Breadth-first Research Articles

Privacy-Preserving Dynamic Multi-Keyword Ranked Search Scheme in Multi-User Settings

With the rapid development of consumer electronics in Industry 5.0, personalized service supplement based on the cloud infrastructure for the Internet of Things (IoT) has been a promising requirement pushed to consumers. During the massive and frequent IoT data interaction from the consumer-centric in Industry 5.0, efficiently searchable encryption for multiple consumers are indispensable. Existing multi-user searchable encryption is based on attribute and predicate encryption technology to realize one-to-many rather than realistic many-to-many scenarios. Moreover, multi-keyword ranked search, dynamic update, and search pattern hiding have not been implemented in the multi-user scenario. In this paper, to address the above problems for consumer electronics in Industry 5.0, we present a privacy-preserving dynamic multi-keyword ranked search scheme over encrypted cloud data to accomplish pay-as-you-consume cloud data security sharing. Our scheme designs a specific tree-based index structure and a “Greedy Breadth-First Search” algorithm to achieve the sub-linear search. To support dynamic updates in the cloud, a novel secure maximum generation protocol is proposed. Finally, the security analysis and experiment evaluation prove our scheme cannot only preserve the privacy of index and search patterns but also support dynamic update operations under the multi-writer/multi-reader setting at an acceptable cost.

Deep reinforcement learning based planning method in state space for lunar rovers

The unmanned lunar rover is essential for lunar exploration and construction. Executing environment differ from what humans get since communication needs time from Earth to Moon. Considering possible discrepancies between the pre-considered environment from the planner and the real environment for sampling tasks on the moon, a planner that generates short plans quickly should be used. Therefore, a planner for both standard and emergency planning based on deep reinforcement learning (DRL) is demonstrated in this paper. This planner can create a full-range plan 13.5 times faster than the traditional planner on complex problems or 10.1 times faster while controlling the rover step-by-step in the state space. Based on a specific moon sampling scenario, we propose a tracking reward guiding the rover searching in the states in the deep reinforcement learning architecture which is presented and created by a state space representation by matrix, randomly available training state pairs and the plans generated by a custom breadth-first search (BFS) planner for the tracking reward. The BFS planner obtains a custom state hash algorithm and a preparation to train state pairs for safety and flexibility. Tests on training and planning are performed to validate the effectiveness, robustness and customization of the proposed method in a planning domain with multiple rovers. Our model can handle three kinds of emergencies, even if they occur frequently. The success rate is beyond the state-of-the-art model. While facing emergencies, the average response time of our model is 324 times faster than the classical planner.

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.

The energy complexity of diameter and minimum cut computation in bounded-genus networks

This paper investigates the energy complexity of distributed graph problems in multi-hop radio networks, where the energy cost of an algorithm is measured by the maximum number of awake rounds of a vertex. Recent works revealed that some problems, such as broadcast, breadth-first search, and maximal matching, can be solved with energy-efficient algorithms that consume only polylog⁡n energy. However, there exist some problems, such as computing the diameter of the graph, that require Ω(n) energy to solve. To improve energy efficiency for these problems, we focus on a special graph class: bounded-genus graphs. We present algorithms for computing the exact diameter, the exact global minimum cut size, and a (1±ϵ)-approximate s-t minimum cut size with O˜(n) energy for bounded-genus graphs. Our approach is based on a generic framework that divides the vertex set into high-degree and low-degree parts and leverages the structural properties of bounded-genus graphs to control the number of certain connected components in the subgraph induced by the low-degree part.

Mining top-k high average-utility itemsets based on breadth-first search

Mining top-k high average-utility itemsets based on breadth-first search

Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach

A novel non-centralised dispatch strategy is presented for wake redirection to optimise large-scale offshore wind farms operation, creating a balanced control between power production and fatigue thrust loads evenly among the wind turbines. This approach is founded on a graph sparsification partitioning strategy that takes into account the impact of wake propagation. More specifically, the breadth-first search algorithm is employed to identify the subgraph based on the connectivity of the wake direction graph, while the PageRank centrality computation algorithm is utilised to determine and rank scores for the shared turbines’ affiliation with the subgraphs. By doing so, the wind farm is divided into smaller subsets of partitioned turbines, resulting in decoupling. The objective function is then formulated by incorporating penalty terms, specifically the standard deviation of fatigue thrust loads, into the maximum power equation. Meanwhile, the non-centralisation sequential quadratic programming optimisation algorithm is subsequently employed within each partition to determine the control actions while considering the objectives of the respective controllers. Finally, the simulation results of case studies prove to reduce computational costs and improve wind farm power production by balancing accumulated fatigue thrust loads over the operational lifetime as much as possible.

Finding biclique partitions of co-chordal graphs

The biclique partition number (bp) of a graph G is referred to as the least number of complete bipartite (biclique) subgraphs that are required to cover the edges of the graph exactly once. In this paper, we show that the biclique partition number of a co-chordal (complementary graph of chordal) graph G=(V,E) is less than the number of maximal cliques (mc) of its complementary graph: A chordal graph Gc=(V,Ec). We first provide a general framework of the “divide and conquer” heuristic of finding minimum biclique partitions of co-chordal graphs based on clique trees. Furthermore, a heuristic of complexity O(|V|(|V|+|Ec|)) is proposed by applying a lexicographic breadth-first search to find structures called moplexes. Either heuristic gives us a biclique partition of G with a size of mc(Gc)−1. In addition, we prove that both of our heuristics can solve the minimum biclique partition problem on G exactly if its complement Gc is chordal and clique vertex irreducible. We also show that mc(Gc)−2≤bp(G)≤mc(Gc)−1 if G is a split graph.

Cascading Delays for the High-Speed Rail Network Under Different Emergencies: A Double Layer Network Approach

High-speed rail (HSR) has formed a networked operational scale in China. Any internal or external disturbance may deviate trains' operation from the planned schedules, resulting in primary delays or even cascading delays on a network scale. Studying the delay propagation mechanism could help to improve the timetable resilience in the planning stage and realize cooperative rescheduling for dispatchers. To quickly and effectively predict the spatial-temporal range of cascading delays, this paper proposes a max-plus algebra based delay propagation model considering trains' operation strategy and the systems' constraints. A double-layer network based breadth-first search algorithm based on the constraint network and the timetable network is further proposed to solve the delay propagation process for different kinds of emergencies. The proposed model could deal with the delay propagation problem when emergencies occur in sections or stations and is suitable for static emergencies and dynamic emergencies. Case studies show that the proposed algorithm can significantly improve the computational efficiency of the large-scale HSR network. Moreover, the real operational data of China HSR is adopted to verify the proposed model, and the results show that the cascading delays can be timely and accurately inferred, and the delay propagation characteristics under three kinds of emergencies are unfolded.

Assessment of topological boundaries for microgrids in an active power distribution network during emergencies

Microgrid (MG) operation has become an intriguing possibility to enhance the sustainability, reliability, and resilience of electricity distribution systems in emergencies. However, identifying suitable physical boundaries of MGs from an active power delivery infrastructure is a highly challenging task. This study describes a novel approach for determining the topological boundary of multiple MGs to restore power supply in a power distribution system during the unavailability of grid power supply. This method tries to enhance the self-adequacy of the MGs by increasing power flow within the MG boundaries. In this context, possible demand at the nodes of the power delivery infrastructure during an emergency is anticipated by an autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) network-based time series forecast models. A modified backward-forward sweep (MBFS) power flow analysis, tailored for islanded mode, is employed to assess the power flow status in the MGs during emergencies. The MGs are supplied with locally available dispatchable distributed generators (DDGs). A particle swarm optimization (PSO) algorithm intertwined with the MBFS power flow method has been employed to determine the boundary of MGs by identifying minimum power flow line in the shortest path between the consecutive DDGs. Further, this study demonstrates the evaluation of the shortest path between paths between the consecutive DDGs with the help of a breadth-first search (BFS) algorithm. The proposed strategy has been tested on a modified 33-bus, 69-bus and 118-bus power distribution test systems. The experimented results are verified with the results obtained from similar existing approaches to explore the opportunities in the proposed methodology.

Mobile application for finding the best route on campus

The actual task of developing a mobile application for finding the optimal route on a university campus is considered. The mathematical basis for designing a mobile application is the task of finding the shortest route in a graph whose vertices are classrooms, laboratories, libraries, departments, dean's offices, etc. in the buildings of the university.The analysis of the main characteristics of existing mobile applications for walking navigation has been carried out, their advantages and disadvantages have been identified. The well-known algorithms for finding the shortest path in a graph are considered: breadth-first search, greedy algorithm, and Dijkstra shortest path algorithm. For the task under consideration, a software implementation and an auxiliary service have been developed to create maps and bring them to a format in which a graph is automatically created for the correct operation of the application. The novelty of the proposed development lies in the possibility of displaying, adding and editing various maps. Further improvement of the developed mobile application can be carried out in the direction of increasing the accuracy of navigation by combining algorithms for finding paths in a graph, as well as in translating maps from 2D to 3D. The results of the study can be used to find the best routes in other universities.

A Multi-Stage Real-Time Fast Search Method for Closed-Loop Paths Based on Grid Hierarchical Partitioning Characteristics

Prior to switching operations, the dispatch automation system is required to be able to search for closed-loop paths quickly in real time. In order to improve the efficiency of closed-loop path search, this paper proposes a multi-stage real-time fast search method for closed-loop paths based on the characteristics of power grid hierarchical partitioning. First, we divide the closed-loop judgement process into three phases: initialization, acceptance of the closed-loop judgement command, and post-operation. Then, we define three types of nodes, including root nodes, same-layer contact nodes, and common nodes, and construct path data models for each type of node in accordance with the node liaison relationship in grid hierarchical zoning. In the initialization phase, we design a method for the automatic generation of real-time nodal path data models and study a hierarchical partitioned closed-loop path search method based on a nodal path data model for the phase of accepting closed-loop judgement commands. Next, we design a fast local correction method for nodal path models after the switch closure and disconnection operations in the post-operation idle phase are performed. Finally, a real power grid model is used as an example to test the above closed-loop path search method, which improves the search efficiency by eight times compared with the conventional breadth-first search method. The method is applicable to real large and complex power grids.

Urban traffic volume estimation using intelligent transportation system crowdsourced data

Traffic volume is a crucial information for many different fields, such as city planner, logistic planning and more. However, installing sensors on each road to collect traffic volume data for the whole traffic network is impractical due to high cost and human labour. Most recent studies implement machine learning, mathematical and statistical methods to learn the behaviour of traffic volume. However, the randomness of traffic volume can hardly be defined by equations or statistical models which leads to the proposed machine learning model. This paper proposed a novel spatial prediction to fill up the traffic volume of a whole network with an estimated 10% of ground truth data. To make up for the lack of data, a spatial-temporal weightage is assigned to each road before fitting the training sample into a tree ensemble model to perform a prediction of the connecting roads. The weightage is first computed using the 10% ground truth data and then the weightage is spread to connecting roads via an innovative repetitive breadth-first search (BFS) method that capture the spatial correlation of a traffic network. Various experiments were conducted to assess the significance of spatial weighting and it was observed that incorporating the weighting resulted in a 1.69% improvement in the Mean Absolute Percentage Error (MAPE). The temporal relationship can be learnt from the trend of hourly traffic data for every day of the week. The proposed model achieved an average percentage error of 2.63% with reduced average percentage error by 95% compared to existing methods.

Identification of Influential Nodes in Social Network: Graph Approach

The identification of nodes that are actually influencing other nodes is one of the core and fundamental research problems in the social network data sciences. There are multiple proven techniques available globally that depict the influence of the root node on the remaining nodes in the given network. Although methods and methodology is already proven and multiple experiments have been carried out in this area, the current research work is an extension of the influential node identification problem with the perspective of graph traversal. To prove the methodology and new approach a tree and AUC (area under the curve) - based analysis method has been proposed, TArank in which the information collected from the breadth-first search tree is used to identify influential nodes. From the obtained results and analysis, the effectiveness of TARank in the identification of influential nodes has been validated. The concentration of data at a particular segment of the plot is observed. Additionally, the tree view of the analysis predicts the root and leaf node thus helping in the identification of the most influential nodes in the online social media network. Keywords: Influential Node, PCC, TARank, Degree Centrality, PCC

Iterative Level-0: A new and fast algorithm to traverse mating networks calculating the inbreeding and relationship coefficients

In population medical genetics, the study of autosomal recessive disorders in highly endogamous populations is a major topic where calculating the inbreeding and relationship coefficients on mating networks is crucial. However, a challenge arises when dealing with large and complex mating networks, making their traversal difficult during the calculation process. For this calculation, we propose using Iterative Level-0 (IL0) as a new and faster algorithm that traverses mating networks more efficiently. The purpose of this work is to explain in detail the IL0 algorithm and prove its superiority by comparing it with two algorithms based on the best-known algorithms in the area: Depth First Search (DFS) and Breadth First Search (BFS). A Cytoscape application has been developed to calculate the inbreeding and relationship coefficients of individuals composing any mating network. In this application, the IL0 proposal together with DFS-based and BFS-based algorithms have been implemented. Any user can access this freely available Cytoscape application (https://apps.cytoscape.org/apps/inbreeding) that allows the comparison between the IL0 proposal and the best-known algorithms (based on DFS and BFS). In addition, a diverse set of mating networks has been collected in terms of complexity (number of edges) and species (humans, primates, and dogs) for the experiments. The runtime obtained by the IL0, DFS-based, and BFS-based algorithms when calculating the inbreeding and relationship coefficients proved the improvement of IL0. In fact, a speedup study reflected that the IL0 algorithm is 7.60 to 127.50 times faster than DFS-based and BFS-based algorithms. Moreover, a scalability study found that the growth of the IL0 runtime has a linear dependence on the number of edges of the mating network, while the DFS-based and BFS-based runtimes have a quadratic dependence. Therefore, the IL0 algorithm can solve the problem of calculating the inbreeding and relationship coefficients many times faster (up to 127.50) than the two algorithms based on the famous DFS and BFS. Furthermore, our results demonstrate that IL0 scales much better as the complexity of mating networks increases.

Algorithm Visualizer

Abstract: In recent years, computer science education has become increasingly important as technology continues to play a dominant role in our lives. The understanding of algorithms and their implementation is a crucial aspect of computer science education. Visualizing algorithms can be a powerful tool to help students understand and retain the concepts behind them. This paper presents a new algorithm visualizer that focuses on two main types of algorithms: sorting algorithms and graph pathfinding algorithms. The algorithm visualizer was created using React.js, a popular JavaScript library, and provides visualizations for various sorting algorithms, such as merge sort, quick sort, heap sort, and bubble sort. Additionally, the visualizer includes visualizations for graph pathfinding algorithms such as breadth-first search, depth-first search, and A*. The visualizer also includes mazes and patterns that can be solved using the pathfinding algorithms, allowing users to see the algorithms in action. The algorithm visualizer provides a user-friendly interface that allows users to step through the algorithms and see how they work. This interactive approach to learning algorithms provides a valuable resource for students and educators alike. The visualizer is also highly customizable, allowing users to adjust the speed and complexity of the algorithms to fit their needs. This paper provides a comprehensive overview of the design, implementation, and evaluation of the algorithm visualizer.

Erosion mechanism for submarine pipeline on graded sediments using a coupled PORO-CFD-DEM model

To investigate the meso-mechanism of scouring process, a numerical model that couples the porous medium model, computational fluid dynamics and discrete elements method (PORO-CFD-DEM model) is established and validated. Two pipeline conditions, suspended and partially buried, are considered. The porous medium is used to replace most of the discrete elements, significantly reducing the computational requirements. Two particle generation methods, namely fictious grid method (FGM) and breath-first search method (BFM), are proposed, and three kinds of particle samples are generated with different gradations: Gaussian, gap and continuous. Detailed analyses of flow, fluid-particle interaction and sediment motion is conducted with an increasing inlet velocity. When the pipeline is suspended, the seepage flow is induced by the gap flow beneath the pipeline, with the pressure dropping along the seepage path. Around partially buried pipeline, the seepage flow is much weaker and the pressure concentration appears. The seepage force fluctuates, and the loose bed is disturbed, leading to randomly collapses. Different particle movement patterns, including the jumping, suspended transportation and the formation of sand slope, are captured during the simulations. It’s confirmed that the PORO-CFD-DEM model can capture the meso-movement of the particles and provides insights into the theory of sediment transportation.

MWFormer: Mesh Understanding with Window-based Transformer

Polygonal mesh has been proven to be a powerful representation of 3D shapes, given its efficiency in expressing shape surface while maintaining geometric and topological information. Increasing efforts have been made to design elaborate deep convolutional neural networks for meshes. However, these methods naturally ignore the global connectivity among mesh primitives due to the locality nature of convolution operations. In this paper, we introduce a transformer-like self-attention mechanism with down-sampling architectures for mesh learning to capture both the global and local relationships among mesh faces. To achieve this, we propose BFS-Pooling, which can convert a connected mesh into discrete tokens (i.e., a set of adjacent faces) with breath-first-search (BFS) and naturally build hierarchical architectures for mesh learning by pooling mesh tokens. Benefiting from BFS-Pooling, we design a hierarchical transformer architecture with a window-based local attention mechanism, Mesh Window Transformer (MWFormer). Experimental results demonstrate that MWFormer achieves the best or competitive performance in both mesh classification and mesh segmentation tasks. Code will be available.

Closeness Centrality on Uncertain Graphs

Centrality is a family of metrics for characterizing the importance of a vertex in a graph. Although a large number of centrality metrics have been proposed, a majority of them ignores uncertainty in graph data. In this article, we formulate closeness centrality on uncertain graphs and define the batch closeness centrality evaluation problem that computes the closeness centrality of a subset of vertices in an uncertain graph. We develop three algorithms, MS-BCC , MG-BCC, and MGMS-BCC , based on sampling to approximate the closeness centrality of the specified vertices. All these algorithms require to perform breadth-first searches (BFS) starting from the specified vertices on a large number of sampled possible worlds of the uncertain graph. To improve the efficiency of the algorithms, we exploit operation-level parallelism of the BFS traversals and simultaneously execute the shared sequences of operations in the breadth-first searches. Parallelization is realized at different levels in these algorithms. The experimental results show that the proposed algorithms can efficiently and accurately approximate the closeness centrality of the given vertices. MGMS-BCC is faster than both MS-BCC and MG-BCC because it avoids more repeated executions of the shared operation sequences in the BFS traversals.

Route choice modeling for cyclists on urban networks

This paper presents the results of route choice models for cyclists in the city of Zurich. The data includes approx. 4400 cycling trajectories, including approx. 850 from e-bikes. The network is sourced from Open Street Map (OSM) and the choice set generation is based on the breadth-first search on link elimination (BFSLE) algorithm. We present descriptive statistics and model results which specifically point out the difference between regular and e-bikes. We provide results of a simple path size Logit (PSL) and a more complex mixed PSL, both estimated in Value-of-Distance (VoD) space, and both suited to directly derive VoD indicators. The results show anticipated effects for cycling infrastructure, speed limits, traffic signals, gradients and traffic volumes. Numerous interaction effects shed light on the taste heterogeneity.

Diameter-2-critical graphs with at most 13 nodes

Diameter-2-critical graphs (abbr. D2C) are diameter 2 graphs whose diameter increases by removing any edge. The procedure used to obtain the list of D2C graphs of the order at most 13 is described. This is achieved by incorporating the diameter 2 test and the criticality test into geng, the program from the package nauty that generates the list of all non-isomorphic connected graphs. Experiments with the two heuristics in diameter 2 test, which is intensively used during the search, show that it is slightly more efficient to start the test with the largest degree node using BFS algorithm. As an application of the obtained list, the three conjectures concerning the maximum number of edges in D2C graphs were checked for graphs of the order at most 13 and one counterexample was found. Index Terms: diameter-2-critical graphs, graph diameter, primitive graph.