scholarly journals Research on Vertical Search Method of Multidimensional Resources in English Discipline Based on Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yi Xie
Keyword(s):  
Search Time ◽  
Recall Rate ◽  
Search Method ◽  
Edge Computing ◽  
Experimental Results ◽  
Local Optimum ◽  
Vertical Search ◽  
Data Search ◽  
Resource Data ◽  
English Subject

The traditional vertical search method only considers the content of the webpage, and the global master node is not enough, which will lead to premature convergence and fall into the local optimum, resulting in insufficient multi-dimensional search of resources. Therefore, this paper proposes a multidimensional resource vertical edge based on the calculation of English subject search method. This paper analyzes the architecture of search engine firstly and then introduces the multiaccess edge computing architecture. At last, it constructs the vertical search task computing model of multidimensional resources in English discipline. By associating and traversing the attributes of multidimensional resources of English discipline, the vertical search of attribute information is realized offline, and the vertical search method of multidimensional resources of English discipline based on edge calculation is designed. In order to verify the effectiveness of the proposed method, a comparative experiment is designed. Experimental results show that the method can improve the resource search ratio and recall ratio, and it can also effectively improve the search efficiency. For an English subject resource data of 50 MB, the calculation methods of edge multidimensional resource data search recall rate can reach 97% and multidimensional resource data search time consumption is only 39 ms. The experimental results show that the performance of English subject multidimensional resources vertical search is much better.

scholarly journals Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110264
Author(s):  
Jiqing Chen ◽  
Chenzhi Tan ◽  
Rongxian Mo ◽  
Hongdu Zhang ◽  
Ganwei Cai ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Path Length ◽  
Search Time ◽  
Experimental Results ◽  
Artificial Potential Field ◽  
A Algorithm ◽  
Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

scholarly journals Algorithm Implementation Crochemore Perrin On The Dictionary Application Of Latin Terms Flora And Fauna Based On Android

2020 ◽  
Vol 2 (1) ◽  
pp. 140-148
Author(s):  
Bobby Aris Sandy ◽  
Paska Marto Hasugian
Keyword(s):  
String Matching ◽  
Smartphone Application ◽  
Search Method ◽  
Search Process ◽  
Smartphone Applications ◽  
Matching Process ◽  
Data Search ◽  
String Search ◽  
Algorithm Implementation ◽  
Very High

Searching is the process of selecting the information needed from a collection of data that already exists, data search is often also called a tablelook-up or store and retrieval information. Along with the development of technology that is now so very rapid, one of them is the Smartphone application. Smartphone applications that are currently in demand are increasing sharply, that is Android. Even though the presentation is limited to the screen, Smartphones are quite efficient for its users where the mobility is very high and affordable in all circles of society. used is the string search method. String search method is a string search process or often also called string matching. In the string matching process, there is the Crochemore perrin algorithm, which is an algorithm that factoring a pattern into two parts, namely Pattrenkiri and pattern right. This method is perfect for dictionary search applications of Latin terms flora and fauna.

SCESS: a WFSA-based automated simplified chinese essay scoring system with incremental latent semantic analysis

2014 ◽  
Vol 22 (2) ◽  
pp. 291-319 ◽  
Author(s):  
SHUDONG HAO ◽  
YANYAN XU ◽  
DENGFENG KE ◽  
KAILE SU ◽  
HENGLI PENG
Keyword(s):  
Latent Semantic Analysis ◽  
Scoring System ◽  
Semantic Analysis ◽  
Scoring Systems ◽  
Recall Rate ◽  
Experimental Results ◽  
Important Indicator ◽  
Simplified Chinese ◽  
Language Tests ◽  
Essay Scoring

AbstractWriting in language tests is regarded as an important indicator for assessing language skills of test takers. As Chinese language tests become popular, scoring a large number of essays becomes a heavy and expensive task for the organizers of these tests. In the past several years, some efforts have been made to develop automated simplified Chinese essay scoring systems, reducing both costs and evaluation time. In this paper, we introduce a system called SCESS (automated Simplified Chinese Essay Scoring System) based on Weighted Finite State Automata (WFSA) and using Incremental Latent Semantic Analysis (ILSA) to deal with a large number of essays. First, SCESS uses ann-gram language model to construct a WFSA to perform text pre-processing. At this stage, the system integrates a Confusing-Character Table, a Part-Of-Speech Table, beam search and heuristic search to perform automated word segmentation and correction of essays. Experimental results show that this pre-processing procedure is effective, with a Recall Rate of 88.50%, a Detection Precision of 92.31% and a Correction Precision of 88.46%. After text pre-processing, SCESS uses ILSA to perform automated essay scoring. We have carried out experiments to compare the ILSA method with the traditional LSA method on the corpora of essays from the MHK test (the Chinese proficiency test for minorities). Experimental results indicate that ILSA has a significant advantage over LSA, in terms of both running time and memory usage. Furthermore, experimental results also show that SCESS is quite effective with a scoring performance of 89.50%.

The Research on VRP Based on Max-Min Ant Colony Algorithm

2011 ◽  
Vol 219-220 ◽  
pp. 1285-1288 ◽  
Author(s):  
Chang Min Chen ◽  
Wei Cheng Xie ◽  
Song Song Fan
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Ant Colony ◽  
Local Optimum ◽  
Simulation Data ◽  
Routing Problem ◽  
Np Hard Problem ◽  
The Cost

Vehicle routing problem (VRP) is the key to reducing the cost of logistics, and also an NP-hard problem. Ant colony algorithm is a very effective method to solve the VRP, but it is easy to fall into local optimum and has a long search time. In order to overcome its shortcomings, max-min ant colony algorithm is adopted in this paper, and its simulation system is designed in GUI of MATLAB7.0. The results show that the vehicle routing problem can well achieves the optimization of VRP by accessing the simulation data of database.

scholarly journals Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning

2019 ◽  
Vol 33 ◽  
pp. 6722-6729 ◽  
Author(s):  
Ziming Li ◽  
Julia Kiseleva ◽  
Maarten De Rijke
Keyword(s):  
Reinforcement Learning ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Imitation Learning ◽  
Local Optimum ◽  
Inverse Reinforcement Learning ◽  
High Quality ◽  
Overall Performance

The performance of adversarial dialogue generation models relies on the quality of the reward signal produced by the discriminator. The reward signal from a poor discriminator can be very sparse and unstable, which may lead the generator to fall into a local optimum or to produce nonsense replies. To alleviate the first problem, we first extend a recently proposed adversarial dialogue generation method to an adversarial imitation learning solution. Then, in the framework of adversarial inverse reinforcement learning, we propose a new reward model for dialogue generation that can provide a more accurate and precise reward signal for generator training. We evaluate the performance of the resulting model with automatic metrics and human evaluations in two annotation settings. Our experimental results demonstrate that our model can generate more high-quality responses and achieve higher overall performance than the state-of-the-art.

Imperialist Competitive Algorithms with Perturbed Moves for Global Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 3135-3139 ◽  
Author(s):  
Jun Lin Lin ◽  
Chun Wei Cho ◽  
Hung Chjh Chuan
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Population Based ◽  
Experimental Results ◽  
Local Optimum ◽  
Competitive Algorithm ◽  
Competitive Algorithms

Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. Previous works have shown that ICA converges quickly but often to a local optimum. To overcome this problem, this work proposed two modifications to ICA: perturbed assimilation move and boundary bouncing. The proposed modifications were applied to ICA and tested using six well-known benchmark functions with 30 dimensions. The experimental results indicate that these two modifications significantly improve the performance of ICA on all six benchmark functions.

Application of Tullis' Visual Search Model to Highlighted and Non-Highlighted Tabular Displays

1987 ◽  
Vol 31 (11) ◽  
pp. 1221-1225
Author(s):  
Pratapray (Paul) Thacker ◽  
T. S. Tullis ◽  
A. J. G. Babu
Keyword(s):  
Visual Search ◽  
Search Time ◽  
Experimental Results ◽  
Search Model ◽  
Information Density ◽  
Series Of Experiments

This paper presents a comparison of experimental results with predictions obtained from Tullis' (1984) model of search times for tabular displays. Three levels of information density for displays with and without highlighting were used in a series of experiments. The highlighting of information was done by adding graphic boundaries (lines). Two levels of highlighting were used. A question-answer type of visual search was performed for two different tasks. The search time results are discussed and a method for utilizing Tullis' model for highlighted displays is suggested.

A HYBRID CLUSTERING ALGORITHM COMBINING CLOUD MODEL IWO AND K-MEANS

2014 ◽  
Vol 28 (06) ◽  
pp. 1450015 ◽  
Author(s):  
GUO PAN ◽  
KENLI LI ◽  
AIJIA OUYANG ◽  
XU ZHOU ◽  
YUMING XU
Keyword(s):  
Iterative Process ◽  
Clustering Algorithm ◽  
Cloud Model ◽  
Initial Guess ◽  
Experimental Results ◽  
Local Optimum ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Hybrid Clustering ◽  
Clustering Problems

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability.

Chinese New Word Identification Using N-Gram and PPM Models

2011 ◽  
Vol 109 ◽  
pp. 612-616 ◽  
Author(s):  
Dun Li ◽  
Wei Tu ◽  
Lei Shi
Keyword(s):  
Information Processing ◽  
Word Identification ◽  
Recall Rate ◽  
Word Segmentation ◽  
Experimental Results ◽  
New Method ◽  
New Words ◽  
Chinese Information Processing ◽  
N Gram

New word identification is one of the difficult problems of the Chinese information processing. This paper presents a new method to identify new words. First of all, the text is segmented using N-Gram; then PPM is used to identify the new words which are in the text; finally, the new identified words are added to update the dictionary using LRU. Compared with three well-known word segmentation systems, the experimental results show that this method can improve the precision and recall rate of new word identification to a certain extent.

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