The Exploration of the Application and Management of Project Cost in Smart Buildings Using BIM Technology

In recent years, the slow development of engineering construction management in China has been attributed to outdated management models and low level of informatization. To address the deficiencies in project cost management, relevant departments have proposed the application of BIM technology. BIM technology encompasses a wide range of areas, including planning, construction processes, and cost management. It enables the simulation of these processes to create dynamic real-time building models. Additionally, BIM technology facilitates the rapid transmission of various information during the construction process, thereby improving the overall efficiency of the construction project. Information technology has become ubiquitous in people’s daily lives, and this foundation has led to the emergence of smart buildings. The operation and development of smart building projects require effective project cost management. Accurate cost estimation can help construction enterprises effectively control project costs and increase economic benefits. However, many companies still rely on traditional methods such as manual measurement based on drawings, bill of quantities, or engineering rates, which often lead to calculation errors. The application of BIM technology in project cost management can help alleviate this problem

Architecture Information Communication in Two OSS Projects: the Why, Who, When, and What

Architecture information is vital for Open Source Software (OSS) development, and mailing list is one of the widely used channels for developers to share and communicate architecture information. This work investigates the nature of architecture information communication (i.e., why, who, when, and what) by OSS developers via developer mailing lists. We employed a multiple case study approach to extract and analyze the architecture information communication from the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during their development life-cycle of over 18 years. Our main findings are: (a) architecture negotiation and interpretation are the two main reasons (i.e., why) of architecture communication; (b) the amount of architecture information communicated in developer mailing lists decreases after the first stable release (i.e., when); (c) architecture communications centered around a few core developers (i.e., who); (d) and the most frequently communicated architecture elements (i.e., what) are Architecture Rationale and Architecture Model. There are a few similarities of architecture communication between the two OSS projects. Such similarities point to how OSS developers naturally gravitate towards the four aspects of architecture communication in OSS development.Comment: Preprint accepted for publication in Journal of Systems and Software, 202

Spectral Adversarial Training for Robust Graph Neural Network

Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as adversarial examples. To address this issue, robust training methods against adversarial examples have received considerable attention in the literature. \emph{Adversarial Training (AT)} is a successful approach to learning a robust model using adversarially perturbed training samples. Existing AT methods on GNNs typically construct adversarial perturbations in terms of graph structures or node features. However, they are less effective and fraught with challenges on graph data due to the discreteness of graph structure and the relationships between connected examples. In this work, we seek to address these challenges and propose Spectral Adversarial Training (SAT), a simple yet effective adversarial training approach for GNNs. SAT first adopts a low-rank approximation of the graph structure based on spectral decomposition, and then constructs adversarial perturbations in the spectral domain rather than directly manipulating the original graph structure. To investigate its effectiveness, we employ SAT on three widely used GNNs. Experimental results on four public graph datasets demonstrate that SAT significantly improves the robustness of GNNs against adversarial attacks without sacrificing classification accuracy and training efficiency.Comment: Accepted by TKDE. Code availiable at https://github.com/EdisonLeeeee/SA

Infinitely many solutions for a quasilinear Schrödinger equation with Hardy potentials

In this article, we study the following quasilinear Schr\"odinger equation \begin{equation*} -\Delta u-\mu\frac{u}{|x|^{2}}+V(x)u-(\Delta(u^{2}))u=f(x,u),\qquad x\in \mathbb{R}^{N}, \end{equation*} where $V(x)$ is a given positive potential and the nonlinearity $f(x,u)$ is allowed to be sign-changing. Under some suitable assumptions, we obtain the existence of infinitely many nontrivial solutions by a change of variable and Symmetric Mountain Pass Theorem