Depth-first Research Articles

Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database. Most previous studies focused on finding these itemsets in row (temporal) databases and disregarded the occurrences of these itemsets in columnar databases. Furthermore, the naïve approach of transforming a columnar database into a row database and then applying the existing algorithms to find interesting itemsets is not practicable due to computational reasons. With this motivation, this paper proposes a framework to discover stable periodic-frequent itemsets in columnar databases. Our framework employs a novel depth-first search algorithm that compresses a given columnar database into a unified dictionary and mines it recursively to find all stable periodic-frequent itemsets. The dictionary holds the information pertaining to itemsets and their temporal occurrences in a database. Experimental results on six databases demonstrate that the proposed algorithm is computationally efficient and scalable.

Prediction of influential proteins and enzymes of certain diseases using a directed unimodular hypergraph.

Protein-protein interaction (PPI) analysis based on mathematical modeling is an efficient means of identifying hub proteins, corresponding enzymes and many underlying structures. In this paper, a method for the analysis of PPI is introduced and used to analyze protein interactions of diseases such as Parkinson's, COVID-19 and diabetes melitus. A directed hypergraph is used to represent PPI interactions. A novel directed hypergraph depth-first search algorithm is introduced to find the longest paths. The minor hypergraph reduces the dimension of the directed hypergraph, representing the longest paths and results in the unimodular hypergraph. The property of unimodular hypergraph clusters influential proteins and enzymes that are related thereby providing potential avenues for disease treatment.

Method and utility for minimizing bitsliced representations of 4×4 S-boxes

The article is devoted to methods and tools for generating bitsliced descriptions of bijective 4×4 S-Boxes with a reduced number of gates/instructions. Bitsliced descriptions generated by the proposed method make it possible to improve the security and performance of both software implementations of cryptoalgorithms using 4×4 S-Boxes on various processor architectures, as well as FPGA and ASIC based hardware. The paper develops a heuristic method of minimization that uses standard logical instructions AND, OR, XOR, NOT, which are available in most 8/16/32/64-bit processors. Due to the combination of different heuristic techniques (preliminary calculations, exhaustive search to a certain depth, DFS algorithm, refining search) in the method, it was possible to reduce the number of gates in bitsliced descriptions of S-Boxes compared to other known methods. The corresponding software in the form of a utility in the Python language was developed and its operation was tested on 225 S-Boxes of various cryptoalgorithms. It is found that the developed method generates a bitsliced description with a smaller number of gates in 57 % of cases compared to the best known methods implemented in the LIGHTER/Peigen utilities.

An Optimized Double-Nested Anti-Missile Force Deployment Based on the Deep Kuhn–Munkres Algorithm

In view of a complex multi-factor interaction relationship and high uncertainty of a battlefield environment in the anti-missile troop deployment, this paper analyzes the relationships between the defending stronghold, weapon system, incoming target, and ballistic missile. In addition, a double nested optimization architecture is designed by combining deep learning hierarchy concept and hierarchical dimensionality reduction processing. Moreover, a deployment model based on the double nested optimization architecture is constructed with the interception arc length as an optimization goal and based on the basic deployment model, kill zone model, and cover zone model. Further, by combining the target full coverage adjustment criterion and depth-first search, a deep Kuhn–Munkres algorithm is proposed. The model is validated by simulations of typical scenes. The results verify the rationality and feasibility of the proposed model, high adaptability of the proposed algorithm. The research of this paper has important enlightenment and reference function for solving the force deployment optimization problems in uncertain battlefield environment.

Route Planning for Autonomous Driving Based on Traffic Information via Multi-Objective Optimization

Route planning for autonomous driving is a global road planning method based on a given starting point and target point combined with current traffic flow information. The optimal global route can reduce traffic jams and improve the safety and economy of autonomous vehicles. The current optimization method of route planning for autonomous driving only considers a single objective or a chain of single objectives, which cannot meet the requirements of drivers. In this paper, we devise a general framework for the route planning method based on multi-objective optimization. Different from planning optimization based on not only traffic information, the framework considers travel time, distance, cost and personal preference, but focuses more on vehicle status and driver requirements. We use an improved depth-first search algorithm to find the optimal route. The evaluations of our method on real-world traffic data indicate the feasibility and applicability of the framework. Our study contributes to a better understanding of route planning and reveals that exploitation of personal preference can more flexibly configure the corresponding route according to the driver’s requirements.

A depth-first search algorithm for oligonucleotide design in gene assembly.

When synthesizing a gene with a long DNA sequence, it is usually necessary to divide it into several fragments. Based on these fragments, a set of oligonucleotides for gene assembly is produced. Each oligonucleotide is synthesized separately by the chemical reaction, and then the obtained oligonucleotides are assembled into the full gene sequence, in a specific environment, by polymerase chain reaction (PCR) or ligase chain reaction (LCR). In this paper, an effective and efficient algorithm to divide long genes into oligonucleotide sets is presented. First, according to the length of the overlapping oligonucleotide region, the long DNA sequence to be synthesized is divided into fragments of approximately equal length. Second, the length of these fragments is iterated to dynamically optimize the length of the overlapping regions to reduce melting temperature fluctuations. Then, the improved depth-first search algorithm is used according to the design principle of pruning optimization to obtain a uniform set of oligonucleotides with very close melting temperatures. This will decrease the errors in gene assembly with PCR or LCR. Lastly, the oligonucleotides that have homologous melting temperatures needed for PCR-based synthesis and two-step assembly of the target gene are deduced and outputted.

Bougie location algorithm of lattice biochip based on YOLOv5

Bougie location is the crucial step of information extraction in biochips. Many researchers have used algorithms, such as image brightness information or autocovariance, to grid the biochip images. However, these algorithms cannot resolve problems such as the bougie lattice being tilted or there being some bougies with insufficient hybridization. To resolve such problems, we propose a grid location algorithm based on the output of the fifth version of You Only Look Once (YOLOv5) for bougies in lattice biochip images. First, we use the YOLOv5 algorithm to detect and locate the bougies with sufficient hybridization. Second, we build a linear model for bougie location based on the least squares method and use the depth-first search algorithm to detect and locate the bougies with insufficient hybridization. Finally, we use Otsu’s method to extract the gray values of grid areas. The algorithm was tested on a dataset consisting of 100 lattice biochip images. The experimental results show that the average accuracy is 99.76%, and the average detection time is 5.04 s. The algorithm has good robustness and high accuracy and can accurately locate the bougies.

Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator.

This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.

Performance analysis of breadth-first search and depth-first search on MANET for health monitoring system

The development of mobile ad-hoc network (MANET) technology is increasing and is directly proportional to the need. MANET is a wireless network consisting of several nodes moving freely and without using the infrastructure. MANET can be applied in various fields such as the military, natural disasters, and health services. The health sector, especially the health monitoring system, gets more attention because it involves maintaining patient safety. However, wireless devices installed in health services have not been able to reach patients. This condition causes the required time and energy consumption to increase. Therefore, the breadth-first search (BFS) and depth-first search (DFS) methods are indispensable in determining the quality of service (QoS) in MANET. QoS parameters analyzed include packet delivery ratio (PDR), throughput, packet loss, delay, and energy consumption. This study aims to choose a health data traffic route that can improve QoS when transmitting data. The simulation results using the 2018 version of MATLAB show that BFS has a better performance than DFS on delay and energy consumption parameters. Meanwhile, DFS has better performance than BFS on PDR, throughput, and packet loss parameters.

Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network

Crowd intelligence based transaction network (CIbTN) is a new generation of e-commerce. In a CIbTN, buyers, sellers, and other institutions are all independent and intelligent agents. Each agent stores the commodity information in a local node. The agents interconnect through a circle of friends and construct an unstructured network. To conduct the commodity search task in a network more efficiently and in an energy-saving manner when a buyer presents a commodity demand, a hybrid breadth-depth search algorithm (HBDA) is proposed, which combines the search logic of the breadth-first search algorithm and the depth-first search algorithm. We defined the correlation degree of nodes in a network, optimized the rules of search and forwarding paths using the correlation degree between a node and its neighboring nodes in the circle of friends, and realized the HBDA based on the PeerSim simulation tool and Java. Experimental results show that, in general, the proposed HBDA has a better search success rate, search time, commodity matching degree, and search network consumption over the two blind search algorithms. The HBDA also has good expansibility, thus allowing it to be used for commodity search efficiently with a high success rate in large-scale networks.

Finding all minimum cost flows and a faster algorithm for the K best flow problem

This paper addresses the problem of determining all optimal integer solutions of a linear integer network flow problem, which we call the all optimal integer flow (AOF) problem. We derive an O ( F ( m + n ) + m n + M ) time algorithm to determine all F many optimal integer flows in a directed network with n nodes and m arcs, where M is the best time needed to find one minimum cost flow. We remark that stopping Hamacher’s well-known method for the determination of the K best integer flows (Hamacher, 1995) at the first sub-optimal flow results in an algorithm with a running time of O ( F m ( n log n + m ) + M ) for solving the AOF problem. Our improvement is essentially made possible by replacing the shortest path sub-problem with a more efficient way to determine a so-called proper zero cost cycle using a modified depth-first search technique. As a byproduct, our analysis yields an enhanced algorithm to determine the K best integer flows that runs in O ( K n 3 + M ) . Besides, we give lower and upper bounds for the number of all optimal integer and feasible integer solutions. Our bounds are based on the fact that any optimal solution can be obtained by an initial optimal tree solution plus a conical combination of incidence vectors of all induced cycles with bounded coefficients.

Efficient Top-K Identical Frequent Itemsets Mining without Support Threshold Parameter from Transactional Datasets Produced by IoT-Based Smart Shopping Carts.

Internet of Things (IoT)-backed smart shopping carts are generating an extensive amount of data in shopping markets around the world. This data can be cleaned and utilized for setting business goals and strategies. Artificial intelligence (AI) methods are used to efficiently extract meaningful patterns or insights from such huge amounts of data or big data. One such technique is Association Rule Mining (ARM) which is used to extract strategic information from the data. The crucial step in ARM is Frequent Itemsets Mining (FIM) followed by association rule generation. The FIM process starts by tuning the support threshold parameter from the user to produce the number of required frequent patterns. To perform the FIM process, the user applies hit and trial methods to rerun the aforesaid routine in order to receive the required number of patterns. The research community has shifted its focus towards the development of top-K most frequent patterns not using the support threshold parameter tuned by the user. Top-K most frequent patterns mining is considered a harder task than user-tuned support-threshold-based FIM. One of the reasons why top-K most frequent patterns mining techniques are computationally intensive is the fact that they produce a large number of candidate itemsets. These methods also do not use any explicit pruning mechanism apart from the internally auto-maintained support threshold parameter. Therefore, we propose an efficient TKIFIs Miner algorithm that uses depth-first search strategy for top-K identical frequent patterns mining. The TKIFIs Miner uses specialized one- and two-itemsets-based pruning techniques for topmost patterns mining. Comparative analysis is performed on special benchmark datasets, for example, Retail with 16,469 items, T40I10D100K and T10I4D100K with 1000 items each, etc. The evaluation results have proven that the TKIFIs Miner is at the top of the line, compared to recently available topmost patterns mining methods not using the support threshold parameter.

Flexible Real-time Railway Crew Rescheduling using Depth-first Search

Crew rescheduling is one of the most important factors one must consider during the recovery process following the disruption of a railway service. If it is not properly considered when the timetable is adjusted, it can jeopardise the prompt return to stable service. A new method, namely depth-first search crew recovery (DFSCR), is developed and proposed as a satisfactory way of dealing with the real-time crew rescheduling problem. DFSCR takes into account practical considerations of the railway environment and implements flexible, parameterised rescheduling constraints in the model to generate multiple solutions. The method has been tested on real-world scenarios and its capabilities are tested against two pre-existing methods. Sensitivity tests on five parameters (maximum permitted working hours, for example) are conducted and results indicate that small adjustments to the allowed ranges of parameter values can often generate acceptable solutions where there would have been none otherwise. This parameter relaxation is implemented via a feedback mechanism in DFSCR which takes results of a run of the algorithm to inform which parameters should be relaxed and by how much in order to maximise one's chances of finding a solution in the subsequent run of the algorithm.

Pressure management in water distribution systems through PRVs optimal placement and settings

Optimal pressure management is a standard strategy for water loss minimization in water distribution systems (WDS). A pragmatic solution to regulating water pressures and leakage is introducing pressure-reducing valves (PRVs). This paper presents a valve positioning algorithm for optimally deciding the positions and setpoints of PRVs in a WDS. The algorithm derives the hydraulic solution of a WDS as a directed graph, established on the flow directions, using EPANET 2.2 and develops the downstream network supplied by water flowing out of every pipe in the network by applying the depth-first search method. The algorithm later recognizes the pipes leading to the most extended downstream networks, with pressures above the minimum required service pressure, and prioritizes them as the ideal locations for PRV placement. In this way, the proposed algorithm overcomes the limitations of the state-of-the-art in realistically conceptualizing the leakage reduction for optimally positioning the PRVs in WDS. Four studies with varying complexities were selected to demonstrate the algorithm's applicability for deriving pressure management solutions. The solution time for PRV positioning was in seconds for the first three networks and several minutes for the extensive fourth case study. The results corroborate the algorithm's ability to pinpoint the critical nodes with the most increased potential for downstream pressure control and for maintaining the pressure at the least required service pressure level through optimally allocating the PRVs, with acceptable setpoint values, within the pipe network.

On the Performance of the Depth First Search Algorithm in Supercritical Random Graphs

We consider the performance of the Depth First Search (DFS) algorithm on the random graph $G\left(n,\frac{1+\epsilon}{n}\right)$, $\epsilon>0$ a small constant. Recently, Enriquez, Faraud and Ménard proved that the stack $U$ of the DFS follows a specific scaling limit, reaching the maximal height of $\left(1+o_{\epsilon}(1)\right)\epsilon^2n$. Here we provide a simple analysis for the typical length of a maximum path discovered by the DFS.

Effective algorithms to mine skyline frequent-utility itemsets

Skyline frequent-utility itemset mining is used to discover itemsets that are non-dominated by considering both support and utility factors. It is an extension of high-utility itemset mining. Most existing algorithms are based on the utility-list structure to mine skyline frequent-utility itemsets. A major limitation of utility-list based algorithms is that numerous join operations consume a huge amount of time and memory. To address this issue, two algorithms named EMSFUI-D and EMSFUI-B are proposed to mine skyline frequent-utility itemsets. EMSFUI-D performs the depth-first search to explore the search space of all itemsets. EMSFUI-B discovers itemsets based on the breadth-first search. Both algorithms utilize two pruning strategies to limit the search space. Moreover, in order to further facilitate the mining performance, the ISU-1 and ISU-2 structures are presented in EMSFUI-D to provide tighter utility upper bounds. These structures maintain the support and utility information of all 1-itemsets and 2-itemsets, respectively. Thus, there is no need to use these structures to prune search space in the breadth-first search algorithm. An extensive experimental study on real and synthetic datasets shows that our proposed algorithms outperform the state-of-the-art SKYFUP-D and SKYFUP-B algorithms in terms of execution time, memory consumption and pruning performance. Moreover, our designed algorithms are scalable for handling a large number of distinct items and transactions.

Implementasi Algoritma Freeman Chain Code dan Algoritma K-Nearest Neighbor Dalam Pengenalan Huruf Mandarin

Mandarin is the second international language used by the world's population and is the language most studied by students in Indonesia. This research was conducted to determine the success rate of the Freeman Chain Code algorithm and the K-Nearest Neighbor algorithm in mandarin letter recognition. In recognizing Chinese characters, there are several stages that must be passed, namely pre-processing, feature extraction, and letter recognition. The pre-processing stage uses grayscale, Gaussian Blur, binaryization, and thinning. The feature extraction stage uses the Freeman Chain Code Algorithm and the Depth First Search (DFS) Algorithm. The classification stage uses the K-Nearest Neighbor Algorithm and the L1-Metric Algorithm (Manhattan Distance). In this study, there were 10 letter classes with each letter having 100 sample images. The distribution ratio of this research is 70% training data and 30% testing data. This research produces an application that is able to recognize Chinese characters. The success rate resulting from this study was 72% with 216 of the 300 images successfully recognized

I S pan : Parallel Identification of Strongly Connected Components with Spanning Trees

Detecting strongly connected components (SCCs) in a directed graph is crucial for understanding the structure of graphs. Most real-world graphs have one large SCC that contains the majority of the vertices as well as many small SCCs whose sizes are reversely proportional to the frequency of their occurrences. For both types of SCCs, current approaches that rely on depth first search (DFS) or breadth first search (BFS) face the challenges of both strict synchronization requirements and high computation cost. In this article, we advocate a new paradigm of identifying SCCs with simple spanning trees since SCC detection requires only the knowledge of connectivity among the vertices. We have developed a prototype called i S pan , which consists of parallel, relaxed synchronization construction of spanning trees for detecting large and small SCCs combined with fast trims for small SCCs. We further scale i S pan to the distributed memory system by applying different distribution strategies to the data and task parallel jobs. Not limited, we also extend i S pan to the GPU architecture. The evaluations show that i S pan is able to significantly outperform current state-of-the-art DFS- and BFS-based methods by an average 18× and 4×, respectively.

Rapid-SL identifies synthetic lethal sets with an arbitrary cardinality

The multidrug resistance of numerous pathogenic microorganisms is a serious challenge that raises global healthcare concerns. Multi-target medications and combinatorial therapeutics are much more effective than single-target drugs due to their synergistic impact on the systematic activities of microorganisms. Designing efficient combinatorial therapeutics can benefit from identification of synthetic lethals (SLs). An SL is a set of non-essential targets (i.e., reactions or genes) that prevent the proliferation of a microorganism when they are “knocked out” simultaneously. To facilitate the identification of SLs, we introduce Rapid-SL, a new multimodal implementation of the Fast-SL method, using the depth-first search algorithm. The advantages of Rapid-SL over Fast-SL include: (a) the enumeration of all SLs that have an arbitrary cardinality, (b) a shorter runtime due to search space reduction, (c) embarrassingly parallel computations, and (d) the targeted identification of SLs. Targeted identification is important because the enumeration of higher order SLs demands the examination of too many reaction sets. Accordingly, we present specific applications of Rapid-SL for the efficient targeted identification of SLs. In particular, we found up to 67% of all quadruple SLs by investigating about 1% of the search space. Furthermore, 307 sextuples, 476 septuples, and over 9000 octuples are found for Escherichia coli genome-scale model, iAF1260.

Maximizing network utility while considering proportional fairness for rail transit systems: Jointly optimizing passenger allocation and vehicle schedules

To deal with the contradiction between oversaturated demand and the restricted transport infrastructure and capacity during peak periods in a rail transit system, several passenger flow control methods that include soft pricing incentive, rigorous physical barrier and so on are widely used in current scenarios. However, the traffic disorderliness at the bottleneck and the control unfairness for the flows on specific stations may occur with those methods in the rail transit corridor. To alleviate those problems, this paper focus on the passenger allocation and matching with the corresponding schedules by considering the fairness of passengers and cost of operation from a macro perspective, where the passenger trip plan can be collected via a reservation platform in advance. A passenger utility maximization model that programs passenger allocation is proposed firstly, and we theoretically prove that there is an optimal traffic flow allocation with maximum utility under the oversaturated flow condition for any network. The model is solved by the Newton iteration algorithm and further proven to effectively make a tradeoff between efficiency and fairness. Furthermore, the transportation network utility maximization model is constructed by jointly optimizing the passenger allocation and vehicle schedules, thereby saving the cost of vehicle operation while guaranteeing the fairness of passenger allocation macroscopically. The model is solved by Multi-tree depth-first search algorithm based on Lagrangian relaxation with a subgradient in an iterative manner. A real-world case from the Batong subway line in Beijing is studied to validate the proposed models and algorithms. The results show that the model can jointly optimize the passenger allocation and vehicle schedules effectively in both an economical and fair manner, and the passengers can pass through the bottleneck in an orderly way.