With the rapid development of Internet of Things (IoT), biometric-based authentication systems have been widely used for access control. The wide application of biometric recognition systems has brought convenience but also raised privacy and security concerns. When unprotected templates are stolen, it will permanently leak the original biometric data. Therefore, it is important to ensure the security of biometric templates while meeting the real-time device requirements. Motivated by these issues, in this paper, we proposed a scheme based on partial Cake-cutting transformation and spherical hashing to generate cancelable binary face templates. Firstly, with external random parameters, partial Cake-cutting transformation is established to introduce randomness and preserve the relative distance similarity of face features. Then spherical hashing is utilized to encode the face features into protected binary codes. The protected template has the advantages of high entropy value, low storage consumption, and fast generation speed. Extensive experiments conducted on LFW, CFPW, and CASIA-FaceV5 databases along with theoretical analyses indicate that the proposed scheme shows good matching accuracy and strong resistance to various attacks. Besides, the protected templates can achieve equal or even better accuracy than the unprotected counterparts. Furthermore, the proposed scheme also satisfies the requirements of cancelable biometrics, i.e., irreversibility, revocability, and unlinkability.

OBJECTIVE: We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson's disease (PD) patients and healthy controls (HC). METHODS: We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein-protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson's Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS. RESULTS: We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS. CONCLUSIONS: This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS.

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.7k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 72k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query- by-example search over UIs.

To evaluate the performance of mobile app designs, designers and researchers employ techniques such as A/B, usability, and analytics-driven testing. While these are all useful strategies for evaluating known designs, comparing many divergent solutions to identify the most performant remains a costly and difficult problem. This paper introduces a design performance testing approach that leverages existing app implementations and crowd workers to enable comparative testing at scale. This approach is manifest in ZIPT, a zero-integration performance testing platform that allows designers to collect detailed design and interaction data over any Android app -- including apps they do not own and did not build. Designers can deploy scripted tests via ZIPT to collect aggregate user performance metrics (e.g., completion rate, time on task) and qualitative feedback over third-party apps. Through case studies, we demonstrate that designers can use ZIPT's aggregate data and visualizations to understand the relative performance of interaction patterns found in the wild, and identify usability issues in existing Android apps.

Design plays an important role in adoption of apps. App design, however, is a complex process with multiple design activities. To enable data-driven app design applications, we present interaction mining -- capturing both static (UI layouts, visual details) and dynamic (user flows, motion details) components of an app's design. We present ERICA, a system that takes a scalable, human-computer approach to interaction mining existing Android apps without the need to modify them in any way. As users interact with apps through ERICA, it detects UI changes, seamlessly records multiple data-streams in the background, and unifies them into a user interaction trace. Using ERICA we collected interaction traces from over a thousand popular Android apps. Leveraging this trace data, we built machine learning classifiers to detect elements and layouts indicative of 23 common user flows. User flows are an important component of UX design and consists of a sequence of UI states that represent semantically meaningful tasks such as searching or composing. With these classifiers, we identified and indexed more than 3000 flow examples, and released the largest online search engine of user flows in Android apps.