This paper addresses the problem of ground node localization in satellite-denied environments by employing unmanned aerial vehicles (UAVs) as mobile beacons. Existing approaches often neglect the joint impact of node prior uncertainty, UAV dynamic errors, and TOA/FOA measurement noise. To this end, we propose a joint localization algorithm, termed SDTL-q, based on maximum likelihood estimation. The method integrates time-of-arrival (TOA), frequency-of-arrival (FOA), UAV motion models, and prior constraints, and is solved using the Gauss-Newton method. A corresponding Cramér-Rao lower bound (CRLB) is derived under coupled error conditions. Simulation results demonstrate that SDTL-q achieves over 30% improvement in positioning accuracy under high-quality priors and maintains approximately 1.8 m accuracy even with degraded priors, indicating strong robustness to prior uncertainty. In addition, analysis of the sampling period reveals the trade-off between measurement frequency and energy efficiency. These findings highlight the applicability of the proposed algorithm in real-world UAV-assisted localization scenarios with imperfect prior information.

Mutation faults are the core of mutation testing and have been widely used in many software testing tasks. Hence, efficiently constructing high-quality mutation faults is critical. To address the effectiveness limitations of traditional and deep learning-based mutation techniques, we first proposed LEAM , utilizing a syntax-guided encoder–decoder architecture with extended grammar rules. While LEAM significantly enhances the effectiveness, it does not consider the associated testing cost. To further improve the efficiency of LEAM , we propose LEAM++ , adopting a novel selective mutation fault construction module based on the probability of grammar rule sequences and the similarity of mutation faults. We extensively evaluate LEAM++ using Defects4J. Regarding effectiveness, the results demonstrate that the mutation faults constructed by LEAM++ can better represent real faults than two traditional techniques ( Major and PIT ) and the deep learning-based technique ( DeepMutation ), and substantially boost three downstream applications, i.e., mutation-based test case prioritization, mutation-based fault localization, and mutation-based bug detection. Regarding efficiency, LEAM++ demonstrates superiority over the four selective mutation testing techniques across three scenarios, i.e., mutation testing, mutation-based test case prioritization, and mutation-based fault localization. Our work serves as an important step toward the efficiently automated construction of mutation faults.

Compiler testing is an important task for assuring the quality of compilers, but investigating test failures is very time-consuming. This is because many test failures are caused by the same compiler bug (known as bug duplication problem). In particular, this problem becomes much more challenging on silent compiler bugs (also called wrong code bugs), since these bugs can provide little information (unlike crash bugs that can produce error messages) for bug de-duplication. In this work, we propose a novel technique (called D3) to solve the duplication problem on silent compiler bugs. Its key insight is to characterize the silent bugs from the testing process and identify three-dimensional information (i.e., test program, optimizations, and test execution) for bug de-duplication. However, there are huge amount of bug-irrelevant details on the three dimensions, D3 then systematically conducts causal analysis to identify bug-causal features from each of the three dimensions for more accurate bug de-duplication. Finally, D3 ranks the test failures that are more likely to be caused by different silent bugs higher by measuring the distance among test failures based on the three-dimensional bug-causal features. Our experimental results on four datasets (including duplicate bugs of both GCC and LLVM) demonstrate the significant superiority of D3 over the two state-of-the-art compiler bug de-duplication techniques, achieving the average improvement of 19.36% and 51.43% in identifying unique silent compiler bugs when analyzing the same number of test failures.

Recent progresses on Facial Expression Recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertainties caused by ambiguous expressions, low-quality facial images, and the subjectiveness of annotators, which limits FER performance. To address this challenge, this paper introduces novel Rayleigh and weighted-softmax loss from two aspects. First, we propose Rayleigh loss to extract discriminative representation, which aims at minimizing within-class distances and maximizing inter-class distances simultaneously. Moreover, Rayleigh loss has a Euclidean form which make it easily be optimized with SGD and be combined with other forms. Second, we introduce a weight to measure the uncertainty of a given sample, by considering its distance to class center. Extensive experiments on RAF-DB, FERPlus and AffectNet show the effectiveness of our method with SOTA performance.

BACKGROUND: Quantitative areas is of great measurement of wound significance in clinical trials, wound pathological analysis, and daily patient care. 2D methods cannot solve the problems caused by human body curvatures and different camera shooting angles. Our objective is to simply collect wound areas, accurately measure wound areas and overcome the shortcomings of 2D methods. RESULTS: We propose a method with 3D transformation to measure wound area on a human body surface, which combines structure from motion (SFM), least squares conformal mapping (LSCM), and image segmentation. The method captures 2D images of wound, which is surrounded by adhesive tape scale next to it, by smartphone and implements 3D reconstruction from the images based on SFM. Then it uses LSCM to unwrap the UV map of the 3D model. In the end, it utilizes image segmentation by interactive method for wound extraction and measurement. Our system yields state-of-the-art results on a dataset of 118 wounds on 54 patients, and performs with an accuracy of 0.97. The Pearson correlation, standardized regression coefficient and adjusted R square of our method are 0.999, 0.895 and 0.998 respectively. CONCLUSIONS: A smartphone is used to capture wound images, which lowers costs, lessens dependence on hardware, and avoids the risk of infection. The quantitative calculation of the 3D wound area is realized, solving the challenges that 2D methods cannot and achieving a good accuracy.

*: Background Consisting of dictated free-text documents such as discharge summaries, medical narratives are widely used in medical natural language processing. Relationships between anatomical entities and human body parts are crucial for building medical text mining applications. To achieve this, we establish a mapping system consisting of a Wikipedia-based scoring algorithm and a named entity normalization method (NEN). The mapping system makes full use of information available on Wikipedia, which is a comprehensive Internet medical knowledge base. We also built a new ontology, Tree of Human Body Parts (THBP), from core anatomical parts by referring to anatomical experts and Unified Medical Language Systems (UMLS) to make the mapping system efficacious for clinical treatments. *: Result The gold standard is derived from 50 discharge summaries from our previous work, in which 2,224 anatomical entities are included. The F1-measure of the baseline system is 70.20%, while our algorithm based on Wikipedia achieves 86.67% with the assistance of NEN. *: Conclusions We construct a framework to map anatomical entities to THBP ontology using normalization and a scoring algorithm based on Wikipedia. The proposed framework is proven to be much more effective and efficient than the main baseline system.