Narrow Results

In this paper, we investigate the equivalence of connectedness for the Sierpinski-like sponge and skeleton networks, and find out the relation between the geodesic distance on the sponge and renormalized shortest path distance on the skeleton networks. Furthermore, under some assumption on the IFS, we obtain the comparability of the Manhattan distance and the geodesic distance on the sponge.

There is a need for a probabilistic linguistic term set model for go/no-go product screening problem for new product development to meet a firm’s expectation. This paper develops a novel 3-tuple linguistic distance-based model to evaluate whether an overall respondents perception meets a firm’s expectation (“go”) for new product development. The respondent’s perception is collected by a Kansei-based survey as an interval-linguistic term. Then, an expected distance between the firm’s expectation and the respondent’s perception is computed by a target-based Manhattan distance measure. The expected distance is compared with a threshold to shows that what product attribute meets the firm’s expectation based on customers’ perceptions. A real case study of Thai-tea soy milk packaging design is provided. The proposed model is compared to the existing model to show its effectiveness and applicability. Experimental results show that the proposed model can effectively point out the inferior product attributes, which leads to redesign the product until all product concepts meet the target attributes before launching the product to the market. Thus, it can significantly reduce the risk of failure of the product in a real market. This paper has significant contributions in that it allows respondents to provide their opinions with uncertainty by providing an interval linguistic assessment, handles a bias of the heterogeneity of respondents by determining the weight of respondents, and overcomes limitations of existing models by applying target-oriented linguistic terms.

Trajectory data often provides useful information that can be utilized in real-life applications, such as traffic planning and location-based advertising. Because people’s trajectory information can result in serious personal privacy leakage, trajectory privacy protection methods are employed. However, existing methods assume and use the same privacy requirements for all trajectories, which affect privacy protection efficiency and data utilization. This paper proposes a trajectory privacy protection method based on user requirement. By dividing different time intervals, it sets different privacy protection parameters for different trajectories to provide more detailed privacy protection. The proposed method utilizes the divided time intervals and privacy protection requirements to form a privacy requirement matrix, to construct an anonymous trajectory equivalence class and undirected graph. Then, trajectories are processed to form anonymous sets. Euclidean distance is also replaced with Manhattan distance in calculating the distance of the trajectories, which would improve the privacy protection and data utility and narrow the gap between the theoretical privacy protection and the actual protective effects. Comparative experiments demonstrate that the proposed method outperforms other similar methods in regards to both privacy protection and data utilization.