Depth-first Research Articles

Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN

Software-Defined Networking (SDN) enhances network control but faces Distributed Denial of Service (DDoS) attacks due to centralized control and flow-table constraints in network devices. To overcome this limitation, we introduce a multi-path routing algorithm for SDN called Trust-Based Proximal Policy Optimization (TBPPO). TBPPO incorporates a Kullback–Leibler divergence (KL divergence) trust value and a node diversity mechanism as the security assessment criterion, aiming to mitigate issues such as network fluctuations, low robustness, and congestion, with a particular emphasis on countering DDoS attacks. To avoid routing loops, differently from conventional ‘Next Hop’ routing decision methodology, we implemented an enhanced Depth-First Search (DFS) approach involving the pre-computation of path sets, from which we select the best path. To optimize the routing efficiency, we introduced an improved Proximal Policy Optimization (PPO) algorithm based on deep reinforcement learning. This enhanced PPO algorithm focuses on optimizing multi-path routing, considering security, network delay, and variations in multi-path delays. The TBPPO outperforms traditional methods in the Germany-50 evaluation, reducing average delay by 20%, cutting delay variation by 50%, and leading in trust value by 0.5, improving security and routing efficiency in SDN. TBPPO provides a practical and effective solution to enhance SDN security and routing efficiency.

Enhancing Privacy in Graph Algorithms: Data-Oblivious Approaches to DFS and Dijkstra's Algorithm

Data obliviousness is characterized by a consistent sequence of operations irrespective of input data and data-independent memory accesses, making it a suitable solution for users utilizing outsourced storage data services who aim to conceal their data access patterns. In ACM SIGSAC 2013, data-oblivious algorithms were introduced for breadth-first search, single-source single-destination (SSSD) algorithms, maximum flow, and minimum spanning tree. In this study, we present novel data-oblivious algorithms designed for depth-first search and single-source shortest path (Dijkstra’s algorithm). Our proposed data-oblivious algorithms demonstrate efficiency comparable to non-data-oblivious counterparts, particularly for graphs containing fewer than 1000 nodes. This research contributes to advancing data privacy in outsourced storage services by providing effective data-oblivious solutions for common graph algorithms.

Battery-powered automated guided vehicles scheduling problem in automated container terminals for minimizing energy consumption

Battery-Powered Automated Guided Vehicles (B-AGVs) are important equipment used to transfer containers between the seaside and the landside in automated container terminals. Due to their limited battery capacity, the B-AGVs require battery recovery when their battery reaches the threshold. However, the processing capacity of electrical battery swapping stations is limited, it is crucial to avoid congestion in swapping station. Considering that the scheduling problem of B-AGV in the terminal is time intensive, this paper proposes a set partitioning method based on breadth-first and depth-first search to solve the established mixed integer programming model. Combined with the operational characteristics of the terminal, this paper adopts the flexible pooling strategy to effectively matches the inbound and outbound containers to form dual-cycle operation of B-AGVs, which can reduce the computational complexity and thus improving the solution efficiency. In addition, the record-to-record travel algorithm is applied to improve the quality of the solution. Finally, the effectiveness of the proposed model and solution method is evaluated through numerical experiments. Besides, the applicability of the flexible pooling strategy to different container terminals is studied, and the impact of the number of B-AGVs and the capacity of swapping stations on the energy consumption of B-AGVs is investigated, and some managerial insights are given accordingly.

Voxel-based variable width continuous spiral path planning for 3D printing

Discontinuous printing paths in 3D printing lead to wasted time in fabricating products. This paper proposes a voxel-based continuous spiral path planning algorithm for 3D printing parts with less time consumption. Interestingly, we found our method can also improve the surface qualities and mechanical properties of the printed parts. The proposed path planning strategy in this paper is based on voxels which is different from traditional path planning strategies in literature. Firstly, our proposed algorithm determines the voxel size by considering the printable width of the printer nozzle. It then generates discrete elements by voxelizing the model and creates spirals and line segments by restricting the print direction of the connectable discrete elements. Next, the algorithm employs the Euler circuit to generate continuous sub-paths for the spirals and line segments. It utilizes the depth-first traversal algorithm to traverse and open the continuous sub-paths, resulting in printable continuous paths. To maintain surface accuracy for complex models, contour lines are introduced on the model's exterior and connected internally to ensure global path continuity. Lastly, the algorithm controls the path width in different directions by adjusting the extrusion rate of the printhead, preventing path overlap. Experimental results demonstrate that the proposed algorithm outperforms traditional path planning algorithms and the Connected Fermat spirals algorithm. The proposed strategy effectively mitigates the negative effects of path discontinuity, reduces printing time, and improves printed accuracy, and mechanical properties.

BigSMARTS: A Topologically Aware Query Language and Substructure Search Algorithm for Polymer Chemical Structures.

Molecular search is important in chemistry, biology, and informatics for identifying molecular structures within large data sets, improving knowledge discovery and innovation, and making chemical data FAIR (findable, accessible, interoperable, reusable). Search algorithms for polymers are significantly less developed than those for small molecules because polymer search relies on searching by polymer name, which can be challenging because polymer naming is overly broad (i.e., polyethylene), complicated for complex chemical structures, and often does not correspond to official IUPAC conventions. Chemical structure search in polymers is limited to substructures, such as monomers, without awareness of connectivity or topology. This work introduces a novel query language and graph traversal search algorithm for polymers that provides the first search method able to fully capture all of the chemical structures present in polymers. The BigSMARTS query language, an extension of the small-molecule SMARTS language, allows users to write queries that localize monomer and functional group searches to different parts of the polymer, like the middle block of a triblock, the side chain of a graft, and the backbone of a repeat unit. The substructure search algorithm is based on the traversal of graph representations of the generating functions for the stochastic graphs of polymers. Operationally, the algorithm first identifies cycles representing the monomers and then the end groups and finally performs a depth-first search to match entire subgraphs. To validate the algorithm, hundreds of queries were searched against hundreds of target chemistries and topologies from the literature, with approximately 440,000 query-target pairs. This tool provides a detailed algorithm that can be implemented in search engines to provide search results with full matching of the monomer connectivity and polymer topology.

Finding strong components using depth-first search

We survey three algorithms that use depth-first search to find the strong components of a directed graph in linear time: (1) Tarjan’s algorithm; (2) a cycle-finding algorithm; and (3) a bidirectional search algorithm.

An optimal pruned traversal tree-based fast minimum cut solver in dense graph

As an important problem in graph theory, the minimum-cut (min-cut) problem has various applications in many related fields. Among many acceleration strategies, one commonly used strategy is preprocessing the original graph to facilitate the min-cut calculations. In this paper, an optimal pruned tree-based min-cut acceleration algorithm (PTMA) is proposed for the problem by exploiting the mapping between the cuts and pruned depth-first traversal trees, which has not yet been investigated in the existing work. In different types of dense graphs with an average degree of no less than 10, using efficient dynamic programming-based preprocessing with a time complexity of O(M) (M is the total number of edges), a large number of optimal pruned depth-first traversal trees can be found, which are then used to quickly obtain accurate min-cuts of more than 99.9% node pairs. The algorithm can be used as an effective alternative to existing algorithms, and due to the inherent randomness, it is easy to fine tune the balance between overhead and precision, such as increasing the number of preprocessing passes to improve accuracy.

Artificial Intelligence-Based Chatbot to Support Public Health Services in Indonesia

The aim of this study is to build an artificial intelligence chatbot application to support public health services. The chatbot acts as an information service that can replace the role of humans. The analysis of functional needs was obtained from information submitted by one of the heads of public health centers in Indonesia. This study uses the Scrum method with pregame stages to produce a plan consisting of functional and non-functional requirements analysis and conceptual design of the chatbot, which will be developed using Unified Modeling Language (UML) diagrams. The process of finding answers uses the matching graph master technique, which is a backtrack matching that utilizes a depth-first search strategy. There are 6 topics of chatbot services, including service schedules, health information, registration, diseases, drugs, and early care services for chatbot users. Tests conducted on these 6 topics showed an average correct answer ratio of 93.1% out of a total of 251 questions. The result of the usability measurement on the chatbot application that has been built obtained a system usability scale value of 80.1, indicating that the developed chatbots are acceptable for use.

A few words about maps

In this paper, we survey some properties, encoding, and bijections involving combinatorial maps, double occurrence words, and chord diagrams. We particularly study quasi-trees from a purely combinatorial point of view and derive a topological representation of maps with a given spanning quasi-tree using two fundamental polygons, which extends the representation of planar maps based on the equivalence with bipartite circle graphs. Then, we focus on Depth-First Search trees and their connection with a poset we define on the spanning quasi-trees of a map. We apply the bijections obtained in the first section to the problem of enumerating loopless rooted maps. Finally, we return to the planar case and discuss a decomposition of planar rooted loopless maps and its consequences on planar rooted loopless map enumeration.

Research on the Co-simulation System of Subway Control Circuits

In order to solve the problems of complex control logic and the difficulty of simulating function modules due to the high coupling degree of subway control circuits, a subway control circuit co-simulation system is designed in this paper. By analysing the components data and connection relationship in the subway control circuit schematic diagrams, the network topologies are automatically generated, and the mathematical relationships between nodes, branches and components states are established, so as to build a complete logical control model. The depth-first search (DFS) algorithm is adopted to search the effective path across the topology, the event management unit is added to solve the linkage control of the relay, and the logic function simulation of the function module is realized by embedded script. Finally, the feasibility of the simulation system is verified through the simulation test of the subway door control circuits and the analysis of the simulation results. The system is conducive to the functional test and algorithm verification at the subway design stage, and has a good application prospect in the field of subway driver training.

Cooperative Merging Strategy in Mixed Traffic Based on Optimal Final-State Phase Diagram With Flexible Highway Merging Points

The cooperation between connected and automated vehicles (CAVs) has emerged as a promising way to improve traffic efficiency and safety for ramp merging on highways. Existing research mostly focused on the collaboration of individual CAVs, while the cooperative merging strategy in mixed traffic considering vehicle platoons has been less explored. To address the above problem, this study aims to build connections between sequence scheduling and motion planning in mixed traffic, where individual CAVs, CAVs platoons, and mixed platoons coexist. First, optimal control strategies are presented for vehicles with flexible merging points based on Pontryagin’s minimum principle (PMP), and the final vehicle states in variable conditions are summarized in a phase diagram. Subsequently, the optimal final-state phase diagram is introduced into the passing sequence tree search process, which is designed for different vehicle groups in mixed traffic. Heuristic pruning rules are added to the depth-first search strategy to facilitate finding the optimal solution. Finally, an event-triggered receding horizon optimization algorithm is developed for continuous implementation. The numerical simulations are conducted in multiple traffic volumes, and the simulation results reveal that our proposed algorithm significantly improves the overall traffic efficiency and reduces vehicle-passing delays compared with the traditional FIFO-based cooperation method.

Community Detection On Multi-layer Graph using Intra-layer and Inter-layer Linkage Graphs (CDMIILG)

Effective communication in complex networks is crucial for users and organizations, so graph theory is being used to provide solid groundwork for modeling and evaluating these networks. However, users often need help with group splits and communication issues due to ineffective community models. In addressing these limitations, this study proposed a community discovery technique based on Community Detection on Multi-layer Graph using Intra-layer and Inter-layer Linkage Graphs (CDMIILG) to model multiple user relationships. The CDMIILG technique adopts centrality measures, depth-first search models, recursive and transitivity, with the latter playing significant roles in the network. The practicability and effectiveness of the proposed method were evaluated using modern centrality measures and a graph-based machine-learning approach. The model’s performance was evaluated utilizing notable clustering metrics, including Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Modularity Approach, and Statistical Metrics, on seven real-world data sets. The study findings revealed that the proposed model is more effective than existing models for multi-layer graphs by yielding a 2%–7% increment.

Competitive network restructuring with spatially loyal customers. A bilevel facility delocation problem

In this paper we address the problem faced by a company that needs to decide the number and location of facilities to close in order to reduce the size of their retail distribution network, whilst minimising the market share that is lost to their competitor, who is also engaged in a network reduction process. The problem is modelled as a bilevel competitive delocation model and is formulated as a variant of the well-known (r|p)-centroid problem. The formulation takes into account the existence of a certain level of customer loyalty, which implies that customers may decide to visit a more distant facility of their regular provider rather than rely on a closer competitor’s facility. A depth-first search implicit enumeration algorithm is proposed for solving a diverse set of instances of the problem. The analysis of an instance based on a real-life setting, and intensive numerical experimentation, provide evidence of the effectiveness of the algorithm for the solution of real-size problems, and confirm the practical validity of our modelling approach. Our results also highlight the importance of considering spatial loyalty in network restructuring scenarios.

Offshore Electrical-Oil Production Coupling System Reliability Analysis

The role played by offshore oil resources in energy supply is becoming more and more important, and the normal operation of oil production system on offshore oil platforms requires the reliable operation of the electric power system as a prerequisite; the failure of the components in the oil production system may lead to a large-scale production shutdown, and cause the related electrical components to shut down, making the power system ineffective in a certain range. In this paper, a coupled system model of the electric power system and production system of an offshore oil platform based on massively parallel computing is proposed. By analysing the connection between the oil production system and the electric power system, the coupled system model is established and the load data are calculated. Meanwhile, the reliability model of the coupling components is established based on the fault data, and the electric-related reliability index and the production-related reliability index of the coupling system are proposed. The broad-first search (BFS) algorithm was used to determine whether the coupling nodes were powered, and the depth-first search (DFS) algorithm was used to determine the connectivity of the coupling system. In addition, a minimum load-shedding model based on the integrated fault degree is proposed to reduce the load shedding of the coupled system.

MCoR-Miner: Maximal Co-Occurrence Nonoverlapping Sequential Rule Mining

The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given sequence. Although various SPM methods have been investigated, most of these focus on mining all of the patterns. However, users sometimes want to mine patterns with the same specific prefix pattern, called co-occurrence pattern. Since sequential rule mining can make better use of the results of SPM, and obtain better recommendation performance, this paper addresses the issue of maximal co-occurrence nonoverlapping sequential rule (MCoR) mining and proposes the MCoR-Miner algorithm. To improve the efficiency of support calculation, MCoR-Miner employs depth-first search and backtracking strategies equipped with an indexing mechanism to avoid the use of sequential searching. To obviate useless support calculations for some sequences, MCoR-Miner adopts a filtering strategy to prune the sequences without the prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies the frequent item and binomial enumeration tree strategies. To avoid searching for the maximal rules through brute force, MCoR-Miner uses a screening strategy. To validate the performance of MCoR-Miner, eleven competitive algorithms were conducted on eight sequences. Our experimental results showed that MCoR-Miner outperformed other competitive algorithms, and yielded better recommendation performance than frequent co-occurrence pattern mining. All algorithms and datasets can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner</uri> .

Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation

Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation

Unauthorized Access Detection for Network Device Firmware WEB Pages

WEB technology is utilized for the configuration, interaction, and management of network equipment, which has become ubiquitous in the intelligent industry and consumer electronics field. Unauthorized access on WEB allows unauthorized users to access authorized information, causing security vulnerabilities such as information leakage and command execution. However, commonly used vulnerability detection techniques for WEB unauthorized access face increasing challenges and more efficiently identify potentially sensitive pages. We propose WEBUAD, a WEB Unauthorized Access Detection framework, for the vulnerability detection of WEB service IoT network devices. WEBUAD utilizes the depth-first search algorithm to fully mine available information on device firmware and generate a potential-visit page set as well as a similarity–matching algorithm of machine learning to calculate the similarity of the responses of a web request. Finally, we evaluate WEBUAD on 9 real physical devices from four vendors and 190 device firmware from seven vendors. The result shows that compared with the state-of-the-art tool such as IoTScope, WEBUAD discovered 5007 potentially available pages, of which 658 were accessible and 9 sensitive pages existed, taking 50 s. Furthermore, WEBUAD exposed 13 security-critical vulnerabilities. Our approach can be used to automate the discovery of the WEB unauthorized access vulnerabilities of IoT devices.

Deep Reinforcement Learning for Intelligent Penetration Testing Path Design

Penetration testing is an important method to evaluate the security degree of a network system. The importance of penetration testing attack path planning lies in its ability to simulate attacker behavior, identify vulnerabilities, reduce potential losses, and continuously improve security strategies. By systematically simulating various attack scenarios, it enables proactive risk assessment and the development of robust security measures. To address the problems of inaccurate path prediction and difficult convergence in the training process of attack path planning, an algorithm which combines attack graph tools (i.e., MulVAL, multi-stage vulnerability analysis language) and the double deep Q network is proposed. This algorithm first constructs an attack tree, searches paths in the attack graph, and then builds a transfer matrix based on depth-first search to obtain all reachable paths in the target system. Finally, the optimal path for target system attack path planning is obtained by using the deep double Q network (DDQN) algorithm. The MulVAL double deep Q network(MDDQN) algorithm is tested in different scale penetration testing environments. The experimental results show that, compared with the traditional deep Q network (DQN) algorithm, the MDDQN algorithm is able to reach convergence faster and more stably and improve the efficiency of attack path planning.

Key deviation source diagnosis of complex thin-walled structures based on complex networks and weighted transfer entropy

Abstract There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.

Techniques for accelerating branch-and-bound algorithms dedicated to sparse optimization

Sparse optimization–fitting data with a low-cardinality linear model–is addressed through the minimization of a cardinality-penalized least-squares function, for which dedicated branch-and-bound algorithms clearly outperform generic mixed-integer-programming solvers. Three acceleration techniques are proposed for such algorithms. Convex relaxation problems at each node are addressed with dual approaches, which can early prune suboptimal nodes. Screening methods are implemented, which fix variables to their optimal value during the node evaluation, reducing the subproblem size. Numerical experiments show that the efficiency of such techniques depends on the node cardinality and on the structure of the problem matrix. Last, different exploration strategies are proposed to schedule the nodes. Best-first search is shown to outperform the standard depth-first search used in the related literature. A new strategy is proposed which first explores the nodes with the lowest least-squares value, which is shown to be the best at finding the optimal solution–without proving its optimality. A C++ solver with compiling and usage instructions is made available.