection

Most of existing methods for few-shot object detection follow the fine-tuning paradigm, which potentially assumes that the class-agnostic generalizable knowledge can be learned and transferred implicitly from base classes with abundant samples to novel classes with limited samples via such a two-stage training strategy. However, it is not necessarily true since the object detector can hardly distinguish between class-agnostic knowledge and class-specific knowledge automatically without explicit modeling. In this work we propose to learn three types of class-agnostic commonalities between base and novel classes explicitly: recognition-related semantic commonalities, localization-related semantic commonalities and distribution commonalities. We design a unified distillation framework based on a memory bank, which is able to perform distillation of all three types of commonalities jointly and efficiently. Extensive experiments demonstrate that our method can be readily integrated into most of existing fine-tuning based methods and consistently improve the performance by a large margin

Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods mainly extract multi-modal features (e.g., appearance, object semantics, human pose) and then fuse them together to directly predict HOI triplets. However, most of these methods focus on seeking for self-triplet aggregation, but ignore the potential cross-triplet dependencies, resulting in ambiguity of action prediction. In this work, we propose to explore Self- and Cross-Triplet Correlations (SCTC) for HOI detection. Specifically, we regard each triplet proposal as a graph where Human, Object represent nodes and Action indicates edge, to aggregate self-triplet correlation. Also, we try to explore cross-triplet dependencies by jointly considering instance-level, semantic-level, and layout-level relations. Besides, we leverage the CLIP model to assist our SCTC obtain interaction-aware feature by knowledge distillation, which provides useful action clues for HOI detection. Extensive experiments on HICO-DET and V-COCO datasets verify the effectiveness of our proposed SCTC

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature

Cast Shadow Detection for Surveillance System Based on Tricolor Attenuation Model

Abstract. Shadows bring some undesirable problems in computer vision, such as object detecting in outdoor scenes. In this paper, we propose a novel method for cast shadow detecting for moving target in surveillance system. This measure is based on tricolor attenuation model, which describes the relationship of three color channel&apos;s attenuation in image when shadow happens. According to this relationship, the cast shadow is removed from the detected moving area, only the target area is left. Some experiments were done, and their results validate the performance of our method

RGBD Salient Object Detection via Deep Fusion

Numerous efforts have been made to design different low level saliency cues for the RGBD saliency detection, such as color or depth contrast features, background and color compactness priors. However, how these saliency cues interact with each other and how to incorporate these low level saliency cues effectively to generate a master saliency map remain a challenging problem. In this paper, we design a new convolutional neural network (CNN) to fuse different low level saliency cues into hierarchical features for automatically detecting salient objects in RGBD images. In contrast to the existing works that directly feed raw image pixels to the CNN, the proposed method takes advantage of the knowledge in traditional saliency detection by adopting various meaningful and well-designed saliency feature vectors as input. This can guide the training of CNN towards detecting salient object more effectively due to the reduced learning ambiguity. We then integrate a Laplacian propagation framework with the learned CNN to extract a spatially consistent saliency map by exploiting the intrinsic structure of the input image. Extensive quantitative and qualitative experimental evaluations on three datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods.Comment: This paper has been submitted to IEEE Transactions on Image Processin

Bumblebees with big teeth: revising the subgenus Alpigenobombus with the good, the bad and the ugly of numts (Hymenoptera: Apidae)

Williams, Paul H., Dorji, Phurpa, Huang, Jiaxing, Jaffar, Saleem, Japoshvili, George, Narah, Jaya, Ren, Zongxin, Streinzer, Martin, Thanoosing, Chawatat, Tian, Li, Orr, Michael C. (2023): Bumblebees with big teeth: revising the subgenus Alpigenobombus with the good, the bad and the ugly of numts (Hymenoptera: Apidae). European Journal of Taxonomy 892 (1): 1-65, DOI: https://doi.org/10.5852/ejt.2023.892.2283, URL: https://europeanjournaloftaxonomy.eu/index.php/ejt/article/download/2283/985