Self-adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location-aware navigation or...
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Self-adaptive applications are becoming increasingly attractive, with the ability to smartly understand their runtime environments (or contexts) and deliver adaptive services, for example, location-aware navigation or resource-sensitive suggestions. However, due to inherent noises in the process of sensing and interpreting environmental information, there is a growing demand for guarding the consistency of collected contexts to avoid application misbehaviour and, at the same time, minimize extra costs. Existing work attempted to achieve this by speeding up the kernel constraint checking module inside the consistency guarding process. Most of these efforts were spent on reusing previous checking results or parallelizing the checking process, but they all leave one central step of constraint checking, that is, link generation, untouched. In this step, the checking engine provides reasons to explain the violation of constraints under check. It occupies a substantial part of the total time cost. Focusing on this key link generation step, we proposed MG, which deploys a rigourous analysis to automatically identify and avoid redundancy in the link generation without harming any correctness of the checking results. MG has been proven sound (always guaranteeing correctness) and complete (entirely removing redundancy). Moreover, based on our observation that MG's redundancy elimination also assists another core step of constraint checking to reduce unnecessary computation further, we additionally enhance MG with an escape-condition optimization to escape unnecessary evaluation of truth values to further improve the efficiency of constraint checking in an aspect other than link generation. We call it MG+ for distinguishing. Our experiments with synthesized and real-world consistency constraints reported that, compared with existing work, MG eliminates all link redundancy (83% to 0%), and based on it, MG+ further reduces significant truth value calculations (e.g., 49.74% reduc
Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature o...
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The advances in 3D reconstruction technology, such as photogrammetry and LiDAR scanning, have made it easier to reconstruct accurate and detailed 3D models for urban scenes. Nevertheless, these reconstructed models of...
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To alleviate the shortage of computing power faced by clients in training deep neural networks (DNNs) using federated learning (FL), we leverage the edge computing and split learning to propose a model-splitting allow...
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Network traffic anomaly detection plays a crucial role in today's network security and performance management. In response to the challenges in current network traffic data processing, such as insufficient structu...
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Context: Open-source software development has become a widely adopted approach to software creation. However, developers’ activities extend beyond social coding platforms (e.g., GitHub), encompassing social Q&A p...
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Context: Open-source software development has become a widely adopted approach to software creation. However, developers’ activities extend beyond social coding platforms (e.g., GitHub), encompassing social Q&A platforms (e.g., StackOverflow) and social media platforms (e.g., Twitter). Therefore, cross-platform research is essential for a deeper understanding of the nature of software development activities. Objective: This paper focuses on open-source platforms and systematically summarizes relevant cross-platform research. It aims to assess the current state of cross-platform research and provide insights into the challenges and future developments in this field. Method: This paper reviews 69 cross-platform research papers related to open-source software from 2013 to 2024, with a focus on several key areas, including platform interconnections, research themes, experimental design methods, challenges and research opportunities. Results: Through the analysis of 69 papers, we found that cross-platform research primarily involves platforms such as social coding, social Q&A, and social media. Researchers typically rely on information traces, including user personal info, technical info, project/post/bug report metadata, interaction info, to facilitate connections between platforms. Cross-platform research in the open-source domain mainly focuses on problem classification and feature extraction. The predominant research methods include data-driven approaches, qualitative studies, modeling and machine learning, and tool development and implementation. Despite these advancements, common challenges remain, such as subjective evaluation bias in manual data classification, insufficient data source coverage, and inaccurate data recognition. Future research opportunities may focus on increasing the diversity of data sources, improving data recognition accuracy, optimizing data classification methods, and clarifying user skill requirements. Conclusions: Based on our findings, w
The current urban intelligent transportation is in a rapid development stage, and coherence control of vehicle formations has important implications in urban intelligent transportation research. This article focuses o...
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Multiple patterning lithography (MPL) has been introduced in the integrated circuits manufacturing industry to enhance feature density as the technology node advances. A crucial step of MPL is assigning layout feature...
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The recent transport measurements of La_(3)Ni_(2)O_(7)uncovered a“right-triangle”shape of the superconducting dome in the pressure-temperature(P-T)phase *** by this,we perform theoretical first-principles studies of...
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The recent transport measurements of La_(3)Ni_(2)O_(7)uncovered a“right-triangle”shape of the superconducting dome in the pressure-temperature(P-T)phase *** by this,we perform theoretical first-principles studies of La_(3)Ni_(2)O_(7)with the pressure ranging from 0 to 100 ***,we reveal a pressure dependence of the Ni-d_(z^(2))electron density at the Fermi energy(n_(z)^(E_(F)))that highly coincides with such *** this basis,we further explore the electronic structure under uniaxial *** tracking the stress response of n_(z)^(E_(F)),we propose that superconductivity can be achieved by applying only ~2GPa of compression along the c *** idea is further exemplified from the perspectives of lattice distortion,band structure,Fermi surface and superconducting phase *** also discuss the possible charge modulation under the stress and provide an insight into the relation between nz E Fand the superconducting T_(c)in La_(3)Ni_(2)O_(7)*** study provides new routes to the search of high-T_(c)superconductors in future experiments.
Enhanced regular expressions (EREs), which extend standard regular expressions with shuffle and counting operators, provide exponentially more succinct descriptions of regular languages. The membership problem, determ...
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