Pattern Matching Process Research Articles

Efficient Post-Quantum Pattern Matching on Encrypted Data

Pattern matching methods are essential in various applications where users must disclose highly sensitive information. Among these applications are genomic data analysis, financial records inspection, and intrusion detection processes, all of which necessitate robust privacy protection mechanisms. Balancing the imperative of protecting the confidentiality of analyzed data with the need for efficient pattern matching presents a significant challenge. In this paper, we propose an efficient post-quantum secure construction that enables arbitrary pattern matching over encrypted data while ensuring the confidentiality of the data to be analyzed. In addition, we address scenarios where a malicious data sender, intended to send an encrypted content for pattern detection analysis, has the ability to modify the encrypted content. We adapt the data fragmentation technique to handle such a malicious sender. Our construction makes use of a well-suited Homomorphic Encryption packing method in the context of fragmented streams and combines homomorphic operations in a leveled mode (i.e. without bootstrapping) to obtain a very efficient pattern matching detection process. In contrast to the most efficient state-of-the-art scheme, our construction achieves a significant reduction in the time required for encryption, decryption, and pattern matching on encrypted data. Specifically, our approach decreases the time by factors of 1850 , 10 6 , and 245 , respectively, for matching a single pattern, and by factors of 115 , 10 5 , and 12 , respectively, for matching 2 10 patterns.

Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning.

Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.

Clustering of Feature Vectors and Recognition of Bodo Phoneme Using MLP Technique

The process through which a computer can identify spoken words is termed as speech recognition. After analysis and finding of features of the speech sound, one can go towards the recognition of the speech. The extraction of feature vector is known as the feature extraction process or the front-end process. This front-end process is considered as the 1st stage of speech recognition. Pattern matching process is the 2nd stage or final stage of speech recognition where actual search is carried out to decode the spoken utterances by matching the sequence of feature vectors against the acoustic and language models stored in the recognizer. To reduce this problem, clustering technique is used. Clustering makes it possible to look at properties of whole clusters instead of individual objects - a simplification that might be useful when handling large volume of data. Clustering is nothing but the assignment of a set of observations into subsets so that the observations in the same cluster are similar in some sense.

Accelerating Pattern Matching Using a Novel Multi-Pattern-Matching Algorithm on GPU

Nowadays, almost all network traffic is encrypted. Attackers hide themselves using this traffic and attack over encrypted channels. Inspections performed only on packet headers and metadata are insufficient for detecting cyberattacks over encrypted channels. Therefore, it is important to analyze packet contents in applications that require control over payloads, such as content filtering, intrusion detection systems (IDSs), data loss prevention systems (DLPs), and fraud detection. This technology, known as deep packet inspection (DPI), provides full control over the communication between two end stations by keenly analyzing the network traffic. This study proposes a multi-pattern-matching algorithm that reduces the memory space and time required in the DPI pattern matching compared to traditional automaton-based algorithms with its ability to process more than one packet payload character at once. The pattern-matching process in the DPI system created to evaluate the performance of the proposed algorithm (PA) is conducted on the graphics processing unit (GPU), which accelerates the processing of network packets with its parallel computing capability. This study compares the PA with the Aho-Corasick (AC) and Wu–Manber (WM) algorithms, which are widely used in the pattern-matching process, considering the memory space required and throughput obtained. Algorithm tables created with a dataset containing 500 patterns use 425 and 688 times less memory space than those of the AC and WM algorithms, respectively. In the pattern-matching process using these tables, the PA is 3.5 and 1.5 times more efficient than the AC and WM algorithms, respectively.

A Question-Answering Model Based on Knowledge Graphs for the General Provisions of Equipment Purchase Orders for Steel Plants Maintenance

Recently, equipment replacement and maintenance repair and operation (MRO) optimization have substantially increased owing to the aging and deterioration of industrial plants, such as steel-making factories in Korea. Therefore, plant owners are required to quickly review equipment supply contracts, i.e., purchase order (PO) documents, with suppliers and vendors. Currently, there is inconsistency in the time and quality required for the PO document review process by engineers, depending on their manual skills and practice. This study developed a general provisions question-answering model (GPQAM) by combining knowledge graph (KG) and question-answering (QA) techniques to search for semantically connected contract clauses through the definition of relationships between entities during the review of equipment purchase contracts. The PO documents analyzed in this case study were based on one steel-making company’s general provisions (GP). GPQAM is a machine learning (ML)-based model with two sub-models (i.e., KG and QA) that automatically generates the most relevant answers to semantic search questions through a cypher query statement in GP for the PO engineers. First, based on the developed GP lexicon and its classifying taxonomy to be stored in the Neo4j graph database (GDB), the KG sub-model finds the corresponding synonyms and consequently shows GP-related information in a graphic form. Second, the QA sub-model is a function to find and answer contract information within the KG and applies pattern-matching technology based on the Aho–Corasick (AC) algorithm. Third, nodes with the meaning most similar to the question are selected using similarity measurement if a response cannot be extracted through the pattern-matching process. Forty-five pilot test questions were created and applied to the GPQAM model evaluation. The F1 score was 82.8%, indicating that the unsupervised training methods developed in this study could be better applied to a semantic QA process in plant engineering documents, where sufficient training data are limited and bargained. An expert survey of PO practitioners confirmed that the semantic QA capability of GPQAM might be efficient and useful for their work. As the first case of applying KG technology to semantic QA for plant equipment PO contracts, this study might be a meaningful contribution to the steel plant industry and, therefore, extended to construction and engineering contract applications.

A fast two part direct sampling method based on interpolation

Multiple-point simulation methods are effective in reproducing complex spatial structures, however intensive computations are needed in the pattern matching process. We propose a new method to alleviate the computation burden of direct sampling. This method first constructs a mixed sparse part using a regular sub grid and random cells from the target image. Next, direct sampling is only performed on this part and all other cells are estimated from neighboring conditioning cells rather than simulated. Two estimation methods are proposed for categorical and continuous variables based on the Bayes’ theorem and regression under the assumption that different conditioning cells contribute individually to the estimation of the central cell. Comparisons are made between the proposed method and direct sampling. Experiments show that the proposed method can save at most 90% and 40% computation time for the simulation of categorical and continuous variables, which is faster than not only the direct sampling but also the previously proposed two stage direct sampling method, which saves only at most 50% and 13% simulation time to generate results with similar simulation quality.

Semi-Supervised Graph Pattern Matching and Rematching for Expert Community Location

Graph pattern matching (GPM) is widely used in social network analysis, such as expert finding, social group query, and social position detection. Technically, GPM is to find matched subgraphs that meet the requirements of pattern graphs in big social networks. In the application of expert community location, the nodes in the pattern graph and data graph represent expert entities, and the edges represent previous cooperations between them. However, the existing GPM methods focus on shortening the matching time and without considering the preference of the decision maker (DM), which makes it difficult for the DM to find ideal teams from numerous matches to complete the assigned task. In this article, as for the process of graph pattern matching and rematching, with a preferred expert set, i.e., the DM hopes that one or more experts in this set will appear in matched subgraphs, we propose a Dual Simulation-based Edge Sequencing-oriented Semi-Supervised GPM method (DsEs-ssGPM). In addition, considering a preferred expert set and a dispreferred expert set together, the DM hopes that experts in the dispreferred expert set will not appear in final matches, so we have the DsEs-ssGPM+ method. Technically, these DsEs-ssGPM methods conduct the matching process from the preferred expert set during dual simulation-based edge sequencing, and based on the edge sequence, these edges are searched recursively. Especially, as for the rematching process, when the preferred and/or the dispreferred expert sets change continuously, to process the GPM again is unnecessary and it is possible to revise the previous matched results partially with DsEs-ssGPM methods. Experiments on four large datasets demonstrate the effectiveness, efficiency and stability of our proposed DsEs-ssGPM methods, and the necessity of introducing an edge sequencing mechanism.

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach.

Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

An Improved Approach of Iris Biometric Authentication Performance and Security with Cryptography and Error Correction Codes

One of the most challenging parts of integrating biometrics and cryptography is the intra variation in acquired identifiers between the same users. Due to noise in the environment or different devices, features of the iris may differ when it is acquired at different time periods. This research focuses on improving the performance of iris biometric authentication and encrypting the binary code generated from the acquired identifiers. The proposed biometric authentication system incorporates the concepts of non-repudiation and privacy. These concepts are critical to the success of a biometric authentication system. Iris was chosen as the biometric identifier due to its characteristics of high accuracy and the permanent presence throughout an individual’s lifetime. This study seeks to find a method of reducing the noise and error associated with the nature of dissimilarity acquired by each biometric acquisition. In our method, we used Reed Solomon error-correction codes to reduce dissimilarities and noise in iris data. The code is a block-based error correcting code that can be easily decoded and has excellent burst correction capabilities. Two different distance metric measurement functions were used to measure the accuracy of the iris pattern matching identification process, which are Hamming distance and weighted Euclidean distance. The experiments were conducted with the CASIA 1.0 iris database. The results showed that the False Acceptance Rate is 0%, the False Rejection Rate is 1.54%, and the Total Success Rate is 98.46%. The proposed approach appears to be more secure, as it is able to provide a low rate of false rejections and false acceptances.

Business-Civil Society Collaborations in South Korea: A Multi-Stage Pattern Matching Study

In this study, we use an empirical example to demonstrate how a multi-stage pattern matching process can inform and substantiate the construction of partial least squares (PLS) models and the subsequent interpretation of and theorizing from the findings. We document the research process underlying our empirical investigations of business – civil society collaborations in South Korea. The four-step process we outline in this paper can be used to ensure the meaningfulness of the structural model as well as to maximize the use of PLS for theorizing. This methodological advancement is particularly helpful in situations when literature reference points exist, but further contextual information may add nuances to prevalent knowledge. The findings from the qualitative flexible pattern matching part of the study prompted us to conduct a multi-group analysis. The resulting path changes in the base model led to the identification of four partnering strategies for business-CSO collaborations: (1) partnering for visibility; (2) partnering for compliance; (3) partnering for responsibility outsourcing; and (4) partnering for value co-creation.

Business-Civil Society collaborations in South Korea: A Multi-stage Pattern Matching Study

Our study seeks to achieve three objectives. First, we identify the main dimensions that shape the outcomes of business-CSO collaborations from the literature. Second, due to the scarcity of research on this topic in the context of South Korea, we examine to what extent the knowledge predominantly derived from other contexts is applicable in this research setting. Lastly, based on our findings, we theorize about the implications for MNEs seeking to enter South Korea. To do this, we followed a multi-stage pattern matching process comprising of qualitative flexible pattern matching and full pattern matching using SmartPLS.

Hotspot prediction of e-commerce network users based on improved K-nearest neighbour algorithm

Aiming at the problems of low accuracy and poor prediction effect in traditional e-commerce network users' hot spot prediction, this paper proposes an e-commerce network users' hot spot prediction method based on improved K-nearest neighbour algorithm. According to the users' hotspot prediction impact indicator, the K-nearest neighbour algorithm is improved in pattern matching process. The key point method is used to remove the noise interference of the original time series. The dynamic time warping algorithm is used to measure the similarity of time series of users' hotspot. The distance weight and trend coefficient are introduced according to the difference of users' hotspot time series to deduce the future users' hotspot and realise hotspot prediction of e-commerce network users. Experimental results show that the method in this paper greatly reduces the deviation of prediction results, which fully shows that the method has better prediction effect.

Algorithm Design for Deterministic Finite Automata for a Given Regular Language with Prefix Strings

Computer Science and Engineering have given us the field of automata theory, one of the largest areas that is concerned with the efficiency of an algorithm in solving a problem on a computational model. Various classes of formal languages are represented using Chomsky hierarchy. These languages are described as a set of specific strings over a given alphabet and can be described using state or transition diagrams. The state/transition diagram for regular languages is called a finite automaton which is used in compiler design for recognition of tokens. Other applications of finite automata include pattern matching, speech and text processing, CPU machine operations, etc. The construction of finite automata is a complicated and challenging process as there is no fixed mathematical approach that exists for designing Deterministic Finite Automata (DFA) and handling the validations for acceptance or rejection of strings. Consequently, it is difficult to represent the DFA’s transition table and graph. Novel learners in the field of theoretical computer science often feel difficulty in designing of DFA The present paper proposes an algorithm for designing of deterministic finite automata (DFA) for a regular language with a given prefix. The proposed method further aims to simplify the lexical analysis process of compiler design.

Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic learning function that predicts the disease affected by previous imaging analysis. The optimized algorithm-based selected regions are further examined using the distribution pattern-matching rule. The pattern-matching process recognizes the disease effectively, and the system's efficiency is evaluated using the MATLAB implementation process. This process ensures high accuracy of up to 99.02% to 99.37% and reduces computational complexity.

OPTIMIZING LOW-COST UAV AERIAL IMAGE MOSAICING FOR CROP GROWTH MONITORING

Abstract. High spatial resolution images acquired with drones can provide useful information to farmers for devising suitable management practices and increase crop yield. Data collected as individual frames or images have to be mosaiced using pattern recognition and matching process. Most flight missions collect hundreds of photos with high overlap and side overlap in order to generate mosaic without data gaps or distortion. These frames are aligned using the location information associated with each image. The same features are identified in multiple frames for generating the mosaic. In this process, it is common to use all or most of the images which requires a lot of resources. Uploading and processing hundreds of images could take several hours to days. Many farmers and crop consultants in developing countries may not have the necessary resources to upload hundreds of images. This study assessed the optimal number of images required to generate an image mosaic for a crop field without any data gaps or distortion. Images were collected at two different heights and directions. First, the mosaic was generated using all (100%) frames followed by subsets containing 90%, through 50% of images. Results obtained will assist us to plan the settings in future flight missions for acquiring optimal number of images required for generating image mosaic.

Twelve tips for using Pattern Matching in data analysis for qualitative medical education research

Pattern Matching (PM) is a data analysis method used in qualitative research. This article outlines a step-by-step approach to using PM to analyse qualitative data through the example of the author’s experience in its use for a master in medical education dissertation. The recommended twelve tips, outlined as steps to be used sequentially in the PM process are: (1) Assess if PM is a suitable technique for you and your research, (2) Consider alternatives, (3) Decide to proceed with PM, (4) Perform literature review, (5) Reflect about your experiences, (6) Define Propositions, (7) Collect your data, (8) Code data, (9) Match data to propositions, (10) Celebrate congruency, (11) Explain discrepancies and, (12) Discuss relevance and utility of findings in your context. PM, when rigorously applied, can increase the internal validity and transferability of qualitative research in medical education. A clear data analysis protocol enhances reliability and dependability of any research.

Pattern-Matching Unit for Medical Applications

We explore the application of concepts developed in High Energy Physics (HEP) for advanced medical data analysis. Our study case is a problem with high social impact: clinically-feasible Magnetic Resonance Fingerprinting (MRF). MRF is a new, quantitative, imaging technique that replaces multiple qualitative Magnetic Resonance Imaging (MRI) exams with a single, reproducible measurement for increased sensitivity and efficiency. A fast acquisition is followed by a pattern matching (PM) task, where signal responses are matched to entries from a dictionary of simulated, physically-feasible responses, yielding multiple tissue parameters simultaneously. Each pixel signal response in the volume is compared through scalar products with all dictionary entries to choose the best measurement reproduction. MRF is limited by the PM processing time, which scales exponentially with the dictionary dimensionality, i.e. with the number of tissue parameters to be reconstructed. We developed for HEP a powerful, compact, embedded system, optimized for extremely fast PM. This system executes real-time tracking for online event selection in the HEP experiments, exploiting maximum parallelism and pipelining. Track reconstruction is executed in two steps. The Associative Memory (AM) ASIC first implements a PM algorithm by recognizing track candidates at low resolution. The second step, which is implemented into FPGAs (Field Programmable Gate Arrays), refines the AM output finding the track parameters at full resolution. We propose to use this system to perform MRF, to achieve clinically reasonable reconstruction time. This paper proposes an adaptation of the HEP system for medical imaging and shows some preliminary results.

A user-knowledge dynamic pattern matching process and optimization strategy based on the expert knowledge recommendation system

When automated pattern matching tools are used to execute user-knowledge pattern matching (UKPM) in the expert knowledge recommendation system (EKRS), user-knowledge matching is uncertain and the matching efficiency is low. To solve the above problems, the dynamic UKPM mathematical model is established and the “Entropy-Beta” method of crowdsourcing task assignment is designed to solve the model in the study. Firstly, the concept of Entropy is combined with crowdsourcing. The uncertainty of user-knowledge matching results is measured and the magnitude of the uncertainty is calculated. Secondly, based on the Beta distribution function, the accuracy of matching results is measured. The optimal matching results are selected and the matching results were sent to EKRS according to the matching probability. Thirdly, the knowledge recommendation process of UKPM is dynamically adjusted according to the matching probability. Finally, the comparison results of several algorithms showed that the Entropy-Beta algorithm could largely improve the accuracy, efficiency, dynamic regulation, and other performances of EKRS.

Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa

To address the energy shortage problem in rural areas, significant attention has been paid to off-grid solar power plants. However, ensuring the security of these plants, improving the utilization rate of energy and, finally, proposing a sustainable energy development scheme for rural areas are still challenges. Under this, this work proposes a novel regression model-based stand-alone power plant load management system. This not only shows great potential in increasing load prediction in the real-time process but also provides effective anomaly detection for improving energy efficiency. The proposed predictor is a hybrid model that can effectively reduce the influence of fitting problems. Meanwhile, the proposed detector exhibits an efficient pattern matching process. That is, for the first time, a support vector machine (SVM) and the fruit fly optimization algorithm (FOA) are combined and applied to the field of energy consumption anomaly detection. This method was applied to manage the load of an off-grid solar power plant in a rural area in Tanzania with more than 50 households. In this paper, both the prediction and detection of our method are proven to exhibit better results than those of some previous works, and a comprehensive discussion on the establishment of a real-time energy management system has also been proposed.

DETECTION OF HIDDEN FUNCTIONALITIES OF SMART PHONE MALWARE APP USING PATTERN-MATCHING TECHNIQUES

Malware developers are progressively using advanced techniques to defeat malware detection tools. One such technique commonly observed in recent malware samples consists of hiding and obfuscating modules containing malicious functionality in places that static analysis tools overlook. In this paper, we describe a dynamic analysis approach for detecting such hidden or obfuscated malware components distributed as parts of an app package. The key idea is behavioral differences between the original app and a number of automatically generated versions of it, where a number of modifications (faults) have been carefully injected. The differential signature is analyzed through a pattern-matching process driven by rules that relate different types of hidden functionalities with patterns found in the signature. A thorough justification and a description of the proposed model are provided.