Undecidability of static analysis
ACM Letters on Programming Languages and Systems (LOPLAS)
Manufacturing cheap, resilient, and stealthy opaque constructs
POPL '98 Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
Tamper Resistant Software: An Implementation
Proceedings of the First International Workshop on Information Hiding
Protection of Software-Based Survivability Mechanisms
DSN '01 Proceedings of the 2001 International Conference on Dependable Systems and Networks (formerly: FTCS)
A New Class of Invertible Mappings
CHES '02 Revised Papers from the 4th International Workshop on Cryptographic Hardware and Embedded Systems
A security architecture for survivability mechanisms
A security architecture for survivability mechanisms
Obfuscation of executable code to improve resistance to static disassembly
Proceedings of the 10th ACM conference on Computer and communications security
Applied Statistics and the SAS Programming Language (5th Edition)
Applied Statistics and the SAS Programming Language (5th Edition)
Control flow based obfuscation
Proceedings of the 5th ACM workshop on Digital rights management
IEEE Transactions on Software Engineering
Opaque predicates detection by abstract interpretation
AMAST'06 Proceedings of the 11th international conference on Algebraic Methodology and Software Technology
Code obfuscation against static and dynamic reverse engineering
IH'11 Proceedings of the 13th international conference on Information hiding
Hi-index | 0.00 |
This paper discusses the problem of decision support systems in the organization. The procedure (linear combination) developed with the aim to combine some predicted results obtained with simulation of linear and nonlinear regression models (experts), multiple regression model, nonparametric regression model, and semi parametric regression model. This adjustment procedure enforce some statistical characteristics like the expected value of the gross production rate based on Cobb-Douglas production function is unbiased for the actual value, and the total weights (importance) of all models (experts) is equal to one. We used modeling and simulation techniques to generate our data and to apply the procedure.