String Matching Techniques Research Articles

Pseudo 2D String Matching Technique for High Efficiency Screen Content Coding

This paper proposes a pseudo 2D string matching (P2SM) technique for high efficiency screen content coding (SCC). The technique uses a primary reference buffer (PRB) and a secondary reference buffer (SRB) for string matching and string copying. In the encoder, optimal reference string searching is performed in both PRB and SRB, and either a PRB or an SRB string is selected as an optimal reference string on a string-by-string basis. If no reference string of at least one pixel is founded for a current pixel, then the current pixel is coded as an unmatched pixel. Compared with HM-16.4 ${+}$ SCM-4.0 reference software, the proposed P2SM technique achieves up to 37.7% Y BD-rate reduction for a screen snapshot of a spreadsheet. On average, using HEVC SCC common test condition and YUV test sequences in text and graphics with motion category, the proposed technique achieves Y BD-rate reduction of 7.7%, 5.0%, 2.6% for all intra (AI), random access (RA) and low-delay B (LB) configurations, respectively in lossy coding with both intra block copy (IBC) and P2SM having the same 4 coding tree units (CTUs) searching range, and bit-rate saving of 6.0%, 3.9%, 3.1% for AI, RA, LB configurations, respectively in lossless coding with IBC having full frame searching range while P2SM having only 2 CTUs searching range, at very low additional encoding and decoding complexity.

On the feasibility of character n-grams pseudo-translation for Cross-Language Information Retrieval tasks

The field of Cross-Language Information Retrieval relates techniques close to both the Machine Translation and Information Retrieval fields, although in a context involving characteristics of its own. The present study looks to widen our knowledge about the effectiveness and applicability to that field of non-classical translation mechanisms that work at character n-gram level. For the purpose of this study, an n-gram based system of this type has been developed. This system requires only a bilingual machine-readable dictionary of n-grams, automatically generated from parallel corpora, which serves to translate queries previously n-grammed in the source language. n-Gramming is then used as an approximate string matching technique to perform monolingual text retrieval on the set of n-grammed documents in the target language.The tests for this work have been performed on CLEF collections for seven European languages, taking English as the target language. After an initial tuning phase in order to analyze the most effective way for its application, the results obtained, close to the upper baseline, not only confirm the consistency across languages of this kind of character n-gram based approaches, but also constitute a further proof of their validity and applicability, these not being tied to a given implementation.

Airborne Behaviour Monitoring Using Regular-Expressions Based Pattern Matching

This paper addresses the problem of target manoeuvre detection and behaviour recognition using string-matching techniques which could bring a great impact to the multiple UAV operations. The overall proposed solution consists of a trajectory analysis tool, which classifies the target position in time into a sequence of driving modes, and a string-matching method. For the latter, two techniques are described and compared: a classical approach based on simple strings and a newly proposed regular-expressions-based method. The advantages obtained from the use of regular expressions are extensively analysed, and results from numerical simulations supporting our conclusions are provided.

IJARCCE - Computer and Communication Engineering

2 Abstract: An intrusion is essentially an attack on security layer in a TCP/IP protocol stack. Such attacks have definite signature for instance specific ports or RTT. Hence such attacks can be detected by cross verifying current communication signature with attack signatures. However attack signatures are evolutionary. Therefore using a string matching technique is neither robust nor fast. Hence several machine learning techniques are developed which are mainly based on classifier. These classifiers lack generalization capabilities which result in less performance leading to high false positives. However a specific attack can have wide range of signatures and a signature may of wide range of attacks. Therefore conventional classifiers like Neural Network needs frequent training when a new signature is discovered. Again discovery of such new signature also needs a regression with existing signature database. In order to avoid exploding the training nodes of Neural Network, it is important that a benchmark is set of introduce new nodes. In this paper we use Artificial Immune System mark the signatures as genes. A packet or network level signature is verified for closeness with existing model. In case of significant diversification is detected, the signature is marked as new which is regressed with the existing signature model to automate the grouping of the signature. Signature similar to existing ones is subjected to regression using PLS method and is then classified by neural network. This paper evaluates the performance of technique using the publicly available KDD Cup dataset and compares the result with conventional Neural Network Based Classifier, Support Vector Machine based Classifier, pure regression based technique and conventional string matching technique. Further we investigate the real time applicability of the technique by using PLS Regression to detect anomaly in CIT college router dataset. We consider that the connections by peer clients should only be accessing internet. Other activities like using Bittorrent are considered as anomaly. First we take router log and extract the features. We then select a specific data row and classify it using auto regression.

Probabilistic correlation-based similarity measure on text records

Large scale unstructured text records are stored in text attributes in databases and information systems, such as scientific citation records or news highlights. Approximate string matching techniques for full text retrieval, e.g., edit distance and cosine similarity, can be adopted for unstructured text record similarity evaluation. However, these techniques do not show the best performance when applied directly, owing to the difference between unstructured text records and full text. In particular, the information are limited in text records of short length, and various information formats such as abbreviation and data missing greatly affect the record similarity evaluation.In this paper, we propose a novel probabilistic correlation-based similarity measure. Rather than simply conducting the matching of tokens between two records, our similarity evaluation enriches the information of records by considering correlations of tokens. The probabilistic correlation between tokens is defined as the probability of them appearing together in the same records. Then we compute weights of tokens and discover correlations of records based on the probabilistic correlations of tokens. The extensive experimental results demonstrate the effectiveness of our proposed approach.

Construction and Influence Analysis of Co-Author Network Based on Network Science

In this paper, we are devoted to the construction and analysis of research network. Firstly, on the basis of data Erdos1, a co-author network is built and two novel measures are proposed to analyze properties of the co-author network. A data extraction method using string matching technique is developed and the network is visualized using UCINET. Then, the first-order and second-order entropy are defined to depict the complexity of the network, and the node’s invalidity probability and load-bearing capacity are defined to depict the robustness of the network.

Improvement of Soundex Algorithm for Indian Language Based on Phonetic Matching

In a system with a large database, there always has been a problem that names may not be spe lled well or might not be spelled in a way thatone expected. So, data in the database gets degrad ed. In this case it is required to search the duplicates and merge them in the single entity. In doing so, one problem is that the way in which the strings would be compared. In such cases rather than looking for exact match, approximate string matching would be appreciable . One of the string matching techniques isPhonetic matching which is used to compare the name based on the pronunciation of the words. The similar sounding words could be retrieved from the large database using different phonetic matching algorithm and best known algorithm is Soundex algorithm. Phonetic matching is needed when many people from different culture come together. They either spe ak with different pronunciation or their writing habits are different. This scenario is very common in India, as we have many different languages like Hindi, Gujarati, Marathi, Tamil etc. In this research work Soundex algorithm is used for Hindi and Gujara ti language and applied on the names along with their variations in order to retrieve the output with minimum false hits.

A java API interface for the search of DTV services in embedded multimedia devices

The majority of DTV sets and set top boxes are embedded systems based on Linux. Utilizing network connectivity, they can provide many different kinds of information coming from the Internet such as news, sport events, weather and social networks through a large number of applications that are available for download. Considering the vast amount of data that is available to users, these platforms include specialized applications for unified search and presentation of this data. These applications currently do not support searching of data coming from DTV broadcast stream. This paper presents a proposal for the integration of data coming from DTV broadcast stream into a search mechanism on embedded systems which support the Java programming language. The proposal includes two parts: the Java API specification that is used to access DTV content from search applications, and the DTV native layer API. The latter is used to connect the native code to the Java API and includes approximate string matching technique for reducing spelling errors in search criterion.

Face Recognition Using Ensemble String Matching

In this paper, we present a syntactic string matching approach to solve the frontal face recognition problem. String matching is a powerful partial matching technique, but is not suitable for frontal face recognition due to its requirement of globally sequential representation and the complex nature of human faces, containing discontinuous and non-sequential features. Here, we build a compact syntactic Stringface representation, which is an ensemble of strings. A novel ensemble string matching approach that can perform non-sequential string matching between two Stringfaces is proposed. It is invariant to the sequential order of strings and the direction of each string. The embedded partial matching mechanism enables our method to automatically use every piece of non-occluded region, regardless of shape, in the recognition process. The encouraging results demonstrate the feasibility and effectiveness of using syntactic methods for face recognition from a single exemplar image per person, breaking the barrier that prevents string matching techniques from being used for addressing complex image recognition problems. The proposed method not only achieved significantly better performance in recognizing partially occluded faces, but also showed its ability to perform direct matching between sketch faces and photo faces.

A Semantic-aware method for polymorphic signature generation

It is difficult for security experts to generate polymorphic signatures by using traditional string mining and matching techniques. A semantic-aware method is presented to generate a kind of two-level signature that includes both polymorphic semantics and string patterns. It first analyzes the characteristics of polymorphic engines and categorizes the data flows into different clusters and then uses static data flow methods to extract invariable semantic instructions. And then, it combines traditional string methods to generate the signature. In comparison with other methods, experimental results show that it may effectively reduce false positives and false negatives.

Fast detection of exact clones in business process model repositories

As organizations reach higher levels of business process management maturity, they often find themselves maintaining very large process model repositories, representing valuable knowledge about their operations. A common practice within these repositories is to create new process models, or extend existing ones, by copying and merging fragments from other models. We contend that if these duplicate fragments, a.k.a. exact clones, can be identified and factored out as shared subprocesses, the repository's maintainability can be greatly improved. With this purpose in mind, we propose an indexing structure to support fast detection of clones in process model repositories. Moreover, we show how this index can be used to efficiently query a process model repository for fragments. This index, called RPSDAG, is based on a novel combination of a method for process model decomposition (namely the Refined Process Structure Tree), with established graph canonization and string matching techniques. We evaluated the RPSDAG with large process model repositories from industrial practice. The experiments show that a significant number of non-trivial clones can be efficiently found in such repositories, and that fragment queries can be handled efficiently.

Software Bertillonage

Deployed software systems are typically composed of many pieces, not all of which may have been created by the main development team. Often, the provenance of included components--such as external libraries or cloned source code--is not clearly stated, and this uncertainty can introduce technical and ethical concerns that make it difficult for system owners and other stakeholders to manage their software assets. In this work, we motivate the need for the recovery of the provenance of software entities by a broad set of techniques that could include signature matching, source code fact extraction, software clone detection, call flow graph matching, string matching, historical analyses, and other techniques. We liken our provenance goals to that of Bertillonage, a simple and approximate forensic analysis technique based on bio-metrics that was developed in 19th century France before the advent of fingerprints. As an example, we have developed a fast, simple, and approximate technique called anchored signature matching for identifying the source origin of binary libraries within a given Java application. This technique involves a type of structured signature matching performed against a database of candidates drawn from the Maven2 repository, a 275 GB collection of open source Java libraries. To show the approach is both valid and effective, we conducted an empirical study on 945 jars from the Debian GNU/Linux distribution, as well as an industrial case study on 81 jars from an e-commerce application.

A Framework for Improving the Performance of Ontology Matching Techniques in Semantic Web

Ontology matching is the process of finding correspondences between semantically related entities of different ontologies. We need to apply this process to solve the heterogeneity problems between different ontologies. Some ontologies may contain thousands of entities which make the ontology matching process very complex in terms of space and time requirements. This paper presents a framework that reduces the search space by removing entities (classes, properties) that have less probability of being matched. In order to achieve this goal we have introduced a matching strategy that uses multi matching techniques specifically; string, structure, and linguistic matching techniques. The results obtained from this framework have indicated a good quality matching outcomes in a low time requirement and a low search space in comparisons with other matching frameworks. It saves from the search space from (43% - 53%), and saves on the time requirement from (38% - 45%).

Automatic Heart Diseases Detection Techniques using Musical Approaches

In this study, a musical approach to provide an automatic heart disease detection system is proposed. Heart sounds are recorded with audio format. Audio files are converted to semi-structured music files that can be represented textually. Samples were captured from different heart diseases and were stored in a database. Two different approaches which are information retrieval based on n-gram and longest common subsequence are used to retrieve the similarity of a given sample with existing heart diseases in the database. Since the frequency of heart sound is relative to age and physical characteristics of a patient, an important feature of using n-gram in this study is to retrieve diseases without respect to the different heart sounds frequencies. The effects of window sizes for n-gram approach on the accuracy of the information retrieval were tested and a proper window size was extracted. The results of the performed experiments showed that window size of 5 notes revealed a high performance in comparison with other window sizes. Hence, the proposed technique can detect and recognize a heart disease with a reliable accuracy. Average of precision values for around 85% in information retrieval and 55% in longest common subsequence technique were obtained for the retrieval of heart sound categories. Moreover, the results of string matching technique demonstrated that threshold level of 65% could appropriately detect heart disease.

SAS: semantics aware signature generation for polymorphic worm detection

String extraction and matching techniques have been widely used in generating signatures for worm detection, but how to generate effective worm signatures in an adversarial environment still remains a challenging problem. For example, attackers can freely manipulate byte distributions within the attack payloads and thus inject well-crafted noisy packets to contaminate the suspicious flow pool. To address these attacks, we propose SAS, a novel Semantics Aware Statistical algorithm for automatic signature generation. When SAS processes packets in a suspicious flow pool, it uses data flow analysis techniques to remove non-critical bytes. We then apply a hidden Markov model (HMM) to the refined data to generate state-transition-graph-based signatures. To our best knowledge, this is the first work combining semantic analysis with statistical analysis to automatically generate worm signatures. Our experiments show that the proposed technique can accurately detect worms with concise signatures. Moreover, our results indicate that SAS is more robust to the byte distribution changes and noise injection attacks compared to Polygraph and Hamsa.

Fast Visual Retrieval Using Accelerated Sequence Matching

We present an approach to represent, match, and index various types of visual data, with the primary goal of enabling effective and computationally efficient searches. In this approach, an image/video is represented by an ordered list of feature descriptors. Similarities between such representations are then measured by the approximate string matching technique. This approach unifies visual appearance and the ordering information in a holistic manner with joint consideration of visual-order consistency between the query and the reference instances, and can be used for automatically identifying local alignments between two pieces of visual data. This capability is essential for tasks such as video copy detection where only small portions of the query and the reference videos are similar. To deal with large volumes of data, we further show that this approach can be significantly accelerated along with a dedicated indexing structure. Extensive experiments on various visual retrieval and classification tasks demonstrate the superior performance of the proposed techniques compared to existing solutions.

Adaptive Subspace Symbolization for Content-Based Video Detection

Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to extract a series of dominant subspaces of variable lengths from the whole visual feature space without the constraint of dimension consecutiveness. A stable visual dictionary is built by clustering the video keyframes over each dominant subspace. A compact video representation model is developed by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a series of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence context of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video retrieval. The experimental results demonstrate the high effectiveness and efficiency of SUDS.

Author Disambiguation using an Hybrid Approach of Queries and String Matching Techniques

Common problems when searching for publications by author names are misspellings, name abbreviations, and format differences. This paper attempted to solve this problem by combining database SQL queries with the error detection and correction properties of string comparisons. The use of these combination of methods increased the amount of relevant records retrieved while reducing the number of irrelevant records compared to other well know string matching techniques.

MTSSM - A Framework for Multi-Track Segmentation of Symbolic Music

Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the internal structure of a composition. Structural information about a composition can improve several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization. The authors of this paper present the MTSSM framework, a twolayer framework for the multi-track segmentation of symbolic music. The strength of this framework lies in the combination of existing methods for local track segmentation and the application of global structure information spanning via multiple tracks. The first layer of the MTSSM uses various string matching techniques to detect the best candidate segmentations for each track of a multi-track composition independently. The second layer combines all single track results and determines the best segmentation for each track in respect to the global structure of the composition.

Does dictionary based bilingual retrieval work in a non-normalized index?

Many operational IR indexes are non-normalized, i.e. no lemmatization or stemming techniques, etc. have been employed in indexing. This poses a challenge for dictionary-based cross-language retrieval (CLIR), because translations are mostly lemmas. In this study, we face the challenge of dictionary-based CLIR in a non-normalized index. We test two optional approaches: FCG (Frequent Case Generation) and s-gramming. The idea of FCG is to automatically generate the most frequent inflected forms for a given lemma. FCG has been tested in monolingual retrieval and has been shown to be a good method for inflected retrieval, especially for highly inflected languages. S-gramming is an approximate string matching technique (an extension of n-gramming). The language pairs in our tests were English–Finnish, English–Swedish, Swedish–Finnish and Finnish–Swedish. Both our approaches performed quite well, but the results varied depending on the language pair. S-gramming and FCG performed quite equally in all the other language pairs except Finnish–Swedish, where s-gramming outperformed FCG.