Efficient Matching Algorithm Research Articles

Improving the deferred acceptance algorithm for efficient sponsor-recipient matching

As the gap between rich and poor in China has become increasingly prominent, various charity platforms have emerged. However, due to information asymmetry, many recipients cannot receive effective donations, and the emergence of fraudulent donations also makes donors unable to fulfill their intentions. We designed some improvements to the delayed acceptance (DA) algorithm and applied it flexibly in the donation platform. This can improve the efficiency of matching between donors and recipients. The variant of the DA algorithm is designed to carry out multiple rounds of matching, give donors and recipients the opportunity to select the matching priority list based on various factors and use a reasonable matching mechanism to achieve Pareto efficient, strategy-proof, and stable matching. Therefore, the application of the variation of the DA algorithm in the donation problem can better match the needs, so that the platform can complete a more reasonable and efficient donation matching.

TC-Match: Fast Time-Constrained Continuous Subgraph Matching

Continuously monitoring structural patterns in streaming graphs is a critical task in many real-time graph-based applications. In this paper, we study the problem of time-constrained continuous subgraph matching (shorted as TCSM) over streaming graphs. Given a query graph Q with timing order constraint and a data graph stream G , TCSM aims to report all incremental matches of Q in G for each update of G , where a match should obey both structure constraint (i.e., isomorphism) and timing order constraint of Q. Although TCSM has a wide range of applications, such as cyber-attack detection and credit card fraud detection, we note that this problem has not been well addressed. The state-of-the-art bears the limitations of high index space cost and intermediate result maintenance cost. In this paper, we propose TC-Match, an effective approach to TCSM. First, we design a space and time cost-effective index CSS, which is essentially a k -partite graph structure where a node corresponds to an edge in G. By carefully creating links between nodes, we can encapsulate into CSS the partial embedding and timing order information between edges in G. We theoretically show that CSS has polynomial space and construction time complexities. Second, based on the property of CSS, we develop an efficient incremental matching algorithm with an effective node merging optimization. Extensive experiments show that TC-Match can achieve up to 3 orders of magnitude query performance improvement over the baseline methods, and meanwhile the memory consumption is reduced by 48.7%-86.7%.

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphological analysis, such as identifying neuron types and large-scale neuron retrieval, all of which require accurate measuring and efficient matching algorithms. Recently, many studies have been conducted to describe neuronal morphologies quantitatively using predefined measurements. However, hand-crafted features are usually inadequate for distinguishing fine-grained differences among massive neurons. In this article, we propose a novel morphology-aware contrastive graph neural network (MACGNN) for unsupervised neuronal morphological representation learning. To improve the retrieval efficiency in large-scale neuronal morphological datasets, we further propose Hash-MACGNN by introducing an improved deep hash algorithm to train the network end-to-end to learn binary hash representations of neurons. We conduct extensive experiments on the largest dataset, NeuroMorpho, which contains more than 100 000 neurons. The experimental results demonstrate the effectiveness and superiority of our MACGNN and Hash-MACGNN for large-scale neuronal morphological analysis.

Novel linkage approach to join community-acquired and national data

BackgroundCommunity optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.MethodsAs the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.ResultsA sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.ConclusionsThe potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Online MEC Offloading for V2V Networks

As an enabling technology for vehicle-to-vehicle (V2V) networks, multi-access edge computing (MEC) provides a feasible platform for sharing power and resources, and offloading some of the computation-intensive tasks between vehicles. This, however, is challenging with the unpredictable variations in road traffic conditions and vehicle mobility in MEC-enabled V2V networks. Consequently, such computation task offloading can be easily disrupted, which may require frequent switching of task offloading between vehicles and degrade the Quality of Service (QoS). In this paper, we focus on the computation offloading problem under unstable connections in MEC-enabled V2V networks. We first model this as a distributed online service optimization problem, which is proved to be NP-hard. In order to minimize the out-of-service time (i.e., the service mismatching, switching and compromise time), we propose a distributed Online Instability-aware Computation Offloading (OICO) heuristic algorithm to improve the service efficiency and quality. Specifically, in order to minimize the service mismatching rate, we design an efficient Service Path Matching (SPM) algorithm for matching pairs of customer vehicles (which require offload computing services) and server vehicles (which provide edge computing services) that share the longest matching path. We evaluate OICO through real-world traces, i.e., GAIA open dataset from DiDi. Extensive simulation results demonstrate that OICO can increase the service matching rate by 25% and reduce the power consumption by about 54% per customer vehicle compared with the existing schemes.

Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Efficient bat-inspired block matching algorithm with novel motion energy directional histograms for dynamic texture fast recognition

Implementation of the Turbo Boyer Moore Method in Searching Thesis Titles at the Website-Based Faculty of Economics

Every year, students are required to complete a final project in the form of a thesis as one of the graduation requirements. However, in determining a thesis title that is relevant to the chosen scientific field, students often experience difficulties. The number of thesis titles available in libraries and websites can confuse students in choosing the right title. One method that can be used to achieve this goal is the Turbo Boyer-Moore pattern search method. Turbo Boyer-Moore is one of the efficient pattern matching algorithms to search for certain words or phrases in text. From the results of the tests carried out, it can be concluded that the implementation of the designed program in searching and detecting the same title with a similarity percentage.

Orthogonal dictionary learning based on l 4-Norm maximisation for seismic data interpolation

Due to geological conditions, acquisition environment, and economic restrictions, acquired seismic data are often incomplete and irregularly distributed, and this affects subsequent migration imaging and inversion. Sparse constraint-based methods are widely used for seismic data interpolation, including fixed-base transform and dictionary learning. Fixed-base transform methods are fast and simple to implement, but the basis function needs to be pre-selected. The dictionary learning method is more adaptive, and provides a means of learning the sparse representation from corrupted data. K-singular value decomposition (K-SVD) is a classical dictionary learning method that combines sparse coding and dictionary updating iteratively. However, the dictionary atoms are updated column-by-column, leading to high computational complexity due to long SVD calculation times. In this study, we evaluated the dictionary learning method via l 4-norm maximisation using an orthogonal dictionary, which is different from the traditional l 0-norm or l 1-norm minimisation, and interpolated the missing traces in the projection onto convex sets (POCS) framework. The optimal objection function is convex, but can be solved using a simple and efficient Matching, Stretching and Projection (MSP) algorithm, which greatly reduces the dictionary learning time. Numerical experiments using synthetic and field data demonstrate the effectiveness of the proposed method.

GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H-13C HSQC-Based Metabolomics.

Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.

Efficient Matching Algorithms between Logs and Indicators for Automatic Incident Response System

Efficient Matching Algorithms between Logs and Indicators for Automatic Incident Response System

Modified Cuckoo Search Algorithm for Motion Vector Estimation

Motion estimation and motion compensation are the accepted process in H.264 and H.265 video coding standard to reduce temporal redundancy. Several fast block matching algorithms have been developed to reduce the calculation cost in the motion estimation process. But quick block matching algorithms often lead to a local minimum. Several researchers used different population-based nature-inspired algorithms to perform block matching. Algorithms like genetic algorithm, differential evolution, particle swarm optimization were used in numerous motion estimation algorithms. Different algorithms used a fitness approximation strategy to reduce computation cost. Jaya algorithm-based block matching is the most efficient block matching algorithm in the available literature. Jaya algorithm is free from algorithmic specific parameter which speeds up the process. This article proposes a few modifications to the traditional cuckoo search algorithm and then, a block matching algorithm was proposed based on the modified cuckoo search algorithm. Fitness approximation, adaptive termination, and zero motion prejudgment modules were used with the modified cuckoo search algorithm to reduce the number of redundant calculations. The performance of the proposed algorithm was compared with the exhaustive search algorithm and other benchmarking algorithms in terms of Peak Signal to Noise Ratio (PSNR), Structure Similarity Index (SSIM), and average search point required to calculate a motion vector for a block. The proposed algorithm delivers better performance compared to the benchmarking algorithms.

An efficient SIFT-based matching algorithm for optical remote sensing images

ABSTRACT Scale-invariant feature transform (SIFT) and its improved versions have been widely used to match the remote-sensing optical images. However, it is still a challenging task to get enough correct matching pairs by using SIFT-based methods. In this letter, an efficient matching scheme is proposed to increase the number of correct matches. The proposed matching scheme uses scale, orientation, and translation differences between the input images to remove the outliers and to retain the correct matches. At first, the initial matching candidates are obtained by a SIFT-based algorithm. Then, our proposed matching scheme is used to select the correct matching pairs. The proposed method can obtain more correct matches than the state-of-the-art methods. Experiments are performed on five pairs of optical images to verify the performance of the proposed method.

A weak approach to suffix automata simulation for exact and approximate string matching

String matching is one of the most extensively studied problems in computer science, mainly due to its direct applications to such diverse areas as text, image and signal processing, speech analysis and recognition, information retrieval, data compression, computational biology and chemistry. In the last few decades a myriad of alternative solutions have been proposed, based on very different techniques. However, automata have always played a very important role in the design of efficient string matching algorithms. In this paper we present the Range Automaton, a weak yet efficient variant of the non-deterministic suffix automaton of a string whose configuration can be encoded in a very simple form and which is particularly suitable to be used for solving a multitude of text-searching problems. We will firstly develop our approach in the case of exact string matching and present an efficient algorithm, named Backward Range Automaton Matcher, which turns out to be very fast in many practical cases. Later, we will show how the Range Automaton can be adapted in an effective way also to non-standard string matching problems such as swap matching and multiple string matching. Experimental results suggest that our approach is flexible and effective for all three search problems addressed, especially in the case of long patterns.

2021 Census to Census Coverage Survey Matching Results.

The 2021 England and Wales Census was matched to the Census Coverage Survey (CCS). This was an essential requisite for estimating undercount in the Census. To ensure outputs could be produced within a year of census day, matching had to be completed in eight weeks.
 We used the Data Access Platform to write efficient and accurate automatic matching algorithms. Probabilistic and associative matching methods were used to present candidate pairs that could not be automatically matched but likely consisted of many true matches. These were resolved by matchers in the clerical matching system. Active learning was used to iteratively improve probabilistic parameters as matches were made.
 We also developed a pre-search algorithm that simplifies clerical matching by replacing clerical searching (here’s a record, can you find a match?) with clerical resolution (here are two records, do they match?).
 As a result of our improvements, we increased our automatic matching rates from 70% (2011 Census) to 93% (2021 Census) for person matching and from 60% to 95% for household matching, without loss of accuracy. Precision and recall were estimated to both be 99.96% for person matching and 100% and 99.78% respectively for household matching. For both persons and households, the same trends seen in 2011 are seen here too; however, most of the biases have decreased and most match rates have increased.
 Dual system estimation using the matched and unmatched records enabled us to estimate that coverage in the 2021 England and Wales Census was 97%.
 The matching methodology implemented for the 2021 Census to CCS matching improved both person and household matching greatly when compared with the 2011 matching. The clerical matching system enabled clerical matching at scale and at pace. Thus, we successfully completed all clerical matching within our eight-week deadline.

Efficient energy-based orthogonal matching pursuit algorithm for multiple sound source localization with unknown source count

In this paper, we propose a compressed sensing (CS) sound source localization algorithm based on signal energy to solve the problem of stopping the iteration condition of the orthogonal matching pursuit (OMP) reconstruction algorithm in CS. The orthogonal matching tracking algorithm needs to stop iteration according to the number of sound sources or the change of residual. Generally, the number of sound sources cannot be known in advance, and the residual often leads to unnecessary calculation. Because the sound source is sparsely distributed in space, and its energy is concentrated and higher than that of the environmental noise, the comparison of the signal energy at different positions in each iteration reconstruction signal is used to determine whether the new sound source is added in this iteration. At the same time, the block sparsity is introduced by using multiple frequency points to avoid the problem of different iteration times for different frequency points in the same frame caused by the uneven energy distribution in the signal frequency domain. Simulation and experimental results show that the proposed algorithm retains the advantages of the orthogonal matching tracking sound source localization algorithm, and can complete the iteration well. Under the premise of not knowing the number of sound sources, the maximum error between the number of iterations and the set number of sound sources is 0.31. The experimental results show that the proposed algorithm has good positioning accuracy and has certain anti-reverberation capability. Compared with other OMP algorithms, the proposed algorithm has better iterative ability and stability. This work is helpful in promoting the development of multiple sound source localization.

P-smoother: efficient PBWT smoothing of large haplotype panels.

MotivationAs large haplotype panels become increasingly available, efficient string matching algorithms such as positional Burrows-Wheeler transformation (PBWT) are promising for identifying shared haplotypes. However, recent mutations and genotyping errors create occasional mismatches, presenting challenges for exact haplotype matching. Previous solutions are based on probabilistic models or seed-and-extension algorithms that passively tolerate mismatches.ResultsHere, we propose a PBWT-based smoothing algorithm, P-smoother, to actively ‘correct’ these mismatches and thus ‘smooth’ the panel. P-smoother runs a bidirectional PBWT-based panel scanning that flips mismatching alleles based on the overall haplotype matching context, which we call the IBD (identical-by-descent) prior. In a simulated panel with 4000 haplotypes and a 0.2% error rate, we show it can reliably correct 85% of errors. As a result, PBWT algorithms running over the smoothed panel can identify more pairwise IBD segments than that over the unsmoothed panel. Most strikingly, a PBWT-cluster algorithm running over the smoothed panel, which we call PS-cluster, achieves state-of-the-art performance for identifying multiway IBD segments, a challenging problem in the computational community for years. We also showed that PS-cluster is adequately efficient for UK Biobank data. Therefore, P-smoother opens up new possibilities for efficient error-tolerating algorithms for biobank-scale haplotype panels.Availability and implementationSource code is available at github.com/ZhiGroup/P-smoother.

Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List

Efficiently identifying the mated gallery fingerprint of a latent fingerprint in a large database requires a highly accurate and efficient fingerprint matching algorithm. The common strategy to achieve this goal is to combine an efficient indexing algorithm with a slow but accurate matching algorithm. Despite of the importance of latent indexing, it has received far less attention than rolled and plain fingerprint indexing. Due to the small fingerprint area, poor image quality and huge variety in information quantity of latent fingerprints, existing rolled and plain fingerprint indexing approaches cannot be simply migrated to the latent fingerprint indexing. In this paper, we propose (1) a multi-scale fixed-length representation approach for latent fingerprint indexing, and (2) a fingerprint information quantity estimation approach for adaptive candidate list reduction. The representation scheme is designed to deal with small finger area and low image quality of latents. The information quantity of a latent is a predictor of the indexing score of its mated gallery fingerprint and thus can be used to determine a proper threshold for its candidate list. Extensive experimental results on NIST SD27, MOLF, N2N, and Hisign latent fingerprint databases show that the proposed method achieved the state-of-the-art indexing accuracy on latent fingerprints, and significantly improved the efficiency of state-of-the-art latent matching algorithm.

Efficient Trajectory Matching Algorithm Based on Spatial Coordinate Rotation

Trajectory similarity estimation is the key to discover vehicle motion characteristics and trajectory classification, but the calculation of trajectory similarity is slow, and improving the speed of trajectory matching can help to carry out trajectory characteristics mining fast. Therefore, an efficient trajectory matching algorithm based on the rotation of spatial coordinate is proposed. Firstly, the trajectory curve is converted into the mean and variance curve which the number of points is equal to the number of rotations by multiple rotation of the spatial coordinate system. Then, Pearson correlation coefficient and Fréchet distance are used to measure the correlation of the mean and variance curves. Finally, the similarity between the original trajectories is determined indirectly according to the values of four parameters: Fréchet mean, Fréchet var, Pearson mean and Pearson var. Based on the taxi trajectory data of Hangzhou, under different trajectory sampling points and spatial coordinate rotation times, compared trajectory matching accuracy and speed with the traditional Hausdorff trajectory matching algorithm. The several experiments show that the algorithm can improve the trajectory matching speed by 85% on average while ensuring the accuracy of trajectory matching. By constructing a visual analysis system that includes three major modules: a map overview showing the results of trajectory matching, several visual interactive components that explore the differences in trajectory matching results, and a module for selecting trajectory matching parameters, explore the differences in the results of the four trajectory matching methods to help road network researchers to match real driving trajectories and find similar trajectory groups faster.

A Computationally Efficient Outlier-Robust Cubature Kalman Filter for Underwater Gravity Matching Navigation

Gravity-aided navigation is one of key techniques for the navigation of underwater vehicles. Cubature Kalman filter (CKF)-based matching algorithm improves the positioning accuracy of traditional Sandia inertial terrain aided navigation method. However, the gravity sensor may suffer from outlier interferences due to complex and changeable underwater environments, which degrades the performance of CKF-based matching algorithm remarkably. To address this problem, in this article, a novel computationally efficient outlier-robust CKF-based matching algorithm is proposed for an underwater gravity-aided navigation system with outlier-contaminated measurements. The convergence analysis and the stability discussions are given to show the effectiveness of the proposed algorithm, and the discussion on computational complexity illustrates the good real-time performance of the proposed algorithm. Simulation and experimental results demonstrate the advantages of the proposed matching algorithm as compared with existing state-of-the-art matching algorithms.

Formula omitted]: Efficient keyword matching for network traffic classification

Network traffic classification is required for a range of network management activities like meeting the Quality of Service demands of applications and security monitoring. Deep Packet Inspection (DPI) based methods achieve better classification accuracy compared to other techniques. However, DPI is computationally demanding and requires searching patterns in the payload. Methods found in the literature suffer from performance issues as they perform multiple scans of payload. In this paper, we describe KeyClass, which is a DPI based traffic classifier and can classify network flows with single scan of payload using keyword based signatures. KeyClass achieves performance gains (speed of classification) with a combination of two things. It quickly identifies potential application(s) by scanning few initial bytes of payload and optimize the number of character comparisons while searching remaining keywords of potential application(s). In order to identify potential applications, it uses a finite state machine constructed with first keyword of every application using classic Aho–Corasick multi-pattern matching algorithm. KeyClass has an application specific signature which is generated with the remaining set of keywords of an application. By skipping portions of payload from inspection, coupled with an efficient string matching algorithm, it practically achieves sub-linear search complexity. We evaluate the classification and execution performance of KeyClass with experiments using two large datasets containing 173619 and 885405 network flows and report that it has a good average classification accuracy of ≈ 98%. In our evaluation, KeyClass is found to be 3.79 times faster than state of the art methods.