Hierarchical time series data comprises a collection of time series aggregated at multiple levels based on categorical, geographical, or physical constraints, the analysis of which aids analysts across various domains like retail, finance, and energy, in gaining valuable insights and making informed decisions. However, existing interactive exploratory analysis approaches for hierarchical time series data fall short in analyzing time series across different aggregation levels and supporting more complex analytical tasks beyond common ones like summarize and compare. These limitations motivate us to develop a new visual analytics approach. We first generalize a taxonomy to delineate various tasks in hierarchical time series analysis, derived from literature survey and expert interviews. Based on this taxonomy, we develop ChronoDeck, an interactive system that incorporates a multi-column hierarchical time series visualization for implementing various analytical tasks and distilling insights from the data. ChronoDeck visualizes each aggregation level of hierarchical time series with a combination of coordinated dimensionality reduction and small multiples visualizations, alongside interactions including highlight, align, filter, and select, assisting users in the visualization, comparison, and transformation of hierarchical time series, as well as identifying the entities of interest. The effectiveness of ChronoDeck is demonstrated by case studies on three real-world datasets and expert interviews.

Summary Modern smartphone platforms offer a multitude of useful features to their users, but at the same time, they highly affect privacy, which may lead to unnecessary personal data being collected. In this paper, we proposed an evolutionary game model to study permission request strategies of the bounded rational application providers. We study the proposed model with detailed simulations. Initial results demonstrate that evolution processes are influenced by four factors: revenue increase ratio, market share, credit cost, and market attraction. It also suggests that establishing a privacy alarm mechanism not only can improve the users' privacy awareness but also decrease the market share when providers over‐request permissions and push them request permissions properly.

Summary A major concern of the deployment of location based services (LBSs) is the safeguards of the user's location data collected by service providers, since personal location data may imply sensitive private information. However, most available works proposed so far rely on syntactic privacy models such as k‐anonymity and location perturbation, which are proved to quiver in the balance with privacy and usability requirements. In this article, we provide a new ‐map mechanism to help users better understand the privacy/accuracy tradeoff process and preserve location data. In our ‐map model, the user can specify a geographic region to hide her precise location and suppress the following queries in the same area to meet her privacy requirement, while maintaining and understanding its usability. In addition, we propose a new notion of ‐privacy based on differential privacy to account for the temporal‐spatial correlation and history correlation in the case of crossing region, which is the major privacy concern of a moving user's trace. Finally, we evaluate our framework by using an online LBS with real‐world data sets. The results not only indicate that the ‐map is significantly useful for identifying the privacy and utility tradeoffs but also show the effectiveness and practicality of the proposed ‐privacy.

Recently, convolutional neural networks (CNNs) have achieved impressive progress in multi‐focus image fusion (MFF). However, it always fails to capture sufficient discrimination features due to the local receptive field limitations of the convolutional operator, restricting most current CNN‐based methods’ performance. To address this issue, by leveraging self‐attention (SA) mechanism, the authors propose Siamese SA network (SSAN) for MFF. Specifically, two kinds of SA modules, position SA (PSA) and channel SA (CSA) are utilised to model the long‐range dependencies across focused and defocused regions in the multi‐focus image, alleviating the local receptive field limitations of convolution operators in CNN. To search a better feature representation of the input image for MFF, the captured features obtained by PSA and CSA are further merged through a learnable 1 × 1 convolution operator. The whole pipeline is in a Siamese network fashion to reduce the complexity. After training, the authors SSAN can accomplish well the fusion task with no post‐processing. Experiments demonstrate that their approach outperforms other current state‐of‐the‐art methods, not only in subjective visual perception but also in the quantitative assessment.