Approximate Matching Research Articles

Combinatorial Algorithms for String Sanitization

String data are often disseminated to support applications such as location-based service provision or DNA sequence analysis. This dissemination, however, may expose sensitive patterns that model confidential knowledge (e.g., trips to mental health clinics from a string representing a user’s location history). In this article, we consider the problem of sanitizing a string by concealing the occurrences of sensitive patterns, while maintaining data utility, in two settings that are relevant to many common string processing tasks. In the first setting, we aim to generate the minimal-length string that preserves the order of appearance and frequency of all non-sensitive patterns. Such a string allows accurately performing tasks based on the sequential nature and pattern frequencies of the string. To construct such a string, we propose a time-optimal algorithm, TFS-ALGO. We also propose another time-optimal algorithm, PFS-ALGO, which preserves a partial order of appearance of non-sensitive patterns but produces a much shorter string that can be analyzed more efficiently. The strings produced by either of these algorithms are constructed by concatenating non-sensitive parts of the input string. However, it is possible to detect the sensitive patterns by “reversing” the concatenation operations. In response, we propose a heuristic, MCSR-ALGO, which replaces letters in the strings output by the algorithms with carefully selected letters, so that sensitive patterns are not reinstated, implausible patterns are not introduced, and occurrences of spurious patterns are prevented. In the second setting, we aim to generate a string that is at minimal edit distance from the original string, in addition to preserving the order of appearance and frequency of all non-sensitive patterns. To construct such a string, we propose an algorithm, ETFS-ALGO, based on solving specific instances of approximate regular expression matching. We implemented our sanitization approach that applies TFS-ALGO, PFS-ALGO, and then MCSR-ALGO, and experimentally show that it is effective and efficient. We also show that TFS-ALGO is nearly as effective at minimizing the edit distance as ETFS-ALGO, while being substantially more efficient than ETFS-ALGO.

Occupational exposure as a risk factor for cognitive impairment in Vietnamese nail salon workers

AbstractBackgroundNail salon technicians are continuously exposed to neurotoxic and carcinogenic properties in nail products that may put them at risk for central nervous system impairments linked to Alzheimer’s disease. However, there are very few studies of the nail‐care workplace, and even fewer focus on Vietnamese female nail technicians, who make up the overwhelming part of the workforce. This study provides preliminary data on clinical assessments that will inform the cognitive functioning of a vulnerable population.MethodSample includes 300 Vietnamese females aged 50 or above in Northern California. The nail‐technician group (n=155) self‐identified as a current or former full or part‐time nail technician. The non‐nail group (n=145) are those who have never worked in the cosmetic industry, including nail or hair salons, but were an approximate match with the nail technicians on demographics and acculturation variables. Participants completed the Montreal Cognitive Assessment (MoCA), the Center for Epidemiologic Studies Depression Scale (CES‐D), and a self‐reported bilingual survey regarding their occupational exposure and demographics.ResultThe average MoCA score was 18.8 (SD ±4.50) for non‐nail technicians, which is significantly higher than 17.60 (SD ±4.30) for nail technicians (p = 0.017). However, there was no significant difference between the two groups on the average CES‐D score (p = 0.22). Based on results of multiple linear regression, with an increase of 100 in exposure index (i.e., years working in current nail salon x hours worked per week), the MoCA score is expected to decrease by 0.29 (95% CI: 0.05, 0.53, p = 0.017).ConclusionAfter adjusting for potential confounders, Vietnamese nail technicians were found to have lower cognitive functioning that those without occupational exposure to nail salon work. The results of this study suggest the need for future longitudinal research to better elucidate the processes underlying this association.

Short Read Alignment Based on Maximal Approximate Match Seeds.

Sequence alignment is a critical step in many critical genomic studies, such as variant calling, quantitative transcriptome analysis (RNA-seq), and metagenomic sequence classification. However, the alignment performance is largely affected by repetitive sequences in the reference genome, which extensively exist in species from bacteria to mammals. Aligning repeating sequences might lead to tremendous candidate locations, bringing about a challenging computational burden. Thus, most alignment tools prefer to simply discard highly repetitive seeds, but this may cause the true alignment to be missed. Using maximal approximate matches (MAMs) as seeds is an option, but MEMs seeds may fail due to sequencing errors or genomic variations in MEMs seeds. Here, we propose a novel sequence alignment algorithm, named MAM, which can efficiently align short DNA sequences. MAM first builds a modified Burrows-Wheeler transform (BWT) structure of a reference genome to accelerate approximate seed matching. Then, MAM uses maximal approximate matches (MAMs) seeds to reduce the candidate locations. Finally, MAM applies an affine-gap-penalty dynamic programming to extend MAMs seeds. Experimental results on simulated and real sequencing datasets show that MAM achieves better performance in speed than other state-of-the-art alignment tools. The source code is available at https://github.com/weiquan/mam.

Statistic analysis of lightning transients on wind turbines

In view of the actual random behavior of lightning strikes, an efficient algorithm is proposed in this paper for statistical analysis of the lightning transients on wind turbines. The equivalent circuits are established for blades, moving contact site, tower and grounding arrangement. A set of formulas is provided for evaluating their respective circuit parameters. With the equivalent circuits connected in sequence, a complete circuit model of wind turbines (WTs) is constructed for calculating lightning transients. The lightning transient responses can be obtained at different locations on a wind turbine by using the circuit model to perform the transient calculation. For checking the validity of the circuit model, the experiment measurement is also made on a laboratory-scale WT. An approximate match appears between the measured and calculated results. On the basis of the circuit model, the transient calculation is performed sequentially for each discrete amplitude in a large interval of lightning current. The random distribution of lightning current is taken into account by a cumulative probability function of exponential type. The corresponding probability density function is derived to weight the peak values of the lightning transient responses. The statistical values of the lightning transient responses can be determined by totalizing the weighted peak values in the interval of lightning current. Then, a case study is carried out to contrast the statistical values with the non-statistical ones calculated from the standard lightning current amplitudes, and a significant difference is discovered between them.

A methodology based on strain analysis for identifying potential discontinuous deformation zones in sublevel caving mines

Underground mining operations using the sublevel caving method often result in discontinuous ground deformations involving surface cracks. Determining the limit of the discontinuous deformation zone is important for the relocation of neighbouring residential areas, mining infrastructure, and the planning of underground mining operations. The limit of the discontinuous deformation zone is characterised by the outermost surface crack, which is a tensile crack when it occurs and is correlated with the horizontal ground tensile strain. In this study, a mathematical procedure combining the horizontal displacement, as periodically monitored by total station and GPS, multiquadric radial basis function (MQ-RBF) interpolation, and continuum mechanics is provided to calculate the strain state at any point on the ground. Then the relationship between a new tensile crack and horizontal ground tensile strain is studied to determine the criterion representing the presence of a new tensile crack. This means that, when the criterion at any point on the ground is satisfied, the point will have the potential to crack. Based on this, the potential discontinuous deformation zone where surface cracks may occur can be identified, potential break angles at representative sections can then be determined. A case study is presented to illustrate the results given by the above methodology. The result shows that when major principal horizontal ground strain ε1 at any point on the ground reaches the potential cracking major principal strain, PCMPS (6 mm/m in this case study), the point will have the potential to crack. Subsequently, the area within the outermost contour of the PCMPS can be defined as the potential discontinuous deformation zone. Moreover, the approximate matching of the limits of potential discontinuous deformation zones and surface cracks verifies the reliability of the mathematical procedure and the rationality of the PCMPS in identifying the discontinuous deformation zone. Additionally, in this case study, the potential break angles at two respective sections are significantly different, indicating that the break angles should be further studied and care taken in design of works in these two zones, respectively.

CDKAM: a taxonomic classification tool using discriminative k-mers and approximate matching strategies

BackgroundCurrent taxonomic classification tools use exact string matching algorithms that are effective to tackle the data from the next generation sequencing technology. However, the unique error patterns in the third generation sequencing (TGS) technologies could reduce the accuracy of these programs.ResultsWe developed a Classification tool using Discriminative K-mers and Approximate Matching algorithm (CDKAM). This approximate matching method was used for searching k-mers, which included two phases, a quick mapping phase and a dynamic programming phase. Simulated datasets as well as real TGS datasets have been tested to compare the performance of CDKAM with existing methods. We showed that CDKAM performed better in many aspects, especially when classifying TGS data with average length 1000–1500 bases.ConclusionsCDKAM is an effective program with higher accuracy and lower memory requirement for TGS metagenome sequence classification. It produces a high species-level accuracy.

Heavy traffic analysis of approximate max-weight matching algorithms for input-queued switches

In this paper, we propose a class of approximation algorithms for the max-weight matching (MWM) policy for input-queued switches, called expected 1-APRX. We establish the state space collapse (SSC) result for expected 1-APRX, and characterize its queue length behavior in the heavy-traffic limit. Our results indicate that expected 1-APRX can approximately approach the optimal queue length scaling in the heavy-traffic regime. We further propose an expected 1-APRX based policy, called MWM with adaptive update (MWM-AU), for reducing communication cost due to queue information update. We prove that MWM-AU is throughput optimal and characterize its heavy-traffic limit behavior. Our simulation results demonstrate that the proposed policy can significantly reduce queue update overhead, while maintaining the delay performance comparable to that of MWM.

Lift Charts-Based Binary Classification in Unsupervised Setting for Concept-Based Retrieval of Emotionally Annotated Images from Affective Multimedia Databases

Evaluation of document classification is straightforward if complete information on the documents’ true categories exists. In this case, the rank of each document can be accurately determined and evaluated. However, in an unsupervised setting, where the exact document category is not available, lift charts become an advantageous method for evaluation of the retrieval quality and categorization of ranked documents. We introduce lift charts as binary classifiers of ranked documents and explain how to apply them to the concept-based retrieval of emotionally annotated images as one of the possible retrieval methods for this application. Furthermore, we describe affective multimedia databases on a representative example of the International Affective Picture System (IAPS) dataset, their applications, advantages, and deficiencies, and explain how lift charts may be used as a helpful method for document retrieval in this domain. Optimization of lift charts for recall and precision is also described. A typical scenario of document retrieval is presented on a set of 800 affective pictures labeled with an unsupervised glossary. In the lift charts-based retrieval using the approximate matching method, the highest attained accuracy, precision, and recall were 51.06%, 47.41%, 95.89%, and 81.83%, 99.70%, 33.56%, when optimized for recall and precision, respectively.

2-Dimensional palindromes with k mismatches

This paper extends the problem of 2-dimensional palindrome search into the area of approximate matching. Using the Hamming distance as the measure, we search for 2D palindromes that allow up to k mismatches. We consider two different definitions of 2D palindromes and describe efficient algorithms for both of them. The first definition implies a square, while the second definition (also known as a centrosymmetric factor), can be any rectangular shape. Given a text of size (n×m), the time complexity of the first algorithm is O(nm(log⁡m+log⁡n+k)) and for the second algorithm it is O(nmlog⁡m+klog⁡k(nm+occ)) where occ is the size of the output.

Data loss prevention (DLP) by using MRSH-v2 algorithm

Sensitive data may be stored in different forms. Not only legal owners but also malicious people are interesting of getting sensitive data. Exposing valuable data to others leads to severe Consequences. Customers, organizations, and /or companies lose their money and reputation due to data breaches. There are many reasons for data leakages. Internal threats such as human mistakes and external threats such as DDoS attacks are two main reasons for data loss. In general, data may be categorized based into three kinds: data in use, data at rest, and data in motion. Data Loss Prevention (DLP) are good tools to identify important data. DLP can do analysis for data content and send feedback to administrators to make decision such as filtering, deleting, or encryption. Data Loss Prevention (DLP) tools are not a final solution for data breaches, but they consider good security tools to eliminate malicious activities and protect sensitive information. There are many kinds of DLP techniques, and approximation matching is one of them. Mrsh-v2 is one type of approximation matching. It is implemented and evaluated by using TS dataset and confusion matrix. Finally, Mrsh-v2 has high score of true positive and sensitivity, and it has low score of false negative.

ChiSeL

Subgraph querying is one of the most important primitives in many applications. Although the field is well studied for deterministic graphs, in many situations, the graphs are probabilistic in nature. In this paper, we address the problem of subgraph querying in large probabilistic labeled graphs. We employ a novel algorithmic framework, called ChiSeL, that uses the idea of statistical significance for approximate subgraph matching on uncertain graphs that have uncertainty in edges. For each candidate matching vertex in the target graph that matches a query vertex, we compute its statistical significance using the chi-squared statistic. The search algorithm then proceeds in a greedy manner by exploring the vertex neighbors having the largest chi-square score. In addition to edge uncertainty, we also show how ChiSeL can handle uncertainty in labels and/or vertices. Experiments on large real-life graphs show the efficiency and effectiveness of our algorithm.

Output sensitive algorithms for approximate incidences and their applications

An ε-approximate incidence between a point and some geometric object (line, circle, plane, sphere) occurs when the point and the object lie at distance at most ε from each other. Given a set of points and a set of objects, computing the approximate incidences between them is a major step in many database and web-based applications in computer vision and graphics, including robust model fitting, approximate point pattern matching, and estimating the fundamental matrix in epipolar (stereo) geometry.In a typical approximate incidence problem of this sort, we are given a set P of m points in two or three dimensions, a set S of n objects (lines, circles, planes, spheres), and an error parameter ε>0, and our goal is to report all pairs (p,s)∈P×S that lie at distance at most ε from one another. We present efficient output-sensitive approximation algorithms for quite a few cases, including points and lines or circles in the plane, and points and planes, spheres, lines, or circles in three dimensions. Several of these cases arise in the applications mentioned above. Our algorithms report all pairs at distance ≤ε, but may also report additional pairs, all of which are guaranteed to be at distance at most αε, for some problem-dependent constant α>1. Our algorithms are based on simple primal and dual grid decompositions and are easy to implement. We note that (a) the use of duality, which leads to significant improvements in the overhead cost of the algorithms, appears to be novel for this kind of problems; (b) the correct choice of duality in some of these problems is fairly intricate and requires some care; and (c) the correctness and performance analysis of the algorithms (especially in the more advanced versions) is fairly non-trivial. We analyze our algorithms and prove guaranteed upper bounds on their running time and on the “distortion” parameter α.

Approximate matching-based unsupervised document indexing approach: application to biomedical domain

Document indexing is considered as a crucial phase in the information retrieval field because textual information is constantly increasing. With this accumulation of documents, the satisfaction of user needs becomes more and more complex. For these reasons, several information retrieval systems have been designed in order to respond to user requests. The main contribution of the current work resides in the suggestion of a novel hybrid approach for biomedical document indexing. We improve the estimation of the correspondence between a document and a given concept using two methods: vector space model (VSM) and description logics (DL). VSM performs partial matching between documents and external resource terms. DL allows representing knowledge in a relevant manner for better matching. The proposed contribution reduces the limitation of exact matching. It serves to index documents by exploiting medical subject headings (MeSH) thesaurus services with approximate matching. The latter partially matches document terms with biomedical vocabularies to extract other morphological variants in that resource. It also generates irrelevant concepts. The filtering step solves this problem and grants the selection of the most important concepts by exploiting the knowledge provided by MeSH. The experiments, carried out on different corpora, show encouraging results of around 25% improvement in average accuracy compared to other approaches studied in the literature.

Improved light-matter interaction for storage of quantum states of light in a thulium-doped crystal cavity

We design and implement an atomic frequency comb quantum memory for 793 nm wavelength photons using a monolithic cavity based on a thulium-doped Y$_3$Al$_5$O$_{12}$ (Tm:YAG) crystal. Approximate impedance matching results in the absorption of approximately $90\%$ of input photons and a memory efficiency of (27.5$\pm$ 2.7)% over a 500 MHz bandwidth. The cavity enhancement leads to a significant improvement over the previous efficiency in Tm-doped crystals using a quantum memory protocol. In turn, this allows us for the first time to store and recall quantum states of light in such a memory. Our results demonstrate progress toward efficient and faithful storage of single photon qubits with large time-bandwidth product and multi-mode capacity for quantum networking.

Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks

Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban traffic congestion. We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers' trips in intelligent transportation platforms (e.g., DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban traffic systems. The dynamic pricing strategy retrieves a matching between n travelers' trips and the potential travel routes (each trip has k potential routes) to minimize the global traffic congestion. We believe that DPS holds the potential to benefit society and the environment, such as reducing traffic congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist kn matching possibilities). We develop an efficient and effective approximate matching algorithm based on local search, as well as pruning techniques to further enhance the matching efficiency. The accuracy and efficiency of the dynamic pricing strategy are verified by extensive experiments on real datasets.

An Improved Bytewise Approximate Matching Algorithm Suitable for Files of Dissimilar Sizes

The goal of digital forensics is to recover and investigate pieces of data found on digital devices, analysing in the process their relationship with other fragments of data from the same device or from different ones. Approximate matching functions, also called similarity preserving or fuzzy hashing functions, try to achieve that goal by comparing files and determining their resemblance. In this regard, ssdeep, sdhash, and LZJD are nowadays some of the best-known functions dealing with this problem. However, even though those applications are useful and trustworthy, they also have important limitations (mainly, the inability to compare files of very different sizes in the case of ssdeep and LZJD, the excessive size of sdhash and LZJD signatures, and the occasional scarce relationship between the comparison score obtained and the actual content of the files when using the three applications). In this article, we propose a new signature generation procedure and an algorithm for comparing two files through their digital signatures. Although our design is based on ssdeep, it improves some of its limitations and satisfies the requirements that approximate matching applications should fulfil. Through a set of ad-hoc and standard tests based on the FRASH framework, it is possible to state that the proposed algorithm presents remarkable overall detection strengths and is suitable for comparing files of very different sizes. A full description of the multi-thread implementation of the algorithm is included, along with all the tests employed for comparing this proposal with ssdeep, sdhash, and LZJD.

Response of second-harmonic generation of Lamb wave propagation to microdamage thickness in a solid plate

The response features of second-harmonic generation (SHG) of primary Lamb wave propagation to the thickness of microdamage layer (MDL) in a solid plate have been theoretically and numerically investigated in this paper. Here the solid plate with a MDL is regarded as a double-layered plate in analysis of nonlinear Lamb wave propagation. On the basis of a second-order perturbation approximation and modal expansion analysis, the physical process of cumulative SHG by primary Lamb wave propagation in a solid plate with a MDL has been investigated. The influence of variation in the thickness of MDL on the effect of SHG of primary S0 mode, which satisfies an approximate phase velocity matching in the low frequency region, has been theoretically analyzed, and then the finite element (FE) simulation has been carried out to validate the results of the theoretical predictions. A close agreement between the theoretical analyses and FE simulations validates the effectiveness of using the effect of SHG of primary S0 mode for characterizing changes in the thickness of MDL. Moreover, change mechanism of nonlinear acoustic parameter with the thickness of MDL is revealed It is expected that the results obtained can provide a convenient means for accurately characterizing nonhomogeneous microdamage (MDL thickness) in layered plates.

A Simplified Variant of a Protocol for Privacy-preserving String Comparison

Due to the frequency of spelling and typographical errors, in practical applications, Record Linkage algorithms have to use string similarity functions. Therefore, algorithms for computing string similarity functions with encrypted identifiers are essential for approximate string matching in private record linkage. In this paper, we propose a new simplified protocol for Privacy-preserving Record Linkage.

Communication complexity of approximate maximum matching in the message-passing model

We consider the communication complexity of finding an approximate maximum matching in a graph in a multi-party message-passing communication model. The maximum matching problem is one of the most fundamental graph combinatorial problems, with a variety of applications. The input to the problem is a graph G that has n vertices and the set of edges partitioned over k sites, and an approximation ratio parameter $$\alpha $$ . The output is required to be a matching in G that has to be reported by one of the sites, whose size is at least factor $$\alpha $$ of the size of a maximum matching in G. We show that the communication complexity of this problem is $$\varOmega (\alpha ^2 k n)$$ information bits. This bound is shown to be tight up to a $$\log n$$ factor, by constructing an algorithm, establishing its correctness, and an upper bound on the communication cost. The lower bound also applies to other graph combinatorial problems in the message-passing communication model, including max-flow and graph sparsification.

RESULTING APPROXIMATE QUERIES USING XML DATA

Daunting for users to formulate precise queries and search correct answers. Therefore, approximate matching is introduced to trot out the problem in respondent users’ queries, and this matching might be addressed by 1st reposeful the structure and content of a given question, so longing for answers that match the relaxed queries. Ranking and returning the foremost relevant results of a question became the foremost widespread paradigm in XML query process. However, the present proposals don't adequately take structures into consideration and those them so lack the strength to elegantly mix structures with contents to answer the relaxed queries. To handle this drawback, we have a tendency to 1st propose a classy framework of question relaxations for supporting approximate queries over XML information. The answers underlying this framework aren't compelled to strictly satisfy the given question formulation, instead they will be based on properties inferable from the initial question. we have a tendency to then develop a unique top-k retrieval approach.