Common Subsequence Research Articles

Move schedules: fast persistence computations in coarse dynamic settings

AbstractMatrix reduction is the standard procedure for computing the persistent homology of a filtered simplicial complex with m simplices. Its output is a particular decomposition of the total boundary matrix, from which the persistence diagrams and generating cycles are derived. Persistence diagrams are known to vary continuously with respect to their input, motivating the study of their computation for time-varying filtered complexes. Computing persistence dynamically can be reduced to maintaining a valid decomposition under adjacent transpositions in the filtration order. Since there are $$O(m^2)$$ O ( m 2 ) such transpositions, this maintenance procedure exhibits limited scalability and is often too fine for many applications. We propose a coarser strategy for maintaining the decomposition over a 1-parameter family of filtrations. By reduction to a particular longest common subsequence problem, we show that the minimal number of decomposition updates d can be found in $$O(m \log \log m)$$ O ( m log log m ) time and O(m) space, and that the corresponding sequence of permutations—which we call a schedule—can be constructed in $$O(d m \log m)$$ O ( d m log m ) time. We also show that, in expectation, the storage needed to employ this strategy is actually sublinear in m. Exploiting this connection, we show experimentally that the decrease in operations to compute diagrams across a family of filtrations is proportional to the difference between the expected quadratic number of states and the proposed sublinear coarsening. Applications to video data, dynamic metric space data, and multiparameter persistence are also presented.

Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial–temporal–semantic features

AbstractStudying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS‐weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix‐projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID‐19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID‐19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were “spindle‐shaped,” meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was “wave‐shaped,” indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies.

Optimized RNA structure alignment algorithm based on longest arc-preserving common subsequence

<abstract> <p>Ribonucleic acid (RNA) structure alignment is an important problem in computational biology to identify structural similarity of RNAs. Obtaining an efficient method for this problem is challenging due to the high computational time for the optimal solution and the low accuracy of a heuristic solution. In this paper, an efficient algorithm is proposed based on a mathematical model called longest arc-preserving common subsequence. The proposed algorithm uses a heuristic technique and high-performance computing to optimize the solution of RNA structure alignment, both in terms of the running time and the accuracy of the output. Extensive experimental studies on a multicore system are conducted to show the effectiveness of the proposed algorithm on two types of data. The first is simulated data that consists of 450 comparisons of RNA structures, while the second is real biological data that consists of 357 comparisons of RNA structures. The results show that the proposed algorithm outperforms the best-known heuristic algorithm in terms of execution time, with a percentage improvement of 71% and increasing the length of the output, i.e., accuracy, by approximately 45% in all studied cases. Finally, future approaches are discussed.</p> </abstract>

Identification of pattern mining algorithm for rugby league players positional groups separation based on movement patterns.

The application of pattern mining algorithms to extract movement patterns from sports big data can improve training specificity by facilitating a more granular evaluation of movement. Since movement patterns can only occur as consecutive, non-consecutive, or non-sequential, this study aimed to identify the best set of movement patterns for player movement profiling in professional rugby league and quantify the similarity among distinct movement patterns. Three pattern mining algorithms (l-length Closed Contiguous [LCCspm], Longest Common Subsequence [LCS] and AprioriClose) were used to extract patterns to profile elite rugby football league hookers (n = 22 players) and wingers (n = 28 players) match-games movements across 319 matches. Jaccard similarity score was used to quantify the similarity between algorithms' movement patterns and machine learning classification modelling identified the best algorithm's movement patterns to separate playing positions. LCCspm and LCS movement patterns shared a 0.19 Jaccard similarity score. AprioriClose movement patterns shared no significant Jaccard similarity with LCCspm (0.008) and LCS (0.009) patterns. The closed contiguous movement patterns profiled by LCCspm best-separated players into playing positions. Multi-layered Perceptron classification algorithm achieved the highest accuracy of 91.02% and precision, recall and F1 scores of 0.91 respectively. Therefore, we recommend the extraction of closed contiguous (consecutive) over non-consecutive and non-sequential movement patterns for separating groups of players.

DwMLCS: An Efficient MLCS Algorithm based on Dynamic and Weighted Directed Acyclic Graph.

The problem of finding the longest common subsequence (MLCS) for multiple sequences is a computationally intensive and challenging problem that has significant applications in various fields such as text comparison, pattern recognition, and gene diagnosis. Currently, the dominant point-based MLCS algorithms have become popular and extensively studied. Generally, they construct the directed acyclic graph (DAG) of matching points and convert the MLCS problem into a search for the longest paths in the DAG. Several improvements have been made, focusing on decreasing model size and reducing redundant computations. These include 1) hash methods for eliminating duplicated nodes, 2) dynamic structures for supporting smaller DAG and 3) path pruning strategy and so on. However, the algorithms are still too limited when facing large-scale MLCS problem due to 1) the dynamic structures are too time-consuming to maintain and 2) the path pruning relies heavily on the tightness of the lower and upper bound of the MLCS. These factors contribute to the large-scale MLCS problem remaining a challenge. We propose a novel algorithm for the large-scale MLCS problem, named dwMLCS. It is based on two models: one is a dynamic DAG model which is both space and time efficient. It can decrease the size of the DAG significantly. The other is a weighted DAG model with new successor strategies. With this model, we design the algorithm for finding a tighter lower bound of the MLCS. Then, the path pruning is conducted to further reduce the size of the DAG and eliminate redundant computation. Additionally, we propose an upper bound method for improving the efficiency of the path pruning strategy. The experimental results demonstrate that the effectiveness and efficiency of the models and algorithms proposed are better than state-of-the-art algorithms. The source codes of dwMLCS can be downloaded from web site https://github.com/BioLab310/dwMLCS.

Algorithms for the Uniqueness of the Longest Common Subsequence.

Given several number sequences, determining the longest common subsequence is a classical problem in computer science. This problem has applications in bioinformatics, especially determining transposable genes. Nevertheless, related works only consider how to find one longest common subsequence. In this paper, we consider how to determine the uniqueness of the longest common subsequence. If there are multiple longest common subsequences, we also determine which number appears in all/some/none of the longest common subsequences. We focus on four scenarios: (1) linear sequences without duplicated numbers; (2) circular sequences without duplicated numbers; (3) linear sequences with duplicated numbers; (4) circular sequences with duplicated numbers. We develop corresponding algorithms and apply them to gene sequencing data.

Similarity-Based Remaining Useful Lifetime Prediction Method Considering Epistemic Uncertainty

Measuring the similarity between two trajectories is fundamental and essential for the similarity-based remaining useful life (RUL) prediction. Most previous methods do not adequately account for the epistemic uncertainty caused by asynchronous sampling, while others have strong assumption constraints, such as limiting the positional deviation of sampling points to a fixed threshold, which biases the results considerably. To address the issue, an uncertain ellipse model based on the uncertain theory is proposed to model the location of sampling points as an observation drawn from an uncertain distribution. Based on this, we propose a novel and effective similarity measure metric for any two degradation trajectories. Then, the Stacked Denoising Autoencoder (SDA) model is proposed for RUL prediction, in which the models can be first trained on the most similar degradation data and then fine-tuned by the target dataset. Experimental results show that the predictive performance of the new method is superior to prior methods based on edit distance on real sequence (EDR), longest common subsequence (LCSS), or dynamic time warping (DTW) and is more robust at different sampling rates.

Battery defect detection for real world vehicles based on Gaussian distribution parameterization developed LCSS

Accurate detection of early faults in lithium-ion (Li-ion) battery packs plays an important role in preventing safety accidents and reducing property damage. At present, fault diagnosis research based on actual vehicle single-cell voltage consistency has become a hot topic, but the consistency evolution law is not uniform due to complex environmental conditions and operating conditions, so the generalization of current methods is poor. To this end, this paper proposes a multi-layer fault diagnosis framework, which first considers the evolution law of single-unit voltage difference to determine whether there is a potential risk, and proposes a flexible screening method for state of charge (SOC) interval segments, which improves the accuracy and efficiency of inspection. Next, the thresholds required by the developed longest common subsequence (DLCSS) algorithm are parameterized based on Gaussian distribution and combined with z-score to construct fault diagnosis indicators. The parameters of the framework are calculated from different segment data statistics, enhancing the objectivity and interpretability of the parameters. The results are validated using actual vehicle data and show that the proposed framework can greatly reduce the required time and identify faulty batteries more accurately with a 96.2 % precision and a 72.4 % detection rate, which is better than other algorithms and has better robustness.

An Improved Algorithm of Finding a Maximal Common Subsequence

An Improved Algorithm of Finding a Maximal Common Subsequence

Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink

Bus transportation system has become the primary mode of traffic for urban residents. Every day, thousands of buses provide services for millions of passengers. Efficiently monitoring bus trajectories is essential for evaluating service quality and ensuring public safety. In this study, we propose a Flink-based solution to detect anomalies for bus trajectories in real time. Specifically, it can identify two types of anomalies. The first type is when a bus deviates from its designated route during a trip. The second type is when a bus arrives at a scheduled stop along its route but fails to stop. This solution employs CEP (Complex Event Processing) to determine bus arrival events and control the detection process. In this process, it utilizes the state management mechanism to save and update a bus’s actual trajectory, which is derived from the raw GPS trajectory and maintained as a stop sequence. Subsequently, it uses LCSS (Longest Common Subsequence) to measure the trajectory similarity between the actual bus trajectory and the scheduled route. We validate the solution using a large-scale real dataset in a Flink cluster with six virtual machines. The experimental results show that (1) each core can handle anomaly detection on 12.5 buses simultaneously and (2) the detection accuracies of the two anomalies are 90.5% and 89.3%, respectively.

A Fast Algorithm for Finding a Maximal Common Subsequence of Multiple Strings

A Fast Algorithm for Finding a Maximal Common Subsequence of Multiple Strings

Image captioning to aid blind and visually impaired outdoor navigation

Artificial intelligence technology has dramatically improved the quality of services for human needs, one of which is technology to improve the quality of services for the blind and visually impaired, particularly technology that can help them understand visual sights to facilitate navigation in their daily lives. This study developed an image captioning model to aid the blind and visually impaired in outdoor navigation. The image captioning model employs the encoder-decoder method, with the convolutional neural network (CNN) feature extraction and attention layer as encoders and the long short-term memory (LSTM) as decoders. ResNet101 and ResNet152 are used in the encoder to extract image features. The results of the extraction and caption are forwarded to the attention layer and the LSTM network. The attention layer uses the Bahdanau attention mechanism. The accuracy of the model is calculated using the bilingual evaluation understudy score (BLEU), metric for evaluation of translation with explicit ordering (METEOR) and recall-oriented understudy for gisting evaluation-longest common subsequence (ROUGE-L). ResNet101 performed the best on BLEU-4, scoring 91.811% and 94.0337% in the METEOR evaluation. The captioning results show that the model is quite successful in displaying a simple caption that is suitable for each image.

Capturing the variety of clinical pathways in patients with schizophrenic disorders through state sequences analysis

BackgroundCare pathways are increasingly being used to enhance the quality of care and optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting, classical statistical tools were insufficient to consider a phenomenon with such high variability adequately and have to be integrated with novel data mining techniques suitable for identifying patterns in complex data structures. Data-driven techniques can potentially support empirically identifying effective care sequences by extracting them from data collected routinely. The purpose of this study is to perform a state sequence analysis (SSA) to identify different patterns of treatment and to asses whether sequence analysis may be a useful tool for profiling patients according to the treatment pattern.MethodsThe clinical application that motivated the study of this method concerns the mental health field. In fact, the care pathways of patients affected by severe mental disorders often do not correspond to the standards required by the guidelines in this field. In particular, we analyzed patients with schizophrenic disorders (i.e., schizophrenia, schizotypal or delusional disorders) using administrative data from 2015 to 2018 from Lombardy Region. This methodology considers the patient’s therapeutic path as a conceptual unit, composed of a succession of different states, and we show how SSA can be used to describe longitudinal patient status.ResultsWe define the states to be the weekly coverage of different treatments (psychiatric visits, psychosocial interventions, and anti-psychotic drugs), and we use the longest common subsequences (dis)similarity measure to compare and cluster the sequences. We obtained three different clusters with very different patterns of treatments.ConclusionsThis kind of information, such as common patterns of care that allowed us to risk profile patients, can provide health policymakers an opportunity to plan optimum and individualized patient care by allocating appropriate resources, analyzing trends in the health status of a population, and finding the risk factors that can be leveraged to prevent the decline of mental health status at the population level.

A methodology for part family formation in reconfigurable manufacturing systems using Shannon's entropy method

For effective utilization of resources, Operation Management plays an important role in businesses and organizations. It basically coordinates the efforts of people to fulfil the goals and objectives decided by the organization. Operation Management is a supporting system to carry out businesses. From the point of generation of raw material to the point of consumption of goods, it directs and controls the organization. It makes a path to a manufacturing system to fulfil its goal. For full utilization of resources in an uncertain environment, the manufacturing system faces various challenges in this century. The manufacturing systems must be responding rapidly to market changes. For effective management, a new paradigm in the manufacturing system is the Reconfigurable manufacturing systems, which are built around a part family to face such challenges in an uncertain environment. The authors have developed a methodology for part family formation using attributes modularity, reconfiguration effort, longest common subsequence, etc. in the present work. The developed methodology has been discussed using an example.

TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graph

This paper proposes to learn the graph of accessible indoor routes by crowd-sourcing inertial sensor data from mobile phones. The inertial sensor data collected by each phone is processed by Pedestrian Dead Reckoning (PDR) algorithm to generate motion trajectories. These motion trajectories are noisy, unlabeled, and are in different coordinate frames. Then the crucial problem to learn the route graph is reduced to firstly find associations among the motion trajectories according to intrinsic features of the trajectories, and then to register the trajectories to generate the route graph, which is called a TrackPuzzle problem. Since finding the longest common subsequence on multiple trajectories is NP-hard, a key contribution of this paper is to convert the multiple trajectory association problem to a trajectory-to-graph association problem. A trajectory is selected as a leader graph and all other trajectories are associated and registered with the leader graph in turn and expand the leader graph gradually. A dynamic programming based Longest Common Sub-sequence on Graph (LCSG) searching method is proposed to solve the trajectory-to-graph association in each step. We further employ a DBSCAN-based outlier detection method to filter out the edges in the association set which are highly inconsistent with others. Then filtered associated edges are registered non-rigidly to expand the leader graph. At last, a hierarchical graph optimization process is applied to the final graphs of different floors to generate multi-floor route graph. Extensive experiments in a synthetic environment and two real-world environments show the more feasibility, efficiency and accuracy of the proposed methods than the state-of-the-art methods.

Leveraging similarity analysis to understand variability in movement behavior

AbstractThe increasingly large volume of trajectories of moving entities obtained through GPS and cellphone tracking, telemetry, and other location‐aware technologies motivates researchers to understand the implicit patterns hidden in movement trajectories and understand how movement is influenced by the environmental context. Trajectory similarity serves as an important tool in computational movement analysis and as the foundation of revealing those patterns. However, there are various trajectory similarity measures, each of which has its own strengths and weaknesses. In this article, we present a hierarchical clustering framework that integrates five commonly used similarity measures, including Fréchet distance, dynamic time warping, Hausdorff distance, longest common subsequence, and normalized weighted edit distance, a special kind of edit distance for movement analysis. The framework aims at clustering similar patterns and identifying variability in movement. The optimal number of clusters are first obtained. Then, the clusters are characterized by environmental variables to explore the associations between variability in movement and the environmental conditions. We evaluate the proposed framework using 15 years of tracking data of turkey vultures, tracked at 1‐ to 3‐h sampling intervals, during their fall and spring migration seasons. The results suggest that, at 5% significance level, turkey vultures select their movement paths intentionally and those selections appear to be related to certain environmental context variables, including thermal uplift, vegetation state (observed indirectly through Normalized Difference Vegetation Index), temperature, precipitation, tailwind, and crosswind. And interestingly, there exist preferential differences among individuals. Although the preference of the same turkey vulture is not strictly consistent over different years, each individual tends to preserve a more similar preference over different years, compared with the preferences of other turkey vultures.

Adaptive-Modeling Multi-Agent Learning System for Video Behavioral Clustering Recognition

Multi-agent systems are suitable for handling complex problems due to their high parallelism and autonomous evolution ability. In this paper, we propose an adaptive clustering multi-agent learning system for intelligent applications with continuously changing requirements. Each agent model changes temporal sequences using the longest common subsequence (LCS) algorithm. Multiple agents collaborate in a multilayer decentralized approach to enhance learning adaptability and achieve self-supervised behavioral clustering. The system is constructed using a “memory-like” method and operates primarily on memory access and comparison, avoiding extensive matrix operations of artificial neural networks while achieving learning and prediction functions. We chose an unsupervised vehicle behavioral clustering scenario for feasibility validation in which the system’s cognitive objective is to cluster and recognize vehicle behaviors. In a low computational environment, the system can complete clustering functions and exhibit continuous learning capabilities when new behavioral changes occur. The proposed approach achieves an accuracy of 97.4% while processing at a speed 1–5 times faster than similar clustering algorithms. The verification results indicate that this system has excellent potential to enhance intelligent sensing front ends.

Longest common substring in Longest Common Subsequence’s solution service: A novel hyper-heuristic

The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a new criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer (S2D) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present our proposed hyper-heuristic that exploits the S2D and one of the internal properties of the given strings to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show that our proposed dichotomizer (i.e., S2D) can classify datasets with 98% of accuracy. Also, our proposed hyper-heuristic obtains competitive performance in comparison with the best methods and outperforms best hyper-heuristics for uncorrelated datasets in terms of both quality of solutions and run time factors. All supplementary files, including the source codes and datasets, are publicly available on GitHub.11https://github.com/BioinformaticsIASBS/LCS-DSclassification.

Across atoms to crossing continents: Application of similarity measures to biological location data.

Biological processes involve movements across all measurable scales. Similarity measures can be applied to compare and analyze these movements but differ in how differences in movement are aggregated across space and time. The present study reviews frequently-used similarity measures, such as the Hausdorff distance, Fréchet distance, Dynamic Time Warping, and Longest Common Subsequence, jointly with several measures less used in biological applications (Wasserstein distance, weak Fréchet distance, and Kullback-Leibler divergence), and provides computational tools for each of them that may be used in computational biology. We illustrate the use of the selected similarity measures in diagnosing differences within two extremely contrasting sets of biological data, which, remarkably, may both be relevant for magnetic field perception by migratory birds. Specifically, we assess and discuss cryptochrome protein conformational dynamics and extreme migratory trajectories of songbirds between Alaska and Africa. We highlight how similarity measures contrast regarding computational complexity and discuss those which can be useful in noise elimination or, conversely, are sensitive to spatiotemporal scales.

Linear-space S-table algorithms for the longest common subsequence problem

Given two sequences A and B of lengths m and n, respectively, the consecutive suffix alignment (CSA) problem is to compute the longest common subsequence (LCS) between A and each suffix of B. The data structure of two-dimensional matrix for solving the CSA problem is named S-table. The S-table would be infeasible due to its quadratic-space requirement for long sequences. The linear-space S-table, proposed by Alves et al. (2005), consists of the first row of the S-table and the changes between every two consecutive rows. However, there is no further discussions and practical applications for the linear-space S-table. This paper proposes algorithms for improving three problems, related to LCS and S-table, by using the linear-space S-table. Suppose that A = A(1)A(2) (concatenation of two substrings), and we are given the S-table of A(2) and B, as well as the alignment result (LCS length) of A(1) to each prefix of B. The concatenated LCS (CoLCS) problem is to find the alignment result of A and B. For the CoLCS problem, this paper proposes an O(n)-time algorithm with the technique of set union-find to achieve the linear space. Next, for merging two linear S-tables, we propose an O(nlog⁡n)-time algorithm with the technique of range query and update to improve the quadratic-time algorithm using quadratic-space S-tables. Then, we propose an O(n)-time algorithm to update the linear-space S-table of A and B to obtain the linear-space S-table of Aσ and B, where σ denotes a new character appended to the tail of A. For further applications to long sequences by using the linear-space S-table, the algorithms proposed in this paper are essential and necessary.