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Introduction: In response to the issues of complexity and low efficiency in line loss calculations for actual distribution networks, this paper proposes a fast power flow calculation method for distribution networks based on Neo4j graph models and a hierarchical forward-backward sweep parallel algorithm. Methods: Firstly, Neo4j is used to describe the distribution network structure as a simple graph model composed of nodes and edges. Secondly, a hierarchical forward-backward sweep method is adopted to perform power flow calculations on the graph model network. Finally, during the computation of distribution network subgraphs, the method is combined with the Bulk Synchronous Parallel (BSP) computing model to quickly complete the line loss analysis. Results and Discussion: Results from the IEEE 33-node test system demonstrate that the proposed method can calculate network losses quickly and accurately, with a computation time of only 0.175s, which is lower than the MySQL and Neo4j graph methods that do not consider hierarchical parallel computing. [ABSTRACT FROM AUTHOR]

This work aims to stimulate a reflection on the fact that organizations that have or adopt very complex organizational structures may encounter results in risk assessment that are not entirely reliable or compromised by their complexity. The work examines the main approaches to risk analysis organizations adopt, particularly the models and techniques used to assess processes and information management risks. Risk analysis is an essential process to ensure adequate governance for the organization. For it to be effective, it is also necessary to define a Risk Analysis Model (a methodology) capable of preserving specific properties such as completeness and repeatability. These are some of the indispensable elements to ensure a (recurrent) risk assessment that can consider the evolution of the corporate asset protection system over time and thus determine whether the system as a whole increases or decreases its performance. In this context, some factors can jeopardize the quality and reliability of the results obtained, and one of these factors is the complexity of the organization. Organisations are becoming increasingly complex due to their structure, processes, and rapidly changing external environment, and we know that some limitations exist in using the graph theory applied to RACI. The study of corporate complexity measurement is a vast subject, but this simplified tool is intended to support risk analysis processes in complex organisations specifically. Measuring organizational complexity is a subject that can be addressed with multiple approaches. This work proposes a strategy based on graph and flow network tools applied to RACI matrices. This tool can reproduce an organization starting from its representation produced through RACI matrices and, with the necessary assumptions, express an index of the complexity of the organization itself using the algebraic properties of graphs. The reflection that emerges is that risk assessment could be subject to errors caused by the detection of input data used to calculate the level of risk. The generation of these errors is linked to the size and complexity of an organization. To manage this phenomenon, the work introduces a correction parameter of the results produced by risk analysis (an "adjustment" factor) that can mitigate any measurement errors produced precisely by the complexity dimension. Complexity can significantly compromise the reliability of risk assessment results, and organizations must implement correction parameters to mitigate such measurement errors, ensuring better governance and performance over time. [ABSTRACT FROM AUTHOR]

We provide a well-posedness theory for a class of nonlocal continuity equations on co-evolving graphs. We describe the connection among vertices through an edge weight function and we let it evolve in time, coupling its dynamics with the dynamics on the graph. This is relevant in applications to opinion dynamics and transportation networks. Existence and uniqueness of suitably defined solutions is obtained by exploiting the Banach fixed-point theorem. We consider different time scales for the evolution of the weight function: faster and slower than the flow defined on the graph. The former leads to graphs whose weight functions depend nonlocally on the density configuration at the vertices, while the latter induces static graphs. Furthermore, we prove a discrete-to-continuum limit for the PDEs under study as the number of vertices converges to infinity. [ABSTRACT FROM AUTHOR]

Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution. [ABSTRACT FROM AUTHOR]

In recent years, the heat kernel has proven to be a valuable tool for graph characterization and graph-based object clustering. It serves as the fundamental solution to the heat diffusion equation associated with the discrete graph Laplacian, describing how information flows across graph edges over time by exponentiating the Laplacian eigensystem. This paper focuses on the novel concept of the generalized heat kernel of a network, recently introduced by Kalala Mutombo et al. These authors build upon the k-path Laplacian operator for graphs, pioneered by Estrada et al., to incorporate long-range interactions (LRI) into information transmission across nodes and edges of a network/graph. LRI are handled through the use of Mellin and Laplace transforms. This paper makes a notable contribution by highlighting the practical application of the generalized heat kernel. We achieve this by demonstrating the effectiveness of the generalised heat kernel invariants in object clustering with real data using principal component analysis (PCA). Moreover, we investigate how incorporating long-range interactions (LRI) impacts object characterization and clustering, revealing superior results compared to conventional diffusion methods. Through experimentation, we show that object clustering remains achievable even with small values of the Mellin and Laplace parameters, contrasting with the requirement of an infinite value in the absence of LRI. • Application of graph generalised heat kernel invariants in object clustering via PCA. • Long-range interactions enhance graph-based object clustering. • Object clustering achieved with small Mellin/Laplace parameters. [ABSTRACT FROM AUTHOR]

In this paper, the concept of circular r $r$‐flow in a mono‐directed signed graph (G,σ) $(G,\sigma)$ is introduced. That is a pair (D,f) $(D,f)$, where D $D$ is an orientation on G $G$ and f:E(G)→(−r,r) $f:E(G)\to (-r,r)$ satisfies that ∣f(e)∣∈[1,r−1] $| f(e)| \in [1,r-1]$ for each positive edge e $e$ and ∣f(e)∣∈[0,r2−1]∪[r2+1,r) $| f(e)| \in [0,\frac{r}{2}-1]\cup [\frac{r}{2}+1,r)$ for each negative edge e $e$, and the total in‐flow equals the total out‐flow at each vertex. This is the dual notion of circular colorings of signed graphs and is distinct from the concept of circular flows in bi‐directed graphs associated with signed graphs studied in the literature. We first explore the connection between circular 2kk−1 $\frac{2k}{k-1}$‐flows and modulo k $k$‐orientations in signed graphs. Then we focus on the upper bounds for the circular flow indices of signed graphs in terms of the edge‐connectivity, where the circular flow index of a signed graph is the minimum value r $r$ such that it admits a circular r $r$‐flow. We prove that every 3‐edge‐connected signed graph admits a circular 6‐flow and every 4‐edge‐connected signed graph admits a circular 4‐flow. More generally, for k≥2 $k\ge 2$, we show that every (3k−1) $(3k-1)$‐edge‐connected signed graph admits a circular 2kk−1 $\frac{2k}{k-1}$‐flow, every 3k $3k$‐edge‐connected signed graph has a circular r $r$‐flow with r<2kk−1 $r\lt \frac{2k}{k-1}$, and every (3k+1) $(3k+1)$‐edge‐connected signed graph admits a circular 4k+22k−1 $\frac{4k+2}{2k-1}$‐flow. Moreover, the (6k−2) $(6k-2)$‐edge‐connectivity condition is shown to be sufficient for a signed Eulerian graph to admit a circular 4k2k−1 $\frac{4k}{2k-1}$‐flow, and applying this result to planar graphs, we conclude that every signed bipartite planar graph of negative girth at least 6k−2 $6k-2$ admits a homomorphism to the negative even cycles C−2k ${C}_{-2k}$. [ABSTRACT FROM AUTHOR]

Coarse-Grain Reconfigurable Arrays (CGRAs) represent emerging low-power architectures designed to accelerate Compute-Intensive Loops (CILs). The effectiveness of CGRAs in providing acceleration relies on the quality of mapping: how efficiently the CIL is compiled onto the platform. State-of-the-Art (SoA) compilation techniques utilize modulo scheduling to minimize the Iteration Interval (II) and use graph algorithms like Max-Clique Enumeration to address mapping challenges. Our work approaches the mapping problem through a satisfiability (SAT) formulation. We introduce the Kernel Mobility Schedule (KMS), an ad hoc schedule used with the Data Flow Graph and CGRA architectural information to generate Boolean statements that, when satisfied, yield a valid mapping. Experimental results demonstrate SAT-MapIt outperforming SoA alternatives in almost 50% of explored benchmarks. Additionally, we evaluated the mapping results in a synthesizable CGRA design and emphasized the runtime metrics trends, i.e., energy efficiency and latency, across different CILs and CGRA sizes. We show that a hardware-agnostic analysis performed on compiler-level metrics can optimally prune the architectural design space, while still retaining Pareto-optimal configurations. Moreover, by exploring how implementation details impact cost and performance on real hardware, we highlight the importance of holistic software-to-hardware mapping flows, as the one presented herein. [ABSTRACT FROM AUTHOR]

In the fish passage facility design, understanding the coupled effects of hydrodynamics on fish behaviour is particularly important. The flow field caused by fish movement however are usually obtained via time-consuming transient numerical simulation. Hence, a hybrid deep neural network (HDNN) approach is designed to predict the unsteady flow field around fish. The basic architecture of HDNN includes the UNet convolution (UConv) module and the bidirectional convolutional long-short term memory (BiConvLSTM) module. Specifically, the UConv module extracts crucial features from the flow field graph, while the BiConvLSTM module learns the evolution of low-dimensional spatio-temporal features for prediction. The numerical results showcase that the HDNN achieves accurate multi-step rolling predictions of the effect of fish movement on flow fields under different tail-beat frequency conditions. Specifically, the average and standard deviation of PSNR and SSIM for the proposed HDNN model for 60 time-step rolling predictions on the entire sequences of four test sets being respectively larger than 34 dB and 0.9. The HDNN delivers a speedup of over 130 times compared to the numerical simulator. Moreover, the HDNN demonstrates commendable generalisation capabilities, enabling the prediction of spatial-temporal evolution within unsteady flow fields even at unknown tail-beat frequencies. [ABSTRACT FROM AUTHOR]

Malicious reverse engineering of software has served as a valuable technique for attackers to infringe upon and steal intellectual property. We can employ obfuscation techniques to protect against such attackers as useful tools to safeguard software. Applying obfuscation techniques to source code can prevent malicious attackers from reverse engineering a program. However, the ambiguity surrounding the protective efficacy of these source code obfuscation tools and techniques presents challenges for users in evaluating and comparing the varying degrees of protection provided. This paper addresses these issues and presents a methodology to quantify the effect of source code obfuscation. Our proposed method is based on three main types of data: (1) the control flow graph, (2) the program path, and (3) the performance overhead added to the process—all of which are derived from a program analysis conducted by human experts and automated tools. For the first time, we have implemented a tool that can quantitatively evaluate the quality of obfuscation techniques. Then, to validate the effectiveness of the implemented framework, we conducted experiments using four widely recognized commercial and open-source obfuscation tools. Our experimental findings, based on quantitative values related to obfuscation techniques, demonstrate that our proposed framework effectively assesses obfuscation quality. [ABSTRACT FROM AUTHOR]

Inter-city population mobility, a critical phenomenon in the modern urbanisation process, is closely related to urban industrial structure and socioeconomic development. This paper aims to investigate the dynamics of population flows and their intricate ties to industrial structure, so we employ the graph neural networks (GNNs) method to simulate inter-city population flows in China, which efficiently integrates demographic and socioeconomic data with Tencent migration big data while accounts for geographical relationships between cities. The results show that the model's predictive accuracy using the CPC index was high for road and rail traffic and moderate for air transportation. A comparison with real-world data verified the model's effectiveness in predicting the urban hierarchy and regional aggregation of flows. Using GNNExplainer, the results indicated that population size positively influenced population flow, while developed manufacturing reduced population mobility for road and rail traffic but increased it for air transportation. By conducting scenario simulations in Northeast China, we found that enhancing the region's industry and consumer service industry could mitigate negative population outflows. The conclusions drawn from this study offer valuable perspectives to policymakers and urban planners, enabling them to make well-informed and judicious choices concerning urban planning, transportation, and resource allocation. [ABSTRACT FROM AUTHOR]

Our work concerns algorithms for a variant of Maximum Flow in unweighted graphs. In the All-Pairs Connectivity (APC) problem, we are given a graph G on n vertices and m edges, and are tasked with computing the maximum number of edge-disjoint paths from s to t (equivalently, the size of a minimum (s, t)-cut) in G, for all pairs of vertices (s, t). Significant algorithmic breakthroughs have recently shown that over undirected graphs, APC can be solved in n 2 + o (1) time, which is essentially optimal. In contrast, the true time complexity of APC over directed graphs remains open: this problem can be solved in O ~ (m ω) time, where ω ∈ [ 2 , 2.373) is the exponent of matrix multiplication, but no matching conditional lower bound is known. Following [Abboud et al. In: 46th International colloquium on automata, languages, and programming, ICALP 2019, July 9-12, 2019, Patras, Greece, Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2019], we study a bounded version of APC called the k-Bounded All Pairs Connectivity (k-APC) problem. In this variant of APC, we are given an integer k in addition to the graph G, and are now tasked with reporting the size of a minimum (s, t)-cut only for pairs (s, t) of vertices with min-cut value less than k (if the minimum (s, t)-cut has size at least k, we can just report it is "large" instead of computing the exact value). Our main result is an O ~ ((k n) ω) time algorithm solving k-APC in directed graphs. This is the first algorithm which solves k-APC faster than simply solving the more general APC problem exactly, for all k ≥ 3 . This runtime is O ~ (n ω) for all k ≤ poly (log n) , which essentially matches the optimal runtime for the k = 1 case of k-APC, under popular conjectures from fine-grained complexity. Previously, this runtime was only achieved for k ≤ 2 in general directed graphs [Georgiadis et al. In: 44th international colloquium on automata, languages, and programming (ICALP 2017), volume 80 of Leibniz International Proceedings in Informatics (LIPIcs), Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2017], and for k ≤ o (log n) in the special case of directed acyclic graphs [Abboud et al. In: 46th international colloquium on automata, languages, and programming, ICALP 2019, July 9–12, 2019, Patras, Greece, Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2019]. Our result employs the same algebraic framework used in previous work, introduced by [Cheung et al. In: FOCS, 2011]. A direct implementation of this framework involves inverting a large random matrix. Our new algorithm is based off the insight that for solving k-APC, it suffices to invert a low-rank random matrix instead of a generic random matrix. We also obtain a new algorithm for a variant of k-APC, the k-Bounded All-Pairs Vertex Connectivity (k-APVC) problem, where we are now tasked with reporting, for every pair of vertices (s, t), the maximum number of internally vertex-disjoint (rather than edge-disjoint) paths from s to t if this number is less than k, and otherwise reporting that there are at least k internally vertex-disjoint paths from s to t. Our second result is an O ~ (k 2 n ω) time algorithm solving k-APVC in directed graphs. Previous work showed how to solve an easier version of the k-APVC problem (where answers only need to be returned for pairs of vertices (s, t) which are not edges in the graph) in O ~ ((k n) ω) time [Abboud et al. In: 46th International colloquium on automata, languages, and programming, ICALP 2019, July 9–12, 2019, Patras, Greece, Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2019]. In comparison, our algorithm solves the full k-APVC problem, and is faster if ω > 2 . [ABSTRACT FROM AUTHOR]

This article discusses the development of an automated plot extraction system for narrative texts. Acknowledging the distinction between plot, as an object of study with its own rich history and literature, and features of a text that may be automatically extractable, we begin by characterizing a text's scatter plot of entities. This visualization of a text reveals entity density patterns characterizing the particular telling of the story under investigation and leads to effective scene partitioning. We then introduce the concept of narrative flow , a graph representation of the narrative ordering of scenes (the syuzhet) that includes how entities move through scenes from the text, and investigate the degree to which narrative flow can be automatically extracted given a glossary of plot-important objects, actors, and locations. Our subsequent analysis then explores the correlation between subjective notions of plot and the information extracted through these visualizations. In particular, we discuss narrative structures commonly found within the graphs and make comparisons with ground truth narrative flow graphs, showing mixed results highlighting the difficulty of plot extraction. However, the visual artifacts and common structural relationships seen in the graphs provide insight into narrative and its underlying plot. [ABSTRACT FROM AUTHOR]

In the landscape of software development, the selection of compilation tools and settings plays a pivotal role in the creation of executable binaries. This diversity, while beneficial, introduces significant challenges for reverse engineers and security analysts in deciphering the compilation provenance of binary code. To this end, we present MulCPI, short for Multi-representation Fusion-based Compilation Provenance Identification, which integrates the features collected from multiple distinct intermediate representations of the binary code for better discernment of the fine-grained function-level compilation details. In particular, we devise a novel graph-oriented neural encoder improved upon the gated graph neural network by subtly introducing an attention mechanism into the neighborhood nodes' information aggregation computation, in order to better distill the more informative features from the attributed control flow graph. By further integrating the features collected from the normalized assembly sequence with an advanced Transformer encoder, MulCPI is capable of capturing a more comprehensive set of features manifesting the multi-faceted lexical, syntactic, and structural insights of the binary code. Extensive evaluation on a public dataset comprising 854,858 unique functions demonstrates that MulCPI exceeds the performance of current leading methods in identifying the compiler family, optimization level, compiler version, and the combination of compilation settings. It achieves average accuracy rates of 99.3%, 96.4%, 90.7%, and 85.3% on these tasks, respectively. Additionally, an ablation study highlights the significance of MulCPI's core designs, validating the efficiency of the proposed attention-enhanced gated graph neural network encoder and the advantages of incorporating multiple code representations. [ABSTRACT FROM AUTHOR]

Control flow critical metadata play a key role in hardware-based control flow integrity (CFI) mechanisms that effectively monitor and secure program control flow based on pre-extracted metadata. The existing control flow analysis tools exhibit some deficiencies, including inadequate compatibility with the RISC-V architecture, a steep learning curve, limited automation capabilities, and restricted data output formats. CFIEE is an open-source tool with a graphical interface for the automated extraction of control flow critical metadata. The tool possesses the capability to analyze RISC-V binary executables, transforming the binary into an intermediate representation (IR) in the form of the disassembled code, and extracting the critical metadata required for studying hardware-based CFI mechanism through a designed control flow transfer relationship analysis algorithm. The extracted metadata include program basic blocks and their corresponding hash values, control flow graphs, function call relationships, distribution of forward transfer instructions, etc. We selected 15 embedded system programs with processor adaptation for functional verification. The results demonstrate the CFIEE's capability to automatically analyze programs within the supported RISC-V instruction set and generate comprehensive and precise metadata files. This tool can significantly enhance the efficiency of control flow metadata extraction and furnish configurable metadata for the hardware-based security mechanisms. [ABSTRACT FROM AUTHOR]

The decoupling of control and forwarding layers brings Software-Defined Networking (SDN) the network programmability and global control capability, but it also poses SDN security risks. The adversaries can use the forwarding and control decoupling character of SDN to forge legitimate traffic, launching saturation attacks targeted at SDN switches. These attacks can cause the overflow of switch flow tables, thus making the switch cannot forward benign network traffic. How to effectively detect saturation attack is a research hotspot. There are only a few graph-based saturation attack detection methods. Meanwhile, the current graph generation methods may take useless or misleading information to the attack detection, thus decreasing the attack detection accuracy. To solve the above problems, this paper proposes TITAN, a bidirecTional forwardIng graph-based saturaTion Attack detectioN method. TITAN defines flow forwarding rules and topology information, and designs flow statistical features. Based on these definitions, TITAN generates nodes of the bi-forwarding graph based on the flow statistics features and edges of the bi-forwarding graph based on the network traffic routing paths. In this way, each traffic flow in the network is transformed into a bi-directional forwarding graph. Then TITAN feeds the above bidirectional forwarding graph into a Graph Convolutional Network (GCN) to detect whether the flow is a saturation attack flow. The experimental results show that TITAN can effectively detect saturation attacks in SDNs with a detection accuracy of more than 97%. [ABSTRACT FROM AUTHOR]

We consider the volume constrained fractional mean curvature flow of a nearly spherical set and prove long time existence and asymptotic convergence to a ball. The result applies in particular to convex initial data under the assumption of global existence. Similarly, we show exponential convergence to a constant for the fractional mean curvature flow of a periodic graph. [ABSTRACT FROM AUTHOR]

The growth of multimedia applications poses new challenges to software facilities in edge computing. Developers must effectively develop edge computing software to accommodate the rapid expansion of multimedia applications. Code search has become a prevalent practice to enhance the efficiency of the construction of edge software infrastructure. Researchers have proposed lots of approaches for code search, and employed deep learning technology to extract features from program representations, such as token, AST, graphs, method name, and API. Nevertheless, two prominent issues remain: 1) there are only a few studies on the effective use of graph representation for code search (especially in Java language), and 2) there is a lack of empirical study on the contributions of different program representations. To address these issues, we conduct an empirical study to explore program representations, especially program graphs. To the best of our knowledge, this is the first attempt to conduct code search with mixed graphs representation for Java language, containing the control flow graph and the program dependence graph. We also present a hybrid approach to capture and fuse the features of a program with representations of Token, AST, and Mixed Graphs (TAMG). The results of our experiment show that our approach possesses the best ability (R@1 with 37% and R@10 with 67.1%). Our graph representation exhibits a positive effect, and the token and AST also have a significant contribution to the code search. Our findings can aid developers in efficiently searching for the desired code while constructing the software infrastructure for edge computing, which is crucial for the rapid expansion of multimedia applications. [ABSTRACT FROM AUTHOR]

Software vulnerabilities pose a significant threat to system security, necessitating effective automatic detection methods. Current techniques face challenges such as dependency issues, language bias, and coarse detection granularity. This study presents a novel deep learning-based vulnerability detection system for Java code. Leveraging hybrid feature extraction through graph and sequence-based techniques enhances semantic and syntactic understanding. The system utilizes control flow graphs (CFG), abstract syntax trees (AST), program dependencies (PD), and greedy longest-match first vectorization for graph representation. A hybrid neural network (GCN-RFEMLP) and the pre-trained CodeBERT model extract features, feeding them into a quantum convolutional neural network with self-attentive pooling. The system addresses issues like long-term information dependency and coarse detection granularity, employing intermediate code representation and inter-procedural slice code. To mitigate language bias, a benchmark software assurance reference dataset is employed. Evaluations demonstrate the system's superiority, achieving 99.2% accuracy in detecting vulnerabilities, outperforming benchmark methods. The proposed approach comprehensively addresses vulnerabilities, including improper input validation, missing authorizations, buffer overflow, cross-site scripting, and SQL injection attacks listed by common weakness enumeration (CWE). [ABSTRACT FROM AUTHOR]

According to the guidelines of the Intergovernmental Panel on Climate Change, carbon emissions are attributed to the producers of goods and services. This approach has been challenged by recent literature, advocating an attribution criterion based on consumers, i.e. accounting for the carbon embedded into the goods imported by each country. Quantifying the effectiveness of such a consumption-based accounting requires understanding the complex structure of the graph induced by the flows of emissions between world countries. To this aim, we have considered a balanced panel of a hundred of countries and constructed the corresponding Carbon Trade Network for each of the past twenty years. Our analysis highlights the tendency of each country to behave either as a 'net producer'—or 'net exporter'—of emissions or as a 'net consumer'—or 'net importer'—of emissions; besides, it reveals the presence of an unexpected, positive feedback: despite individual exchanges having become less carbon-intensive, the increasing trade activity has ultimately risen the amount of emissions directed from 'net exporters' towards 'net importers'. Adopting a consumption-aware accounting would re-distribute responsibility between these two groups, possibly reducing disparities. [ABSTRACT FROM AUTHOR]

Unmanned platforms are becoming more and more widely used in both civilian and military applications. The flight safety of such aircraft is crucial. Therefore, it becomes necessary to formally model and rigorously test their software. Commencement of an unmanned air vehicle's (UAV) operation requires it undergoing a certification process, which further heightens the quality requirements. The validation process of the designed system is presented on the example of verifying the correctness of the operational scenario involving the transfer of control over a UAV between two ground control stations (GCSs). The paper introduces the method of designing unmanned aerial vehicles and testing their functionality based on finite-state machines. The architecture description of the system encompasses the use cases view and logical view of the 1+5 architectural views model. The paper concentrates on system requirements and their transformation into unified modeling language (UML) state machine diagrams. The authors present a method of mapping requirements to functions related to flight safety and system dynamics models expressed as communicating extended finite-state machines (CEFSMs). Moreover, the paper shows the rules for testing the correctness of state machine models using methods based on determining flows in directed graphs. It should be emphasized that the paper considers the certification of UAV software components from the system safety viewpoint, which directly affects the airworthiness of the air platform. [ABSTRACT FROM AUTHOR]

Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph convolutional network (TSADGCN). The dynamic time warping algorithm (DTW) is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension, and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow. By combining graph attention network and time attention network, a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data. Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms. [ABSTRACT FROM AUTHOR]

Directed greybox fuzzing can mainly be used for vulnerability mining and vulnerability replication. However, there are still some issues with existing directional fuzzing tools. One is that after providing problematic changes or patches, it is not possible to quickly target and discover the problem. Secondly, it is difficult to break through the magic byte path, making it difficult to mine deep vulnerabilities. This article proposes a new vulnerability mining and repair framework: American Fuzz Lop Plus (AFL++). Firstly, we utilize alias analysis to enhance inter-procedural control flow graphs and redefine the distance calculation formula to obtain more accurate distances. Secondly, the Newton interpolation method is used for the energy initialization of each seed to prevent test cases from being filtered out due to low energy. A heuristic energy scheduling algorithm is proposed to judiciously schedule the energy of seeds. During the path exploration phase, by adjusting the seed energy, shorter-distance seeds quickly reach the target; with increasing time, seeds tend to explore deeper paths. We then represent the symbolic distance by the number of instructions passed to reach the target and investigate the shortest path search strategy to achieve path pruning, alleviating the problem of path explosion. Finally, based on the above methods, we implement the AFL++ prototype system, integrating directed greybox fuzzing with symbolic execution technology for vulnerability discovery. By interleaving directed symbolic execution and directed greybox fuzzing, the efficiency of vulnerability discovery and reproduction is effectively enhanced. [ABSTRACT FROM AUTHOR]

In the domain of software testing, the generation of test cases is a critical process for detecting system errors and bugs. However, automated test case generation for smart contracts often encounters challenges related to automation, vulnerability diversity, and coverage. This paper presents a novel method, the self-adaptive learning Genetic Algorithm (self-adaptive learning GA), designed to address these issues. Our research methodology incorporates several construction models, namely the Control Dependence Graph (CDG), Control Flow Graph (CFG), and Application Binary Interface (ABI). Initially, the ABI model provides essential information for generating and executing test cases. The CFG model subsequently visualizes potential execution paths through the functions of smart contracts. Ultimately, the CDG model identifies potential vulnerabilities in smart contracts. Using these models, our method enhances automatic test case generation in smart contracts by improving coverage and reducing execution time. We selected a variety of smart contracts from the Decentralized Finance (DeFi) ecosystem for data collection and comparative analysis. The experimental results show superior performance rates, with an average code coverage rate of 98.1%, a total of 3500 vulnerabilities detected, a vulnerability detection rate of 98.7%, a false positive rate of 1.3%, a recall of 98.2%, precision of 98.8%, a path uniqueness rate of 96.4%, false negative rate of 3.5%, an execution time of 25 s, and test case generation time of 16 s. In conclusion, our proposed approach demonstrates a significant improvement over existing methods for test case generation by providing a promising solution for the robustness of smart contracts and security enhancement in the DeFi ecosystem. [ABSTRACT FROM AUTHOR]

Imagine having a knowledge graph that can extract medical health knowledge related to patient diagnosis solutions and treatments from thousands of research papers, distilled using machine learning techniques in healthcare applications. Medical doctors can quickly determine treatments and medications for urgent patients, while researchers can discover innovative treatments for existing and unknown diseases. This would be incredible! Our approach serves as an all-in-one solution, enabling users to employ a unified design methodology for creating their own knowledge graphs. Our rigorous validation process involves multiple stages of refinement, ensuring that the resulting answers are of the utmost professionalism and solidity, surpassing the capabilities of other solutions. However, building a high-quality knowledge graph from scratch, with complete triplets consisting of subject entities, relations, and object entities, is a complex and important task that requires a systematic approach. To address this, we have developed a comprehensive design flow for knowledge graph development and a high-quality entities database. We also developed knowledge distillation schemes that allow you to input a keyword (entity) and display all related entities and relations. Our proprietary methodology, multiple levels refinement (MLR), is a novel approach to constructing knowledge graphs and refining entities level-by-level. This ensures the generation of high-quality triplets and a readable knowledge graph through keyword searching. We have generated multiple knowledge graphs and developed a scheme to find the corresponding inputs and outputs of entity linking. Entities with multiple inputs and outputs are referred to as joints, and we have created a joint-version knowledge graph based on this. Additionally, we developed an interactive knowledge graph, providing a user-friendly environment for medical professionals to explore entities related to existing or unknown treatments/diseases. Finally, we have advanced knowledge distillation techniques. [ABSTRACT FROM AUTHOR]

In the paper, a wideband tunable 4 × 4 Nolen matrix is proposed. By using the presented topology of the Nolen matrix, tunable output port phase differences can be realized by inserting one type of tunable phase shifters (T-PS). Besides, the in-band phase deviation error is minimized by adding compensation phase shifters (C-PSs) to the output ports of the Nolen matrix. Analytical design methods based on signal flow graphs and complex exponential signal are given to obtain the rigorous relationships of the coupling and phase shifts in the Nolen matrix. Besides, a detailed method for output ports phase slope compensation is provided. For validation, a prototype centered at 5.8 GHz is designed and fabricated. Measurement results agree well with the simulated ones. By using only one voltage to control the phase shifts of the T-PS in a 90° range for each input excitation of the Nolen matrix, a full 360° range of the progressive phase difference is realized by switching four input ports. The measured fractional bandwidth under 10 dB return loss and isolation is larger than 24.5% with the in − band ± 1.5 dB amplitude imbalance and ±15° phase deviation error for ports 1-4 excitations. Besides, for a more strict criterion of ±1 dB amplitude imbalance and 10° phase deviation error, the measured bandwidths are larger than 15% for all port excitations. [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

Our overall research aim is to statically derive execution cost and other metrics from program code written in the P4 programming language. For this purpose, we extract a detailed control ow graph (CFG) from the code, that can be turned into a full, formal model of execution, to extract properties - such as execution cost - from the model. While CFG extraction and analysis is well researched area, details are dependent on code representation and therefore application of textbook algorithms (often defined over unstructured code listings) to real programming languages is often non-trivial. Our aim is to present an algorithm for CFG extraction over P4 abstract syntax trees (AST). During the extraction we create direct links between nodes of the CFG and the P4 AST: this way we can access all information in the P4 AST during CFG traversal. We are utilizing Gremlin, a graph query language to take advantage of graph databases, but also for compactness and to formally prove algorithm correctness. [ABSTRACT FROM AUTHOR]

String diagrams are a powerful and intuitive graphical syntax, originating in theoretical physics and later formalised in the context of symmetric monoidal categories. In recent years, they have found application in the modelling of various computational structures, in fields as diverse as Computer Science, Physics, Control Theory, Linguistics, and Biology. In several of these proposals, transformations of systems are modelled as rewrite rules of diagrams. These developments require a mathematical foundation for string diagram rewriting: whereas rewrite theory for terms is well-understood, the two-dimensional nature of string diagrams poses quite a few additional challenges. This work systematises and expands a series of recent conference papers, laying down such a foundation. As a first step, we focus on the case of rewrite systems for string diagrammatic theories that feature a Frobenius algebra. This common structure provides a more permissive notion of composition than the usual one available in monoidal categories, and has found many applications in areas such as concurrency, quantum theory, and electrical circuits. Notably, this structure provides an exact correspondence between the syntactic notion of string diagrams modulo Frobenius structure and the combinatorial structure of hypergraphs. Our work introduces a combinatorial interpretation of string diagram rewriting modulo Frobenius structures in terms of double-pushout hypergraph rewriting. We prove this interpretation to be sound and complete and we also show that the approach can be generalised to rewriting modulo multiple Frobenius structures. As a proof of concept, we show how to derive from these results a termination strategy for Interacting Bialgebras, an important rewrite theory in the study of quantum circuits and signal flow graphs. [ABSTRACT FROM AUTHOR]

As an analogous concept of a nowhere-zero flow for directed graphs, zero-sum flows and constant-sum flows are defined and studied in the literature. For an undirected graph, a zero-sum flow (constant-sum flow resp.) is an assignment of nonzero integers to the edges such that the sum of the values of all edges incident with each vertex is zero (constant h resp.), and we call it a zero-sum k-flow (h-sum k-flow resp.) if the values of the edges are less than k. We extend these concepts to general constant-sum A-flow, where A is an Abelian group, and consider the case A = Z k the additive Abelian cyclic group of integer congruences modulo k with identity 0. In the literature, a graph is alternatively called Z k -magic if it admits a constant-sum Z k -flow, where the constant sum is called a magic sum or an index for short. We define the set of all possible magic sums such that G admits a constant-sum Z k -flow to be I k (G) and call it the magic sum spectrum, or for short, the index set of G with respect to Z k . In this article, we study the general properties of the magic sum spectrum of graphs. We determine the magic sum spectrum of complete bipartite graphs K m , n for m ≥ n ≥ 2 as the additive cyclic subgroups of Z k generated by k d , where d = g c d (m − n , k) . Also, we show that every regular graph G with a perfect matching has a full magic sum spectrum, namely, I k (G) = Z k for all k ≥ 3 . We characterize a 3-regular graph so that it admits a perfect matching if and only if it has a full magic sum spectrum, while an example is given for a 3-regular graph without a perfect matching which has no full magic sum spectrum. Another example is given for a 5-regular graph without a perfect matching, which, however, has a full magic sum spectrum. In particular, we completely determine the magic sum spectra for all regular graphs of even degree. As a byproduct, we verify a conjecture raised by Akbari et al., which claims that every connected 4 k -regular graph of even order admits a 1-sum 4-flow. More open problems are included. [ABSTRACT FROM AUTHOR]

Safety-critical cyber-physical systems (CPSs), such as high-tech cars having cyber capabilities, are highly interconnected. Automotive manufacturers are concerned about cyber attacks on vehicles that can lead to catastrophic consequences. There is a need for a new risk management approach to address and investigate cybersecurity risks. Risk management in the automotive domain is challenging due to technological improvements and advances every year. The current standard for automotive security is ISO/SAE 21434, which discusses a framework that includes threats, associated risks, and risk treatment options such as risk reduction by applying appropriate defences. This paper presents a residual cybersecurity risk management framework aligned with the framework presented in ISO/SAE 21434. A methodology is proposed to develop an integrated attack tree that considers multiple sub-systems within the CPS. Integrating attack trees in this way will help the analyst to take a broad perspective of system security. Our previous approach utilises a flow graph to calculate the residual risk to a system before and after applying defences. This paper is an extension of our initial work. It defines the steps for applying the proposed framework and using adaptive cruise control (ACC) and adaptive light control (ALC) to illustrate the applicability of our work. This work is evaluated by comparing it with the requirements of the risk management framework discussed in the literature. Currently, our methodology satisfies more than 75% of their requirements. [ABSTRACT FROM AUTHOR]

This study aims to compare the longitudinal performance between machine-learning and deep-learning classifiers for Android malware detection, employing different levels of feature abstraction. Using a dataset of 200k Android apps labeled by date within a 10-year range (2013-2022), we propose the LongCGDroid, an image-based effective approach for Android malware detection. We use the semantic Call Graph API representation that is derived from the Control Flow Graph and Data Flow Graph to extract abstracted API calls. Thus, we evaluate the longitudinal performance of LongCGDroid against API changes. Different models are used; machine-learning models (LR, RF, KNN, SVM) and deep-learning models (CNN, RNN). Empirical experiments demonstrate a progressive decline in performance for all classifiers when evaluated on samples from later periods. However, the deep-learning CNN model under the class abstraction maintains a certain stability over time. In comparison with eight state-of-the-art approaches, LongCGDroid achieves higher accuracy. [ABSTRACT FROM AUTHOR]

The graph realization problem seeks an answer to how and under what conditions a graph can be constructed if we know the degrees of its vertices. The problem was widely studied by many authors and in many ways, but there are still new ideas and solutions. In this sense, the paper presents the known necessary and su cient conditions for realization with the description in pseudocode of the corresponding algorithms. Two cases to solve the realization problem are treated: finding one solution, and finding all solutions. In this latter case a parallel approach is presented too, and how to exclude isomorphic graphs from solutions. We are also discussing algorithms using binary integer programming and flow networks. In the case of a bigraphical list with equal out- and in-degree sequences a modified Edmonds–Karp algorithm is presented such that the resulting graph will be always symmetric without containing loops. This algorithm solves the problem of graph realization in the case of undirected graphs using flow networks. [ABSTRACT FROM AUTHOR]

In programming education, the result of plagiarism detection is a crucial criterion for assessing whether or not students can pass course exams. Recently, the prevalent methods for detecting student plagiarism have been proposed by analyzing source code. These methods extract features (such as token, abstract syntax tree and control flow graph) from the source code, examine the similarity of codes using various similarity detection methods, and then perform plagiarism detection based on a predefined plagiarism threshold. However, these previous methods for plagiarism detection have some problems. First, they are less effective in detecting code modification related to structure. Second, they require a considerable number of training data, which demand high computing time and space. Third, they cannot determine whether students plagiarize in time. We propose a novel plagiarism detection method by analyzing the behavioral features of students during the coding process. Specifically, we extract five behavioral features based on students' programming habits. Then, we use a feature ranking-based suspiciousness algorithm to obtain the possibility of student plagiarism. Based on our proposed method, we develop the Online Integrated Programming Platform. To evaluate the accuracy of our method, we conduct a series of experiments. Final experimental results indicate that our method achieves promising results with Accuracy, Precision, Recall and F 1 values of 0.95, 0.90, 0.95 and 0.92, respectively. Finally, we also analyze the correlation between whether students plagiarized and their regular and final grades, which can further verify the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper we construct a tropical moduli space parametrizing roots of divisors on tropical curves. We study the relation between this space and the skeleton of Jarvis moduli space of nets of limit roots on stable curves. We show that the combinatorics of the moduli space of tropical roots is governed by the poset of flows, a poset parametrizing certain flows on graphs. [ABSTRACT FROM AUTHOR]

Securing the operating system kernel is the key to overall system security. Due to developer negligence and the inherent limitations of the code language, kernel data is exposed to various security risks, such as execution flow leakage and privilege hijacking. In binary security, most vulnerabilities are exploited by hijacking the control flow to make the program run according to the attacker's idea of attack. Control flow integrity is a common defense scheme against control flow hijacking attacks. In this paper, we use speculation-free function tables in LLVM IR to achieve the integrity of fine-grained control flow. The technique enforces CFI policies by making logical judgments on jump instruction stubbing. All jump instructions share a common function table, and the information in the table is not repeatedly stored, reducing the additional memory consumption caused by the function table and achieving fine-grained CFI protection. [ABSTRACT FROM AUTHOR]

The new wave of Industry 4.0 is transforming manufacturing factories into data-rich environments. This provides an unprecedented opportunity to feed large amounts of sensing data collected from the physical factory into the construction of digital twin (DT) in cyberspace. However, little has been done to fully utilize the DT technology to improve the smartness and autonomous levels of small and medium-sized manufacturing factories. Indeed, only a small fraction of small and medium-sized manufacturers (SMMs) has considered implementing DT technology. There is an urgent need to exploit the full potential of data analytics and simulation-enabled DTs for advanced manufacturing. Hence, this paper presents the design and development of DT models for simulation optimization of manufacturing process flows. First, we develop a multi-agent simulation model that describes nonlinear and stochastic dynamics among a network of interactive manufacturing things, including customers, machines, automated guided vehicles (AGVs), queues, and jobs. Second, we propose a statistical metamodeling approach to design sequential computer experiments to optimize the utilization of AGV under uncertainty. Third, we construct two new graph models--job flow graph and AGV traveling graph--to track and monitor the real-time performance of manufacturing jobshops. The proposed simulation-enabled DT approach is evaluated and validated with experimental studies for the representation of a real-world manufacturing factory. Experimental results show that the proposed methodology effectively transforms a manufacturing jobshop into a new generation of DT-enabled smart factories. The sequential design of experiments effectively reduces the computation overhead of expensive simulations while optimally scheduling the AGV to achieve production throughput cost-effectively. This research is strongly promised to help SMMs fully utilize big data and DT technology for gaining competitive advantages in the global marketplace. [ABSTRACT FROM AUTHOR]

Programmers who work with smart contract development often encounter challenges in reusing code from repositories. This is due to the presence of two unknowns that can lead to non-functional and functional failures. These unknowns are implicit collaborations between functions and subtle differences among similar functions. Current code mining methods can extract syntax and semantic knowledge (known knowledge), but they cannot uncover these unknowns due to a significant gap between the known and the unknown. To address this issue, we formulate knowledge acquisition as a knowledge deduction task and propose an analytic flow that uses the function clone as a bridge to gradually deduce the known knowledge into the problem-solving knowledge that can reveal the unknowns. This flow comprises five methods: clone detection, co-occurrence probability calculation, function usage frequency accumulation, description propagation, and control flow graph annotation. This provides a systematic and coherent approach to knowledge deduction. We then structure all of the knowledge into a semantic-enriched code Knowledge Graph (KG) and integrate this KG into two software engineering tasks: code recommendation and crowd-scaled coding practice checking. As a proof of concept, we apply our approach to 5,140 smart contract files available on Etherscan.io and confirm high accuracy of our KG construction steps. In our experiments, our code KG effectively improved code recommendation accuracy by 6% to 45%, increased diversity by 61% to 102%, and enhanced NDCG by 1% to 21%. Furthermore, compared to traditional analysis tools and the debugging-with-the-crowd method, our KG improved time efficiency by 30 to 380 seconds, vulnerability determination accuracy by 20% to 33%, and vulnerability fixing accuracy by 24% to 40% for novice developers who identified and fixed vulnerable smart contract functions. [ABSTRACT FROM AUTHOR]

Static analysis is popular for detecting SQL injection vulnerabilities. However, due to the lack of accurate modeling of object-oriented database extensions, current methods fail to accurately detect SQL injection vulnerabilities in applications that use object-oriented database extensions. We propose a program transformation-based SQL injection vulnerability detection method to address this issue. This method consists of two stages: program transformation and vulnerability detection. In the first stage, object-oriented database extensions are automatically transformed into semantically equivalent procedural database extensions through the identification of key statements, call relation verification, and program transformation. In the second stage, application programs are automatically scanned using a combination of control flow graph construction and taint analysis techniques to detect SQL injection vulnerabilities. Based on the proposed method, we have implemented the OODBE-SCAN prototype system and performed experimental analysis on eight modern PHP applications. We compare OODBE-SCAN with two related static analysis tools, RIPS and Seay. The results show that OODBE-SCAN can detect more real-world vulnerabilities and has higher accuracy than existing methods. [ABSTRACT FROM AUTHOR]

Web shell attacks through malicious PHP scripts allow attackers to execute system commands remotely and take control of web servers. Most existing PHP shell detection methods rely on signature matching, which can be evaded by obfuscation. This paper proposes an alternative static taint analysis framework to detect PHP web shell attacks by modeling data flows from untrusted inputs to sensitive sinks. The proposed web shell attacks detector takes PHP source code as input and performs a staged analysis, including lexical analysis to tokenize the code, syntactic analysis to generate a parse tree, semantic analysis to extract variables and functions into a dependency control flow graph (D-CFG), dataflow analysis to track taint through the D-CFG and identify flows from untrusted sources like $_GET to sinks like shell commands, and evaluation to compare identified flows to known malicious patterns and check for indications of a web shell attack. Each stage builds on the previous one, and the whole process aims at reliably detecting PHP web shell threats through static taint analysis of program flows from origin to system execution. It conducts a hybrid analysis using lexical, syntactic, and semantic analysis of the abstract syntax tree. Static taint analysis is a program analysis technique used to identify how untrusted data propagated through a codebase without executing the program. Also, static taint analysis helps find security issues by modeling how untrusted inputs interact with critical operations via a static code inspection rather than dynamic execution. Results on a PHP web shells dataset showed that our framework could achieve 95% recall and 90% precision, outperforming existing static and dynamic analysis methods. The approach also had fewer false positives than signature-based methods. The evaluation demonstrates the framework's capabilities in precisely detecting web shell attacks with high accuracy. [ABSTRACT FROM AUTHOR]

Cross-project software defect prediction (CPDP) aims to enhance defect prediction in target projects with limited or no historical data by leveraging information from related source projects. The existing CPDP approaches rely on static metrics or dynamic syntactic features, which have shown limited effectiveness in CPDP due to their inability to capture higher-level system properties, such as complex design patterns, relationships between multiple functions, and dependencies in different software projects, that are important for CPDP. This paper introduces a novel approach, a graph-based feature learning model for CPDP (GB-CPDP), that utilizes NetworkX to extract features and learn representations of program entities from control flow graphs (CFGs) and data dependency graphs (DDGs). These graphs capture the structural and data dependencies within the source code. The proposed approach employs Node2Vec to transform CFGs and DDGs into numerical vectors and leverages Long Short-Term Memory (LSTM) networks to learn predictive models. The process involves graph construction, feature learning through graph embedding and LSTM, and defect prediction. Experimental evaluation using nine open-source Java projects from the PROMISE dataset demonstrates that GB-CPDP outperforms state-of-the-art CPDP methods in terms of F1-measure and Area Under the Curve (AUC). The results showcase the effectiveness of GB-CPDP in improving the performance of cross-project defect prediction. [ABSTRACT FROM AUTHOR]

The nearest neighbors query problem on road networks constitutes a crucial aspect of location-oriented services and has useful practical implications; e.g., it can locate the k-nearest hotels. However, researches who study road networks still encounter obstacles due to the method's inherent limitations with respect to object mobility. More popular methods employ indexes to store intermediate results to improve querying time efficiency, but these other methods are often accompanied by high time costs. To balance the costs of time and space, a lightweight flow graph index is proposed to reduce the quantity of candidate nodes, and with this index the results of a kNN query can be efficiently obtained. Experiments on real road networks confirm the efficiency and accuracy of our optimized algorithm. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based applications due to their intrinsic capacity in modeling structural and contextual relations between various parts of an image frame. On another front, the rising popularity of deep vision-based applications at the edge has been facilitated by the recent advancements in heterogeneous multi-processor Systems on Chips (MPSoCs) that enable inference under real-time, stringent execution requirements. By extension, GNNs employed for vision-based applications must adhere to the same execution requirements. Yet contrary to typical deep neural networks, the irregular flow of graph learning operations poses a challenge to running GNNs on such heterogeneous MPSoC platforms. In this paper, we propose a novel unified design-mapping approach for efficient processing of vision GNN workloads on heterogeneousMPSoC platforms. Particularly, we developMaGNAS, a mappingaware Graph Neural Architecture Search framework. MaGNAS proposes a GNN architectural design space coupled with prospective mapping options on a heterogeneous SoC to identify model architectures that maximize on-device resource efficiency. To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs. Through designing a supernet derived from the recent Vision GNN (ViG) architecture, we conducted experiments on four (04) state-of-the-art vision datasets using both (i) a real hardware SoC platform (NVIDIA Xavier AGX) and (ii) a performance/cost model simulator for DNN accelerators. Our experimental results demonstrate that MaGNAS is able to provide 1.57x latency speedup and is 3.38x more energy-efficient for several vision datasets executed on the Xavier MPSoC vs. the GPU-only deployment while sustaining an average 0.11% accuracy reduction from the baseline. [ABSTRACT FROM AUTHOR]

Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match. [ABSTRACT FROM AUTHOR]

As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited by Java malware have become increasingly prevalent, which cause threats for multi-platform. Security researchers continuously propose various approaches for fighting against Java malware programs. The low code path coverage and poor execution efficiency of dynamic analysis limit the large-scale application of dynamic Java malware detection methods. Therefore, researchers turn to extracting abundant static features to implement efficient malware detection. In this paper, we explore the direction of capturing malware semantic information by using graph learning algorithms and present BejaGNN (Behavior-based Java malware detection via Graph Neural Network), a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. Specifically, BejaGNN leverages static analysis techniques to extract ICFGs (Inter-procedural Control Flow Graph) from Java program files and then prunes these ICFGs to remove noisy instructions. Then, word embedding techniques are adopted to learn semantic representations for Java bytecode instructions. Finally, BejaGNN builds a graph neural network classifier to determine the maliciousness of Java programs. Experimental results on a public Java bytecode benchmark demonstrate that BejaGNN achieves high F1 98.8% and is superior to existing Java malware detection approaches, which verifies the promise of graph neural network in Java malware detection. [ABSTRACT FROM AUTHOR]

The one-dimensional hyperbolic mean curvature flow for Lagrangian graphs is discussed in this paper. In the beginning, infinitesimal generators, symmetry groups and an optimal system of symmetries for the proposed hyperbolic Lagrangian mean curvature flow are obtained based on the Lie symmetry approach. Additionally, several invariant solutions are discovered using reduced equations. More specifically, we use the power series method to attain explicit solutions. [ABSTRACT FROM AUTHOR]

In this work we consider a generalization of graph flows. A graph flow is, in its simplest formulation, a labelling of the directed edges with real numbers subject to various constraints. A common constraint is conservation in a vertex, meaning that the sum of the labels on the incoming edges of this vertex equals the sum of those on the outgoing edges. One easy fact is that if a flow is conserving in all but one vertex, then it is also conserving in the remaining one. In our generalization we do not label the edges with real numbers, but with elements from an arbitrary group, where this fact becomes false in general. As we will show, graphs with the property that conservation of a flow in all but one vertex implies conservation in all vertices are precisely the planar graphs. [ABSTRACT FROM AUTHOR]

Mode decomposition methods, such as proper orthogonal decomposition and dynamic mode decomposition (DMD), have introduced a novel data-driven approach for flow prediction. These methods aim to identify a collection of modes that capture the essential flow features. Subsequently, the flow field data are projected onto these modes to reconstruct and predict the evolution of the flow field. However, due to their inherent linearity, mode decomposition methods are limited in effectively handling unsteady and nonlinear flow exhibiting significant nonlinearities. In this study, we propose a spectral graph convolutional deep neural network (SGC-DNN). It employs the eigenvectors of the Laplacian matrix as modes to fully utilize the adjacency information within the graph structure to solve flow on an unstructured grid better. Additionally, we employ a DNN (deep neural network) to model the temporal evolution of each mode, thereby enhancing the model's adaptability to nonlinear flow fields. To evaluate the performance of our proposed SGC-DNN, we compare its prediction results with those obtained using DMD and DNN for the flow around a cylinder on unstructured grids at various Reynolds numbers (ranging from 1000 to 500 000). We also compared the predictive results of these three models for flow with complex geometries, such as the Da Vinci pipeline flow and intracranial aneurysm blood flow. The comparative analysis demonstrates that SGC-DNN outperformed the other models, yielding lower L2 relative errors and higher R2 values. These outcomes highlight the superiority of SGC-DNN in accurately predicting unsteady and nonlinear flow characterized by graph structures. [ABSTRACT FROM AUTHOR]

Software cross-modal retrieval is a popular yet challenging direction, such as bug localization and code search. Previous studies generally map natural language texts and codes into a homogeneous semantic space for similarity measurement. However, it is not easy to accurately capture their similar semantics in a homogeneous semantic space due to the semantic gap. Therefore, we propose to map the multi-modal data into heterogeneous semantic spaces to capture their unique semantics. Specifically, we propose a novel software cross-modal retrieval framework named Deep Hypothesis Testing (DeepHT). In DeepHT, to capture the unique semantics of the code's control flow structure, all control flow paths (CFPs) in the control flow graph are mapped to a CFP sample set in the sample space. Meanwhile, the text is mapped to a CFP correlation distribution in the distribution space to model its correlation with different CFPs. The matching score is calculated according to how well the sample set obeys the distribution using hypothesis testing. The experimental results on two text-to-code retrieval tasks (i.e., bug localization and code search) and two code-to-text retrieval tasks (i.e., vulnerability knowledge retrieval and historical patch retrieval) show that DeepHT outperforms the baseline methods. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting is a prerequisite guarantee for the realization of intelligent transportation. Due to the complex time and space features of traffic flow, its forecasting has always been a research hotspot in this field. Aiming at the difficulty of capturing and modelling the temporal and spatial correlation and dynamic features of traffic flow, this paper proposes a novel graph convolutional network traffic flow forecasting model (STAGCN) based on the temporal and spatial attention mechanism. STAGCN model is mainly composed of three modules: Spatio-temporal Attention (STA-Block), Graph Convolutional Network (GCN) and Standard Convolutional Network (CN), model the periodicity, spatial correlation and time dependence of traffic flow respectively. STA-Block module models the spatio-temporal correlation between different time steps through the spatio-temporal attention mechanism and gating fusion mechanism, and uses GCN and CN to capture the spatial and temporal features of traffic flow respectively. Finally, the output of the three components is predicted through a gated fusion mechanism. A large number of experiments have been conducted on two data sets of PeMS. The experimental results demonstrate that compared with the baseline method, the STAGCN model proposed in this paper has better forecasting performance. [ABSTRACT FROM AUTHOR]