Lot-to-lot verification is an important laboratory activity that is performed to monitor the consistency of analytical performance over time. In this opinion paper, the concept, clinical impact, ...challenges and potential solutions for lot-to-lot verification are exained.
CHEX Lu, Long; Li, Zhichun; Wu, Zhenyu ...
Proceedings of the 2012 ACM conference on Computer and communications security,
10/2012
Conference Proceeding
An enormous number of apps have been developed for Android in recent years, making it one of the most popular mobile operating systems. However, the quality of the booming apps can be a concern 4. ...Poorly engineered apps may contain security vulnerabilities that can severally undermine users' security and privacy. In this paper, we study a general category of vulnerabilities found in Android apps, namely the component hijacking vulnerabilities. Several types of previously reported app vulnerabilities, such as permission leakage, unauthorized data access, intent spoofing, and etc., belong to this category.
We propose CHEX, a static analysis method to automatically vet Android apps for component hijacking vulnerabilities. Modeling these vulnerabilities from a data-flow analysis perspective, CHEX analyzes Android apps and detects possible hijack-enabling flows by conducting low-overhead reachability tests on customized system dependence graphs. To tackle analysis challenges imposed by Android's special programming paradigm, we employ a novel technique to discover component entry points in their completeness and introduce app splitting to model the asynchronous executions of multiple entry points in an app.
We prototyped CHEX based on Dalysis, a generic static analysis framework that we built to support many types of analysis on Android app bytecode. We evaluated CHEX with 5,486 real Android apps and found 254 potential component hijacking vulnerabilities. The median execution time of CHEX on an app is 37.02 seconds, which is fast enough to be used in very high volume app vetting and testing scenarios.
Kinship verification is a very important technique in many real-world applications, e.g., personal album organization, missing person investigation and forensic analysis. However, it is extremely ...difficult to verify a family pair with generation gap, e.g., father and son, since there exist both age gap and identity variation. It is essential to well fight off such challenges to achieve promising kinship verification performance. To this end, we propose a towards-young cross-generation model for effective kinship verification by mitigating both age and identity divergences. Specifically, we explore a conditional generative model to bring in an intermediate domain to bridge each pair. Thus, we could extract more effective features through deep architectures with a newly-designed Sparse Discriminative Metric Loss (SDM-Loss), which is exploited to involve the positive and negative information. Experimental results on kinship benchmark demonstrate the superiority of our proposed model by comparing with the state-of-the-art kinship verification methods.
•SM-DTW is a weighted DTW algorithm based on the concept of stability regions and handwriting generation models.•Dissimilarities inside stability regions and similarities outside them are ...penalized.•The performance are evaluated by varying the set of features used for representing signatures.•SM-DTW improves the performance of a classical DTW system and it outperforms some state of the art ASV systems.•Stability regions exploits shape information better than the whole feature set.
Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject’s signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification. We then introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions, i.e. the most similar parts between two signatures, into the distance measure between a pair of signatures computed by the Dynamic Time Warping for signature verification. Experiments were conducted on two datasets largely adopted for performance evaluation. Experimental results show that the proposed algorithm improves the performance of the baseline system and compares favourably with other top performing signature verification systems.
The data race problem is common in the interrupt-driven program, and it is difficult to find as a result of complicated interrupt interleaving. Static analysis is a mainstream technology to detect ...those problems, however, the synchronization mechanism of interrupt is hard to be processed by the existing method, which brings many false alarms. Eliminating false alarms in static analysis is the main challenge for precisely data race detection.
In this paper, we present a framework of static analysis combined with program verification, which performs static analysis to find all potential races, and then verifies every race to eliminate false alarms. The experiment results on related race benchmarks show that our implementation finds all race bugs in the phase of static analysis, and eliminates all false alarms through program verification.
Deep Neural Network (DNN) testing is one of the most widely-used ways to guarantee the quality of DNNs. However, labeling test inputs to check the correctness of DNN prediction is very costly, which ...could largely affect the efficiency of DNN testing, even the whole process of DNN development. To relieve the labeling-cost problem, we propose a novel test input prioritization approach (called PRIMA) for DNNs via intelligent mutation analysis in order to label more bug-revealing test inputs earlier for a limited time, which facilitates to improve the efficiency of DNN testing. PRIMA is based on the key insight: a test input that is able to kill many mutated models and produce different prediction results with many mutated inputs, is more likely to reveal DNN bugs, and thus it should be prioritized higher. After obtaining a number of mutation results from a series of our designed model and input mutation rules for each test input, PRIMA further incorporates learning-to-rank (a kind of supervised machine learning to solve ranking problems) to intelligently combine these mutation results for effective test input prioritization. We conducted an extensive study based on 36 popular subjects by carefully considering their diversity from five dimensions (i.e., different domains of test inputs, different DNN tasks, different network structures, different types of test inputs, and different training scenarios). Our experimental results demonstrate the effectiveness of PRIMA, significantly outperforming the state-of-the-art approaches (with the average improvement of 8.50%~131.01% in terms of prioritization effectiveness). In particular, we have applied PRIMA to the practical autonomous-vehicle testing in a large motor company, and the results on 4 real-world scene-recognition models in autonomous vehicles further confirm the practicability of PRIMA.
This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in ...face and kinship verification, most existing metric learning methods aim to learn a single Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, which cannot capture the nonlinear manifold where face images usually lie on. To address this, we propose a DDML method to train a deep neural network to learn a set of hierarchical nonlinear transformations to project face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. To better use the commonality of multiple feature descriptors to make all the features more robust for face and kinship verification, we develop a discriminative deep multi-metric learning method to jointly learn multiple neural networks, under which the correlation of different features of each sample is maximized, and the distance of each positive pair is reduced and that of each negative pair is enlarged. Extensive experimental results show that our proposed methods achieve the acceptable results in both face and kinship verification.
We are all increasingly dependent on software systems to run the technology we use every day, so we need these systems to be both reliable and safe.This book presents papers from the NATO Advanced ...Study Institute Summer School Dependable Software Systems Engineering, held in Marktoberdorf, Germany, in July and August 2014. Lecturers were drawn from prestigious research groups representing both industry and academia, and the course was designed as an in-depth presentation and teaching of state-of-the-art scientific techniques and methods covering research and industrial practice as well as scientific principles.Topics covered
