Difference Algorithm Research Articles

Enhancing Security through Multi-Factor User Behavior Identification: Moving Beyond the Use of the Longest Common Subsequence (LCS)

Enhancing Security through Multi-Factor User Behavior Identification: Moving Beyond the Use of the Longest Common Subsequence (LCS)

Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system

The reconfigurable manufacturing system (RMS) is an advanced manufacturing strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations to be adapted for new part requirements. Optimizing flow line design to produce various parts involves minimizing reconfigurations and associated costs by enhancing operation sequence similarity. This article proposes a novel sequence optimization using the Longest Common Subsequence (LCS) method to reduce bypassing moves and machine idle times. The study introduces a similarity coefficient derived from LCS and employs average linkage hierarchical clustering to categorize parts in a case study. Unlike traditional methods, this approach considers material movements both before the initial machine and after the final processing station, addressing gaps in bypassing move calculations. The impact of different weighting scenarios for Type-II moves (ω) and machine idleness (β) on clustering was examined. For example, with Type-II move weights (ω) of {1.0, 0.6, 0.3, 0.0} and equal weightings for bypassing moves (α) and machine idleness (β) set at 0.5, a threshold value of 0.3 results in eight clusters, such as Cluster 1 {1, 11, 10, 12} and Cluster 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower threshold values lead to fewer clusters with larger sizes, indicating a more consolidated part family grouping. Various Type-II move weights and material handling scenarios demonstrate how different weighting configurations affect clustering and part family sizes. This approach enhances RMS efficiency by integrating comprehensive material handling considerations and optimizing clustering based on operational similarities.

On the Expressive Power of Languages for Static Variability

Variability permeates software development to satisfy ever-changing requirements and mass-customization needs. A prime example is the Linux kernel, which employs the C preprocessor to specify a set of related but distinct kernel variants. To study, analyze, and verify variational software, several formal languages have been proposed. For example, the choice calculus has been successfully applied for type checking and symbolic execution of configurable software, while other formalisms have been used for variational model checking, change impact analysis, among other use cases. Yet, these languages have not been formally compared, hence, little is known about their relationships. Crucially, it is unclear to what extent one language subsumes another, how research results from one language can be applied to other languages, and which language is suitable for which purpose or domain. In this paper, we propose a formal framework to compare the expressive power of languages for static (i.e., compile-time) variability. By establishing a common semantic domain to capture a widely used intuition of explicit variability, we can formulate the basic, yet to date neglected, properties of soundness, completeness, and expressiveness for variability languages. We then prove the (un)soundness and (in)completeness of a range of existing languages, and relate their ability to express the same variational systems. We implement our framework as an extensible open source Agda library in which proofs act as correct compilers between languages or differencing algorithms. We find different levels of expressiveness as well as complete and incomplete languages w.r.t. our unified semantic domain, with the choice calculus being among the most expressive languages.

Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method

Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements

Dominant point-based sequential and parallel algorithms for the multiple sequential substring constrained-LCS problem

The Longest Common Subsequence (LCS) problem is a well-known and studied problem in computer science and bioinformatics. It consists in finding the longest subsequence that is common to two or more given sequences. In this paper, we address the problem of finding all LCS for the Sequential Substring Constrained-LCS (SSCLCS) problem, called the Multiple SSCLCS problem. To solve this problem, we first propose a dominant point-based sequential algorithm, designed on a new Leveled Direct Acyclic Graph (DAG) that gives the correct evaluation order of subproblems to avoid redundancy due to overlap. Depending on whether the constraints may overlap or not, it requires O ( S | Σ | K + 1 + r + n | Σ |) and O ( S | Σ | K + 1 + n | Σ |) time with \(O(Max\_level+n|\Sigma |) \) space. S is the number of partial SSCLCS in a node, K is the number of DAG levels, n is the length of sequences, r is the total length of constraints, \(Max\_level \) is the number of nodes in the largest level of the DAG, and | Σ | is the length of the alphabet. Then, we derive a coarse-grained multicomputer parallel solution requiring \(O\left(\frac{S|\Sigma |^{K+1}+r+n|\Sigma |}{p}\right) \) and \(O\left(\frac{S|\Sigma |^{K+1}+n|\Sigma |}{p}\right) \) execution time, \(O(Max\_level+n|\Sigma |) \) memory space and O ( K ) communication rounds. p is the number of processors. Experimental results showed that the parallel algorithm is respectively 14.43 × and 19.19 × faster than the sequential algorithm on 32 and 64 processors.

Representative applications of dynamic-programming in different fields: Analysis on problems of shortest-path, 0/1 knapsack and longest common sub-sequence

This article mainly focuses on exploring the current application status, concrete things implemented by dynamic programming and advantages of dynamic programming in problems (the concept and operation of dynamic programming and the role it plays in programs) in different fields, future development trends and areas for improvement. It also introduces the gaps and problems currently encountered by dynamic programming, then provides corresponding solutions to make it more helpful and efficient in the future. In this regard, by consulting different references and online resources written by previous scientists, it finds out that they all have different points of view on dynamic programming and different solutions to its shortcomings. In the current research, dynamic programming is the most widely used algorithm with few limitations that can achieve the global optimal solution at any time; however, it does not mean that it can achieve the local optimal solution at any time, which also indirectly results in the memory space it requires being larger than other optimization algorithms. After analyzing the literature and researching and testing multiple applications, it is obvious that dynamic programming can make the code run easier and faster than traditional methods, even though it will take up more space.

Multi-sensor feature fusion for vehicle detection based on the fuzzy longest common subsequence

To mitigate the impact of sensor misdetection and omission on the multi-sensor feature fusion algorithm, this paper proposes a vehicle detection method for multi-sensor feature fusion based on the fuzzy longest common subsequence algorithm. Firstly, the lightweight YOLOv4 algorithm is utilized to obtain image features. Secondly., an adaptive threshold-based clustering method processes point cloud data, extracting trajectory information from both image and point cloud sources. Then, an enhanced longest common subsequence algorithm, incorporating a fuzzy membership function, is introduced to assess the similarity between the lidar target trajectory and the camera target trajectory. Next, the fusion of lidar and camera detection results is carried out based on the computed similarity scores. Ultimately, experimental validation affirms that this algorithm significantly improves vehicle detection accuracy. This enhancement contributes to a more robust and reliable vehicle environment sensing system.

NLP-based smart decision making for business and academics

Natural Language Processing (NLP) systems enable machines to understand, interpret, and generate human-like language, bridging the gap between human communication and computer understanding. Natural Language Interface to Databases (NLIDB) and Natural Language Interface to Visualization (NLIV) systems are designed to enable non-technical users to retrieve and visualize data through natural language queries. However, these systems often face challenges in handling complex correlation and analytical questions, limiting their effectiveness for comprehensive data analysis. Additionally, current Business Intelligence (BI) tools also struggle with understanding the context and semantics of complex questions, further hindering their usability for strategic decision-making. Also, when building these models for generating the queries from natural language, the system handles only the semantic parsing issues as each column header is being changed manually to their normal names by all existing models which is time-consuming, tedious, and subjective.Recent studies reflect the need for attention to context, semantics, and especially ambiguities in dealing with natural language questions. To address this problem, the proposed architecture focuses on understanding the context, correlation-based semantic analysis, and removal of ambiguities using a novel approach. An Enhanced Longest Common Subsequence (ELCS) is suggested where existing LCS is modified with a memorization component for mapping the natural language question tokens with ambiguous table column headers. This can speed up the overall process as human intervention is not required to manually change the column headers. The same is evidenced by carrying out thorough experimentation and comparative study in terms of precision, recall, and F1 score. By synthesizing the latest advancements and addressing challenges, this paper has proved how NLP can significantly enhance the accuracy and efficiency of information retrieval and visualization, broadening the inclusivity and usability of NLIDB, NLIV, and BI systems.

Faster space-efficient STR-IC-LCS computation

One of the most fundamental method for comparing two given strings A and B is the longest common subsequence (LCS), where the task is to find (the length) of an LCS of A and B. In this paper, we deal with the STR-IC-LCS1 problem which is one of the constrained LCS problems proposed by Chen and Chao [J. Comb. Optim, 2011]. A string Z is said to be an STR-IC-LCS of three given strings A, B, and P, if Z is a longest string satisfying that (1) Z includes P as a substring and (2) Z is a common subsequence of A and B. We present three efficient algorithms for this problem: First, we begin with a space-efficient solution which computes the length of an STR-IC-LCS in O(n2) time and O((ℓ+1)(n−ℓ+1)) space, where ℓ is the length of an LCS of A and B of length n. When ℓ=O(1) or n−ℓ=O(1), then this algorithm uses only linear O(n) space. Second, we present a faster algorithm that works in O(nr/log⁡r+n(n−ℓ+1)) time, where r is the length of P, while retaining the O((ℓ+1)(n−ℓ+1)) space efficiency. Third, we give an alternative algorithm that runs in O(nr/log⁡r+n(n−ℓ′+1)) time with O((ℓ′+1)(n−ℓ′+1)) space, where ℓ′ denotes the STR-IC-LCS length for input strings A, B, and P.

Two-Level Dynamic Programming-Enabled Non-Metric Data Aggregation Technique for the Internet of Things

The Internet of Things (IoT) has become a transformative technological infrastructure, serving as a benchmark for automating and standardizing various activities across different domains to reduce human effort, especially in hazardous environments. In these networks, devices with embedded sensors capture valuable information about activities and report it to the nearest server. Although IoT networks are exceptionally useful in solving real-life problems, managing duplicate data values, often captured by neighboring devices, remains a challenging issue. Despite various methodologies reported in the literature to minimize the occurrence of duplicate data, it continues to be an open research problem. This paper presents a sophisticated data aggregation approach designed to minimize the ratio of duplicate data values in the refined set with the least possible information loss in IoT networks. First, at the device level, a local data aggregation process filters out outliers and duplicates data before transmission. Second, at the server level, a dynamic programming-based non-metric method identifies the longest common subsequence (LCS) among data from neighboring devices, which is then shared with the edge module. Simulation results confirm the approach’s exceptional performance in optimizing the bandwidth, energy consumption, and response time while maintaining high accuracy and precision, thus significantly reducing overall network congestion.

DLLog: An Online Log Parsing Approach for Large-Scale System

Syslog is a critical data source for analyzing system problems. Converting unstructured log entries into structured log data is necessary for effective log analysis. However, existing log parsing methods demonstrate promising accuracy on limited datasets, but their generalizability and precision are uncertain when applied to diverse log data. Enhancements in these areas are necessary. This paper proposes an online log parsing method called DLLog, which is based on deep learning and has the longest common subsequence. DLLog utilizes the GRU neural network to mine template words and applies the longest common subsequence to parse log entries in real-time. In the offline stage, DLLog combines multiple log features to accurately extract the template words, creating a log template set to assist online log parsing. In the online stage, DLLog parses log entries by calculating the matching degree between the real-time log entry and the log template in the log template set. This method also supports the incremental update of the log template set to handle new log entries generated by systems. We summarized the previous works and validated DLLog using real log data collected from 16 systems. The results demonstrate that DLLog achieves high parsing accuracy, universality, and adaptability.

Using Sequence Analyses to Quantitatively Measure Oropharyngeal Swallowing Temporality in Point-of-Care Ultrasound Examinations: A Pilot Study.

(1) Background: Swallowing is a complex process that comprises well-timed control of oropharyngeal and laryngeal structures to achieve airway protection and swallowing efficiency. To understand its temporality, previous research adopted adherence measures and revealed obligatory pairs in healthy swallows and the effect of aging and bolus type on the variability of event timing and order. This study aimed to (i) propose a systemic conceptualization of swallowing physiology, (ii) apply sequence analyses, a set of information-theoretic and bioinformatic methods, to quantify and characterize swallowing temporality, and (iii) investigate the effect of aging and dysphagia on the quantified variables using sequence analyses measures. (2) Method: Forty-three participants (17 young adults, 15 older adults, and 11 dysphagic adults) underwent B-mode ultrasound swallowing examinations at the mid-sagittal plane of the submental region. The onset, maximum, and offset states of hyoid bone displacement, geniohyoid muscle contraction, and tongue base retraction were identified and sorted to form sequences which were analyzed using an inventory of sequence analytic techniques; namely, overlap coefficients, Shannon entropy, and longest common subsequence algorithms. (3) Results: The concurrency of movement sequence was found to be significantly impacted by aging and dysphagia. Swallowing sequence variability was also found to be reduced with age and the presence of dysphagia (H(2) = 52.253, p < 0.001, η2 = 0.260). Four obligatory sequences were identified, and high adherence was also indicated in two previously reported pairs. These results provided preliminary support for the validity of sequence analyses for quantifying swallowing sequence temporality. (4) Conclusions: A systemic conceptualization of human deglutition permits a multi-level quantitative analysis of swallowing physiology. Sequence analyses are a set of promising quantitative measurement techniques for point-of-care ultrasound (POCUS) swallowing examinations and outcome measures for swallowing rehabilitation and evaluation of associated physiological conditions, such as sarcopenia. Findings in the current study revealed physiological differences among healthy young, healthy older, and dysphagic adults. They also helped lay the groundwork for future AI-assisted dysphagia assessment and outcome measures using POCUSs. Arguably, the proposed conceptualization and analyses are also modality-independent measures that can potentially be generalized for other instrumental swallowing assessment modalities.

CrptAC: Find the Attack Chain with Multiple Encrypted System Logs

Clandestine assailants infiltrate intelligent systems in smart cities and homes for different purposes. These attacks leave clues behind in multiple logs. Systems usually upload their local syslogs as encrypted files to the cloud for longterm storage and resource saving. Therefore, the identification of pre-attack steps through log investigation is crucial for proactive system protection. Current methodologies involve system diagnosis using logs, often relying on datasets for feature training. Furthermore, the prevalence of mass encrypted logs in the cloud introduces a new layer of complexity to this domain. To tackle these challenges, we introduce CrptAC, a system for Multiple Encrypted Log Correlated Analysis, aimed at reconstructing attack chains to prevent further attacks securely. CrptAC initiates by searching and downloading relevant log files from encrypted logs stored in an untrusted cloud environment. Utilizing the obtained logs, it addresses the challenge of discovering event relationships to establish the attack provenance. The system employs various logs to construct event sequences leading up to an attack. Subsequently, we utilize Weighted Graphs and the Longest Common Subsequences algorithm to identify regular steps preceding an attack without the need for third-party training datasets. This approach enables the proactive identification of pre-attack steps by analyzing related log sequences. We apply our methodology to predict attacks in cloud computing and router breach provenance environments. Finally, we validate the proposed method, demonstrating its effectiveness in constructing attack steps and conclusively identifying corresponding syslogs.

Dynamic-MLCS: Fast searching for dynamic multiple longest common subsequences in sequence stream data

In recent years, data stream mining has become a popular research topic, wherein the dynamic multiple longest common subsequence problem for sequence streams (i.e., D-MLCS) is an important issue with a vast range of applications. However, the existing methods are only for static multiple longest common subsequence problem (i.e., S-MLCS) and there are no algorithms for the D-MLCS problem. Using the existing algorithms for the S-MLCS problem to solve the D-MLCS problem often leads to an overload of the main memory and is extremely time consuming as all the sequence segments should be stored into the main memory when the new segments flow in, and all updated problems should be resolved. To overcome this shortcoming, we designed a scheme for constructing dynamic Directed Acyclic Graphs (DAGs). It only needs to update the current DAG post each sequence segment flows in, without constructing the DAG from the beginning. We propose a novel algorithm for the D-MLCS problem called Dynamic-MLCS, which only needs to store a part of the key information of each streaming sequence segments, instead of storing all streaming sequence segments and scanning them repeatedly. Theoretical analyses demonstrate that the time and space complexities of the proposed algorithm are only relevant and linear to the number of the incomplete points and newly generated nodes, and the experiments conducted on the datasets of sequence streams indicate that the proposed Dynamic-MLCS is efficient and performs better than the compared state-of-the-art algorithms.

Estimation of feeding composition content based on novel variable sliding window method and layered data reconciliation with multiple modes

The real-time detection of feeding composition content holds significant importance in process monitoring and control optimization in industrial systems. However, the current feeding composition content is obtained by manual assay, with low detection frequency and significant hysteresis. Moreover, due to adjustment in production plans and adaptation to market demand, multiple operation modes frequently exist in real processes, making it difficult for a single mode to adapt to the actual site. To address the above problems, firstly, a novel variable sliding window method incorporating the improved longest common subsequence (LCSS) is proposed to achieve steady-state detection and multimode recognition in industrial processes. Secondly, according to the characteristics of different modes, the corresponding layered data reconciliation and parameter estimation (LDRPE) models are built to reduce the errors of the measured data and estimate the unmeasurable data. Finally, an online estimation model of feeding composition content based on multimode LDRPE is designed for estimating the coke content in the mixtures. The effectiveness of the proposed method is verified by the application to a real industrial system.

Image2InChI: Automated Molecular Optical Image Recognition.

The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable representations. Therefore, in this paper, we propose an automated molecular optical image recognition model based on deep learning, called Image2InChI. Additionally, the proposed Image2InChI introduces a novel feature fusion network with attention to integrate image patch and InChI prediction. The improved SwinTransformer as an encoder and the Transformer Decoder as a decoder with patch embedding are applied to predict the image features for the corresponding InChI. The experimental results showed that the Image2InChI model achieves an accuracy of InChI (InChI acc) of 99.8%, a Morgan FP of 94.1%, an accuracy of maximum common structures (MCS acc) of 94.8%, and an accuracy of longest common subsequence (LCS acc) of 96.2%. The experiments demonstrated that the proposed Image2InChI model improves the accuracy and efficiency of molecular image recognition and provided a valuable reference about optical chemical structure recognition for InChI.

Reconstruction algorithms for DNA-storage systems

Motivated by DNA storage systems, this work presents the DNA reconstruction problem, in which a length-n string, is passing through the DNA-storage channel, which introduces deletion, insertion and substitution errors. This channel generates multiple noisy copies of the transmitted string which are called traces. A DNA reconstruction algorithm is a mapping which receives t traces as an input and produces an estimation of the original string. The goal in the DNA reconstruction problem is to minimize the edit distance between the original string and the algorithm’s estimation. In this work, we present several new algorithms for this problem. Our algorithms look globally on the entire sequence of the traces and use dynamic programming algorithms, which are used for the shortest common supersequence and the longest common subsequence problems, in order to decode the original string. Our algorithms do not require any limitations on the input and the number of traces, and more than that, they perform well even for error probabilities as high as 0.27. The algorithms have been tested on simulated data, on data from previous DNA storage experiments, and on a new synthesized dataset, and are shown to outperform previous algorithms in reconstruction accuracy.

An robust N-gram causal inference approach based on multi-model fusion

Causal inference and interpretation generation are currently hot topics in various fields of research. For instance, by describing the causes of accidents and revealing potential accident rules, we can effectively prevent the recurrence of accidents. However, most models can only capture empirical causal patterns from specific datasets, meaning that their performance deteriorates when they are confronted with new samples or applied to other datasets. In this paper, we propose a robust N-Gram causal inference algorithm based on multi-model fusion, which we refer to as MCRN-Gram. Firstly, we innovatively use the Longest Common Subsequence (LCS) algorithm to generate misleading causal pseudo-samples that we randomly inject during model training. This approach encourages the model to develop a more abstract and nuanced understanding of causal relationships while also improving its robustness. Secondly, we use several effective algorithms for text feature extraction to extract the features of the causal relationship dataset respectively. We then use binary cross-entropy loss and N-Gram loss to optimize the loss function. Finally, we compared the evaluation metrics of the proposed algorithm under different N-Gram window sizes and selected the best window size for conducting experiments on three datasets. Through our experiments, we demonstrate that the proposed method outperforms the comparison algorithms in terms of the precision of causal prediction.

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.