Software composition analysis (SCA) tools have been widely adopted to identify vulnerable libraries used in software applications. Such SCA tools depend on a vulnerability database to know affected libraries of each vulnerability. However, it is labor-intensive and error prone for a security team to manually maintain the vulnerability database. While several approaches adopt extreme multi-label learning to predict affected libraries for vulnerabilities, they are practically ineffective due to the limited library labels and the unawareness of ecosystems.

Vulnerability reports play a crucial role in mitigating open-source software risks. Typically, the vulnerability report contains affected versions of a software. However, despite the validation by security expert who discovers and vendors who review, the affected versions are not always accurate. Especially, the complexity of maintaining its accuracy increases significantly when dealing with multiple versions and their differences. Several advances have been made to identify affected versions. However, they still face limitations. First, some existing approaches identify affected versions based on repository-hosting platforms (i.e., GitHub), but these versions are not always consistent with those in package registries (i.e., Maven). Second, existing approaches fail to distinguish the importance of different vulnerable methods and patched statements in face of vulnerabilities with multiple methods and change hunks.

Abstract Notion of optical flow literally refers to the displacements of intensity patterns. In that sense, extracting interested information from 2D scene is analogy to modulation/demodulation in random signal processing. To address the limitations presented in computer vision based on static image, we propose a novel metal component defect detection method, specified as the instance of turbine blade surface detection, using optical flow estimation.To start the specified pattern recognition in 2D presentation, we modulate the brightness constancy assumption equation as illumination varying model, by sampling the second image with function whose frequency was chosen according to the Nyquist sampling theorem, and a sinusoidal factor was introduced as an additive factor. This tunable channel based on 2D image transfers intensity features into optical modes. Then, we implement optical flow estimation on two sequential images. Experimental results reveal grayscale space shows completness in representing the optical modes of turbine blade with various kinds of surface characteristics. By modifying the index of information content, we propose quantitative index to evaluate the performance of our method. Evaluation reveals optical flow algorithm is qualified to examine defects on highly reflective turbine blade, and our method extends the application of optical flow.

Failure mode and effects analysis (FMEA) is a widely used tool of risk assessment to identify and eliminate failures of products and systems. However, the conventional FMEA has some defects, such as the same importance of risk factors, utilizing crisp numbers to evaluate failures without considering vagueness. Although fuzzy methods are used to improve the conventional FMEA, the fuzzy FMEA requires much priori information (e.g., fuzzy membership function), which cannot flexibly reflect the changes of a decision maker's preference. In addition, most of the previous methods suppose that decision makers are totally rational without considering their psychological factors. Actually, FMEA members' judgments on the failure modes are often influenced by their bounded rationality. To solve those problems, an integrated FMEA model is proposed in this paper, which integrates the strength of variable precision rough set theory in handling vagueness and the merit of TODIM (an acronym in Portuguese of Interactive and Multicriteria Decision Making) approach in manipulating bounded rationality of decision makers. Finally, the proposed method is validated with a case study of a steam valve system to demonstrate its effectiveness and efficiency.

Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members' weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.

In this paper, a novel Error-compensated MArginal LINEarization (EMALINE) fuzzy modeling method is proposed. This method models a group of data information to a piecewise linear fuzzy system with high accuracy within a given error bound. It is proved that the fuzzy system generalized by the EMALINE method possesses universal approximation capability for a class of nonlinear systems. In addition, the theoretical approximation error bounds of the fuzzy system generalized by the EMALINE method are established and proved. Theoretical and practical results indicate that the EMALINE has better approximation accuracy than those of previous approaches. Numerical examples are shown to illustrate the validity of the proposed approach.