scholarly journals Survey of Attack Graph Analysis Methods from the Perspective of Data and Knowledge Processing

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jianping Zeng ◽  
Shuang Wu ◽  
Yanyu Chen ◽  
Rui Zeng ◽  
Chengrong Wu
Keyword(s):  
Cost Optimization ◽  
Optimization Method ◽  
Research Community ◽  
Graph Analysis ◽  
Analysis Method ◽  
Knowledge Processing ◽  
Attack Graph ◽  
Analysis Methods ◽  
Basic Concepts ◽  
Future Work

Attack graph can simulate the possible paths used by attackers to invade the network. By using the attack graph, the administrator can evaluate the security of the network and analyze and predict the behavior of the attacker. Although there are many research studies on attack graph, there is no systematic survey for the related analysis methods. This paper firstly introduces the basic concepts, generation methods, and computing tasks of the attack graph, and then, several kinds of analysis methods of attack graph, namely, graph-based method, Bayesian network-based method, Markov model-based method, cost optimization method, and uncertainty analysis method, are described in detail. Finally, comparative study of the methods and future work are provided. We believe that this work would help the research community to understand the attack graph analysis method systematically.

scholarly journals Analysis Methods in Neural Language Processing: A Survey

2019 ◽  
Vol 7 ◽  
pp. 49-72 ◽  
Author(s):  
Yonatan Belinkov ◽  
James Glass
Keyword(s):  
Language Processing ◽  
Network Models ◽  
Research Trends ◽  
Neural Network Models ◽  
Fine Grained ◽  
Survey Paper ◽  
Review Analysis ◽  
Analysis Methods ◽  
New Models ◽  
Future Work

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

scholarly journals Best Suitable Cogeneration Power for Factories from Multi-Objective Data Analysis

2015 ◽  
Vol 14 (4) ◽  
pp. 109-120 ◽  
Author(s):  
Joseph Al Asmar ◽  
Raed Kouta ◽  
Salah Laghrouche ◽  
Joseph El Assad ◽  
Maxime Wack
Keyword(s):  
Data Analysis ◽  
Renewable Energy Sources ◽  
Production Capacity ◽  
Optimization Method ◽  
Industrial Sector ◽  
Analysis Method ◽  
Electrical Grid ◽  
Multi Objective ◽  
Data Analysis Method ◽  
A New Technique

Abstract The cogeneration systems in the industrial sector have become an essential part due to their global efficiency and reduced pollution. These systems may operate from conventional fuel sources, as well as from renewable energy sources (biomass, solar, fuel cell). Cogeneration systems could be installed as a distributed generation and on-site generation source in order to take advantage from the produced heat. The utility can motivate factories to install such systems by permitting them to link and sell their residual production capacity to the electrical grid. This work presents a new technique to find the best solution from multi-objective optimization results, using a sensitivity and data analysis method. Genetic Algorithm (GA) optimization method is used with the data analysis method: Multiple Linear Regression (MLR).

Log-Logistic Analysis of Herbicide Dose-Response Relationships

1995 ◽  
Vol 9 (2) ◽  
pp. 218-227 ◽  
Author(s):  
Steven S. Seefeldt ◽  
Jens Erik Jensen ◽  
E. Patrick Fuerst
Keyword(s):  
Dose Response ◽  
Logistic Model ◽  
Response Analysis ◽  
Analysis Method ◽  
Weed Science ◽  
Response Curves ◽  
Herbicide Resistant ◽  
Logistic Analysis ◽  
Analysis Methods ◽  
The Difference

Dose-response studies are an important tool in weed science. The use of such studies has become especially prevalent following the widespread development of herbicide resistant weeds. In the past, analyses of dose-response studies have utilized various types of transformations and equations which can be validated with several statistical techniques. Most dose-response analysis methods 1) do not accurately describe data at the extremes of doses and 2) do not provide a proper statistical test for the difference(s) between two or more dose-response curves. Consequently, results of dose-response studies are analyzed and reported in a great variety of ways, and comparison of results among various researchers is not possible. The objective of this paper is to review the principles involved in dose-response research and explain the log-logistic analysis of herbicide dose-response relationships. In this paper the log-logistic model is illustrated using a nonlinear computer analysis of experimental data. The log-logistic model is an appropriate method for analyzing most dose-response studies. This model has been used widely and successfully in weed science for many years in Europe. The log-logistic model possesses several clear advantages over other analysis methods and the authors suggest that it should be widely adopted as a standard herbicide dose-response analysis method.

Pengaruh penanaman modal dalam Negeri, penanaman modal asing dan ekspor terhadap PDRB Provinsi Jambi Periode 2000- 2016

2020 ◽  
Vol 8 (3) ◽  
pp. 155-164
Author(s):  
Tarmizi Tarmizi ◽  
Siti Hodijah ◽  
Rosmeli Rosmeli
Keyword(s):  
Quantitative Analysis ◽  
Foreign Investment ◽  
Descriptive Analysis ◽  
Domestic Investment ◽  
Analysis Method ◽  
Analysis Methods ◽  
Study Results ◽  
Investment Growth

This study aims to analyze the development of GRDP, domestic investment, foreign investment, and exports in Jambi Province for the period 2000-2016, as well as to study the effect of domestic investment, foreign investment, and exports on the growth of GRDP of Jambi Province in the period 2000-2016. 2016. This research uses descriptive and quantitative analysis methods. The descriptive analysis method is used to analyze the development of each research variable, namely domestic investment, foreign investment, and exports. Quantitative analysis methods are used to analyze the effect of domestic investment, foreign investment, and exports on the growth of GRDP in Jambi province for the period 2000-2016. Based on the study results, the Jambi Province GRDP growth for the 2000-2016 period was 7.21 percent, domestic investment growth was 11.64 percent, foreign investment was 18.69 percent, and export development was 17.83 percent. And during the period 2000-2016, domestic investment, foreign investment, and exports had a significant effect on GRDP growth in Jambi Province. Keywords: Domestic investment, Foreign investment, Exports, PDRB Growth

Foundation of XML Stream Processing Techniques

2013 ◽  
pp. 48-59
Author(s):  
Weidong Yang ◽  
Hao Zhu
Keyword(s):  
Optimization Technique ◽  
Stream Processing ◽  
Research Community ◽  
Processing Methods ◽  
Xml Data ◽  
Research Status ◽  
Basic Concepts ◽  
Xml Stream ◽  
Processing Techniques ◽  
Area Section

The problem of processing streaming XML data is gaining widespread attention from the research community, and various XML stream processing methods are put forward, including automaton-based methods, index-based methods, and so forth. In this chapter, the basic concepts and several existing typical approaches of XML stream processing are discussed. Section 1 introduces the background and current research status of this area. Section 2 focuses on the discussion of automaton-based methods, for example, X/YFilter, XPush, et cetera. In section 3, the index-based methods are given. In section 4, other methods such us Fist and XTrie are discussed briefly. Section 4 discusses some optimization technique of XML stream processing. Section 5 summarizes this chapter.

scholarly journals SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4457 ◽  
Author(s):  
She ◽  
Zhu ◽  
Tian ◽  
Wang ◽  
Yokoi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Analysis Methods ◽  
Movement Recognition ◽  
S Transform

Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.

Supporting Open-Ended Programming Assignments

2009 ◽  
pp. 368-380
Author(s):  
Caitlin Kelleher
Keyword(s):  
Deep Learning ◽  
Formal Education ◽  
Potential Model ◽  
Classroom Setting ◽  
Programming Environment ◽  
Traditional Curriculum ◽  
Design And Implementation ◽  
Basic Concepts ◽  
Student Projects ◽  
Future Work

Self-directed, open-ended projects can enable students to pursue their own interests and lead to deep learning. However, it can be difficult to incorporate these kinds of projects into a traditional curriculum in which all students must master a set of basic skills. In this chapter, the authors describe the design and implementation of Storytelling Alice, a programming environment that presents computer programming as a means to the end of creating animated stories. By studying the kinds of animated movies that students envision creating, the chapter’s authors were able to design the system such that typical student projects naturally motivate the set of basic concepts we want students to learn. The authors present a potential model for incorporating Storytelling Alice into a classroom setting using open-ended projects. The chapter concludes with a discussion of some directions for future work that may help to enable the use more open-ended projects in formal education.

Multi-objective optimization for repetitive scheduling under uncertainty

2019 ◽  
Vol 26 (7) ◽  
pp. 1294-1320 ◽  
Author(s):  
Tarek Salama ◽  
Osama Moselhi
Keyword(s):  
Cost Optimization ◽  
Indirect Costs ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Software Module ◽  
Content Type ◽  
Project Duration ◽  
Multi Objective ◽  
Work Interruptions ◽  
Cost Calculations

Purpose The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters. Design/methodology/approach The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module. Findings For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules. Originality/value Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

Rice Paper Classification Study Based on Signal Processing and Statistical Methods in Image Texture Analysis

2014 ◽  
Vol 2 (3) ◽  
pp. 1-14
Author(s):  
Haotian Zhai ◽  
Hongbin Huang ◽  
Shaoyan He ◽  
Weiping Liu
Keyword(s):  
Texture Analysis ◽  
Classification Accuracy ◽  
Image Texture ◽  
Analysis Method ◽  
Classification Result ◽  
Image Texture Analysis ◽  
Analysis Methods ◽  
Paper Classification ◽  
Regular Texture ◽  
Better Than

Texture analysis plays an important role in image processing. In the field of texture analysis, the regular texture has been studied a lot, but the natural texture with complex backgrounds is less studied. This paper brings texture analysis into the study of rice paper's classification. First of all it shows the processing flow chart of rice paper classification. By comparing the different kinds of texture analysis methods it chooses the LAWS texture method and uncertainty texture spectrum method to achieve the rice paper classification. When it uses the two texture analysis methods separately, the classification accuracy of rice paper is lower, so it tries to combine the two texture analysis methods. The experimental results show that the classification result got with two combined texture analysis methods is better than that got with one single texture analysis method. The classification accuracy of rice paper has been distinctly improved after the combination of the two texture analysis methods.

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