The selection of the right suppliers is crucial to ensure supply chain resilience. This study proposes a two-stage robust optimization model that combines box uncertainty sets with a supplier delivery outcome that is described by a portfolio state represented as a binary vector indicating for every supplier whether it delivers or fails. The model aims to maximize the total profit by considering the uncertainty of the market selling price and the risk of supply failures, while also incorporating flexible production capacity constraints. The experimental results demonstrate that enhancing supplier capacity flexibility and prioritizing low-risk suppliers can significantly increase total profit and reduce unmet demand losses. Maintaining price fluctuations within a reasonable range also contributes to higher overall profit.

We consider weakly supervised segmentation where only a fraction of pixels have ground truth labels (scribbles) and focus on a self-labeling approach optimizing relaxations of the standard unsupervised CRF/Potts loss on unlabeled pixels. While WSSS methods can directly optimize such losses via gradient descent, prior work suggests that higher-order optimization can improve network training by introducing hidden pseudo-labels and powerful CRF sub-problem solvers, e.g. graph cut. However, previously used hard pseudo-labels can not represent class uncertainty or errors, which motivates soft self-labeling. We derive a principled auxiliary loss and systematically evaluate standard and new CRF relaxations (convex and non-convex), neighborhood systems, and terms connecting network predictions with soft pseudo-labels. We also propose a general continuous sub-problem solver. Using only standard architectures, soft self-labeling consistently improves scribble-based training and outperforms significantly more complex specialized WSSS systems. It can outperform full pixel-precise supervision. Our general ideas apply to other weakly-supervised problems/systems.

We are interested in unsupervised reconstruction of complex near-capillary vasculature with thousands of bifurcations where supervision and learning are infeasible. Unsupervised methods can use many structural constraints, e.g. topology, geometry, physics. Common techniques use variants of MST on geodesic tubular graphs minimizing symmetric pairwise costs, i.e. distances. We show limitations of such standard undirected tubular graphs producing typical errors at bifurcations where flow "directedness" is critical. We introduce a new general concept of confluence for continuous oriented curves forming vessel trees and show how to enforce it on discrete tubular graphs. While confluence is a high-order property, we present an efficient practical algorithm for reconstructing confluent vessel trees using minimum arborescence on a directed graph enforcing confluence via simple flow-extrapolating arc construction. Empirical tests on large near-capillary sub-voxel vasculature volumes demonstrate significantly improved reconstruction accuracy at bifurcations. Our code has also been made publicly available1.

We propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence. As one important example of this general idea, we focus on vector fields modelling blood flow pattern that should be divergent in arteries and convergent in veins. We show that this previously ignored regularization constraint can significantly improve the quality of vessel tree reconstruction particularly around bifurcations where non-zero divergence is concentrated. Our divergence prior is critical for resolving (binary) sign ambiguity in flow orientations produced by standard vessel filters, \eg Frangi. Our vessel tree centerline reconstruction combines divergence constraints with robust curvature regularization. Our unsupervised method can reconstruct complete vessel trees with near-capillary details on synthetic and real 3D volumes.

Android is characterized by the complicated relations among its components and apps, through which one party interacts with the other (e.g., starting its activity) by referring to its attributes like package, activity, service, action names, authorities and permissions. Such relations can be easily compromised during a customization: e.g., when an app is removed to fit an Android version to a new device model, while references to the app remain inside that OS. This conflict between the decentralized, unregulated Android customization process and the interdependency among different Android components and apps leads to the pervasiveness of hanging attribute references (Hares), a type of vulnerabilities never investigated before. In our research, we show that popular Android devices are riddled with such flaws, which often have serious security implications: when an attribute (e.g., a package/authority/action name) is used on a device but the party defining it has been removed, a malicious app can fill the gap to acquire critical system capabilities, by simply disguising as the owner of the attribute. More specifically, we discovered in our research that on various Android devices, the malware can exploit their Hares to steal the user's voice notes, control the screen unlock process, replace Google Email's account settings activity and collect or even modify the user's contact without proper permissions. We further designed and implemented Harehunter, a new tool for automatic detection of Hares by comparing attributes defined with those used, and analyzing the references to undefined attributes to determine whether they have been protected (e.g., by signature checking). On the factory images for 97 most popular Android devices, Harehunter discovered 21557 likely Hare flaws, demonstrating the significant impacts of the problem. To mitigate the hazards, we further developed an app for detecting the attempts to exploit Hares on different devices and provide the guidance for avoiding this pitfall when building future systems.

Based on the observations that spy-on-user attacks by calling Android APIs will be detected out by Android API auditing, we studied the possibility of a "transplantation attack", through which a malicious app can take privacy-harming pictures to spy on users without the Android API auditing being aware of it. Usually, to take a picture, apps need to call APIs of Android Camera Service which runs in mediaserver process. Transplantation attack is to transplant the picture taking code from mediaserver process to a malicious app process, and the malicious app can call this code to take a picture in its own address space without any IPC. As a result, the API auditing can be evaded. Our experiments confirm that transplantation attack indeed exists. Also, the transplantation attack makes the spy-on-user attack much more stealthy. The evaluation result shows that nearly a half of 69 smartphones (manufactured by 8 vendors) tested let the transplantation attack discovered by us succeed. Moreover, the attack can evade 7 Antivirus detectors, and Android Device Administration which is a set of APIs that can be used to carry out mobile device management in enterprise environments. The transplantation attack inspires us to uncover a subtle design/implementation deficiency of the Android security.

Two key factors in the design of anonymity schemes are the scalability and the security of the relay node selection. In this paper, a scalable, secure anonymity scheme based on DHT inquiry mechanism is presented. Unlike the most existing schemes, every relay node's routing information (RRI) is stored as normal data in DHT overlay. The routing information can be inquired just with corresponding relay node's Relay ID (RID).All RID is maintained by SA to fill a dynamic range. So, user only needs to get the range of RID to select relay nodes, which is a datum with constant size. Such a mechanism significantly improve the scalability of scheme. Furthermore, RID assigned by SA also provides a more stable and provable relationship between relay nodes, which can be used to help validation of RRI storage. With this innovation, security measures are introduced. The framework of the scheme, key technical details and security analysis are described in this paper. In addition, simulation experiments are conducted to validate the effectiveness of this scheme.

Based on the characteristics of color matching and the mixed color theory, the main related factors in the process of color matching are analyzed and abstracted. Mathematical modeling and curve fitting methods are applied to the study of color matching in this paper. On the basis of the data analysis of dyeing samples, the mathematical model of color matching in textile dyeing is established based on the different concentration of three mixed colorants. Furthermore, least square Fitting is applied to solve this model. Experiment illustrates that this color matching model is simple, clear and effective which provides the good basis for solving the exact concentration of three colorants.