Pattern Matching Research Articles

Forward linearised tree pattern matching using tree pattern border array

We define a tree pattern border array as a property of linearised trees analogous to border arrays from the string domain. We consider tree patterns with node wildcards and subtree wildcards. Then we use the tree pattern border array in a new forward tree pattern matching algorithm for ordered ranked trees. The algorithm finds all occurrences of a single linearised tree pattern in a linearised input tree. As with the classical Morris–Pratt algorithm for searching in strings, the tree pattern border array is used to determine shift lengths in the searching phase of the tree pattern matching algorithm. We compare this new algorithm with the best performing previously existing algorithms based on backward linearised tree pattern matching algorithms, (non-)linearised tree pattern matching algorithms using finite tree automata or stringpath matchers. We show that our new algorithm outperforms other tree pattern matching algorithms in single tree pattern matching.

Searching long patterns with BNDM

AbstractWe present new algorithms for exact string matching of long patterns. Our algorithms read ‐grams at constant distances and are variations of the simplified BNDM algorithm. We demonstrate the competitiveness of our solutions through practical experiments. Many of our algorithms were faster than previous methods for English and DNA patterns between 400 and 50,000 in length. Our algorithms were still better when the preprocessing time was taken into account or when the patterns were taken from a different text of the same type.

An Intelligent Food Recommendation System for Dine-in Customers with Non-Communicable Diseases History

The rising prevalence of diet-related diseases necessitates a focus on individual food selection to enhance nutrition intake and promote overall health. This study introduces a novel food recommender system utilizing artificial intelligence, specifically a genetic algorithm (GA), to intelligently match diverse nutritional needs with available food items. The research incorporates machine learning methodologies, such as collaborative and content-based filtering, to develop a recommendation model. Data from a commercial restaurant, Nutrisurvey, and the Indonesian food composition list inform the nutritional analysis of five menu items. Consumer variability, considering factors like sex, body mass index, medical conditions, and physical activity, are integrated into the GA framework for personalized food pattern matching. The presented results demonstrate the efficacy of the proposed model in offering tailored food recommendations for consumers with non-communicable diseases (NCDs), such as diabetes, hypertension, and heart disease. The multi-objective optimization technique employed in the system ensures a balance between nutritional adequacy and individual preferences. The presented GA-based approach holds promise for promoting healthier food choices tailored to individual needs, contributing to the broader goal of fostering a sustainable and personalized food system.

CURRENT TRENDS IN AEROSPACE IMAGE PROCESSING AND PATTERN RECOGNITION

This paper aims to analyze current trends in the processing and recognition of aerospace images. Such images are an important source of information for various industries, such as military and civilian cartography, agriculture, and ecology. The process of processing and analyzing large-scale aerospace images requires significant time and resources, so there is a need to use modern machine learning and image processing methods. This paper describes various methods for processing aerospace images, such as linear filtering, independent component analysis, pattern matching, and generative adversarial networks. The use of modern methods for processing and pattern recognition of aerospace images is an essential step in improving the efficiency and accuracy of large-scale image analysis, which can be useful for various industries that use aerospace images.

Towards efficient simulation-based constrained temporal graph pattern matching

Towards efficient simulation-based constrained temporal graph pattern matching

Improved LINE-1 Detection through Pattern Matching by Increasing Probe Length.

Long Interspersed Element-1 (LINE-1 or L1) is an autonomous transposable element that accounts for 17% of the human genome. Strong correlations between abnormal L1 expression and diseases, particularly cancer, have been documented by numerous studies. L1PD (LINE-1 Pattern Detection) had been previously created to detect L1s by using a fixed pre-determined set of 50-mer probes and a pattern-matching algorithm. L1PD uses a novel seed-and-pattern-match strategy as opposed to the well-known seed-and-extend strategy employed by other tools. This study discusses an improved version of L1PD that shows how increasing the size of the k-mer probes from 50 to 75 or to 100 yields better results, as evidenced by experiments showing higher precision and recall when compared to the 50-mers. The probe-generation process was updated and the corresponding software is now shared so that users may generate probes for other reference genomes (with certain limitations). Additionally, L1PD was applied to other non-human genomes, such as dogs, horses, and cows, to further validate the pattern-matching strategy. The improved version of L1PD proves to be an efficient and promising approach for L1 detection.

Fuzzy Ranking-Based Preference Completion via Graph Pattern Matching and Rematching

Fuzzy Ranking-Based Preference Completion via Graph Pattern Matching and Rematching

PTME: A Regular Expression Matching Engine Based on Speculation and Enumerative Computation on FPGA

Fast regular expression matching is an essential task for deep packet inspection. In previous works, the regular expression matching engine on FPGA struggled to achieve an ideal balance between resource consumption and throughput. Speculation and enumerative computation exploits the statistical properties of deterministic finite automata, allowing for more efficient pattern matching. Existing related designs mostly revolve around vector instructions and multiple processors/cores or SIMD instruction sets, with a lack of implementation on FPGA platforms. We design a parallelized two-character matching engine on FPGA for efficiently fast filtering off fields with no pattern features. We transform the state transitions with sequential dependencies to the existing problem of elements in one set, enabling the proposed design to achieve high throughput with low resource consumption and support dynamic updates. Results show that compared with the traditional DFA matching, with a maximum resource consumption of 25% for on-chip FFs (74323/1045440) and LUTs (123902/522720), there is an improvement in throughput of 8.08-229.96 × speedup and 87.61-99.56% speed-up(percentage improvement) for normal traffic, and 11.73-39.59 × speedup and 91.47-97.47% speed-up(percentage improvement) for traffic with high-frequency match hits. Compared with the state-of-the-art similar implementation, our circuit on a single FPGA chip is superior to existing multi-core designs.

The value of oestrogen receptor, progesterone receptor and keratins 5 and 14 immunohistochemistry in the evaluation of epithelial proliferations at cauterised margins in breast-conserving surgery specimens

In breast conservative surgery, it is sometimes difficult to decide whether the cauterised tissue at the inked margin represents normal / hyperplastic or neoplastic tissue. We retrospectively assessed the value of ER, PR, CK5 and CK14 IHC in clarifying the nature of cauterised tissues at the margins concerning 34 lesions of 23 patients. 27 cases belonged to lesions that could not be adequately classified on the basis of the HE stains. Two thirds of them could be classified as non-neoplastic or neoplastic and two thirds of the remaining could be favourised as neoplastic or non-neoplastic, with 3/27 cases remaining uncertain. All 4 IHC reactions were helpful in classifying the lesions in almost half of the cases. However, 3 or 4 immunostains were supportive of the classification in 19/27. The most useful stains were the keratins, generally demonstrating a matching pattern of cell labelling with CK5 and CK14. ER and PR were somewhat less useful in classifying uncertain lesions. Considering all the 27 questionable lesions, IHC with ER, PR, CK5 and CK14 clarified the lesions at the cauterised margins in 23 cases. Taken all these considerations into account, CK5, CK14, PR and ER IHC may help in distinguishing between cautery damaged neoplastic and non-neoplastic tissues. All four IHC may yield the best support for decision making, but CK5 and/or CK14 may be sufficient in their own. The essential approach is that the results must be interpreted with caution, in the context of the given patient’s disease, to avoid misinterpretations.

Preventing Fraudulent Vehicle Listings: Digital ADS Detection

Abstract: With the increasing popularity of online selling platforms for vehicles, the risk of fraudulent advertisements has also risen significantly. This research paper addresses the challenge of detecting and preventing digital fraud ads, particularly those related to vehicle listings, on typical online vehicle selling web portals. The proposed approach leverages advanced algorithms to identify and filter out fraudulent vehicle ads, including unregistered vehicles and unnecessary spam ads posted by sellers. One of the key techniques employed in this research is pattern matching, which involves comparing the details of posted vehicles with the records from Regional Transport Offices (RTOs) to determine their registration status. By analyzing the vehicle details provided in the ads and cross-referencing them with RTO data, the system can flag unregistered vehicles, thus mitigating the risk of fraudulent listings. Additionally, the study incorporates the use of the You Only Look Once (YOLO) algorithm for object detection in the images accompanying the ads. By applying YOLO, the system can accurately identify vehicles in the images and extract relevant information, such as make, model, and license plate details. This allows for further validation of the authenticity of the listings and helps in filtering out spam ads containing irrelevant or misleading images.

Enhanced performance of microstrip antenna fabricated on a composite dielectric substrate coupled with multiple dielectric superstrates

In this study, we introduce a unique geometrical design of a rectangular patch antenna for sub-6 GHz 5G applications. The proposed antenna is designed at 5.2 GHz, and design criteria and performance characteristics have been investigated through numerical simulations and verified using measured data. A composite structure is suggested to overcome the restrictive parameters of conventional antennas. Two different materials, Rogers RT5880, and RT5870, are utilized, achieving a maximum gain of 6.210 dB, and a 90.2% radiation efficiency was attained. Four layers of superstrates are placed above the antenna to enhance the antenna's radiation performance further. The fabricated antenna is evaluated by measuring its radiation characteristics and return loss. The final antenna prototype offers a maximum bandwidth of about 896 MHz at a −10 dB reflection coefficient with a maximum gain of 9 dB. The proposed model achieved ultimate efficiency as high as 89% by employing four superstrates. The results showed good impedance matching (S11, VSWR), high efficiency, and appropriate radiation patterns. The outcomes from simulation and measurement are well aligned, making this antenna suitable for high-performance 5G applications.

Accelerating range minimum queries with ray tracing cores

Over the past decade, GPU technology has undergone a notable transformation, evolving from pure general-purpose computation to the integration of application-specific integrated circuits (ASICs), including Tensor Cores and Ray Tracing (RT) cores. While these specialized GPU cores were initially developed to enhance specific domains like AI and real-time rendering, recent research has successfully harnessed their capabilities to expedite other tasks traditionally reliant on conventional GPU computing. One GPU task that is still yet to find its way into RT cores is the processing of range minimum queries (RMQs) in parallel, which is fundamental in fields such as information retrieval or pattern matching, among others. In this context, accelerating RMQs with RT cores would impact many of the applications that heavily rely on this task. In this work we present RTXRMQ, a new approach that can compute RMQs with RT cores. The main contribution is the proposal of a geometric solution for RMQ, where elements become triangles that are placed and shaped according to the element’s value and position in the array, respectively, such that the closest hit of a ray launched from a point given by the query parameters corresponds to the result of that query. Experimental results show that RTXRMQ is currently best suited for small query ranges relative to the input size, achieving up to 5× and 2.3× of speedup over parallel state of the art CPU and GPU approaches, respectively. For medium and large query ranges RTXRMQ is still slower than the state of the art GPU approach, but still competitive by being 2.5× and 4× faster than a state of the art CPU method running in parallel as well. Furthermore, performance scaling experiments across the latest RTX GPU architectures show that if the current RT core scaling trend continues, then RTXRMQ’s performance would scale at a higher rate than the other compared approaches, making it an attractive tool for future high performance applications that employ many batches of RMQs.

SAT-Based Algorithms for Regular Graph Pattern Matching

Graph matching is a fundamental problem in pattern recognition, with many applications such as software analysis and computational biology. One well-known type of graph matching problem is graph isomorphism, which consists of deciding if two graphs are identical. Despite its usefulness, the properties that one may check using graph isomorphism are rather limited, since it only allows strict equality checks between two graphs. For example, it does not allow one to check complex structural properties such as if the target graph is an arbitrary length sequence followed by an arbitrary size loop. We propose a generalization of graph isomorphism that allows one to check such properties through a declarative specification. This specification is given in the form of a Regular Graph Pattern (ReGaP), a special type of graph, inspired by regular expressions, that may contain wildcard nodes that represent arbitrary structures such as variable-sized sequences or subgraphs. We propose a SAT-based algorithm for checking if a target graph matches a given ReGaP. We also propose a preprocessing technique for improving the performance of the algorithm and evaluate it through an extensive experimental evaluation on benchmarks from the CodeSearchNet dataset.

Combining Multiple Supervision for Robust Zero-Shot Dense Retrieval

Recently, dense retrieval (DR) models, which represent queries and documents with fixed-width vectors and retrieve relevant ones via nearest neighbor search, have drawn increasing attention from the IR community. However, previous studies have shown that the effectiveness of DR critically relies on sufficient training signals, which leads to severe performance degradation when applied in out-of-domain scenarios, where large-scale training data are usually unavailable. To solve this problem, existing studies adopt a data-augmentation-plus-joint-training paradigm to construct weak/pseudo supervisions on the target domain and combine them with the large-scale human annotated data on the source domain to train the DR models. However, they don't explicitly distinguish the data and the supervision signals in the training process and simply assume that the DR models are mighty enough to capture and memorize different domain knowledge and relevance matching patterns without guidance, which, as shown in this paper, is not true. Based on this observation, we propose a Robust Multi-Supervision Combining strategy (RMSC) that decouples the domain and supervision signals by explicitly telling the DR models how the domain data and supervision signals are combined in the training data with specially designed soft tokens. With the extra soft tokens to store the domain-specific and supervision-specific knowledge, RMSC allows the DR models to conduct retrieval based on human-like relevance matching patterns and target-specific language distribution on the target domain without human annotations. Extensive experiments on zero-shot DR benchmarks show that RMSC significantly improves the ranking performance on the target domain compared to strong DR baselines and domain adaptation methods, while being stable during training and can be combined with query generation or second-stage pre-training.

Fracture density reconstruction using direct sampling multiple-point statistics and extreme value theory

The aim of this work is to present a methodology for the reconstruction of missing fracture density within highly fractured intervals, which can represent preferential fluid flow pathways. The lack of record can be very common due to the intense presence of fractures, dissolution processes, or data acquisition issues. The superposition of numerous fractures makes the definition of fracture surfaces impossible, as a consequence, modeling such zones is challenging. In order to address this issue, the usage of direct sampling multiple-point statistics to perform gap filling in well logs is demonstrated as an alternative to other techniques. It reproduces data patterns and provides several models representing uncertainty. The method was tested in intervals from a highly fractured well, by removing previously known fracture density data, and simulating different scenarios with direct sampling. Simulation results are compared to the observed data using cross-validation and continuous rank probability score. The reference scenario training data set consists in one well and two variables: fracture density and fracture occurrence. A sensitivity analysis is carried out considering additional variables, additional wells, different intervals, resampling with extremes, and other gap filling techniques. The auxiliary variable plays an important role in pattern matching, but adding wells and logs increases the complexity of the method without improving pattern retrieval. Best results are obtained applying extreme values theory for stochastic process with the enrichment of the fracture density data at the tail region, followed by resampling of the new values. The enriched data is used for the gap filling resulting in lower continuous rank probability score, and the achievement of extreme fracture density values.

Microstrip Patch Antenna Design for Enhanced Bandwidth and Harmonic Suppression

Antennas are essential components in wireless communication systems, often requiring a delicate balance between achieving wide bandwidth and suppressing harmonics effectively. This study focuses on the integration of defected ground structures (DGS) to enhance antenna performance. Through rigorous experimentation and analysis, the research aimed to optimize microstrip patch antenna designs for improved bandwidth while maintaining efficient harmonic suppression. The investigation involved parametric studies to determine the optimal dimensions and configurations of DGS for wideband-stop characteristics. Simulation tools were utilized to analyse the impact of DGS on the antenna's behaviour, leading to the development of an accurate equivalent circuit model. Current distribution analysis provided insights into the radiation pattern and impedance matching of the antennas with and without DGS. Experimental results demonstrated significant improvements in antenna performance. The measured reflection coefficient values indicated successful harmonic suppression, with reductions around 22 dB at the third harmonic frequency. The input impedance values were optimized to utilize a modified ground plane featuring diagonal edges and a rectangular slot cut to design compact antennas, contributing to enhanced bandwidth and improved overall performance. Radiation pattern measurements validated the effectiveness of the optimized designs. This study demonstrates successful integration of DGS into slot antennas, achieving wider bandwidth with robust harmonic suppression. Experimental results validate design strategies, offering insights for high-performance slot antennas in modern communication systems.

Design and simulation of light field patterns for angular color variation reduction in micro-LED and mini-LED RGB arrays.

We propose a methodology to mitigate angular color variation in full-color micron-scale LED arrays. By simulating light field distribution for red (AlGaAs) and green/blue (GaN) light across various RGB micro-LED sizes, we can select matching light field patterns for RGB chips, reducing angular color variation from 0.0201 to 0.0030. Applying this method to full-color mini-LED assemblies achieves a reduction from 0.0128 to 0.0032 by matching light field patterns with varying substrate thicknesses. This straightforward approach aligns with current mass transfer processes, offering practical implementation.

Pattern matching for the iris biometric recognition system uses KNN and fuzzy logic classifier techniques

Pattern matching for the iris biometric recognition system uses KNN and fuzzy logic classifier techniques

Split-based elevational localization of photoacoustic guidewire tip by 1D array probe using spatial impulse response.

Objective. Photoacoustic emitters on the tip of a therapeutic device have been intensively studied for echo-guided intervention purposes. In this study, a novel method for localizing the guidewire tip emitter in the elevation direction using a 1D array probe is proposed to resolve the issue of the tip potentially deviating from the ultrasound-imaged plane.Approach. Our method uses the 'interference split' that appears when the emitter is off-plane. Here, a point source from the emitter splits into two points in images. Based on the split, 'split-based elevation localization (SEL)' is introduced to estimate the absolute elevation position of the emitter. Additionally, 'Signed SEL' incorporates an asymmetric feature into the 1D probe to obtain the sign of the elevation localization. An attenuative coupler is attached to the half side of the probe to control the interference split. In SEL and Signed SEL, we propose a modeled split matching (MSM) algorithm to localize the tip position. MSM performs pattern matching of a measured split waveform with modeled split waveforms corresponding to all emitter positions in a region of interest. The modeled waveforms are precalculated using the spatial impulse response. The proposed method is numerically and experimentally validated.Main results. Numerical simulations for time-domain wave propagation clearly demonstrated the interference split phenomena. In the experimental validation with a vessel-mimicking phantom, the proposed methods successfully estimated the elevation positions,yb.SEL exhibited a root-mean-squared error (RMSE) of 2.0 mm for the range of 0 mm ≤yb≤ 30 mm, while Signed SEL estimated the absolute position with an RMSE of 2.4 mm and the sign with an accuracy of 80.8% for the range of -30 mm ≤yb≤ 30 mm.Significance.These results suggest that the proposed method could provide approximate tip positions and help sonographers track it by fanning the probe.

Chromogenic, Biochemical and Proteomic Identification of Yeast and Yeast-like Microorganisms Isolated from Clinical Samples from Animals of Costa Rica.

Yeast infections are challenging human and animal medicine due to low rates of detection and the emergence of unknown ecology isolates. The aim of this study was to verify the biochemical identification of yeasts and yeast-like microorganisms obtained from animals comparing the results with chromogenic media and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF MS). Between January and August 2023, yeast and yeast-like isolates from samples of animals with suspicion of mycosis were identified using Vitek® 2 Compact, Brilliance® Candida Agar and MALDI Biotyper® MSP. A total of 39 cases were included, and 45 isolations were obtained. Cryptococcus neoformans (15.5%, 7/45), Meyerozyma guilliermondii (13.3%, 6/45), Candida parapsilosis (11.1%, 5/45), Candida albicans and Candida tropicalis (8.9%, each one 4/45) were the most identified organisms. There was full agreement with the three identification methods in 71.1% (32/45) of the isolates, disagreement on species in 17.8% (8/45), disagreement on genus and species in 6.7% (3/45) and, in 4.4% (2/45), there was no matched pattern in MALDI-TOF to compare the results. Biochemical methods are a good option in laboratories where proteomics are not available, and chromogenic media enhances diagnostics by detecting mixed infections. Surveillance must be implemented to improve the detection of agents shared between humans and animals.