The Blog Topic Detection Research Based on Bursty Word

2014 ◽  
Vol 926-930 ◽  
pp. 3406-3409
Author(s):  
Tao Kuang ◽  
Shan Hong Zhu
Keyword(s):  
Similarity Measure ◽  
Clustering Analysis ◽  
Main Idea ◽  
Design Strategy ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Topic Detection ◽  
Media Report ◽  
Word Similarity ◽  
Detecting Method

The emergence of blog hot topic means that the user's interest ,participation behavior and various media report coverage reach to its climax,a detecting method of topics on blog based on blog bursty words is proposed. It includes the use of word similarity measure and text clustering analysis which is combined with design strategy in specific period, the use of the main idea of the sudden vocabulary hot topic detection algorithm has to be used and improved in order to generate the final clustering. The experimental results show that the algorithm can obtain an accurate blog topic detection results.

Blog Topic Detection Based on the Model

2014 ◽  
Vol 926-930 ◽  
pp. 2221-2224
Author(s):  
Tao Kuang ◽  
Shan Hong Zhu
Keyword(s):  
Accurate Result ◽  
Main Idea ◽  
Measuring Method ◽  
Topic Detection ◽  
Information Granularity ◽  
Space Model ◽  
Topic Detection And Tracking ◽  
Detection And Tracking ◽  
Event Information ◽  
Detecting Method

Constructing and updating model from blog is also the process of topic detection and tracking, then, a detecting method of topics on blog based on model is proposed. It includes the use of measuring method Based on the event information to cluster for post and topic information to cluster event set (blog) which is combined with semantic vector space model, the use of the main idea of information granularity of topic detection and tracking algorithm has to be used and improved in order to generate the final clustering. The experimental results show that the algorithm has an accurate result in blog topic detection

Platform Independent Analysis of Probabilities on Multithreaded Programs

2013 ◽  
Vol 1 (3) ◽  
pp. 48-65
Author(s):  
Yuting Chen
Keyword(s):  
Operating System ◽  
Static Analysis ◽  
Main Idea ◽  
Experimental Results ◽  
Machine To Machine ◽  
Concurrent Program ◽  
Acceptable Accuracy ◽  
Multithreaded Programs ◽  
Independent Analysis ◽  
And Performance

A concurrent program is intuitively associated with probability: the executions of the program can produce nondeterministic execution program paths due to the interleavings of threads, whereas some paths can always be executed more frequently than the others. An exploration of the probabilities on the execution paths is expected to provide engineers or compilers with support in helping, either at coding phase or at compile time, to optimize some hottest paths. However, it is not easy to take a static analysis of the probabilities on a concurrent program in that the scheduling of threads of a concurrent program usually depends on the operating system and hardware (e.g., processor) on which the program is executed, which may be vary from machine to machine. In this paper the authors propose a platform independent approach, called ProbPP, to analyzing probabilities on the execution paths of the multithreaded programs. The main idea of ProbPP is to calculate the probabilities on the basis of two kinds of probabilities: Primitive Dependent Probabilities (PDPs) representing the control dependent probabilities among the program statements and Thread Execution Probabilities (TEPs) representing the probabilities of threads being scheduled to execute. The authors have also conducted two preliminary experiments to evaluate the effectiveness and performance of ProbPP, and the experimental results show that ProbPP can provide engineers with acceptable accuracy.

scholarly journals Distributed Beacon Drifting Detection for Localization in Unstable Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming Xia ◽  
Peiliang Sun ◽  
Xiaoyan Wang ◽  
Yan Jin ◽  
Qingzhang Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Location Information ◽  
Detection Accuracy ◽  
Research Issue ◽  
Fundamental Research

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.

Moving Target Detection Based on Principle of Connectivity and Texture Gradient

2012 ◽  
Vol 605-607 ◽  
pp. 2117-2120
Author(s):  
Min Huang ◽  
Yang Zhang ◽  
Gang Chen ◽  
Guo Feng Yang
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Texture Gradient ◽  
Moving Target ◽  
Moving Target Detection ◽  
Gradient Information ◽  
Target Segmentation ◽  
Target Profile

In target detection, “hole” phenomenon is present in the detection result, and the shadow is difficult to remove. To solve these problems, we propose a target detection algorithm based on principle of connectivity and texture gradient. Firstly, we use the connectivity principle to find the largest target prospects connection area to get a complete target contour, secondly we use target texture gradient information to further remove the shadow of the target. At last, the experimental results show that the algorithm can obtain a clear target profile and improve the accuracy of the moving target segmentation.

scholarly journals Evaluating Word Similarity Measure of Embeddings Through Binary Classification

2019 ◽  
Vol 1 (3) ◽  
Author(s):  
A. Aziz Altowayan ◽  
Lixin Tao
Keyword(s):  
Similarity Measure ◽  
Binary Classification ◽  
General Purpose ◽  
Feature Representation ◽  
Entity Recognition ◽  
Language Models ◽  
Data Set ◽  
Word Similarity ◽  
Domain Specific ◽  
Retrieval Rate

We consider the following problem: given neural language models (embeddings) each of which is trained on an unknown data set, how can we determine which model would provide a better result when used for feature representation in a downstream task such as text classification or entity recognition? In this paper, we assess the word similarity measure through analyzing its impact on word embeddings learned from various datasets and how they perform in a simple classification task. Word representations were learned and assessed under the same conditions. For training word vectors, we used the implementation of Continuous Bag of Words described in [1]. To assess the quality of the vectors, we applied the analogy questions test for word similarity described in the same paper. Further, to measure the retrieval rate of an embedding model, we introduced a new metric (Average Retrieval Error) which measures the percentage of missing words in the model. We observe that scoring a high accuracy of syntactic and semantic similarities between word pairs is not an indicator of better classification results. This observation can be justified by the fact that a domain-specific corpus contributes to the performance better than a general-purpose corpus. For reproducibility, we release our experiments scripts and results.

The 3D graph approach for breakdown voltage calculation in BaTiO3 ceramics

2021 ◽  
pp. 2150103
Author(s):  
Vojislav V. Mitic ◽  
Branislav Randjelovic ◽  
Ivana Ilic ◽  
Srdjan Ribar ◽  
An-Lu Chun ◽  
...  
Keyword(s):  
Graph Theory ◽  
Crystal Structures ◽  
Breakdown Voltage ◽  
Main Idea ◽  
Experimental Results ◽  
Grain Structure ◽  
Mathematical Approach ◽  
Electronic Parameters ◽  
Mathematical Graph

After pioneering attempts for the introduction of graph theory in the field of ceramics and microstructures, where 1D and 2D graphs were used, in this paper we applied 3D graphs for the breakdown voltage calculation in BaTiO3 sample with some predefined constraints. We have described the relations between grains in the sample and established a mathematical approach for the calculation of breakdown voltage using experimental results. As a result, we introduced mapping between the property of sample and grain structure, then between the grain structure and mathematical graph, using various crystal structures. The main idea was to apply 3D graph theory for the distribution of electronic parameters between the neighboring grains. With this study, we successfully confirmed the possibilities for applications of graphs as a tool for the determination of properties even at the intergranular level.

An Algorithm of Edge Detection Based on FSVM

2013 ◽  
Vol 321-324 ◽  
pp. 1046-1050
Author(s):  
Ai Ping Cai
Keyword(s):  
Target Identification ◽  
Main Idea ◽  
Detection Algorithm ◽  
Training Data ◽  
Classification Model ◽  
Detection Methods ◽  
Support Vector ◽  
Classification Problems ◽  
Tracking Motion ◽  
Image Edge

The support vector machine (SVM) has been shown to be an efficient approach for a variety of classification problems. It has also been widely used in target identification and tracking, motion analysis, image segmentation technology. Traditional detection methods mostly exist pseudo-edge and poor anti-noise capability. Under these circumstances, developing an efficient method is necessary. In this paper, we propose a new detection algorithm based on FSVM, the main idea is to train classified sample and give all training data a degree of membership, increase punishment to the wrong sub-sample. Then training and testing the FSVM classification model. Finally, extract edge of the image by using FSVM classification model. Experimental results show that the new algorithm can detect a clear image edge and have a good anti-noise nature.

Multi-level topic detection algorithm for Netnews Specials

2013 ◽  
Author(s):  
Yu Peng ◽  
ZhiQing Lin ◽  
Bo Xiao ◽  
Chuang Zhang
Keyword(s):  
Detection Algorithm ◽  
Topic Detection ◽  
Multi Level
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