4621823
On the Testable Implications of Causal Models with Hidden Variables
The validity of a causal model can be tested only if the model imposes constraints on the probability distribution that governs the generated data. In the presence of unmeasured variables, causal models may impose two types of constraints: conditional independencies, as read through the d-separation criterion, and functional constraints, for which no general criterion is available. This paper offers a systematic way of identifying functional constraints and, thus, facilitates the task of testing causal models as well as inferring such models from data.
15872261
Identifying Direct Causal Effects in Linear Models
This paper deals with the problem of identifying direct causal effects in recursive linear structural equation models. Using techniques developed for graphical causal models, we show that a model can be decomposed into a set of submodels such that the identification problem can be solved independently in each submodel. We provide a new identification method that identifies causal effects by solving a set of algebraic equations.
10434536
Identifying Linear Causal Effects
This paper concerns the assessment of linear cause-effect relationships from a combination of observational data and qualitative causal structures. The paper shows how techniques developed for identifying causal effects in causal Bayesian networks can be used to identify linear causal effects, and thus provides a new approach for assessing linear causal effects in structural equation models. Using this approach the paper develops a systematic procedure for recognizing identifiable direct causal effects.
3092732
Inequality Constraints in Causal Models with Hidden Variables
We present a class of inequality constraints on the set of distributions induced by local interventions on variables governed by a causal Bayesian network, in which some of the variables remain unmeasured. We derive bounds on causal effects that are not directly measured in randomized experiments. We derive instrumental inequality type of constraints on nonexperimental distributions. The results have applications in testing causal models with observational or experimental data.
14734990
Reversible data-embedding with a hierarchical structure
Reversible data-embedding embeds data into a digital content (such as digital image, audio or video) such that an authorized party could decode the embedded data and completely restore the original content. In this paper, we present a hierarchically structured, multi-layer algorithm for digital images. Its basic component is an invertible sharpen filter called the difference expansion. The embedding capacity of this algorithm is substantially higher than results reported in previous reversible data-embedding algorithms. The visual quality of images after data-embedding is also among the best in the literature.
19924236
Test-Execution-Based Reliability Measurement and Modeling for Large Commercial Software
The paper studies practical reliability measurement and modeling for large commercial software systems based on test execution data collected during system testing. The application environment and the goals of reliability assessment were analyzed to identify appropriate measurement data. Various reliability growth models were used on failure data normalized by test case executions to track testing progress and provide reliability assessment. Practical problems in data collection, reliability measurement and modeling, and modeling result analysis were also examined. The results demonstrated the feasibility of reliability measurement in a large commercial software development environment and provided a practical comparison of various reliability measurements and models under such an environment. >
622959
Markov Chain Monte Carlo Super-Resolution Image Reconstruction with Simultaneous Adaptation of the Prior Image Model
In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully exploited and shown as an effective approach to perform super-resolution image reconstruction. However, one major challenge lies at the selection of the hyperparameter of the prior image model, which affects the degree of regularity imposed by the prior image model, and consequently, the quality of the estimated high-resolution image. To tackle this challenge, in this paper, we propose a novel approach to automatically adapt the model’s hyperparameter during the MCMC process, rather than the exhaustive, off-line search. Experimental results presented show that the proposed hyperparameter adaptation method yields extremely close performance to that of the optimal prior image model case.
39882874
A comparison of measurement and defect characteristics of new and legacy software systems
Abstract This paper compares the quality characteristics of a large legacy software system and a new one. Defect fixes applied to specific modules during testing are used as the direct quality metric. Indirect quality indicators used in this paper include software metrics of various product and process attributes, including design, size, change, and complexity. We analyze and compare the measurement results by examining their individual distributions, the correlations between defects and quality indicators, and tree-based models linking defects to quality indicators. In both these systems, most of the defects are found to be concentrated on relatively few high-defect modules, which points to the need for appropriate risk identification techniques so that defect removal effort can be focused on those high-defect modules for effective quality improvement. In addition, defects in the legacy system are more closely related to change and data complexity metrics; while defects in the new system are more closely related to various design metrics. These results demonstrate different measurement characteristics for these two types of software systems, and suggest that different quality analysis and improvement methods may be more appropriate and effective for different kinds of software systems.
36734863
Knowledge based fingerprint image enhancement
Fingerprint enhancement is a critical step in fingerprint identification. Most of the existing enhancement algorithms are based on local ridge direction. The main drawback of these methods lies in the fact that false estimate of local ridge direction will lead to poor enhancement. But the estimate of local ridge directions is unreliable in the areas corrupted by noise where enhancement is most needed. In this paper, we proposed a rule-based method to do fingerprint enhancement. We introduced human knowledge about fingerprints into the enhancement process in the form of rules and simulate what an expert will do to enhance a fingerprint image. In our method, the skeleton image is used to give ridge connection information for the enhancement of the binary image. Experiments show our algorithm is fast and has excellent performance.