Abstract Retrieving web information and tailoring it to fit users' needs is a challenge for modern web search engines. Most display information from searches the same way, regardless of the user's needs, interests, or context. This is a weakness for mobile users who expect to adapt results and more flexibility in interacting with the information retrieved. This research presents the development... Retrieving web information and tailoring it to fit users' needs is a challenge for modern web search engines. Most display information from searches the same way, regardless of the user's needs, interests, or context. This is a weakness for mobile users who expect to adapt results and more flexibility in interacting with the information retrieved. This research presents the development of NaviWeb, a multimedia retrieval mobile system that displays multimedia information retrieved from the web through an original and disciplined Graphical User Interface. The system enhances users' search experiences by providing an original way to stimulate learning and captivate their interest while searching the web. Sessions are recorded and managed to comply with the requirements of adaptive mobile learning. NaviWeb has been developed on the Android mobile platform and has been evaluated using a set of metrics. Most users were interested in getting deep knowledge about their initial queries and were keen to resume their previously saved sessions. Read More Read Less

Abstract Innovation failure knowledge sharing plays an essential role in reducing the probability of repeated failure of subsequent innovation and improving the innovation ability of virtual research organizations. However, it is challenging for members to share innovation failure knowledge without incentives actively. This study aims to establish the incentive mechanism of innovation failure knowledge sharing and promote the sharing behavior of... Innovation failure knowledge sharing plays an essential role in reducing the probability of repeated failure of subsequent innovation and improving the innovation ability of virtual research organizations. However, it is challenging for members to share innovation failure knowledge without incentives actively. This study aims to establish the incentive mechanism of innovation failure knowledge sharing and promote the sharing behavior of innovation failure knowledge in a virtual research organization. Design/methodology/approach: By using game theory, considering the risk aversion degree of organization members and the negative effect of innovation faulttolerance environment, the incentive model of innovation failure knowledge sharing of virtual research organization was constructed, and the incentive relationship of innovation failure knowledge sharing between the organization and its members under the influence of different states was analyzed. Findings: Without considering the negative effect of innovation fault-tolerant environment, the optimal incentive coefficient of innovation failure knowledge sharing is positively related to the sharing rate of innovation failure knowledge and the transformation ability of innovation failure knowledge of organization members, and negatively associated with the sharing cost and risk aversion degree of organization members. Considering the negative effect of an innovation fault-tolerant environment, the virtual research organization should make a corresponding modification of sharing incentive intensity according to the estimation of the tolerance degree to an innovation fault-tolerant environment by itself and its members to reduce the knowledge input of the organization. Originality/Value: This study proposes an incentive mechanism of innovation failure knowledge sharing, considering the negative effect of innovation fault-tolerant environment, which provides a novel idea and methods for the design of incentive mechanism of innovation failure knowledge sharing in a virtual research organization. Read More Read Less

Abstract Association rule mining is an optimization problem because of several limitations. Recently, the imperialist competitive algorithm (ICA) has been submitted for solving different optimization problems. This algorithm is based on the socio-political competition among empires. This paper proposes a novel ICA algorithm for automated mining of the exciting and readable association rules without considering the minimum support and confidence thresholds.... Association rule mining is an optimization problem because of several limitations. Recently, the imperialist competitive algorithm (ICA) has been submitted for solving different optimization problems. This algorithm is based on the socio-political competition among empires. This paper proposes a novel ICA algorithm for automated mining of the exciting and readable association rules without considering the minimum support and confidence thresholds. The convergence rate and computational efficiency of ICA have been improved. This study shows that ICA is combined with some operators of genetic algorithms. The experimental results show that this algorithm is more efficient than the methods of mining association rules based on the basic ICA and the genetic algorithm. These modifications are not only valid for association rule mining but also have extensions to other optimization problems. Read More Read Less