Matching Problem Research Articles

Bayesian sparsity and class sparsity priors for dictionary learning and coding

Dictionary learning methods continue to gain popularity for the solution of challenging inverse problems. In the dictionary learning approach, the computational forward model is replaced by a large dictionary of possible outcomes, and the problem is to identify the dictionary entries that best match the data, akin to traditional query matching in search engines. Sparse coding techniques are used to guarantee that the dictionary matching identifies only few of the dictionary entries, and dictionary compression methods are used to reduce the complexity of the matching problem. In this article, we propose a work flow to facilitate the dictionary matching process. First, the full dictionary is divided into subdictionaries that are separately compressed. The error introduced by the dictionary compression is handled in the Bayesian framework as a modeling error. Furthermore, we propose a new Bayesian data-driven group sparsity coding method to help identify subdictionaries that are not relevant for the dictionary matching. After discarding irrelevant subdictionaries, the dictionary matching is addressed as a deflated problem using sparse coding. The compression and deflation steps can lead to substantial decreases of the computational complexity. The effectiveness of compensating for the dictionary compression error and using the novel group sparsity promotion to deflate the original dictionary are illustrated by applying the methodology to real world problems, the glitch detection in the LIGO experiment and hyperspectral remote sensing.

Automatic Knowledge Graph matching via Self-adaptive Designed Genetic Programming

Knowledge Graph (KG) provides a structured representation of domain knowledge by formally defining entities and their relationships. However, distinct communities tend to employ different terminologies and granularity levels to describe the same entity, leading to the KG heterogeneity issue that hampers their communications. KG matching can identify semantically similar entities in two KGs, which is an effective solution to this problem. Similarity Measures (SMs) are the foundation of the KG matching technique, and due to the complexity of entity heterogeneity, it is necessary to construct a high-level SM by selecting and combining the basic SMs. However, the large number of SMs and their intricate relationships make SM construction an open challenge. Inspired by the success of Evolutionary Algorithms (EA) in addressing the entity matching problem, this work further proposes a novel Self-adaptive Designed Genetic Programming (SDGP) to automatically construct the SM for KG matching. To overcome the drawbacks of the classic EA-based matching methods, a new individual representation and a novel fitness function are proposed to enable SDGP automatically explore the SM selection and combination. Then, a new Adaptive Automatic Design (AAD) method is introduced to adaptively trade off SDGP’s exploration and exploitation, which can determine the timing of AAD and efficiently determine the suitable breeding operators and control parameters for SDGP. The experiment uses the Ontology Alignment Evaluation Initiative’s Knowledge Graph (KG) data set to test the performance of SDGP. The experimental results show that SDGP can effectively determine high-quality KG alignments, which significantly outperform state-of-the-art KG matching methods.

Point cloud registration with quantile assignment

Point cloud registration is a fundamental problem in computer vision. The problem encompasses critical tasks such as feature estimation, correspondence matching, and transformation estimation. The point cloud registration problem can be cast as a quantile matching problem. We refined the quantile assignment algorithm by integrating prevalent feature descriptors and transformation estimation methods to enhance the correspondence between the source and target point clouds. We evaluated the performances of these descriptors and methods with our approach through controlled experiments on a dataset we constructed using well-known 3D models. This systematic investigation led us to identify the most suitable methods for complementing our approach. Subsequently, we devised a new end-to-end, coarse-to-fine pairwise point cloud registration framework. Finally, we tested our framework on indoor and outdoor benchmark datasets and compared our results with state-of-the-art point cloud registration methods.

Designing Service Menus for Bipartite Queueing Systems

Designing Fair and Incentive-Compatible Matching Systems Many service systems face the challenge of dynamically matching customers who have heterogeneous preferences with an available pool of service providers in a fair and equitable manner. The first come, first served (FCFS) service discipline offers a straightforward and fair approach to allocate arriving customers to available servers and manage congestion. However, FCFS may limit the ability of service providers to reach an efficient allocation based on the individual preferences of customers and the characteristics of servers. In “Designing Service Menus for Bipartite Queuing Systems,” Caldentey, Hillas, and Gupta develop a queueing framework to address this dynamic matching problem. Their proposed methodology enables service providers to offer a menu of different service classes, each served by a distinct pool of servers. By thoughtfully designing this menu, a service provider can encourage self-interested customers to select service classes in a way that optimizes the trade-off between quality of matching and waiting time delays.

Finding maximal exact matches in graphs

BackgroundWe study the problem of finding maximal exact matches (MEMs) between a query string Q and a labeled graph G. MEMs are an important class of seeds, often used in seed-chain-extend type of practical alignment methods because of their strong connections to classical metrics. A principled way to speed up chaining is to limit the number of MEMs by considering only MEMs of length at least κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document} (κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document}-MEMs). However, on arbitrary input graphs, the problem of finding MEMs cannot be solved in truly sub-quadratic time under SETH (Equi et al., TALG 2023) even on acyclic graphs.ResultsIn this paper we show an O(n·L·dL-1+m+Mκ,L)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n\\cdot L \\cdot d^{L-1} + m + M_{\\kappa ,L})$$\\end{document}-time algorithm finding all κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document}-MEMs between Q and G spanning exactly L nodes in G, where n is the total length of node labels, d is the maximum degree of a node in G, m=|Q|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$m = |Q|$$\\end{document}, and Mκ,L\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_{\\kappa ,L}$$\\end{document} is the number of output MEMs. We use this algorithm to develop a κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document}-MEM finding solution on indexable Elastic Founder Graphs (Equi et al., Algorithmica 2022) running in time O(nH2+m+Mκ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(nH^2 + m + M_\\kappa )$$\\end{document}, where H is the maximum number of nodes in a block, and Mκ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$M_\\kappa$$\\end{document} is the total number of κ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\kappa$$\\end{document}-MEMs. Our results generalize to the analysis of multiple query strings (MEMs between G and any of the strings). Additionally, we provide some experimental results showing that the number of graph MEMs is an order of magnitude smaller than the number of string MEMs of the corresponding concatenated collection.ConclusionsWe show that seed-chain-extend type of alignment methods can be implemented on top of indexable Elastic Founder Graphs by providing an efficient way to produce the seeds between a set of queries and the graph. The code is available in https://github.com/algbio/efg-mems.

Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks

Novel architectures incorporating transport networks and artificial intelligence (AI) are currently being developed for beyond 5G and 6G technologies. Given that the interfacing mobile and transport network nodes deliver high transactional packet volume in downlink and uplink streams, 6G networks envision adopting diverse transport networks, including non-terrestrial types of transport networks such as the satellite network, High-Altitude Platform Systems (HAPS), and DOCSIS cable TV. Hence, there is a need to match the traffic to the transport network. This paper focuses on such a matching problem and defines a method that leverages machine learning and mixed-integer linear programming. Consequently, the proposed scheme in this paper is to develop a traffic steering capability based on types of transport networks, namely, optical, satellite, and DOCSIS cable. Novel findings demonstrate a more than 90% accuracy of steered traffic to respective types of transport networks for dedicated transport network resources.

Pattern Masking for Dictionary Matching: Theory and Practice

Data masking is a common technique for sanitizing sensitive data maintained in database systems which is becoming increasingly important in various application areas, such as in record linkage of personal data. This work formalizes the Pattern Masking for Dictionary Matching (PMDM) problem: given a dictionary D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {D}$$\\end{document} of d strings, each of length ℓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell $$\\end{document}, a query string q of length ℓ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell $$\\end{document}, and a positive integer z, we are asked to compute a smallest set K⊆{1,…,ℓ}\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$K\\subseteq \\{1,\\ldots ,\\ell \\}$$\\end{document}, so that if q[i] is replaced by a wildcard for all i∈K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$i\\in K$$\\end{document}, then q matches at least z strings from D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {D}$$\\end{document}. Solving PMDM allows providing data utility guarantees as opposed to existing approaches. We first show, through a reduction from the well-known k-Clique problem, that a decision version of the PMDM problem is NP-complete, even for binary strings. We thus approach the problem from a more practical perspective. We show a combinatorial O((dℓ)|K|/3+dℓ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {O}((d\\ell )^{|K|/3}+d\\ell )$$\\end{document}-time and O(dℓ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {O}(d\\ell )$$\\end{document}-space algorithm for PMDM for |K|=O(1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$|K|=\\mathscr {O}(1)$$\\end{document}. In fact, we show that we cannot hope for a faster combinatorial algorithm, unless the combinatorial k-Clique hypothesis fails (Abboud et al. in SIAM J Comput 47:2527–2555, 2018; Lincoln et al., in: 29th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2018). Our combinatorial algorithm, executed with small |K|, is the backbone of a greedy heuristic that we propose. Our experiments on real-world and synthetic datasets show that our heuristic finds nearly-optimal solutions in practice and is also very efficient. We also generalize this algorithm for the problem of masking multiple query strings simultaneously so that every string has at least z matches in D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {D}$$\\end{document}. PMDM can be viewed as a generalization of the decision version of the dictionary matching with mismatches problem: by querying a PMDM data structure with string q and z=1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z=1$$\\end{document}, one obtains the minimal number of mismatches of q with any string from D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {D}$$\\end{document}. The query time or space of all known data structures for the more restricted problem of dictionary matching with at most k mismatches incurs some exponential factor with respect to k. A simple exact algorithm for PMDM runs in time O(2ℓd)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {O}(2^\\ell d)$$\\end{document}. We present a data structure for PMDM that answers queries over D\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {D}$$\\end{document} in time O(2ℓ/2(2ℓ/2+τ)ℓ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {O}(2^{\\ell /2}(2^{\\ell /2}+\ au )\\ell )$$\\end{document} and requires space O(2ℓd2/τ2+2ℓ/2d)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {O}(2^{\\ell }d^2/\ au ^2+2^{\\ell /2}d)$$\\end{document}, for any parameter τ∈[1,d]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au \\in [1,d]$$\\end{document}. We complement our results by showing a two-way polynomial-time reduction between PMDM and the Minimum Union problem [Chlamtáč et al., ACM-SIAM Symposium on Discrete Algorithms (SODA) 2017]. This gives a polynomial-time O(d1/4+ϵ)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathscr {O}(d^{1/4+\\epsilon })$$\\end{document}-approximation algorithm for PMDM, which is tight under a plausible complexity conjecture. This is an extended version of a paper that was presented at International Symposium on Algorithms and Computation (ISAAC) 2021.

A many-to-many matching method considering priority for shared private idle parking spaces and demanders

There has been widespread and growing concern about parking. This paper attempts to provide decision support for a shared parking system to reduce parking difficulty. We study a many-to-many matching problem between shared private idle parking spaces and their demanders. A novelty is that the demanders are allowed to use different parking spaces successively in parking relocation service support. This can further reintegrate the idle time of the parking spaces and improve their utilization rate. A multi-objective optimization model is constructed to maximize the number of matched demanders, the total priority of the parking spaces, and the total priority of the demanders. More importantly, the priorities of the parking spaces and the demanders are innovatively considered. Each of the parking spaces and the demanders is given a priority for the matching and the priority of a parking space or a demander will be increased if the parking space or demander rarely gets matched successfully. This helps reduce the withdrawal of parking spaces and the demanders from the parking platform. In addition, an NSGA-II algorithm is designed to solve the model efficiently. Finally, the feasibility of the proposed method is illustrated via an example.

Load-aware task migration algorithm toward adaptive load balancing in Edge Computing

The rapid advancement of the Internet of Things (IoT) is leading to more and more devices joining the network to interact with information, which requires improving the performance of IoT applications to accommodate more data, faster response times, and more complex tasks. Edge computing, as a new computing paradigm, brings resource contention and load imbalance while reducing communication overhead and task latency. This paper addresses the workload distribution challenge in edge networks, aiming to optimize resource utilization and thereby enhance IoT application performance. To achieve this goal, we present the Load-aware Task Migration (LATM) algorithm. Firstly, we present a load state detection model that captures edge nodes’ workloads and dynamically classifies them according to their resource requirements. We then propose an innovative optimization problem, transforming task migration into a weighted tripartite graph matching problem. This problem leverages the Kuhn–Munkres(KM) task migration algorithm to attain the optimal matching between tasks and nodes. Finally, we assess the algorithm’s performance through experimental simulation. The experimental results underscore the algorithm’s substantial potential in reducing task response times, task execution times, load balance, and enhancing resource utilization.

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.

Simon's congruence pattern matching

The Simon's congruence problem is to determine whether or not two strings have the same set of subsequences of length no greater than a given integer, and the problem can be answered in linear time. We consider the Simon's congruence pattern matching problem that looks for all substrings of a text that are congruent to a pattern under the Simon's congruence. We propose a linear time algorithm by reusing results from previous computations with the help of new data structures called X-trees and Y-trees. Moreover, we investigate several variants of the problem such as identifying the shortest substring or subsequence of the text that is congruent to the pattern under the Simon's congruence, or finding frequent matchings. We design efficient algorithms for these problems. We conclude the paper with two open problems: finding the longest congruent subsequence and optimizing the pattern matching problem.

Space limited linear-time graph algorithms on big data

We study algorithms for graph problems in which the graphs are of extremely large size N so that super-linear time ω(N) or linear space Θ(N) would become impractical. We use a parameter k to characterize the computational power of a normal computer that can provide additional time and space bounded by polynomials of k in dealing with the large graphs. In particular, we are interested in strict linear-time algorithms using space O(kO(1)). In our case studies, as examples, we present (1) a randomized greedy algorithm of time O(N) and space O(k2) for a parameterized version of the Maximal Matching problem; and (2) randomized kernelization algorithms of time O(N) and space O(kO(1)) for a number of well-known NP-hard problems. Our kernelization algorithms have their kernel sizes match the best kernel sizes by known polynomial-time kernelization algorithms with no space constraints for the problems. We also study the relationship between our proposed model and the streaming model. This study motivates a new streaming kernelization algorithm for the famous Vertex Cover problem that has an optimal update time complexity while matches the best known space complexity of streaming algorithms for the problem.

Abstract formulation of the spacetime matching problem and null thin shells

Abstract formulation of the spacetime matching problem and null thin shells

A roommate problem and room allocation in dormitories using mathematical modeling and multi-attribute decision-making techniques

PurposeIn recent decades, the student population in dormitories has increased notably, primarily attributed to the growing number of international students. Dormitories serve as pivotal environments for student development. The coordination and compatibility among students can significantly influence their overall success. This study aims to introduce an innovative method for roommate selection and room allocation within dormitory settings.Design/methodology/approachIn this study, initially, using multi-attribute decision-making methods including the Bayesian best-worst method and weighted aggregated sum product assessment, the incompatibility rate among pairs of students is calculated. Subsequently, using a linear mathematical model, roommates are selected and allocated to dormitory rooms pursuing the twin objectives of minimizing the total incompatibility rate and costs. Finally, the grasshopper optimization algorithm is applied to solve large-sized instances.FindingsThe results demonstrate the effectiveness of the proposed method in comparison to two common alternatives, i.e. random allocation and preference-based allocation. Moreover, the proposed method’s applicability extends beyond its current context, making it suitable for addressing various matching problems, including crew pairing and classmate pairing.Originality/valueThis novel method for roommate selection and room allocation enhances decision-making for optimal dormitory arrangements. Inspired by a real-world problem faced by the authors, this study strives to offer a robust solution to this problem.

Clothing Style Similarity Matching and Classification Recognition Algorithm Based on Double-Layer Model in the Context of the Internet of Things

Abstract The identification of different clothing style attributes is helpful for designers to grasp the clothing style, and it is also helpful for consumers to find clothes with the same style attributes according to their own preferences. This is becoming more important in the clothing design, Internet, and e-commerce industries. It is of great significance to carry out clothing style similarity matching and classification recognition. For the problem of clothing style similarity matching and classification recognition, the traditional algorithm stays in the stage of qualitative analysis and subjective evaluation, unable to quantitatively and objectively determine clothing style, resulting in the decline of clothing style similarity matching effect and classification recognition accuracy. Therefore, a similarity matching, classification, and recognition algorithm of clothing style based on the double-layer model in the context of the Internet of Things is designed. The double-layer model is constructed through the target detection layer and target segmentation layer. The double-layer model is used to realize the segmentation of clothing image. The overall similarity evaluation index of image style is obtained according to the image gradient to complete the similarity matching of clothing style. On this basis, the clothing image features are extracted and input into the support vector machine classifier to complete the clothing classification and recognition. The experimental results show that the proposed garment style similarity matching and classification recognition algorithm based on the two-layer model has high image segmentation accuracy, good style similarity matching effect, high classification recognition accuracy, and high efficiency, which proves that the algorithm is feasible for garment style similarity matching and classification recognition using the two-layer model and can be further applied in the field of garment design.

Fairness-Aware Dynamic Ride-Hailing Matching Based on Reinforcement Learning

The core issue in ridesharing is designing reasonable algorithms to match drivers and passengers. The ridesharing matching problem, influenced by various constraints such as weather, traffic, and supply–demand dynamics in real-world scenarios, requires optimization of multiple objectives like total platform revenue and passenger waiting time. Due to its complexity in terms of constraints and optimization goals, the ridesharing matching problem becomes a central issue in the field of mobile transportation. However, the existing research lacks exploration into the fairness of driver income, and some algorithms are not practically applicable in the industrial context. To address these shortcomings, we have developed a fairness-oriented dynamic matching algorithm for ridesharing, effectively optimizing overall platform efficiency (expected total driver income) and income fairness among drivers (entropy of weighted amortization fairness information between drivers). Firstly, we introduced a temporal dependency of matching outcomes on subsequent matches in the scenario setup and used reinforcement learning to predict these temporal dependencies, overcoming the limitation of traditional matching algorithms that rely solely on historical data and current circumstances for order allocation. Then, we implemented a series of optimization solutions, including the introduction of a time window matching model, pruning operations, and metric representation adjustments, to enhance the algorithm’s adaptability and scalability for large datasets. These solutions also ensure the algorithm’s efficiency. Finally, experiments conducted on real datasets demonstrate that our fairness-oriented algorithm based on reinforcement learning achieves improvements of 81.4%, 28.5%, and 79.7% over traditional algorithms in terms of fairness, platform utility, and matching efficiency, respectively.

Dynamic person-position matching decision method based on hesitant fuzzy number information

In view of the fact that people pay more and more attention to the principle of "getting the position according to the person" and "adapting the person to the position" in person-position matching, a dynamic person-position matching decision method based on hesitant fuzzy numbers is proposed. First, the dynamic person-position matching problem with hesitant fuzzy numbers is described. Then, according to hesitant fuzzy evaluation matrices of positions and candidates, expected score matrices of bilateral subjects are calculated. Furthermore, based on the idea of the generalized optimal order method and the dominant correlation and the missing correlation coefficients, satisfaction means of people and positions are calculated. According to satisfaction means, growth satisfactions at each period are obtained, and then the exponential decay formula is used to determine weights of growth satisfactions. Dynamic satisfactions of bilateral subjects are calculated by aggregating initial satisfaction means and growth satisfactions. On this basis, a stable person-position matching model considering dynamic satisfactions is established and then is solved to obtain the optimal stable person-position matching scheme. Finally, the feasibility and effectiveness of the proposed method are verified by an example analysis of person-position matching. Main contributions of this paper are as follows: an effective calculation method for the missing correlation coefficient is presented; a novel effective calculation method for dynamic satisfactions is proposed by introducing the correlation parameter to combine the missing correlation coefficient with the dominant correlation coefficient; an effective stable person-position matching model considering dynamic satisfactions is established.

Reunion helper: an edge matcher for sibling fragment identification of the Dunhuang manuscript

The Dunhuang ancient manuscripts are an excellent and precious cultural heritage of humanity. However, due to their age, the vast majority of these treasures are damaged and fragmented. Faced with a wide range of sources and numerous fragments, the process of restoration generally involves two core elements: sibling fragments identification and fragment assembly. Currently, fragment restoration still heavily relies on manual labor. During the long practice, a consensus has been reached on the importance of edge features for not only assembly but also for identification. However, accurate extraction of edge features and their use for efficient identification requires extensive knowledge and strong memory. This is a challenge for the human brain. So that in previous studies, fragment edge features have been used for assembly validation but rarely for identification. Therefore, an edge matcher is proposed, working like a bloodhound, capable of “sniffing out” specific “flavors” in edge features and performing efficient sibling fragment identification accordingly, providing guidance when experts perform entity assembly subsequently. Firstly, the fragmented images are standardized. Secondly, traditional methods are used to compress the representation of fragment edges and obtain paired local edge images. Finally, these images are fed into the edge matcher for classification discrimination, which is a CNN-based pairwise similarity metric model proposed in this paper, introducing residual blocks and depthwise separable convolutions, and adding multi-scale convolutional layers. With the edge matcher, a complex matching problem is successfully transformed into a simple classification problem. In the absence of a standard public dataset, a Dunhuang manuscript fragment edge dataset is constructed. Experiments are conducted on that dataset, and the accuracy, precision, recall, and F1 scores of the edge matcher all exceeded 97%. The effectiveness of the edge matcher is demonstrated by comparative experiments, and the rationality of the method design is verified by ablation experiments. The method combines traditional methods and deep learning methods to creatively use the edge geometric features of fragments for sibling fragment identification in a natural rather than coded way, making full use of the computer’s computational and memory capabilities. The edge matcher can significantly reduce the time and scope of searching, matching, and inferring fragments, and assist in the reconstruction of Dunhuang ancient manuscript fragments.

Quasi-Dense Matching for Oblique Stereo Images through Semantic Segmentation and Local Feature Enhancement

This paper proposes a quasi-dense feature matching algorithm that combines image semantic segmentation and local feature enhancement networks to address the problem of the poor matching of image features because of complex distortions, considerable occlusions, and a lack of texture on large oblique stereo images. First, a small amount of typical complex scene data are used to train the VGG16-UNet, followed by completing the semantic segmentation of multiplanar scenes across large oblique images. Subsequently, the prediction results of the segmentation are subjected to local adaptive optimization to obtain high-precision semantic segmentation results for each planar scene. Afterward, the LoFTR (Local Feature Matching with Transformers) strategy is used for scene matching, enabling enhanced matching for regions with poor local texture in the corresponding planes. The proposed method was tested on low-altitude large baseline stereo images of complex scenes and compared with five classical matching methods. Results reveal that the proposed method exhibits considerable advantages in terms of the number of correct matches, correct rate of matches, matching accuracy, and spatial distribution of corresponding points. Moreover, it is well-suitable for quasi-dense matching tasks of large baseline stereo images in complex scenes with considerable viewpoint variations.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations.

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.