Maximum Matching Algorithm Research Articles

Applying RFID and NLP for efficient warehouse picking

This paper proposes an intelligent warehouse-picking approach using radio frequency identification (RFID) indoor positioning and natural language processing (NLP) speech recognition. A forward maximum matching algorithm segments speech into domain terminology. Location was estimated by RFID signal strengths between reference tags and pickers. Simulation results demonstrated a 50% reduction in segmentation runtime versus conventional methods. Speech recognition accuracy reached 90–95%, improving by 23% over baseline. Positioning accuracy also increased substantially. The techniques can reduce picking errors and costs. Further work should evaluate performance in real-world environments.

A comparative study of maximum matching algorithms

One of the central problems of discrete optimization is the problem of determining the maximal matching, which can be solved in various ways, depending on the structure of the initial data. In this article, a comparative study of the efficiency of maximal flow algorithms (Ford-Fulkerson) and the direct algorithm for determining maximal matching (Even & Kariv) on unweighted bipartite graphs is conducted. The analysis is based on a set of high-difficulty computing competition problems. Therefore, the results will be useful not only to those interested in the maximal matching problem but also to all those who are preparing to participate in various programming competitions.

Construction of Online Course Resource Library Based on MEA-BP Neural Networks

Abstract In this paper, the MEA algorithm is applied to the initial optimization of the weight threshold of BP neural network, which improves the stability of the classification of course resources through a two-stage optimization search process. Secondly, the effective recognition of knowledge point entities of teaching resources is realized by combining the LSTM-CRF entity recognition model, the BERT pre-trained language model and the text data enhancement technique based on the maximum bi-directional matching algorithm. In addition, the article proposes a candidate knowledge point entity generation method based on Levenshtein Distance and an entity characterization method based on BERT-TextCNN, which helps to alleviate the noise problem of the text data of curriculum resources and enhance the entity linking effect. The results of the study show that this newly constructed online course resource repository can significantly improve the allocation level of educational resources and reduce the variability of resource allocation among colleges and universities. The coefficient of educational resource allocation variation for SN217 in the university district was reduced from 0.764 to 0.518.In addition, the mean values of this repository in terms of learning path, perceived usefulness, course resources, and intention to use ranged from 1.45 to 1.66.The study in this paper provides valuable methods and insights for optimizing online educational resources.

Hear all parties: Peer effect of digital transformation on long‐term firm investment in China

AbstractThis study explores the increasing significance of digital transformation and its peer effects as key factors influencing the efficiency of long‐term firm investment. A complete digital transformation dictionary is established in this study containing 179 keywords; the frequency of digital‐transformation‐related words is determined using textual mining technology and a reverse maximum matching algorithm. The effects of digital transformation, including peer effect, on long‐term firm investment are investigated using firm‐level panel data analysis. Our empirical findings demonstrate an increase in firms' digital transformation from 2011 to 2020. Digital transformation appears to significantly reduce long‐term firm investment. Industrial peers' digital transformation prohibits a focal firm's long‐term investment, marking a noteworthy divergence from the financial heterogeneous features of focal and peer firms. The digital transformation of industrial peers positively affects the long‐term investment of focal firms by promoting an equity investment structure and an asset‐light operation strategy.

Research on the development of teaching resource library for art design majors based on artificial intelligence technology

Abstract This paper establishes the general architecture of the teaching resource repository through the design of course clusters and specific course resource organizations for the teaching characteristics of art and design majors and builds the business process from two aspects: teacher course resource archiving and student assignment management. For the association of knowledge points of teaching resources in the repository, the coding of text semantics is completed based on BERT, the text data is enhanced by a two-way maximum matching algorithm, and the enhanced data is input into the LSTM-CRF model for training to achieve entity prediction of knowledge points. For the recognition of art design teaching resources, the F1 score of this paper’s method is improved by 9.56% compared with the ATT-CNN model and 2.65% compared with the currently known better model ATT-BLSTM, which can better characterize the semantic information of the text.

Mentor and mentee matching

Pedagogic systems are gaining traction in the provision of training, learning, and continuing professional development (often required to maintain professional qualifications). An essential element in pedagogic systems is the matching of teachers (mentors) and students (mentees). In this paper we present an intelligent context-aware learning system based on profile criteria developed using big data analytic solutions. The proposed system is designed to provide systematic support for mentors based on student profiles. The goal of the proposed system is to match the mentor profiles with the type of pedagogic system, the student profile, the student requirements, and the student’s goals and expectations. The proposed system is predicated on the use of fuzzy logic definitions with a maximal length matching algorithm using expert knowledge. The proposed system implements a mentor (teacher) and mentee (student) matching algorithm based on their profile criteria. The proposed system has been successfully tested by matching mentor and mentee profiles and preferences. Experimental results show that the proposed system can access multi-factorial mentor and mentee profiles, effectively match suitable mentors (teachers) with appropriate mentees (students), and meet the mentee expectations.

High-Efficiency Blockchain-Based Supply Chain Traceability

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. To the best of our knowledge, this paper is the first work studying the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, allocating the record searching primitives to the chunks with maximized parallelization ratio is challenging. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. The experimental results indicate that the time overhead can be reduced by up to 85.1% with affordable storage overhead.

Controllability analysis of the small-world network of neural populations

Controllability analysis of brain networks is the theoretical foundation for neuromodulation feasibility. This paper presents a new framework for studying controllability of certain brain networks on the basis of neural mass models, the minimum driver node, the linearization technique and a controllability index. Firstly, a WS small-world network of Jansen-Rit's neural populations is established to mathematically model complicated neural dynamics. Secondly, an analytical method of analyzing controllability is built based on the bipartite graph maximum matching algorithm, the linearization technique and the matrix condition number. The bipartite graph maximum matching algorithm is applied to determine the minimum driver node sets for the established network while the matrix condition number is applied to define the controllability index which qualitatively evaluates the degree of the controllability of the established network. Finally, the effectiveness of the proposed analytical method is demonstrated by the influence of important parameters on the controllability and the comparison with an existing method. The proposed framework provides theoretical foundation for the study of neuromodulation feasibility, and the results are expected to lead us to better modulate neurodynamics by optimizing network dynamics or designing optimal stimulation protocols.

A Star Pattern Recognition Algorithm Based on the Radial Companion-Circumferential Feature

Attitude measurement is an important core technology of vehicle flight. It is of great significance to ensure the vehicle’s accurate orbit entry and orbit change, high-performance flight, reliable ground communication, high-precision ground observation, and successful completion of various space missions. Star sensor is the core component to realize an autonomous attitude measurement of the vehicle. Autonomous star map recognition is a key technology in star sensor technology, and it is also the focus and difficulty of research. When the star sensor enters the initial attitude acquisition mode, according to the algorithm, the star sensor can quickly obtain the initial attitude and enter the normal working mode. This paper proposes a star pattern recognition algorithm based on the radial companion-circumferential feature with a noise compensation code to address the low recognition rate caused by position noise in the process of constructing star patterns in the traditional star pattern recognition algorithm based on the radial feature. In order to solve the problem of slow matching search speed, a maximum matching number algorithm has been innovatively adopted, which can improve the search efficiency in the process of star pattern recognition. Thus, the capacity of the star pattern recognition feature library is effectively reduced, and the stability and recognition rate of the improved star pattern recognition algorithm are further improved. The improved star pattern recognition algorithm first establishes radial and circumferential feature vectors based on the bit vector, then adds the noise compensation code according to the companion star position error, then modifies the radial and circumferential feature vectors, and finally calculates the minimum similarity difference between the feature vector of the star pattern observed by the star sensor and the feature vector of the navigation star in the feature library to obtain the unique star pattern recognition result. The identification star database adopts the maximum matching number algorithm, which can improve the search efficiency, reduce the amount of redundant matching, and shorten the matching time. The simulation results show that even in the presence of star position and magnitude noise, the improved star pattern recognition algorithm with radial companion-circumferential feature maintains a high recognition rate of more than 97 percent, demonstrating that the algorithm’s robustness is superior to other algorithms. The revised method described in this work outperforms the classic triangle algorithm and the radial feature star pattern recognition algorithm without compensation code in terms of algorithm robustness, recognition success rate, and recognition time.

An Asynchronous Entanglement Distribution Protocol for Quantum Networks

Entanglement-based quantum networks can provide unconditionally secure communication by distributing entangled pairs between distant end nodes. To achieve end-to-end entanglement distribution, multiple operations of entanglement swapping in a quantum repeater chain are always required. However, due to the non-determinism of entanglement swapping caused by imperfect physical devices, the execution pattern of swapping operations has a direct impact on the performance of entanglement distribution, which can be categorized into an entanglement access control (EAC) problem. In this article, we attribute the EAC problem to two aspects: the matching optimization within quantum nodes, and the swapping conflict avoidance between quantum nodes. Accordingly, we propose an asynchronous entanglement distribution protocol which contains a custom weighted maximum matching algorithm, and a reliable signaling interaction mechanism to avoid a swapping conflict, respectively. Based on the proposed protocol, quantum repeaters autonomously decide their behaviors and spontaneously construct the end-to-end entangled pairs asynchronously. Simulation results show that our protocol can significantly improve the entanglement distribution rate and fidelity of end-to-end entangled pairs while simplifying the deployment and management process of the quantum networks.

Minimum Maximal Matchings in Cubic Graphs

We prove that every connected cubic graph with $n$ vertices has a maximal matching of size at most $\frac{5}{12} n+ \frac{1}{2}$. This confirms the cubic case of a conjecture of Baste, Fürst, Henning, Mohr and Rautenbach (2019) on regular graphs. More generally, we prove that every graph with $n$ vertices and $m$ edges and maximum degree at most $3$ has a maximal matching of size at most $\frac{4n-m}{6}+ \frac{1}{2}$. These bounds are attained by the graph $K_{3,3}$, but asymptotically there may still be some room for improvement. Moreover, the claimed maximal matchings can be found efficiently. As a corollary, we have a $\left(\frac{25}{18} + O \left( \frac{1}{n}\right)\right) $-approximation algorithm for minimum maximal matching in connected cubic graphs.

Wake up and join me! An energy-efficient algorithm for maximal matching in radio networks

We consider networks of small, autonomous devices that communicate with each other wirelessly. Minimizing energy usage is an important consideration in designing algorithms for such networks, as battery life is a crucial and limited resource. Working in a model where both sending and listening for messages deplete energy, we consider the problem of finding a maximal matching of the nodes in a radio network of arbitrary and unknown topology. We present a distributed randomized algorithm that produces, with high probability, a maximal matching. The maximum energy cost per node is \(O\big ((\log n)(\log \Delta )\big ),\) and the time complexity is \(O(\Delta \log n)\). Here n is any upper bound on the number of nodes, and \(\Delta \) is any upper bound on the maximum degree; n and \(\Delta \) are parameters of our algorithm that we assume are known a priori to all the processors. We note that there exist families of graphs for which our bounds on energy cost and time complexity are simultaneously optimal up to polylog factors, so any significant improvement would need additional assumptions about the network topology. We also consider the related problem of assigning, for each node in the network, a neighbor to back up its data in case of eventual node failure. Here, a key goal is to minimize the maximum load, defined as the number of nodes assigned to a single node. We present an efficient decentralized low-energy algorithm that finds a neighbor assignment whose maximum load is at most a polylog (n) factor bigger that the optimum.

Targets-Attackers-Defenders Game via Pairwise Outcomes

Decision algorithms are one of the key areas of focus in cluster confrontation research. In this paper, a Targets-Attackers-Defenders (TADs) game that includes an attacking team with [Formula: see text] Attackers, a target team with [Formula: see text] targets and a defending team with [Formula: see text] Defenders is considered. In this game, the players within each team cooperate with each other, and both cooperation and confrontation between the teams ensue. The defending team cooperates with the target team against the attacking team. The attacking team aims to capture [Formula: see text] Targets. The defending team protects [Formula: see text] Targets by intercepting Attackers or rendezvousing with Targets. We present a maximum matching algorithm considering both confrontation and cooperation by using relative distance and velocity parameters, and transform the TADs problem into a real-time target-assignment problem with strong confrontation. Finally, we propose an improved basic variable neighborhood search algorithm to solve the target-assignment problem and give the optimal dynamical strategy for each player.

A Subquadratic-Time Distributed Algorithm for Exact Maximum Matching

For a graph G=(V,E), finding a set of disjoint edges that do not share any vertices is called a matching problem, and finding the maximum matching is a fundamental problem in the theory of distributed graph algorithms. Although local algorithms for the approximate maximum matching problem have been widely studied, exact algorithms has not been much studied. In fact, no exact maximum matching algorithm that is faster than the trivial upper bound of O(n^2) rounds is known for the general instance. In this paper, we propose a randomized O(s_{max}^{3/2}+log n)-round algorithm in the CONGEST model, where s_{\max} is the size of maximum matching. This is the first exact maximum matching algorithm in o(n^2) rounds for general instances in the CONGEST model. The key technical ingredient of our result is a distributed algorithms of finding an augmenting path in O(s_{\max}) rounds, which is based on a novel technique of constructing a sparse certificate of augmenting paths, which is a subgraph of the input graph preserving at least one augmenting path. To establish a highly parallel construction of sparse certificates, we also propose a new characterization of sparse certificates, which might also be of independent interest.

Topology Design for GNSSs Under Polling Mechanism Considering Both Inter-Satellite Links and Ground-Satellite Links

Inter-satellite links (ISLs) are adopted in global navigation satellite systems (GNSSs) for high-precision orbit determination and space-based end-to-end telemetry telecommand control and communications. Due to limited onboard ISL terminals, the polling time division duplex (PTDD) mechanism is usually proposed for space link layer networking. By extending the polling mechanism to ground-satellite links (GSLs), a unified management system of the space segment and the ground segment can be realized. However, under the polling system how to jointly design the topology of ISLs and GSLs that are both time-slotted to improve data interaction has not been studied. In this paper, we formulate the topology design problem as an integer linear programming problem, aiming at minimizing the average delay of data delivery from satellites to ground stations while satisfying the ranging requirement for orbit determination. To tackle the computational complexity problem, we first present a novel modeling method of delay to reduce the number of decision variables. Furthermore, we propose a more efficient heuristic based on maximum weight matching algorithms. Simulation results demonstrate the feasibility of the proposed methods for practical operation in GNSSs with time-slotted GSLs. Comparing the two methods, the heuristic can achieve similar performance with respect to average delay but with significantly less complexity.

A tight bound for shortest augmenting paths on trees

The shortest augmenting path technique is one of the fundamental ideas used in maximum matching and maximum flow algorithms. Since being introduced by Edmonds and Karp in 1972, it has been widely applied in many different settings. Surprisingly, despite this extensive usage, it is still not well understood even in the simplest case: online bipartite matching problem on trees. In this problem a bipartite tree T=(W⊎B,E) is being revealed online, i.e., in each round one vertex from B with its incident edges arrives. It was conjectured by Chaudhuri et al. [6] that the total length of all shortest augmenting paths found is O(nlog⁡n). In this paper we prove a tight O(nlog⁡n) upper bound for the total length of shortest augmenting paths for trees improving over O(nlog2⁡n) bound [3].

Multi-person vision tracking approach based on human body localization features

This paper presents a multi-person vision tracking approach based on human body localization features to address the problem of interactive object localization and tracking in a home monitoring scenario. Firstly, the human body localization model is used to obtain the 3D position of the human body, which is then used to construct the human body motion model based on the Kalman filter method. At the same time, the human appearance model is constructed by fusing human color features and features of the histogram of oriented gradient to better characterize the human body. Secondly, the human body observation model is constructed based on the human body motion model and appearance model to measure the similarities between the human body state sequence in the historical frame and the human body observation result in the current frame, and the cost matrix is then obtained. Thirdly, the Hungarian maximum matching algorithm is employed to match each human body in the current and historical frames, and the exception detection mechanism is simultaneously constructed to further reduce the probability of human tracking and matching failure. Finally, a multi-person vision tracking verification platform was constructed, and the achieved average accuracy was 96.6% in the case of human body overlapping, occlusion, disappearance, and appearance; this verifies the feasibility and effectiveness of the proposed method.

HMM-BiMM: Hidden Markov Model-based word segmentation via improved Bi-directional Maximal Matching algorithm

Combining with the Hidden Markov Model and Bi-directional Maximal Matching algorithm, a new word segmentation algorithm, HMM-BiMM, is presented. In terms of the sub-dictionary matching, it can implement a fast word segmentation. After segmenting the text by the Bi-directional Maximal Matching (BiMM), the remaining text connected by the remaining single words will be segmented again by the strategy of the Hidden Markov Model (HMM). By the HMM, this algorithm can realize the dictionary dynamic update by the new segmentation words and improve the segmentation accuracy accordingly. Compared with five representative algorithms in the real-world clinical text (symptom), we show that the HMM-BiMM algorithm achieves the highest efficiency and accuracy for symptom text segmentation. In detail, this algorithm has around 3% in precision and 70% in running time improved to the BiMM.

Collaborative matching of ridesplitting and connection in the ridesourcing market

The emergence of ridesplitting as a form of ridesourcing reduces the use of vehicles on the road. When connecting multiple ridesplitting orders and single orders to the sharing path, it can achieve higher sharing efficiency. This paper aims to further improve the vehicle sharing rate, and explore the impact of multi-mode sharing using the order data of Haikou, China provided by DiDi Chuxing. A shareability network combining the ridesplitting network and connection network is built based on the order data. We propose the on-demand matching algorithm with four matching objectives to obtain the dispatching strategy. The results show that the percentage of shared trips can reach 99%, the vehicle saving rate can reach 83%, and the average number of shared trips served by shared vehicles can reach about 6 with the time interval 20 min and maximum delay 300 s. When the maximum delay is 300 s, the percentage of orders that can be shared by multiple modes is about 30%. The average delay, idling time and waiting time of shared orders are slightly higher than the corresponding maximum delay, and increase with the increase of the maximum delay, while the change of saving time is the opposite. The proposed algorithm considers the impact of the maximum delay, which, compared with the maximum matching algorithm, has a significant improvement.

An Efficient Minimal Text Segmentation Method for URL Domain Names

Text segmentation of the URL domain name is a straightforward and convenient method to analyze users’ online behaviors and is crucial to determine their areas of interest. However, the performance of popular word segmentation tools is relatively low due to the unique structure of the website domain name (such as extremely short lengths, irregular names, and no contextual relationship). To address this issue, this paper proposes an efficient minimal text segmentation (EMTS) method for URL domain names to achieve efficient adaptive text mining. We first designed a targeted hierarchical task model to reduce noise interference in minimal texts. We then presented a novel method of integrating conflict game into the two-directional maximum matching algorithm, which can make the words with higher weight and greater probability to be selected, thereby enhancing the accuracy of recognition. Next, Chinese Pinyin and English mapping were embedded in the word segmentation rules. Besides, we incorporated a correction factor that considers the text length into the F1-score to optimize the performance evaluation of text segmentation. The experimental results show that the EMTS yielded around 20 percentage points improvement with other word segmentation tools in terms of accuracy and topic extraction, providing high-quality data for the subsequent text analysis.