Feature-Based Uncertainty Visualization

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this study, a suite of feature-based techniques is developed to address these issues. First, a framework of feature-level uncertainty visualization is presented to study the uncertainty of the features in scalar and vector data. The uncertainty in the number and locations of features such as sinks or sources of vector fields are referred to as feature-level uncertainty while the uncertainty in the numerical values of the data is referred to as data-level uncertainty. The features of different ensemble members are indentified and correlated. The feature-level uncertainties are expressed as the transitions between corresponding features through new elliptical glyphs. Second, an interactive visualization tool for exploring scalar data with data-level and two types of feature-level uncertainties — contour-level and topology-level uncertainties — is developed. To avoid visual cluttering and occlusion, the uncertainty information is attached to a contour tree instead of being integrated with the visualization of the data. An efficient contour tree-based interface is designed to reduce users’ workload in viewing and analyzing complicated data with uncertainties and to facilitate a quick and accurate selection of prominent contours. This thesis advances the current uncertainty studies with an in-depth investigation of the feature-level uncertainties and an exploration of topology tools for effective and interactive uncertainty visualizations. With quantified representation and interactive capability, feature-based visualization helps people gain new insights into the uncertainties of their data, especially the uncertainties of extracted features which otherwise would remain unknown with the visualization of only data-level uncertainties

Circular External Difference Families: Construction and Non-Existence

The circular external difference family and its strong version, which themselves are of independent combinatorial interest, were proposed as variants of the difference family to construct new unconditionally secure non-malleable threshold schemes. In this paper, we present new results regarding the construction and non-existence of (strong) circular external difference families, thereby solving several open problems on this topic

Numerical Analysis of Effect of Crack Location on the Crack Breathing Behavior

In this work, a three-dimensional finite element model was developed to investigate the crack breathing behavior at different crack locations considering the effect of unbalance force. A two-disk rotor with a crack is simulated using ABAQUS. The duration of each crack status (open, closed and partially open/closed) during a full shaft rotation was examined to analyse the crack breathing behavior. Unbalanced shaft crack breathing behavior was found to be different at different crack locations. The breathing behavior of crack along the shaft length is divided into different regions depending on the unbalance force and crack location. The simulated results in this work can be further utilised to obtain the time-varying stiffness matrix of the cracked shaft element under the influence of unbalance force

An Effective Index for Truss-based Community Search on Large Directed Graphs

Community search is a derivative of community detection that enables online and personalized discovery of communities and has found extensive applications in massive real-world networks. Recently, there needs to be more focus on the community search issue within directed graphs, even though substantial research has been carried out on undirected graphs. The recently proposed D-truss model has achieved good results in the quality of retrieved communities. However, existing D-truss-based work cannot perform efficient community searches on large graphs because it consumes too many computing resources to retrieve the maximal D-truss. To overcome this issue, we introduce an innovative merge relation known as D-truss-connected to capture the inherent density and cohesiveness of edges within D-truss. This relation allows us to partition all the edges in the original graph into a series of D-truss-connected classes. Then, we construct a concise and compact index, ConDTruss, based on D-truss-connected. Using ConDTruss, the efficiency of maximum D-truss retrieval will be greatly improved, making it a theoretically optimal approach. Experimental evaluations conducted on large directed graph certificate the effectiveness of our proposed method.Comment: 8 pages, 8figure

Fast Butterfly-Core Community Search For Large Labeled Graphs

Community Search (CS) aims to identify densely interconnected subgraphs corresponding to query vertices within a graph. However, existing heterogeneous graph-based community search methods need help identifying cross-group communities and suffer from efficiency issues, making them unsuitable for large graphs. This paper presents a fast community search model based on the Butterfly-Core Community (BCC) structure for heterogeneous graphs. The Random Walk with Restart (RWR) algorithm and butterfly degree comprehensively evaluate the importance of vertices within communities, allowing leader vertices to be rapidly updated to maintain cross-group cohesion. Moreover, we devised a more efficient method for updating vertex distances, which minimizes vertex visits and enhances operational efficiency. Extensive experiments on several real-world temporal graphs demonstrate the effectiveness and efficiency of this solution.Comment: 8 pages, 8 figure

Update-Sensitive Structured Encryption with Backward Privacy

Many recent studies focus on dynamic searchable encryption (DSE), which provides efficient data-search and data-update services directly on outsourced private data. Most encryption schemes are not optimized for update-intensive cases, which say that the same data record is frequently added and deleted from the database. How to build an efficient and secure DSE scheme for update-intensive data is still challenging. We propose UI-SE, the first DSE scheme that achieves single-round-trip interaction, near-zero client storage, and backward privacy without any insertion patterns. UI-SE involves a new tree data structure, named OU-tree, which supports oblivious data updates without any access-pattern leakage. We formally prove that UI-SE is adaptively secure under Type-1$^-$ backward privacy, which is stronger than backward privacy proposed by Bost et al. in CCS 2017. Experimental data also demonstrate UI-SE has low computational overhead, low local disk usage, and high update performance on scalable datasets

Fast Boolean Queries with Minimized Leakage for Encrypted Databases in Cloud Computing

This research revisits the fundamental problem of processing privacy-preserving Boolean queries over outsourced databases on untrusted public clouds. Many current Searchable Encryption (SE) schemes try to seek an appropriate trade-off between security and efficiency, yet most of them suffer from an unacceptable query leakage due to their conjunctive/disjunctive terms that are processed individually. We show, however, this trade-off still can be deeply optimized for more security. We consider a Boolean formula as a set of deterministic finite automatons (DFAs) and propose a novel approach to running an encrypted DFA, which can be effectively and efficiently processed by the cloud. We give three constructions for conjunctive, disjunctive, and Boolean queries, respectively. Their notable advantages are single-round, highly-efficient, adaptively-secure, and leakage-minimized. A lot of experiments are made to evaluate overall efficiency. Testing results show that the schemes achieve enhanced security almost without sacrificing anything of search efficiency