Search Results (635)

Early software defect prediction research primarily relied on software metric features. However, such features struggle to fully capture semantic information in source code. Consequently, methods based on semantic features have gradually become mainstream. Nevertheless, the empirical value of traditional metric features remains significant. To address this, this paper proposes a multi-modal feature fusion method based on a cross-attention gating mechanism for software defect prediction (GMCA-SDP). This method aims to effectively fuse multiple modal features of source code to improve defect prediction performance. Unlike previous approaches, GMCA-SDP can simultaneously integrate traditional metric features and various code semantic features. During the feature fusion stage, it considers both the contribution differences among different types of features and the information interaction between modalities. This paper selects three types of modal features as input for GMCA-SDP: traditional metric features, semantic features extracted from the abstract syntax tree, and structural features extracted from the control flow graph. Our experiments on nine open-source projects demonstrate that the GMCA-SDP method outperforms six mainstream defect prediction models, with average improvements of 18.7% in F1, 10.9% in AUC, and 14.1% in G-mean. Full article

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new ($\xi$, $ϵ$)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the ($\xi$, $ϵ$)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications. Full article

In graph theory and network design, the minimum cut is a fundamental measure of system connectivity and communication capacity. While prior research has largely focused on computing the minimum cut for a fixed source–sink pair, practical scenarios such as data center communication often demand a different objective: identifying the source node whose minimum cut to a designated sink is maximized. This task, which we term the Global Maximum Minimum Cut with Fixed Sink (GMMC-FS) problem, captures the goal of locating a high-capacity source relative to a shared sink node that aggregates multiple servers. The problem is of significant engineering importance, yet it is computationally challenging as it involves a nested max–min optimization. In this paper, we present a recursive reduction (RR) algorithm for solving the GMMC-FS problem. The key idea is to iteratively select pivot nodes, compute their minimum cuts with respect to the sink, and prune dominated candidates whose cut values cannot exceed that of the pivot. By recursively applying this elimination process, RR dramatically reduces the number of max-flow computations required while preserving exact correctness. Compared with classical contraction-based and Gomory–Hu tree approaches that rely on global cut enumeration, the proposed RR framework offers a more direct and scalable mechanism for identifying the source that maximizes the minimum cut to a fixed sink. Its novelty lies in exploiting the structural properties of the sink side of suboptimal cuts, which leads to both theoretical efficiency and empirical robustness across large-scale networks. We provide a rigorous theoretical analysis establishing both correctness and complexity bounds, and we validate the approach through extensive experiments. Results demonstrate that RR consistently achieves optimal solutions while significantly outperforming baseline methods in runtime, particularly on large and dense networks. Full article

Non-coding RNAs (ncRNAs) are involved in many biological processes, making their identification and functional characterization a priority. Among them, long non-coding RNAs (lncRNAs) have been shown to regulate diverse cellular processes, such as cell development, stress response, and transcriptional regulation. The continued identification of new lncRNAs highlights the demand for reliable methods for their detection, with structural analysis offering insightful information. Currently, lncRNAs are identified using tools such as LncFinder, whose database has a large collection of lncRNAs from humans, mice, and chickens, among others. In this work, we present a graph-based algorithm to represent and compare RNA secondary structures. Rooted tree graphs were used to compare two groups of Saccharomyces cerevisiae RNA sequences, lncRNAs and not lncRNAs, by searching for structural similarities between each group. When applied to a novel candidate sequence dataset, the algorithm evaluated whether characteristic structures identified in known lncRNAs recurred. If so, the sequences were classified as likely lncRNAs. These results indicate that graph-based structural analysis offers a complementary methodology for identifying lncRNAs and may complement existing sequence-based tools such as lncFinder or PreLnc. Recent studies have shown that tumor cells can secrete lncRNAs into human biological fluids forming circulating lncRNAs which can be used as biomarkers for cancer. Our algorithm could be applied to identify novel lncRNAs with structural similarities to those associated with tumor malignancy. Full article

Farmland ecosystems present complex pest–predator co-occurrence patterns, posing significant challenges for image-based multitarget recognition and ecological modeling in sensor-driven computer vision tasks. To address these issues, this study introduces a tree-guided Transformer framework enhanced with a knowledge-augmented co-attention mechanism, enabling effective feature extraction from sensor-acquired images. A hierarchical ecological taxonomy (Phylum–Family Species) guides prompt-driven semantic reasoning, while an ecological knowledge graph enriches visual representations by embedding co-occurrence priors. A multimodal dataset containing 60 pest and predator categories with annotated images and semantic descriptions was constructed for evaluation. Experimental results demonstrate that the proposed method achieves 90.4% precision, 86.7% recall, and 88.5% F1-score in image classification, along with 82.3% hierarchical accuracy. In detection tasks, it attains 91.6% precision and 86.3% mAP@50, with 80.5% co-occurrence accuracy. For hierarchical reasoning and knowledge-enhanced tasks, F1-scores reach 88.5% and 89.7%, respectively. These results highlight the framework’s strong capability in extracting structured, semantically aligned image features under real-world sensor conditions, offering an interpretable and generalizable approach for intelligent agricultural monitoring. Full article

Background: Supply chain disruptions from natural hazards, geopolitical tensions, or global events, such as the COVID-19 pandemic, can trigger widespread shortages, with particularly severe consequences in healthcare through drug supply interruptions. Existing methods to assess shortage risks include scoring, simulation, and machine learning, but these approaches face limitations in interpretability, scalability, or computational cost. This study explores the application of probabilistic risk assessment (PRA), a method widely used in high-reliability industries, to evaluate pharmaceutical supply chain risks. Methods: We developed the supply chain probabilistic risk assessment framework and tool, which integrates facility-level failure probabilities and flow data to construct and quantify fault trees and network graphs. Using FDA inspection data from drug manufacturing facilities, the framework generates shortage risk profiles, performs uncertainty analysis, and computes importance measures to rank facilities by risk significance. Results: SUPRA quantified 7567 supply chain models in under eight seconds, producing facility-level importance measures and shortage risk profiles that highlight critical vulnerabilities. The tool demonstrated scalability, interpretability, and efficiency compared with traditional simulation-based methods. Conclusions: PRA offers a systematic, data-driven approach for shortage risk assessment in supply chains. SUPRA enables decision-makers to anticipate vulnerabilities, prioritize mitigation strategies, and strengthen resilience in critical sectors such as healthcare. Full article

The global push for electrification and decarbonization has sharply increased demand for critical raw materials—especially lithium and copper—heightening financial and strategic pressures on firms that lead these supply chains. Yet, the systemic financial risks arising from inter-firm interdependencies in this sector remain largely unexplored. This article presents a novel distance-based network framework to analyze systemic risk among the world’s top 15 lithium and copper producers (2020–2024). Firms are represented through standardized vectors of profitability and risk indicators (liquidity–solvency), from which we construct a two-layer similarity network using Euclidean distances. Graph-theoretic tools—including Minimum Spanning Tree, eigenvector centrality, modularity detection, and contagion simulations—reveal the structural properties and transmission pathways of financial shocks. The results show a robust-yet-fragile topology: while stable under minor perturbations, the network is highly vulnerable to failures of central firms. These findings highlight the utility of distance-based network models in uncovering hidden fragilities in critical commodity sectors, offering actionable insights for macroprudential regulators, investors, and corporate risk managers amid growing geopolitical and financial entanglement. Full article

The constrained bi-objective Minimum Spanning Tree (MST) problem is a fundamental challenge in network design, as it simultaneously requires minimizing both total edge weight and maximum hop distance under strict feasibility limits; however, most existing algorithms tend to emphasize one objective over the other, resulting in imbalanced solutions, limited Pareto fronts, or poor scalability on larger instances. To overcome these shortcomings, this study introduces a Hybrid MOCPO–AGE-MOEA algorithm that strategically combines the exploratory strength of Multi-Objective Crested Porcupines Optimization (MOCPO) with the exploitative refinement of the Adaptive Geometry-based Evolutionary Algorithm (AGE-MOEA), while a Kruskal-based repair operator is integrated to strictly enforce feasibility and preserve solution diversity. Moreover, through extensive experiments conducted on Euclidean graphs with 11–100 nodes, the hybrid consistently demonstrates superior performance compared with five state-of-the-art baselines, as it generates Pareto fronts up to four times larger, achieves nearly 20% reductions in hop counts, and delivers order-of-magnitude runtime improvements with near-linear scalability. Importantly, results reveal that allocating 85% of offspring to MOCPO exploration and 15% to AGE-MOEA exploitation yields the best balance between diversity, efficiency, and feasibility. Therefore, the Hybrid MOCPO–AGE-MOEA not only addresses critical gaps in constrained MST optimization but also establishes itself as a practical and scalable solution with strong applicability to domains such as software-defined networking, wireless mesh systems, and adaptive routing, where both computational efficiency and solution diversity are paramount Full article

In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments. Full article

Probabilistic roadmap (PRM) methods are widely used for robot navigation in both 2D and 3D environments; however, a major drawback is that the raw paths tend to be jagged. Executing a trajectory along such paths can lead to significant overshoots and tight turns, making it difficult to achieve a near-optimal solution under motion constraints. This paper presents an enhanced PRM-based path planning approach designed to improve path quality and computational efficiency. The method integrates advanced sampling strategies, adaptive neighbor selection with spatial data structures, and multi-stage path post-processing. In particular, shortcut smoothing and polynomial fitting are used to generate smoother trajectories suitable for motion-constrained robots. The proposed hybrid sampling scheme biases sample generation toward critical regions—near obstacles, in narrow passages, and between the start and goal—to improve graph connectivity in challenging areas. An adaptive k-d tree-based connection strategy then efficiently builds a roadmap using variable connection radii guided by PRM* theory. Once a path is found using an any-angle graph search, post-processing is applied to refine it. Unnecessary waypoints are removed via line-of-sight shortcuts, and the final trajectory is smoothed using a fitted polynomial curve. The resulting paths are shorter and exhibit gentler turns, making them more feasible for execution. In simulated complex scenarios, including narrow corridors and cluttered environments, the advanced PRM achieved a 100% success rate where standard PRM frequently failed. It also reduced calculation time to 30% and peak turning angle by up to 50% compared to conventional methods. The approach supports dynamic re-planning: when the environment changes, the roadmap is efficiently updated rather than rebuilt from scratch. Furthermore, the use of an adaptive k-d tree structure and incremental roadmap updates leads to an order-of-magnitude speedup in the connection phase. These improvements significantly increase the planner’s path quality, runtime performance, and reliability. Quantitative results are provided to substantiate the performance gains of the proposed method. Full article

Instance segmentation in high-resolution images is essential for applications such as remote sensing, medical imaging, and precision agriculture, yet remains challenging due to factors such as small object sizes, irregular shapes, and occlusions. Tiling-based approaches, such as Slicing-Aided Hyper Inference (SAHI), alleviate some of these challenges by processing smaller patches but introduce border artifacts and increased computational cost. Overlapping tiles can mitigate certain boundary effects but often result in duplicate detections and boundary inconsistencies, particularly along patch edges. Conventional deduplication techniques, including Non-Maximum Suppression (NMS) and Non-Mask Merging (NMM), rely on Intersection over Union (IoU) thresholds and frequently fail to merge fragmented or adjacent masks with low mutual IoU that nonetheless correspond to the same object. To address deduplication and mask fragmentation, Spatial Mask Merging (SMM) is proposed as a graph clustering approach that integrates pixel-level overlap and boundary distance metrics while using R-tree indexing for efficient candidate retrieval. SMM was evaluated on the iSAID benchmark using standard segmentation metrics, with tile overlap configurations systematically examined to determine the optimal setting for segmentation accuracy. The method achieved a nearly 7% increase in precision, with consistent gains in F1 score and Panoptic Quality over existing approaches. The integration of R-tree indexing facilitated faster candidate retrieval, enabling computational performance improvements over standard merging algorithms alongside the observed accuracy gains. Full article

Binary code similarity detection (BCSD) plays an important role in software security. Recent deep learning-based methods have made great progress. Existing methods based on a single feature, such as semantics or graph structure, struggle to handle changes caused by the architecture or compilation environment. Methods fusing semantics and graph structure suffer from insufficient learning of the function, resulting in low accuracy and robustness. To address this issue, we proposed Fus, a method that integrates semantic information from the pseudo-C code and structural features from the Abstract Syntax Tree (AST). The pseudo-C code and AST are robust against compilation and architectural changes and can represent the function well. Our approach consists of three steps. First, we preprocess the assembly code to obtain the pseudo-C code and AST for each function. Second, we employ a Siamese network with CodeBERT models to extract semantic embeddings from the pseudo-C code and Tree-Structured Long Short-Term Memory (Tree LSTM) to encode the AST. Finally, function similarity is computed by summing the respective semantic and structural similarities. The evaluation results show that our method outperforms the state-of-the-art methods in most scenarios. Especially in large-scale scenarios, its performance is remarkable. In the vulnerability search task, Fus achieves the highest recall. It demonstrates the accuracy and robustness of our method. Full article

The globalization of hardware design flows has increased the risk of Hardware Trojan (HT) insertion during the design phase. Graph-based learning methods have shown promise for HT detection at the Register Transfer Level (RTL). However, most existing approaches rely on representing RTL designs through a single graph structure. This single-view modeling paradigm inherently constrains the model’s ability to perceive complex behavioral patterns, consequently limiting detection performance. To address these limitations, we propose GNN-MFF, an innovative multi-view feature fusion model based on Graph Neural Networks (GNNs). Our approach centers on joint multi-view modeling of RTL designs to achieve a more comprehensive representation. Specifically, we construct complementary graph-structural views: the Abstract Syntax Tree (AST) capturing structure information, and the Data Flow Graph (DFG) modeling logical dependency relationships. For each graph structure, customized GNN architectures are designed to effectively extract its features. Furthermore, we develop a feature fusion framework that leverages a multi-head attention mechanism to deeply explore and integrate heterogeneous features from distinct views, thereby enhancing the model’s capacity to structurally perceive anomalous logic patterns. Evaluated on an extended Trust-Hub-based HT benchmark dataset, our model achieves an average F1-score of 97.08% in automated detection of unseen HTs, surpassing current state-of-the-art methods. Full article

Socialized services for rural characteristic industries are becoming a key support for promoting rural industries’ transformation and upgrading. They are permeating the development process of modern agricultural service technologies, achieving significant progress in specialized fields such as mechanized operations and plant-protection services. However, challenges remain, including low efficiency in matching service resources and limited spatiotemporal coordination capabilities. With the deep integration of spatiotemporal information technology and knowledge graph technology, the enormous potential of semantic-level feature spatial representation in intelligent scheduling of service resources has been fully demonstrated, providing a new technical pathway to solve the above problem. This paper systematically analyzes the technological evolution trends of socialized services for rural characteristic industries and proposes a collaborative scheduling framework based on semantic feature space and spatiotemporal maps for characteristic industry service resources. At the technical architecture level, the paper aims to construct a spatiotemporal graph model integrating geographic knowledge graphs and temporal tree technology to achieve semantic-level feature matching between service demand and supply. Regarding implementation pathways, the model significantly improves the spatiotemporal allocation efficiency of service resources through cloud service platforms that integrate spatial semantic matching algorithms and dynamic optimization technologies. This paper conducts in-depth discussions and analyses on technical details such as agricultural semantic feature extraction, dynamic updates of rural service resources, and the collaboration of semantic matching and spatio-temporal matching of supply and demand relationships. It also presents relevant implementation methods to enhance technical integrity and logic, which is conducive to the engineering implementation of the proposed methods. The effectiveness of the proposed collaborative scheduling framework for service resources is proved by the synthesis of principal analysis, logical deduction and case comparison. We have proposed a practical “three-step” implementation path conducive to realizing the proposed method. Regarding application paradigms, this technical system will promote the transformation of rural industry services from traditional mechanical operations to an intelligent service model of “demand perception–intelligent matching–precise scheduling”. In the field of socialized services for rural characteristic industries, it is suggested that relevant institutions promote this technical framework and pay attention to the development trends of new technologies such as knowledge services, spatio-temporal services, the Internet of Things, and unmanned farms so as to promote the sustainable development of rural characteristic industries. Full article

For $a\in \mathbb{R}$, the general sum-connectivity index of a graph G is defined as ${\chi }_a\left(G\right)={\sum }_{uv\in E\left(G\right)}{[d_G\left(u\right)+d_G\left(v\right)]}^a$, where $E\left(G\right)$ is the set of edges of G and $d_G\left(u\right)$ and $d_G\left(v\right)$ are the degrees of vertices u and v, respectively. For trees and unicyclic graphs with given order and number of pendant vertices, we present upper bounds on the general sum-connectivity index ${\chi }_a$, where $0<a<1$. We also present the trees and unicyclic graphs that attain the maximum general sum-connectivity index for $0<a<1$. Full article