Building Information Modeling (BIM) has transformed the Architecture, Engineering, and Construction (AEC) industry by digitizing project data, yet its full potential remains unrealized due to persistent gaps between virtual models and physical sites. These gaps contribute to inefficiencies, with studies reporting substantial waste in labor and coordination. Extended Reality (XR) technologies offer a promising solution by enabling immersive, real-scale visualization of BIM models on-site. This article introduces a Mixed Reality (MR) application for Microsoft HoloLens 2 that superimposes BIM representations onto construction environments at a 1:1 scale, supporting real-time detection of differences between the as-designed BIM model and the as-built construction on site. We present a robust registration pipeline that integrates commercial XR hardware with advanced algorithms to achieve precise alignment under challenging conditions. To validate the system, we conducted a controlled user study comparing three registration paradigms (manual gesture-based, QR-assisted, and fully automatic) and analyzing their impact on alignment accuracy and user experience (UX) in AEC-related tasks. Results show that our automatic approach provides advantages over state-of-the-art alternatives and significantly improves registration precision and usability ratings over the baseline methods. Furthermore, the study demonstrates that alignment errors strongly influence spatial perception and decision-making, highlighting the necessity of high-fidelity registration for effective MR integration in construction workflows.

The use of malware samples is usually required to test cyber security solutions. For that, the correct typology of the samples is of interest to properly estimate the exhibited performance of the tools under evaluation. Although several malware datasets are publicly available at present, most of them are not labeled or, if so, only one class or tag is assigned to each malware sample. We defend that just one label is not enough to represent the usual complex behavior exhibited by most of current malware. With this hypothesis in mind, and based on the varied classification generally provided by automatic detection engines per sample, we introduce here a simple multi-labeling approach to automatically tag the usual multiple behavior of malware samples. In the paper, we first analyze the coherence between the behaviors exhibited by a specific number of well-known malware samples dissected in the literature and the multiple tags provided for them by our labeling proposal. After that, the automatic multi-labeling scheme is executed over four public Android malware datasets, the different results and statistics obtained regarding their composition and representativeness being discussed. We share in a GitHub repository the multi-labeling tool developed, for public usage.

Abstract After several years, crypto‐ransomware attacks still constitute a principal threat for individuals and organisations worldwide. Despite the fact that a number of solutions are deployed to fight against this plague, one main challenge is that of early reaction, as merely detecting its occurrence can be useless to avoid the pernicious effects of the malware. With this aim, the authors introduced in a previous work a novel anti‐ransomware tool for Unix platforms named R‐Locker . The proposal is supported on a honeyfile‐based approach, where ‘infinite’ trap files are disseminated around the target filesystem for early detection and to effectively block the ransomware action. The authors extend here the tool with three main new contributions. First, R‐Locker is migrated to Windows platforms, where specific differences exist regarding FIFO handling. Second, the global management of the honeyfiles around the target filesystem is now improved to maximise protection. Finally, blocking suspicious ransomware is (semi)automated through the dynamic use of white‐/black‐lists. As in the original work for Unix systems, the new Windows version of R‐Locker shows high effectivity and efficiency in thwarting ransomware action.