Risk Assessment of E-Commerce Projects Using Evidential Reasoning
|
|
- Virgil Pearson
- 6 years ago
- Views:
Transcription
1 Risk Assessment of E-Commerce Projects Using Evidential Reasoning Rashid Hafeez Khokhar, David A. Bell, Jiwen Guan, and QingXiang Wu The School of Electronics, Electrical Engineering and Computer Science Queen s University Belfast Belfast, BT7 1NN, N.I. UK Tel.: (44) , (44) ; Fax: (44) {r.khokhar, da.bell, j.guan, q.wu}@qub.ac.uk Abstract. The purpose of this study is to develop a decision making system to evaluate the risks in E-Commerce (EC) projects. Competitive software businesses have the critical task of assessing the risk in the software system development life cycle. This can be conducted on the basis of conventional probabilities, but limited appropriate information is available and so a complete set of probabilities is not available. In such problems, where the analysis is highly subjective and related to vague, incomplete, uncertain or inexact information, the Dempster-Shafer (DS) theory of evidence offers a potential advantage. We use a direct way of reasoning in a single step (i.e., extended DS theory) to develop a decision making system to evaluate the risk in EC projects. This consists of five stages 1) establishing knowledge base and setting rule strengths, 2) collecting evidence and data, 3) determining evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process, 4) combining prior mass and different rules; i.e., the second half of the single step reasoning process, 5) finally, evaluating the belief interval for the best support decision of EC project. We test the system by using potential risk factors associated with EC development and the results indicate that the system is promising way of assisting an EC project manager in identifying potential risk factors and the corresponding project risks. Keywords: Dempster-Shafer Theory, Evidential Reasoning, Software Performance Evaluation, Financial Engineering, E-Commerce Application. 1 Introduction Electronic Commerce (EC) is possibly the most promising information technology application that enterprises have seen in recent years. EC addresses the needs of organizations, suppliers and costumers to reduce costs while improving the quality of goods and services, and increasing the speed of service delivery [8]. The current highly competitive business environment needs a good quality EC system, but EC development is subject to various kinds of risk such as malicious code attacks [2], uncertain legal jurisdiction [7], absence of firewall [8], and lack of using cryptography [10]. L. Wang et al. (Eds.): FSKD 2006, LNAI 4223, pp , Springer-Verlag Berlin Heidelberg 2006
2 622 R.H. Khokhar et al. A comprehensive collection of potential risk factors associated with EC development can be found in Refs [11]. Leung et al., [9] have developed an integrated knowledge-based system that assists project managers to determined potential risk factors and the corresponding project risks. According to this knowledge-base system, most project managers worry about the time involved in risk management when it comes to identifying and assessing risks. However, with the aid of computers and the use of software systems, the time for risk analysis can be significantly reduced. Addison [1] used a Delphi technique to collect the opinion of 32 experts and proposed 28 risks for EC projects. Meanwhile, Carney et al. [3] designed a tool called COTS Usage Risk Evaluation (CURE) to predict the risk areas of COTS products in which he identified four categories comprising 21 risk areas. Ngai and Wat [12] have developed a web-based fuzzy decision support system (FDSS) to assist project manager in identifying potential EC risk factors. However, FDSS has not been tested with real life EC projects, and it can only handle the available risk variables. Also the various membership functions need to be estimated to be as realistically as possible. Cortellessa et al., [4] have introduced a methodology which elaborates annotated UML diagrams to estimate the performance failure probability and combines it with the failure severity estimate which is obtained using the Functional Failure Analysis. This methodology is still have some limitation and only suitable for the analysis of performance-based risk in the early phases of the software life cycle. In this paper, a direct way of reasoning in a single step is presented to develop our evidential reasoning based system to assess the risk in EC projects. A direct way of reasoning in a single step is actually an extended DS theory of evidence. It is a generalization of Yen's [13, 14] model from Bayesian probability theory to the DS theory of evidence. Section 2 presents the system development methodology involved in the system for the risk assessment of EC projects. In section 3, a direct way of reasoning in a single step is described and the experimental results are presented in section 4. Finally, conclusions are given in section 5. 2 System Development Methodology In this paper, a decision making system is developed to assist EC project managers in specifying potential risk factors and evaluating the corresponding EC development risks. Figure 1 presents the methodology involved in the system. Before applying the proposed methodology, it is important to conduct risk identification and compile a list of the most significant uncertainty factors and their descriptions. For this purpose, we use the results of exploratory factor analysis (EFA) by Wat et al., [11] to identifying potential risks associated with EC development. Wat et al., [11] used a source-based approach to categorizing EC development risks is initially used with technical, organizational, and environmental risks as the three primary source categories. Then potential risks associated with EC development was identified with 51 risk items associated with EC development based on a comprehensive literature review and interviewed with EC practitioners.
3 Risk Assessment of E-Commerce Projects Using Evidential Reasoning 623 The project manager first inputs all risk factor and different pieces of evidence. Then system will search for a rule from the existing rules database and get a rule strength for the selected evidence and hypothesis. The rules database is developed according to the support strengths of different pieces of evidence for different conclusions. The user can edit the rules strengths dynamically, this step will help us to handle uncertain situation. The next step is to combine the outputs from the different rules for all risk factors and its corresponding pieces of evidence using the extended DS engine, and to evaluate the belief intervals for the risk assessment of EC projects. Fig. 1. System development methodology for risk assessment of EC projects 3 The Direct Way of Reasoning in Single Step Guan and Bell [5, 6] extended the DS theory by introducing an evidential mapping that uses mass functions as used in the DS theory to express uncertain relationships. It is a generalization of Yen s [13, 14] model from Bayesian probability theory to the DS theory of evidence. The sections below describe the direct way of reasoning in a single step process through an example of its use. 3.1 Rule Strengths in Expert System In the first stage, we describe the knowledge base and rule strengths. Instead of a mass function being used to express strengths for each element of the evidence space, a mass function is used to express strength for each subset of the evidence space. Consider a frame of discernment with 5 hypotheses Θ = { h1, h3,, h5} : h 1 = " VeryLow", h2 = " Low", h3 = " Medium", = " High", h5 = " VeryHigh". Consider a particular piece of evidence e 1 e.g., wrong schedule estimation, which comes from results of an exploratory factor analysis (EFA) [11] for resource
4 624 R.H. Khokhar et al. risk factor. We can obtain a general rule which uses this evidence, when present indicated by { e 1 }, strongly supports h = { h1, h3} of Θ and refutes h = { h 4, h }. 5 When the evidence is not present, indicated by { e 1 }, the support strengths are divided between h and Θ. More specifically, we say here that there is an evidence space = e, } and mass functions s s, s : 2 [0,1] such that s s s 1 { 1 e1 11, Θ, h3} { e1}) = 0.64, s11({, h5} { e1}) = 0.34, s11 = ({ Θ} { e1}) 0.02;, h3} { e1}) = 0.00, s12 ({, h5} { e1}) = 0.50, s12 = ({ Θ} { e1}) 0.50;, h3} { 1}) = 0.25, s13 ({, h5} { 1}) = 0.45, s13 = ({ Θ} { 1}) = 12 = 13 = Guan and Bell [5, 6] used mass function m ( X ) = s( X E) on the power set of hypothesis space Θ to express the rule strength for each subset E of the evidence space. Yen [13, 14] used m ( X ) = s( X e) for each element e of the evidence space to express the rule strength. This means that Guan and Bell [5, 6] have generalized Yen's subset-probability-pair-collection-valued (s-p-p-c-v) mapping to a subset-masspair-collection-valued (s-m-p-c-v) mapping. The s-m-p-c-v mapping Γ from the power set 2 of evidence space to 2 [0,1] 2 Θ 2 [0,1] Γ : (2 { φ }) 2 (1) such that for every non-empty E Γ E) = {( A, s ( A E)),( A, s ( A E)),...,( A, s ( A ))} (2) ( E1 E E1 E2 E E2 En E E Θ E En E Where A E1, AE2,..., AEn 2 ; i. e., A Θ E E1, AE 2,..., AEn are the focal elements of E mass function me ( X ) = se ( X E) on 2 Θ : 0 < se ( AE1 E), se ( AE2 E),..., se ( AEn e E) 1 (3) and Θ (1) A Ei φ for i = 1,...n E ; (2) se ( AEi E) > 0 for i = 1,...n E ; ne (3) = s ( A E) = 1 i 1 E Ei Θ 2 [0,1] Then a rule is a collection RULE =< E, Θ, Γ >, where is an evidence space, Θ is a hypothesis space, and Γ is a s-m-p-c-v mapping from the power set 2 of evidence space to hypothesis space Θ (more precisely, to 2 ). Also, a rule can be expressed by a collection of 2 1 strength mass functions me ( A) = se ( A E) for A Θ, RULE = { se ( A E) E (2 { φ})} (4) is
5 Risk Assessment of E-Commerce Projects Using Evidential Reasoning = { s ( A E ), s ( A E ),..., s ( A E { φ } = { E, E,..., E } ; s s, E φ i = Ei i for i = 1, Consider source of evidence e 2 e.g., project over budget which comes from the same results of EFA [11]. This evidence when present indicated by { e 2 }, strongly support subset h = { h3,, h5} of Θ, and refutes h = { h 1, h 2}. When the evidence is not present, indicated by { e 2 }, the support strengths are divided between h and Θ. More specifically, we say here that there is an evidence space 2 = { e2, e2} and mass functions s s, s : 2 Θ [0,1] such that 21, )}, s 21 ({ h3,, h5} { e2}) = 0.76, s21({ h1 } { e2}) = 0.20, s21 = ({ Θ} { e2}) = 0.04; s 22 ({ h3,, h5} { e2}) = 0.00, s22 } { e2}) = 0.50, s22 = ({ Θ} { e2}) = 0.50; s 23 ({ h3,, h5} { 2}) = 0.65, s23 } { 2}) = 0.20, s23 = ({ Θ} { 2}) = Summarizing, following the method in [5], the knowledge base includes the following rules: RULE-1 IF EVIDENCE { e 1 } THEN h2, h3} WITH STRENGTH s 11, h3} { e1}) = HYPOTHESIS {, h 5} WITH STRENGTH s 11 ({, h5 } e1 ) = WITH STRENGTH s 11 ({ Θ} { e1}) = ELSE IF EVIDENCE { e 1 } THEN h2, h3} WITH STRENGTH s 12, h3} { e1}) = HYPOTHESIS { h 4, h 5} WITH STRENGTH s 12 ({, h5 } { e1 } = WITH STRENGTH s 12 ({ Θ} { e1} = ELSE IF EVIDENCE { 1 } THEN h2, h3} WITH STRENGTH s 13, h3} 1) = HYPOTHESIS {, h 5} WITH STRENGTH s 13 ({, h5 } 1 ) = WITH STRENGTH s 13 ({ Θ} { 1}) = Here 1 = { e1, e1} is an evidence space and m X ) = s ( X ) (5) 11( 11 e1 12 ( X ) s12 ( X e1 ) 13( X ) = s13( X 1 m = (6) m ) (7) are mass functions 2 [0,1] m : 2 [0,1 ] such that m( φ ) = 0, m( Θ) = 1. (8) X Θ
6 626 R.H. Khokhar et al. RULE-2 IF EVIDENCE { e 2 } THEN HYPOTHESIS { h 3,, h5} WITH STRENGTH s 21 ({ h3,, h5} { e2}) = h 2} WITH STRENGTH s 21 } { e2 } = WITH STRENGTH s 21 ({ Θ} e2 ) = ELSE IF EVIDENCE { e 2 } THEN HYPOTHESIS { h 3,, h5} WITH STRENGTH s 22 ({ h3,, h5} { e2} = h 2} WITH STRENGTH s 22 } { e2 } = WITH STRENGTH s 22 ({ Θ} { e2} = ELSE IF EVIDENCE {Θ} THEN HYPOTHESIS { h 3,, h5} WITH STRENGTH s 23 ({ h3,, h5} { 2} = h 2} WITH STRENGTH s 23 } { 2 }) = WITH STRENGTH s 23 ({ Θ} { 2} = Here 2 = { e2, e12} is an evidence space and m X ) = s ( X ) (9) are mass functions 2 Θ [0,1] 3.2 Data and Evidence 21( 21 e2 m X ) = s ( X ) (10) 22 ( 22 e2 m X ) = s ( X ) (11) 23 ( 23 2 Suppose from the above rules and given a particular confidence in the presence of the data items, we can derive pieces of evidence which are in the conventional DS format. The confidence c 1, we have that e 1 evidence is in fact present is as follows: i.e., we have data strength: _ c 1 ({ e1}) = 0.70, c1 ({ e1}) = 0.20, c1 ( 1) = 0.10 Here c 1 is a mass function over the evidence space 1, intuitively representing the confidence we have that e 1 is present. Similarly, evidence e 2 is present into the following data strengths: _ c 2 ({ e2}) = 0.75, c2 ({ e2}) = 0.20, c2 ( 2 ) = 0.05 Here c 2 is again a mass function over 2. Generally, there is a mass function c i : 2 Θ [0,1] over the evidence space i for i = 1,2,...n. 3.3 Hypothesis Strength In this stage we present the procedure to get from evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process.
7 Risk Assessment of E-Commerce Projects Using Evidential Reasoning 627 Now, for each rule we can get a hypothesis strength mass function from the evidence strength and the rule strength. For RULE-1; i.e., for rule strengths s 11, s12, s13 and from evidence c 1, the risk variable mass distribution r : 2 Θ [0,1] is obtained as follows. 1 r 1, h3}) = 0.47, r1 ({, h5}) = 0.38, r1 ({ Θ}) = 0.15 By RULE-2; i.e., for rule strengths s 21, s22, s23 and from evidence c 2 we get the following mass distribution r : 2 Θ [0,1] for the other node: 2 r ({ h3,, h5}) = 0.60, r2 }) = 0.26, r2 ({ Θ}) This is the first half of our reasoning process. 2 = 3.4 Combining Prior Mass and Different Rules Now, let us discuss the second half of the single step reasoning process. If μ 1 and μ 2 are two mass functions corresponding to two independent evidential sources, then the combined mass function μ1 μ2 is calculated according to Dempster rule of combination: 1. ( μ 1 μ2 )( φ) = 0 ; 2. For every A Θ, A φ, ( μ1 μ2 )( A) = θ ( Θ, θ φ X Y = A X Y 0.14 μ1( X ) μ2 ( Y ) [ P( A) ] P( X ) P( Y ) μ1( X ) μ2 ( Y ) [ P( θ ) ]) = θ P( X ) P( Y ) For our example, the intersection table of μ1 μ2 for RULE-1 and RULE-2 is shown in the following table 1. Table1. Intersection table to combine two rules (12) μ h, h, }(0.60) h, }(0.26) {Θ}(0.14) 1 μ 2 { 1 2 h3 { 4, h 5 { 3 4 h5 { 1 h 2 h, h, }(0.47) {{ h 3 }(0.20) { h 1 }(0.19) h, h, }(0.07) { 1 2 h3 { h 4, h 5 h }(0.38) h, }(0.36) φ (0) }(0.05) { 4 h 5 { 3,, h5 {Θ}(0.15) h }(0.09) h, }(0.04) Θ (0.02) { 1 h 2 We get the normalization constant (required to discount for mass committed toφ, the empty set) μ1( X ) μ2 ( Y ) N = P( X Y ) = P( X ) P( Y ) X Y φ (13)
8 628 R.H. Khokhar et al. 3.5 Belief Intervals and Ignorance Finally, we can establish the belief interval for our conclusion after applying this reasoning process. We convert the above results to a set of beliefs for the respective conclusions by adding the masses of all subsets of each conclusion to get the belief allocated to it, and then we get the belief intervals for risk assessment in the EC project using 2 pieces of evidence. So the conclusion is: The resource risk is { h 3 } = Medium using the two pieces of evidence, wrong schedule estimation and project over budget with the belief intervals [ belμ ( A), plsμ ( A)] = [0.2007,0.3657] and ignorance (A) = Case Study The design of the ease of use interface is a key element for the risk assessment of EC developments. Therefore, we design an interface that can be used by any user in EC environments. In this paper, we describe our results with the help of three general steps including: 1) input risk factors and different pieces of evidence 2) edit the rules strengths if required and 3) finally evaluate the belief intervals for the best supported decision. The risk evaluation form is presented in figure 2 to input all potential risk factors, pieces of evidence and data associated with EC project. In this form the project manager/evaluator first inputs all potential risk factors and different pieces of evidence using the results of an EFA [11]. The project manager/evaluator then selects the appropriate hypothesis among five given hypothesis (e.g. VeryLow, Low, Medium, High, and VeryHigh) and assign the data with the help of slider for each evidence. The next step is to determine the rule strengths to get a mass distribution for each rule. Fig. 2. Risk evaluation form to input potential risk associated with EC projects
9 Risk Assessment of E-Commerce Projects Using Evidential Reasoning 629 The system will search for the rules from the existing rules database for the particular piece of evidence involved. The user can also modify the rule strengths to handle uncertainty. Similarly the user inputs all data/evidence and determines the rules for every risk factor. Finally, we apply the DS engine to evaluate the degrees of belief for these risk factors. The risk assessment results using 10 important risk factors from [11] with belief intervals are presented in table 2. In this table different pieces of evidence (in column 3) are presented for the corresponding risk factors. The next columns demonstrate the assessment results with belief intervals and these risk assessment results are explained in figure 3. For example the first result is described as: The resource risk is Low with support (37%), against (46%) and uncertain (17%). No Table 2. Belief intervals and ignorance for potential risk associated with EC projects Risk factors Variables ( pieces of evidence) Conclusion [ bel μ ( A), plsμ ( A)] ign(a) 1 Resources risk V 21, V 22, V 23, V 24, V 25, V 27 Low [0.3700, ] Requirements risk V 14, V 15, V 16, V 17, V 19, V 20 Very Low [0.1393, ] Vendor quality risk V 46, V 47, V 48, V 49 Very Low [0.0598, ] Client-server security risk V 1, V 2, V 3, V 4, V 5 Low [0.2736, ] Legal risk V 38, V 39, V 40 Very Low [0.0244, ] Managerial risk V 28, V 29, V 30, V 31, V 32 Very Low [0.0815, ] Outsourcing risk V 40, V 41, V 42, V 43, V 45 Very Low [0.3091, ] Physical security risk V 7, V 8, V 9, V 10 Low [0.5284, ] Cultural risk V 50, V 51 Low [0.3958, ] Reengineering risk V 33, V 34 Low [0.0554, ] Fig. 3. Risk assessment results using direct way of reasoning
10 630 R.H. Khokhar et al. 5 Conclusions This paper has outlined an approach to the assessment of the risks associated with EC development using a direct way of evidential reasoning with data plus general rule. This method of evidential reasoning has been proposed to assist EC project managers and decision makers in formalizing the types of thinking that are required when assessing the current risk environment of their EC development in a systematic manner. A system has been designed and developed to incorporate is risk analysis model. System evaluation was performed to ascertain whether the system achieved its designed purpose, and the results are satisfactory. The results of the evaluation strongly support the viability of this approach to risk analysis using a direct way of evidential reasoning, and it is demonstrated to be feasible for evaluating EC project risk. References 1. Addison. T.: E-commerce project development risks: evidence from a Delphi survey, International Journal of Information Management. 1 (2003) Bandyopadhyay, K., Mykytyn, P, P., Mykytyn, K.: A framework for integrated risk management in information technology. Management Decision, Vol. 37. No. 5. (1999) Carney, D., Morris, E., Patrick, R.: Identifying commercial off-the-shelf (COTS) product risks: the COTS usage risk evaluation. Technical Report, CMU/SEI (2003)-TR Cortellessa, V., Goseva-Popstojanova, K., Kalaivani Appukkutty, Guedem, A.R., Hassan, A., Elnaggar, R., Abdelmoez, W., Ammar, H.H., Model-based performance risk analysis Software Engineering, IEEE Transactions on Vol. 31. Issue 1. (2005) Guan, J. W., Bell, D. A.: Evidence theory and its applications. Vol.1, Studies in Computer Science and Artificial Intelligence 7, Elsevier, The Netherlands, (1991). 6. Guan, J. W., Bell, D. A.: Evidence theory and its applications. Vol.2, Studies in Computer Science and Artificial Intelligence 8, Elsevier, The Netherlands, (1992). 7. Halpern, J., Mehrotra, A. K.: The tangled web of e-commerce: identifying the legal risks of online marketing. The computer Lawyer, Vol. 17, No. 2, Kalakota, R., Whinston, A.B.: Frontiers of the Electronic Commerce., Addison-Wesley, Reading, MA (1996). 9. Leung, H.M., Chuah, K.B., Tummala, V.M.R.: A knowledge-based system for identifying potential project risks. OMEGA: International Journal of Management Science. Vol. 26, Issue. 5. (1998) Treese, G. W., Stewart, L. C.: Designing Systems for Internet Commerce, Addison Wesley, Massachusetts. 11. Wat, F. K. T., Ngai, E.W.T., Cheng, T.C.E.: Potential risks to e-commerce development using exploratory factor analysis. International Journal of Services Technology and Management (2004), Vol. 6. Part 1, Pages Ngai, E.W.T., Wat, F.K.T.: Fuzzy decision support system for risk analysis in e-commerce development. Journal of Decision Support Systems. Vol. 40. Issue 2. (2005) Yen, J.: A reasoning model based on an extended Dempster-Shafer theory. Proceedings AAAI-(1986) Yen, J.: GERTIS: A Dempster-Shafer Approach to Diagnosing Hierarchical Hypotheses", Communications of the ACM 5 Vol. 32, (1989),
Rough operations on Boolean algebras
Rough operations on Boolean algebras Guilin Qi and Weiru Liu School of Computer Science, Queen s University Belfast Belfast, BT7 1NN, UK Abstract In this paper, we introduce two pairs of rough operations
More informationApplication of Evidence Theory and Discounting Techniques to Aerospace Design
Application of Evidence Theory and Discounting Techniques to Aerospace Design Fiona Browne 1, David Bell 1, Weiru Liu 1, Yan Jin 1, Colm Higgins 1, Niall Rooney 2, Hui Wang 2, and Jann Müller 3 1 School
More informationHandling imprecise and uncertain class labels in classification and clustering
Handling imprecise and uncertain class labels in classification and clustering Thierry Denœux 1 1 Université de Technologie de Compiègne HEUDIASYC (UMR CNRS 6599) COST Action IC 0702 Working group C, Mallorca,
More informationReasoning with Uncertainty
Reasoning with Uncertainty Representing Uncertainty Manfred Huber 2005 1 Reasoning with Uncertainty The goal of reasoning is usually to: Determine the state of the world Determine what actions to take
More informationA novel k-nn approach for data with uncertain attribute values
A novel -NN approach for data with uncertain attribute values Asma Trabelsi 1,2, Zied Elouedi 1, and Eric Lefevre 2 1 Université de Tunis, Institut Supérieur de Gestion de Tunis, LARODEC, Tunisia trabelsyasma@gmail.com,zied.elouedi@gmx.fr
More informationarxiv: v1 [cs.ai] 28 Oct 2013
Ranking basic belief assignments in decision making under uncertain environment arxiv:30.7442v [cs.ai] 28 Oct 203 Yuxian Du a, Shiyu Chen a, Yong Hu b, Felix T.S. Chan c, Sankaran Mahadevan d, Yong Deng
More informationAnalyzing the degree of conflict among belief functions.
Analyzing the degree of conflict among belief functions. Liu, W. 2006). Analyzing the degree of conflict among belief functions. Artificial Intelligence, 17011)11), 909-924. DOI: 10.1016/j.artint.2006.05.002
More informationReasoning Under Uncertainty
Reasoning Under Uncertainty Chapter 14&15 Part Kostas (1) Certainty Kontogiannis Factors E&CE 457 Objectives This unit aims to investigate techniques that allow for an algorithmic process to deduce new
More informationAnalyzing Supply Chain Complexity Drivers using Interpretive Structural Modelling
Analyzing Supply Chain Complexity Drivers using Interpretive Structural Modelling Sujan Piya*, Ahm Shamsuzzoha, Mohammad Khadem Department of Mechanical and Industrial Engineering Sultan Qaboos University,
More informationCanadian Board of Examiners for Professional Surveyors Core Syllabus Item C 5: GEOSPATIAL INFORMATION SYSTEMS
Study Guide: Canadian Board of Examiners for Professional Surveyors Core Syllabus Item C 5: GEOSPATIAL INFORMATION SYSTEMS This guide presents some study questions with specific referral to the essential
More informationDeng entropy in hyper power set and super power set
Deng entropy in hyper power set and super power set Bingyi Kang a, Yong Deng a,b, a School of Computer and Information Science, Southwest University, Chongqing, 40075, China b Institute of Integrated Automation,
More informationThe Semi-Pascal Triangle of Maximum Deng Entropy
The Semi-Pascal Triangle of Maximum Deng Entropy Xiaozhuan Gao a, Yong Deng a, a Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, 610054,
More informationApplication of Evidence Theory to Construction Projects
Application of Evidence Theory to Construction Projects Desmond Adair, University of Tasmania, Australia Martin Jaeger, University of Tasmania, Australia Abstract: Crucial decisions are necessary throughout
More informationManaging Decomposed Belief Functions
Managing Decomposed Belief Functions Johan Schubert Department of Decision Support Systems, Division of Command and Control Systems, Swedish Defence Research Agency, SE-164 90 Stockholm, Sweden schubert@foi.se
More informationMeasure divergence degree of basic probability assignment based on Deng relative entropy
Measure divergence degree of basic probability assignment based on Deng relative entropy Liguo Fei a, Yong Deng a,b,c, a School of Computer and Information Science, Southwest University, Chongqing, 400715,
More informationDecision of Prognostics and Health Management under Uncertainty
Decision of Prognostics and Health Management under Uncertainty Wang, Hong-feng Department of Mechanical and Aerospace Engineering, University of California, Irvine, 92868 ABSTRACT The decision making
More informationSequential adaptive combination of unreliable sources of evidence
Sequential adaptive combination of unreliable sources of evidence Zhun-ga Liu, Quan Pan, Yong-mei Cheng School of Automation Northwestern Polytechnical University Xi an, China Email: liuzhunga@gmail.com
More informationDivergence measure of intuitionistic fuzzy sets
Divergence measure of intuitionistic fuzzy sets Fuyuan Xiao a, a School of Computer and Information Science, Southwest University, Chongqing, 400715, China Abstract As a generation of fuzzy sets, the intuitionistic
More informationSMPS 08, 8-10 septembre 2008, Toulouse. A hierarchical fusion of expert opinion in the Transferable Belief Model (TBM) Minh Ha-Duong, CNRS, France
SMPS 08, 8-10 septembre 2008, Toulouse A hierarchical fusion of expert opinion in the Transferable Belief Model (TBM) Minh Ha-Duong, CNRS, France The frame of reference: climate sensitivity Climate sensitivity
More informationContradiction Measures and Specificity Degrees of Basic Belief Assignments
Contradiction Measures and Specificity Degrees of Basic Belief Assignments Florentin Smarandache Arnaud Martin Christophe Osswald Originally published as: Smarandache F., Martin A., Osswald C - Contradiction
More informationAn Improved Focal Element Control Rule
vailable online at www.sciencedirect.com Procedia ngineering 5 (0) 7 dvanced in Control ngineeringand Information Science n Improved Focal lement Control Rule JIN Hong-bin a, LN Jiang-qiao b a* a ir Force
More informationFuzzy Systems. Possibility Theory.
Fuzzy Systems Possibility Theory Rudolf Kruse Christian Moewes {kruse,cmoewes}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge Processing
More informationUsing Belief Functions in Software Agents to Test the Strength of Application Controls: A Conceptual Framework
Using Belief Functions in Software Agents to Test the Strength of Application Controls: A Conceptual Framework Robert Nehmer Associate Professor Oakland University Rajendra P. Srivastava Ernst and Young
More informationStatistical methods for decision making in mine action
Statistical methods for decision making in mine action Jan Larsen Intelligent Signal Processing Technical University of Denmark jl@imm.dtu.dk, www.imm.dtu.dk/~jl Jan Larsen 1 Why do we need statistical
More informationC Ahmed Samet 1,2, Eric Lefèvre 2, and Sadok Ben Yahia 1 1 Laboratory of research in Programming, Algorithmic and Heuristic Faculty of Science of Tunis, Tunisia {ahmed.samet, sadok.benyahia}@fst.rnu.tn
More informationCHARTING SPATIAL BUSINESS TRANSFORMATION
CHARTING SPATIAL BUSINESS TRANSFORMATION An in-depth look at the business patterns of GIS and location intelligence adoption in the private sector EXECUTIVE SUMMARY The global use of geographic information
More informationThe internal conflict of a belief function
The internal conflict of a belief function Johan Schubert 1 Abstract In this paper we define and derive an internal conflict of a belief function We decompose the belief function in question into a set
More informationIntroduction to belief functions
Introduction to belief functions Thierry Denœux 1 1 Université de Technologie de Compiègne HEUDIASYC (UMR CNRS 6599) http://www.hds.utc.fr/ tdenoeux Spring School BFTA 2011 Autrans, April 4-8, 2011 Thierry
More informationAdaptative combination rule and proportional conflict redistribution rule for information fusion
Adaptative combination rule and proportional conflict redistribution rule for information fusion M. C. Florea 1, J. Dezert 2, P. Valin 3, F. Smarandache 4, Anne-Laure Jousselme 3 1 Radiocommunication &
More informationConditional Deng Entropy, Joint Deng Entropy and Generalized Mutual Information
Conditional Deng Entropy, Joint Deng Entropy and Generalized Mutual Information Haoyang Zheng a, Yong Deng a,b, a School of Computer and Information Science, Southwest University, Chongqing 400715, China
More informationThe Problem. Sustainability is an abstract concept that cannot be directly measured.
Measurement, Interpretation, and Assessment Applied Ecosystem Services, Inc. (Copyright c 2005 Applied Ecosystem Services, Inc.) The Problem is an abstract concept that cannot be directly measured. There
More informationA hierarchical fusion of expert opinion in the Transferable Belief Model (TBM) Minh Ha-Duong, CNRS, France
Ambiguity, uncertainty and climate change, UC Berkeley, September 17-18, 2009 A hierarchical fusion of expert opinion in the Transferable Belief Model (TBM) Minh Ha-Duong, CNRS, France Outline 1. Intro:
More informationToday s s lecture. Lecture 16: Uncertainty - 6. Dempster-Shafer Theory. Alternative Models of Dealing with Uncertainty Information/Evidence
Today s s lecture Lecture 6: Uncertainty - 6 Alternative Models of Dealing with Uncertainty Information/Evidence Dempster-Shaffer Theory of Evidence Victor Lesser CMPSCI 683 Fall 24 Fuzzy logic Logical
More informationREASONING UNDER UNCERTAINTY: CERTAINTY THEORY
REASONING UNDER UNCERTAINTY: CERTAINTY THEORY Table of Content Introduction Certainty Theory Definition Certainty Theory: Values Interpretation Certainty Theory: Representation Certainty Factor Propagation
More informationThe maximum Deng entropy
The maximum Deng entropy Bingyi Kang a, Yong Deng a,b,c, a School of Computer and Information Science, Southwest University, Chongqing, 40075, China b School of Electronics and Information, Northwestern
More informationThe Comprehensive Report
High-Throughput Screening 2002: New Strategies and Technologies The Comprehensive Report Presented by HighTech Business Decisions 346 Rheem Blvd., Suite 208, Moraga, CA 94556 Tel: (925) 631-0920 Fax: (925)
More informationApplication of Rough Set Theory in Performance Analysis
Australian Journal of Basic and Applied Sciences, 6(): 158-16, 1 SSN 1991-818 Application of Rough Set Theory in erformance Analysis 1 Mahnaz Mirbolouki, Mohammad Hassan Behzadi, 1 Leila Karamali 1 Department
More informationA NEW CLASS OF FUSION RULES BASED ON T-CONORM AND T-NORM FUZZY OPERATORS
A NEW CLASS OF FUSION RULES BASED ON T-CONORM AND T-NORM FUZZY OPERATORS Albena TCHAMOVA, Jean DEZERT and Florentin SMARANDACHE Abstract: In this paper a particular combination rule based on specified
More informationA New PCR Combination Rule for Dynamic Frame Fusion
Chinese Journal of Electronics Vol.27, No.4, July 2018 A New PCR Combination Rule for Dynamic Frame Fusion JIN Hongbin 1, LI Hongfei 2,3, LAN Jiangqiao 1 and HAN Jun 1 (1. Air Force Early Warning Academy,
More informationMapping Vulnerability and Risk of Mangrove Conversion to Pond Aquaculture in Myanmar
Aquaculture and Coastal Habitats Report No. 4 Mapping Vulnerability and Risk of Mangrove Conversion to Pond Aquaculture in Myanmar J. Ronald Eastman, Stefano Crema, Katherine Landesman Clark Labs, Clark
More informationRough Set Model Selection for Practical Decision Making
Rough Set Model Selection for Practical Decision Making Joseph P. Herbert JingTao Yao Department of Computer Science University of Regina Regina, Saskatchewan, Canada, S4S 0A2 {herbertj, jtyao}@cs.uregina.ca
More informationReady for INSPIRE.... connecting worlds. European SDI Service Center
Ready for INSPIRE Consultancy SOFTWARE T r a i n i n g Solutions... connecting worlds European SDI Service Center Increasing Added Value with INSPIRE and SDI Components INSPIRE In 2007, the European Commission
More informationURISA QUESTIONNAIRE DRAFT MUNICIPAL GIS CAPABILITY MATURITY MODEL
URISA DRAFT MUNICIPAL GIS CAPABILITY MATURITY MODEL QUESTIONNAIRE G. BABINSKI, GISP, KING COUNTY GIS CENTER JULY 29, 2010 Participant Identification: Name of Municipal GIS Organization: Name of Participant:
More informationA generic framework for resolving the conict in the combination of belief structures E. Lefevre PSI, Universite/INSA de Rouen Place Emile Blondel, BP
A generic framework for resolving the conict in the combination of belief structures E. Lefevre PSI, Universite/INSA de Rouen Place Emile Blondel, BP 08 76131 Mont-Saint-Aignan Cedex, France Eric.Lefevre@insa-rouen.fr
More informationSENSITIVITY ANALYSIS OF BAYESIAN NETWORKS USED IN FORENSIC INVESTIGATIONS
Chapter 11 SENSITIVITY ANALYSIS OF BAYESIAN NETWORKS USED IN FORENSIC INVESTIGATIONS Michael Kwan, Richard Overill, Kam-Pui Chow, Hayson Tse, Frank Law and Pierre Lai Abstract Research on using Bayesian
More informationData Fusion with Imperfect Implication Rules
Data Fusion with Imperfect Implication Rules J. N. Heendeni 1, K. Premaratne 1, M. N. Murthi 1 and M. Scheutz 2 1 Elect. & Comp. Eng., Univ. of Miami, Coral Gables, FL, USA, j.anuja@umiami.edu, kamal@miami.edu,
More informationCHAPTER 3 RESEARCH METHODOLOGY
CHAPTER 3 RESEARCH METHODOLOGY 3.1 INTRODUCTION The research methodology plays an important role in implementing the research and validating the results. Therefore, this research methodology is derived
More informationMulti-criteria Decision Making by Incomplete Preferences
Journal of Uncertain Systems Vol.2, No.4, pp.255-266, 2008 Online at: www.jus.org.uk Multi-criteria Decision Making by Incomplete Preferences Lev V. Utkin Natalia V. Simanova Department of Computer Science,
More informationDevelopment of a System for Decision Support in the Field of Ecological-Economic Security
Development of a System for Decision Support in the Field of Ecological-Economic Security Tokarev Kirill Evgenievich Candidate of Economic Sciences, Associate Professor, Volgograd State Agricultural University
More informationA Static Evidential Network for Context Reasoning in Home-Based Care Hyun Lee, Member, IEEE, Jae Sung Choi, and Ramez Elmasri, Member, IEEE
1232 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART A: SYSTEMS AND HUMANS, VOL. 40, NO. 6, NOVEMBER 2010 A Static Evidential Network for Context Reasoning in Home-Based Care Hyun Lee, Member,
More informationECO 317 Economics of Uncertainty Fall Term 2009 Slides to accompany 13. Markets and Efficient Risk-Bearing: Examples and Extensions
ECO 317 Economics of Uncertainty Fall Term 2009 Slides to accompany 13. Markets and Efficient Risk-Bearing: Examples and Extensions 1. Allocation of Risk in Mean-Variance Framework S states of the world,
More informationApproximation of Belief Functions by Minimizing Euclidean Distances
Approximation of Belief Functions by Minimizing Euclidean Distances Thomas Weiler and Ulrich Bodenhofer Software Competence Center Hagenberg A-4232 Hagenberg, Austria e-mail: {thomas.weiler,ulrich.bodenhofer}@scch.at
More informationSemantics of the relative belief of singletons
Semantics of the relative belief of singletons Fabio Cuzzolin INRIA Rhône-Alpes 655 avenue de l Europe, 38334 SAINT ISMIER CEDEX, France Fabio.Cuzzolin@inrialpes.fr Summary. In this paper we introduce
More informationGIS Capability Maturity Assessment: How is Your Organization Doing?
GIS Capability Maturity Assessment: How is Your Organization Doing? Presented by: Bill Johnstone Principal Consultant Spatial Vision Group November 8, 2018 1. Motivation for Capability Maturity Models
More informationCPDA Based Fuzzy Association Rules for Learning Achievement Mining
2009 International Conference on Machine Learning and Computing IPCSIT vol.3 (2011) (2011) IACSIT Press, Singapore CPDA Based Fuzzy Association Rules for Learning Achievement Mining Jr-Shian Chen 1, Hung-Lieh
More informationMULTINOMIAL AGENT S TRUST MODELING USING ENTROPY OF THE DIRICHLET DISTRIBUTION
MULTINOMIAL AGENT S TRUST MODELING USING ENTROPY OF THE DIRICHLET DISTRIBUTION Mohammad Anisi 1 and Morteza Analoui 2 1 School of Computer Engineering, Iran University of Science and Technology, Narmak,
More informationDistance-based test for uncertainty hypothesis testing
Sampath and Ramya Journal of Uncertainty Analysis and Applications 03, :4 RESEARCH Open Access Distance-based test for uncertainty hypothesis testing Sundaram Sampath * and Balu Ramya * Correspondence:
More informationSoftware Reliability Estimation under Uncertainty: Generalization of the Method of Moments
Software Reliability Estimation under Uncertainty: Generalization of the Method of Moments Katerina Goševa-Popstojanova and Sunil Kamavaram Lane Department of Computer Science and Electrical Engineering
More informationA Fuzzy-Cautious OWA Approach with Evidential Reasoning
Advances and Applications of DSmT for Information Fusion Collected Works Volume 4 A Fuzzy-Cautious OWA Approach with Evidential Reasoning Deqiang Han Jean Dezert Jean-Marc Tacnet Chongzhao Han Originally
More informationGeneralization of Belief and Plausibility Functions to Fuzzy Sets
Appl. Math. Inf. Sci. 6, No. 3, 697-703 (202) 697 Applied Mathematics & Information Sciences An International Journal Generalization of Belief and Plausibility Functions to Fuzzy Sets Jianyu Xiao,2, Minming
More informationEvidence combination for a large number of sources
Evidence combination for a large number of sources Kuang Zhou a, Arnaud Martin b, and Quan Pan a a. Northwestern Polytechnical University, Xi an, Shaanxi 710072, PR China. b. DRUID, IRISA, University of
More informationUncertainty and Rules
Uncertainty and Rules We have already seen that expert systems can operate within the realm of uncertainty. There are several sources of uncertainty in rules: Uncertainty related to individual rules Uncertainty
More informationDra. Aïda Valls Universitat Rovira i Virgili, Tarragona (Catalonia)
http://deim.urv.cat/~itaka Dra. Aïda Valls aida.valls@urv.cat Universitat Rovira i Virgili, Tarragona (Catalonia) } Presentation of the ITAKA group } Introduction to decisions with criteria that are organized
More informationReaxys Improved synthetic planning with Reaxys
R&D SOLUTIONS Reaxys Improved synthetic planning with Reaxys Integration of custom inventory and supplier catalogs in Reaxys helps chemists overcome challenges in planning chemical synthesis due to the
More informationApplication of New Absolute and Relative Conditioning Rules in Threat Assessment
Application of New Absolute and Relative Conditioning Rules in Threat Assessment Ksawery Krenc C4I Research and Development Department OBR CTM S.A. Gdynia, Poland Email: ksawery.krenc@ctm.gdynia.pl Florentin
More informationRough Set Theory Fundamental Assumption Approximation Space Information Systems Decision Tables (Data Tables)
Rough Set Theory Fundamental Assumption Objects from the domain are perceived only through the values of attributes that can be evaluated on these objects. Objects with the same information are indiscernible.
More informationWEB-BASED SPATIAL DECISION SUPPORT: TECHNICAL FOUNDATIONS AND APPLICATIONS
WEB-BASED SPATIAL DECISION SUPPORT: TECHNICAL FOUNDATIONS AND APPLICATIONS Claus Rinner University of Muenster, Germany Piotr Jankowski San Diego State University, USA Keywords: geographic information
More informationFactory method - Increasing the reusability at the cost of understandability
Factory method - Increasing the reusability at the cost of understandability The author Linkping University Linkping, Sweden Email: liuid23@student.liu.se Abstract This paper describes how Bansiya and
More informationA Practical Taxonomy of Methods and Literature for Managing Uncertain Spatial Data in Geographic Information Systems
A Practical Taxonomy of Methods and Literature for Managing Uncertain Spatial Data in Geographic Information Systems Madjid Tavana a,b, * a Business Systems and Analytics Department Distinguished Chair
More informationComparing Three Ways to Update Choquet Beliefs
26 February 2009 Comparing Three Ways to Update Choquet Beliefs Abstract We analyze three rules that have been proposed for updating capacities. First we consider their implications for updating the Choquet
More informationStudy of Fault Diagnosis Method Based on Data Fusion Technology
Available online at www.sciencedirect.com Procedia Engineering 29 (2012) 2590 2594 2012 International Workshop on Information and Electronics Engineering (IWIEE) Study of Fault Diagnosis Method Based on
More informationCompensation Planning Application
Compensation Planning Application Why Physician Compensation? More and more organizations are formally aligning with physicians. These organizations require large support structures to effectively manage
More informationTowards Decision Making under Interval Uncertainty
Journal of Uncertain Systems Vol.1, No.3, pp.00-07, 018 Online at: www.us.org.uk Towards Decision Making under Interval Uncertainty Andrze Pownuk, Vladik Kreinovich Computational Science Program, University
More informationContext-dependent Combination of Sensor Information in Dempster-Shafer Theory for BDI
Context-dependent Combination of Sensor Information in Dempster-Shafer Theory for BDI Sarah Calderwood Kevin McAreavey Weiru Liu Jun Hong Abstract There has been much interest in the Belief-Desire-Intention
More informationCh. 12: Workload Forecasting
Ch. 12: Workload Forecasting Kenneth Mitchell School of Computing & Engineering, University of Missouri-Kansas City, Kansas City, MO 64110 Kenneth Mitchell, CS & EE dept., SCE, UMKC p. 1/2 Introduction
More informationResearch and Development of Nanoparticle Characterization Methods
Research and Development of Nanoparticle Characterization Methods Project Outline This project aims at development of a risk evaluation method based on scientific knowledge for manufactured nanoparticles
More informationUncertain Logic with Multiple Predicates
Uncertain Logic with Multiple Predicates Kai Yao, Zixiong Peng Uncertainty Theory Laboratory, Department of Mathematical Sciences Tsinghua University, Beijing 100084, China yaok09@mails.tsinghua.edu.cn,
More informationIntegrated Electricity Demand and Price Forecasting
Integrated Electricity Demand and Price Forecasting Create and Evaluate Forecasting Models The many interrelated factors which influence demand for electricity cannot be directly modeled by closed-form
More informationEntropy-Based Counter-Deception in Information Fusion
Entropy-Based Counter-Deception in Information Fusion Johan Schubert (&) Department of Decision Support Systems, Division of Defence and Security, Systems and Technology, Swedish Defence Research Agency,
More informationA Generalized Decision Logic in Interval-set-valued Information Tables
A Generalized Decision Logic in Interval-set-valued Information Tables Y.Y. Yao 1 and Qing Liu 2 1 Department of Computer Science, University of Regina Regina, Saskatchewan, Canada S4S 0A2 E-mail: yyao@cs.uregina.ca
More informationJOB TITLE: CURRENT CLASSIFICATION/GRID POSITION # GIS Coordinator AD Grid Level 6(c) # 420
COUNTY OF GRANDE PRAIRIE JOB DESCRIPTION JOB TITLE: CURRENT CLASSIFICATION/GRID POSITION # GIS Coordinator AD Grid Level 6(c) # 420 NOC CODE: 2255 STANDARD HOURS: 35 hours/week (non-management) JOB TITLE
More informationInformation System Design IT60105
Information System Design IT60105 Lecture 19 Project Planning Lecture #19 ISD Project Planning SPMP Documentation System Design Models 2 Why Planning Information System Design? Program vs. Software Size
More informationContradiction measures and specificity degrees of basic belief assignments
Author manuscript, published in "International Conference on Information Fusion, Chicago : United States (2008)" Contradiction measures and specificity degrees of basic belief assignments Florentin Smarandache
More informationFUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH
FUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH M. De Cock C. Cornelis E. E. Kerre Dept. of Applied Mathematics and Computer Science Ghent University, Krijgslaan 281 (S9), B-9000 Gent, Belgium phone: +32
More informationDetecting Anomalous and Exceptional Behaviour on Credit Data by means of Association Rules. M. Delgado, M.D. Ruiz, M.J. Martin-Bautista, D.
Detecting Anomalous and Exceptional Behaviour on Credit Data by means of Association Rules M. Delgado, M.D. Ruiz, M.J. Martin-Bautista, D. Sánchez 18th September 2013 Detecting Anom and Exc Behaviour on
More informationDeveloping the GIS-Expert System for Investigating Land Use Designations
Developing the GIS-Expert System for Investigating Land Use Designations P. Limsupreeyarat a, S. Kaewkeaw b, and P. Charnwasununt c a,b Department of Civil Engineering, Burapha University, Thailand c Department
More informationScientific/Technical Approach
Network based Hard/Soft Information Fusion: Soft Information and its Fusion Ronald R. Yager, Tel. 212 249 2047, E Mail: yager@panix.com Objectives: Support development of hard/soft information fusion Develop
More informationCHAPTER-3 MULTI-OBJECTIVE SUPPLY CHAIN NETWORK PROBLEM
CHAPTER-3 MULTI-OBJECTIVE SUPPLY CHAIN NETWORK PROBLEM 3.1 Introduction A supply chain consists of parties involved, directly or indirectly, in fulfilling customer s request. The supply chain includes
More informationKey Words: geospatial ontologies, formal concept analysis, semantic integration, multi-scale, multi-context.
Marinos Kavouras & Margarita Kokla Department of Rural and Surveying Engineering National Technical University of Athens 9, H. Polytechniou Str., 157 80 Zografos Campus, Athens - Greece Tel: 30+1+772-2731/2637,
More informationUncertain Programming Model for Solid Transportation Problem
INFORMATION Volume 15, Number 12, pp.342-348 ISSN 1343-45 c 212 International Information Institute Uncertain Programming Model for Solid Transportation Problem Qing Cui 1, Yuhong Sheng 2 1. School of
More informationHierarchical Proportional Redistribution Principle for Uncertainty Reduction and BBA Approximation
Hierarchical Proportional Redistribution Principle for Uncertainty Reduction and BBA Approximation Jean Dezert Deqiang Han Zhun-ga Liu Jean-Marc Tacnet Abstract Dempster-Shafer evidence theory is very
More informationCounter-examples to Dempster s rule of combination
Jean Dezert 1, Florentin Smarandache 2, Mohammad Khoshnevisan 3 1 ONERA, 29 Av. de la Division Leclerc 92320, Chatillon, France 2 Department of Mathematics University of New Mexico Gallup, NM 8730, U.S.A.
More informationEntropy and Specificity in a Mathematical Theory of Evidence
Entropy and Specificity in a Mathematical Theory of Evidence Ronald R. Yager Abstract. We review Shafer s theory of evidence. We then introduce the concepts of entropy and specificity in the framework
More informationIJESRT. Scientific Journal Impact Factor: (ISRA), Impact Factor: [Gupta et al., 3(5): May, 2014] ISSN:
IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY Short Term Weather -Dependent Load Forecasting using Fuzzy Logic Technique Monika Gupta Assistant Professor, Marwadi Education
More informationInteractive Random Fuzzy Two-Level Programming through Possibility-based Fractile Criterion Optimality
Interactive Random uzzy Two-Level Programming through Possibility-based ractile Criterion Optimality Hideki Katagiri, Keiichi Niwa, Daiji Kubo, Takashi Hasuike Abstract This paper considers two-level linear
More informationConvex Hull-Based Metric Refinements for Topological Spatial Relations
ABSTRACT Convex Hull-Based Metric Refinements for Topological Spatial Relations Fuyu (Frank) Xu School of Computing and Information Science University of Maine Orono, ME 04469-5711, USA fuyu.xu@maine.edu
More information1 Descriptions of Function
Wide-Area Wind Generation Forecasting 1 Descriptions of Function All prior work (intellectual property of the company or individual) or proprietary (non-publicly available) work should be so noted. 1.1
More informationCredibilistic Bi-Matrix Game
Journal of Uncertain Systems Vol.6, No.1, pp.71-80, 2012 Online at: www.jus.org.uk Credibilistic Bi-Matrix Game Prasanta Mula 1, Sankar Kumar Roy 2, 1 ISRO Satellite Centre, Old Airport Road, Vimanapura
More informationI N T R O D U C T I O N : G R O W I N G I T C O M P L E X I T Y
Global Headquarters: 5 Speen Street Framingham, MA 01701 USA P.508.872.8200 F.508.935.4015 www.idc.com W H I T E P A P E R I n v a r i a n t A n a l y z e r : A n A u t o m a t e d A p p r o a c h t o
More informationUncertainty Sources, Types and Quantification Models for Risk Studies
Uncertainty Sources, Types and Quantification Models for Risk Studies Bilal M. Ayyub, PhD, PE Professor and Director Center for Technology and Systems Management University of Maryland, College Park Workshop
More information