Abstract Rapidity of changes and related complexities has caused organizations to be changeability in all aspects. In order to think about future and take necessary actions, it is needed to create suitable sub – structures and basics by which the organizations are encouraged to have acceptable changeability against future complexities. This is to say that organizations move toward the future on the basis... Rapidity of changes and related complexities has caused organizations to be changeability in all aspects. In order to think about future and take necessary actions, it is needed to create suitable sub – structures and basics by which the organizations are encouraged to have acceptable changeability against future complexities. This is to say that organizations move toward the future on the basis of two main sub structures including “agility” and “multi – faces”. This study includes qualitative and quantitative parts. In qualitative part, we focus on the investigation of terms “agility” and “multi – faces”. As the two dimensions of organizational changeability for moving toward a changeable future. On the other hand, in quantitative part, a questionnaire has been prepared for examining the relationship between the two variables in governmental universities of Yazd province at AMOS space. The results of qualitative part show that organizational experts argue that futuristic organizations should be agile and multi – faced in order to move rapidly. In this regard, the factors needed for changing dimensions include multiple strategies and goals, dynamic capabilities and multidimensional structures. Moreover, the factors needed for achieving agility include flexibility, learning on the base of mistake, time and tempo, the ability of reconfigurable and the ability of recognizing and solving problems. In quantitative part, the reliability of the questions was confirmed by use of Cronbach’s ± up to %90. Moreover, all hypothesis were confirmed by use of confirmed factor analysis model (P>.5). Read More Read Less

Abstract Nowadays, detecting unknown computer worms is a challenging task. Anti-virus is the feasible solution for this problem but anti viruses that contains signature based methods are clueless against new unknown worms and it cannot detect new worms until it is updated with new worm signatures. This paper focuses on improving the accuracy of detecting the unknown worm while reducing the feature set extracted... Nowadays, detecting unknown computer worms is a challenging task. Anti-virus is the feasible solution for this problem but anti viruses that contains signature based methods are clueless against new unknown worms and it cannot detect new worms until it is updated with new worm signatures. This paper focuses on improving the accuracy of detecting the unknown worm while reducing the feature set extracted using DFA. Gain ratio method is implemented to reduce the amount of features. Keygraph and data crystallization classifier is proposed for classifying the unknown attacks. The dataset is collected from the VX Heaven virus collection website which contains the sources and samples of the virus collection. Extensive experiment was performed for testing the feasibility of detecting unknown worms. The evaluation results shows that by applying classification algorithms, the detection ratio obtained for specific unknown worms exceeds 98%. Read More Read Less

Abstract A novel Ant Colony Optimization algorithm (ACO) is combined for the hierarchical multi- label classification problem of protein function prediction. This kind of problem is mainly focused on biometric area, given the large increase in the number of uncharacterized proteins available for analysis and the importance of determining their functions in order to improve the current biological knowledge. Because it is known... A novel Ant Colony Optimization algorithm (ACO) is combined for the hierarchical multi- label classification problem of protein function prediction. This kind of problem is mainly focused on biometric area, given the large increase in the number of uncharacterized proteins available for analysis and the importance of determining their functions in order to improve the current biological knowledge. Because it is known that a protein can perform more than one function and many protein functional-definition schemes are organized in a hierarchical structure, the classification problem in this case is an instance of a hierarchical multi-label problem. In this classification method, each class might have multiple class labels and class labels are represented in a hierarchical structure—either a tree or a directed acyclic graph (DAG) structure. A more difficult problem than conventional flat classification in this approach, given that the classification algorithm has to take into account hierarchical relationships between class labels and be able to predict multiple class labels for the same example. The proposed ACO algorithm discovers an ordered list of hierarchical multi-label classification rules. Read More Read Less