In this new information era,the transfer of data and information has become a very important matter.Transferred data must be kept secured from unauthorized persons using cryptography.The science of cryptography depend...In this new information era,the transfer of data and information has become a very important matter.Transferred data must be kept secured from unauthorized persons using cryptography.The science of cryptography depends not only on complex mathematical models but also on encryption keys.Amino acid encryption is a promising model for data security.In this paper,we propose an amino acid encryption model with two encryption keys.The first key is generated randomly using the genetic algorithm.The second key is called the protein key which is generated from converting DNA to a protein message.Then,the protein message and the first key are used in the modified Playfair matrix to generate the cypher message.The experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only attack.In addition,the proposed model has been tested over different types of characters including white spaces and special characters,as all the data is encoded to 8-bit binary.The performance of the proposed model is compared with other models using encryption time and decryption time.The model also balances all three principles in the CIA triad.展开更多
Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the sof...Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.展开更多
We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary qubits.In particular,we use the power of the entanglemen...We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary qubits.In particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model structure.This model uses one of two techniques to retrieve the solution of a quantum computing problem at hand.In the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or not.In the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean variables.The proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two oracles.The hamming distance is evaluated based on the second technique.It is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean variables.The proposed algorithm is explained via a case study.Finally,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.展开更多
文摘In this new information era,the transfer of data and information has become a very important matter.Transferred data must be kept secured from unauthorized persons using cryptography.The science of cryptography depends not only on complex mathematical models but also on encryption keys.Amino acid encryption is a promising model for data security.In this paper,we propose an amino acid encryption model with two encryption keys.The first key is generated randomly using the genetic algorithm.The second key is called the protein key which is generated from converting DNA to a protein message.Then,the protein message and the first key are used in the modified Playfair matrix to generate the cypher message.The experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only attack.In addition,the proposed model has been tested over different types of characters including white spaces and special characters,as all the data is encoded to 8-bit binary.The performance of the proposed model is compared with other models using encryption time and decryption time.The model also balances all three principles in the CIA triad.
文摘Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their capabilities.Code smells arise as one of the obstacles in the software industry.They are characteristics of software source code that indicate a deeper problem in design.These smells appear not only in the design but also in software implementation.Code smells introduce bugs,affect software maintainability,and lead to higher maintenance costs.Uncovering code smells can be formulated as an optimization problem of finding the best detection rules.Although researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be improved.Previous research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their types.Our research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection rules.Applying this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass variance.The proposed framework adopts if-then detection rules during the software development life cycle.Those rules identify the types for both medium and large projects.Experiments are conducted on five open-source software projects to discover nine smell types that mostly appear in codes.The proposed detection framework has an average of 94.24%precision and 93.4%recall.These accurate values are better than other search-based algorithms of the same field.The proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and cost.Additionally,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.
文摘We present a novel quantum algorithm to evaluate the hamming distance between two unknown oracles via measuring the degree of entanglement between two ancillary qubits.In particular,we use the power of the entanglement degree based quantum computing model that preserves at most the locality of interactions within the quantum model structure.This model uses one of two techniques to retrieve the solution of a quantum computing problem at hand.In the first technique,the solution of the problem is obtained based on whether there is an entanglement between the two ancillary qubits or not.In the second,the solution of the quantum computing problem is obtained as a function in the concurrence value,and the number of states that can be generated from the Boolean variables.The proposed algorithm receives two oracles,each oracle represents an unknown Boolean function,then it measures the hamming distance between these two oracles.The hamming distance is evaluated based on the second technique.It is shown that the proposed algorithm provides exponential speedup compared with the classical counterpart for Boolean functions that have large numbers of Boolean variables.The proposed algorithm is explained via a case study.Finally,employing recently developed experimental techniques,the proposed algorithm has been verified using IBM’s quantum computer simulator.
