A Statistical Language Model for Pre-Trained Sequence Labeling: A Case Study on Vietnamese

By defining the computable word segmentation unit and studying its probability characteristics, we establish an unsupervised statistical language model (SLM) for a new pre-trained sequence labeling framework in this article. The proposed SLM is an optimization model, and its objective is to maximize the total binding force of all candidate word segmentation units in sentences under the condition of no annotated datasets and vocabularies. To solve SLM, we design a recursive divide-and-conquer dynamic programming algorithm. By integrating SLM with the popular sequence labeling models, Vietnamese word segmentation, part-of-speech tagging and named entity recognition experiments are performed. The experimental results show that our SLM can effectively promote the performance of sequence labeling tasks. Just using less than 10% of training data and without using a dictionary, the performance of our sequence labeling framework is better than the state-of-the-art Vietnamese word segmentation toolkit VnCoreNLP on the cross-dataset test. SLM has no hyper-parameter to be tuned, and it is completely unsupervised and applicable to any other analytic language. Thus, it has good domain adaptability.

Dynamic Modeling and Analysis of Micro-Video Teaching System Based on Cloud Computing in College English Teaching

The era is developing and the society is improving. The application research of computer technology in the field of education is a hot topic at present. The cultivation of talents required the training of schools and teachers. How to cultivate talented person who meets the needs of society lied in the comprehensive ability of teaching in colleges and universities, so the college English education professional grammar teaching model is studied based on dynamic programming algorithm. After a brief overview of the dynamic rule algorithm, an algorithm for evaluating English grammar learning in colleges and universities is designed by using dynamic algorithm. The algorithm is composed of dynamic rule algorithm, language processing technology, and regular expression. In the subsequent experiments, it is proved that the algorithm has a good evaluation effect and can meet the actual application requirements.

A Metaheuristic Optimization Algorithm for Solving Higher-Order Boundary Value Problems

An effective metaheuristic algorithm to solve the higher-order boundary value problems, called a genetic programming technique is presented. In this paper, a genetic programming algorithm, which depends on the syntax tree representation, is employed to obtain the analytical solutions of higher- order differential equations with the boundary conditions. The proposed algorithm can be produce an exact or approximate solution when the classical methods lead to unsatisfactory results. To illustrate the efficiency and accuracy of the designed algorithm, several examples are tested. Finally, the obtained results are compared with the existing methods such as the homotopy analysis method, the B-Spline collocation method and the differential transform method.

Sizing of the Propulsion System for a Heavy-Duty Fuel Cell Commercial Vehicle

This paper presents a methodology for the sizing of a heavy-duty fuel cell commercial vehicle. The parameters scanning model and the long-term stochastic drive cycle are adopted for this proposed sizing framework. The dynamic programming algorithm is employed as the energy management strategy to assess the performance of sizing. The efficacy of this framework is evaluated, and a detailed analysis for the hydrogen consumption is given in the results. In addition, a prediction analysis based on the price performance of the next decade is also given in this work.

An Automatic Heuristic Design Approach for Seru Scheduling Problem with Resource Conflicts

In current environments, production systems need the ability of quick response to face the volatile markets. Seru production systems, as a new mode of the production system, have the advantages of quick response, high flexibility, and high efficiency. Seru scheduling, which refers to constructing serus with an exact sequence in limited workspace, is an important decision problem in the operational management of seru production systems and can reflect the reconfiguration nature of seru production systems. This study investigates a seru scheduling problem with resource conflicts, whose objective is to minimize the makespan. An automatic heuristic design approach that combines a genetic programming algorithm and structure similarity-based operators is proposed. Comparative experiments are conducted with human-made rules, basic genetic programming, genetic programming-based algorithm, and with some state-of-the-art scheduling algorithms. The results show the effectiveness and efficiency of the proposed algorithm.

Interpreting Mass Spectra Differing from Their Peptide Models by Several Modifications

Background: In proteomics, mass spectra representing peptides carrying multiple unknown modifications are particularly difficult to interpret. This issue results in a large number of unidentified spectra.Methods: We developed SpecGlob, a dynamic programming algorithm that aligns pairs of spectra – each pair given by a Peptide-Spectrum Match (PSM) – provided by any Open Modification Search (OMS) method. For each PSM, SpecGlob computes the best alignment according to a given score system, interpreting the mass delta within the PSM as one or several unspecified modification(s). All the alignments are provided in a file, using a specific syntax. These alignments are then post-processed by an additional algorithm, which aims at interpreting the detected modifications.Results: Using a large collection of theoretical spectra generated from the human proteome, we demonstrate that running SpecGlob as a post-analysis of an OMS method can significantly increase the number of correctly interpreted spectra, since SpecGlob is able to infer several, and possibly many, modifications. The post-processing algorithm is able to interpret unambiguously most of the modifications detected by SpecGlob in PSMs. In addition, we performed an extensive analysis to provide insight into the potential reasons for incomplete or erroneous interpretations that may remain after alignments of PSMs.Conclusion: SpecGlob is able to correctly align spectra that differ by one or more modification(s) without any a priori. Since SpecGlob explores all possible alignments that may explain the mass delta within a PSM, it reduces interpretation errors generated by incorrect assumptions about the modifications present in the sample or the number and the specificity of modifications carried by peptides. Our results demonstrate that SpecGlob should be relevant to align experimental spectra, even if this consists in a more challenging task.

Efficient Algorithms for Maximum Induced Matching Problem in Permutation and Trapezoid Graphs

We first design an 𝒪(n2) solution for finding a maximum induced matching in permutation graphs given their permutation models, based on a dynamic programming algorithm with the aid of the sweep line technique. With the support of the disjoint-set data structure, we improve the complexity to 𝒪(m+n). Consequently, we extend this result to give an 𝒪(m+n) algorithm for the same problem in trapezoid graphs. By combining our algorithms with the current best graph identification algorithms, we can solve the MIM problem in permutation and trapezoid graphs in linear and 𝒪(n2) time, respectively. Our results are far better than the best known 𝒪(mn) algorithm for the maximum induced matching problem in both graph classes, which was proposed by Habib et al.