Cloud computing provides the capability to con-nect resource-constrained clients with a centralized and shared pool of resources,such as computational power and storage on demand.Large matrix determinant computation i...Cloud computing provides the capability to con-nect resource-constrained clients with a centralized and shared pool of resources,such as computational power and storage on demand.Large matrix determinant computation is almost ubiquitous in computer science and requires large-scale data computation.Currently,techniques for securely outsourcing matrix determinant computations to untrusted servers are of utmost importance,and they have practical value as well as theoretical significance for the scientific community.In this study,we propose a secure outsourcing method for large matrix determinant computation.We em-ploy some transformations for privacy protection based on the original matrix,including permutation and mix-row/mix-column operations,before sending the target matrix to the cloud.The results returned from the cloud need to be de-clypled anul verified U ubtainl te cullett delinall.Il1 comparison with previously proposed algorithms,our new al-gorithm achieves a higher security level with greater cloud ef-ficiency.The experimental results demonstrate the efficiency and effectiveness of our algorithm.展开更多
Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quan...Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61502269)National Key Research and Development Program of China(2017YFA0303903)Zhejiang Province Key R&D Project(2017C01062).
文摘Cloud computing provides the capability to con-nect resource-constrained clients with a centralized and shared pool of resources,such as computational power and storage on demand.Large matrix determinant computation is almost ubiquitous in computer science and requires large-scale data computation.Currently,techniques for securely outsourcing matrix determinant computations to untrusted servers are of utmost importance,and they have practical value as well as theoretical significance for the scientific community.In this study,we propose a secure outsourcing method for large matrix determinant computation.We em-ploy some transformations for privacy protection based on the original matrix,including permutation and mix-row/mix-column operations,before sending the target matrix to the cloud.The results returned from the cloud need to be de-clypled anul verified U ubtainl te cullett delinall.Il1 comparison with previously proposed algorithms,our new al-gorithm achieves a higher security level with greater cloud ef-ficiency.The experimental results demonstrate the efficiency and effectiveness of our algorithm.
文摘Rationality is a fundamental concept in economics. Most researchers will accept that human beings are not fully rational. Herbert Simon suggested that we are ＂bounded rational＂. However, it is very difficult to quantify ＂bounded rationality＂, and therefore it is difficult to pinpoint its impact to all those economic theories that depend on the assumption of full rationality. Ariel Rubinstein proposed to model bounded rationality by explicitly specifying the decision makers＇ decision-making procedures. This paper takes a computational point of view to Rubinstein＇s approach. From a computational point of view, decision procedures can be encoded in algorithms and heuristics. We argue that, everything else being equal, the effective rationality of an agent is determined by its computational power - we refer to this as the computational intelligence determines effective rationality （CIDER） theory. This is not an attempt to propose a unifying definition of bounded rationality. It is merely a proposal of a computational point of view of bounded rationality. This way of interpreting bounded rationality enables us to （computationally） reason about economic systems when the full rationality assumption is relaxed.
