Improving Diversity and Quality of Adversarial Examples in Adversarial Transformation Network

This paper proposes a method to mitigate two major issues of Adversarial Transformation Networks (ATN) including the low diversity and the low quality of adversarial examples. In order to deal with the first issue, this research proposes a stacked convolutional autoencoder based on pattern to generalize ATN. This proposed autoencoder could support different patterns such as all-feature pattern , border feature pattern , and class model map pattern . In order to deal with the second issue, this paper presents an algorithm to improve the quality of adversarial examples in terms of L 0 -norm and L 2 -norm. This algorithm employs an adversarial feature ranking heuristics such as JSMA and COI to prioritize adversarial features. To demonstrate the advantages of the proposed method, comprehensive experiments have been conducted on the MNIST dataset and the CIFAR-10 dataset. For the first issue, the proposed autoencoder can generate diverse adversarial examples with the average success rate above 99%. For the second issue, the proposed algorithm could not only improve the quality of adversarial examples significantly but also maintain the average success rate. In terms of L 0 -norm, the proposed algorithm could decrease from hundreds of adversarial features to one adversarial feature. In terms of L 2 -norm, the proposed algorithm could reduce the average distance considerably. These results show that the proposed method is capable of generating high-quality and diverse adversarial examples in practice.

Dual Class Model and Shareholder Agreements: An Analysis of Italian Companies

This paper analysed the changes in ownership concentration of the Italian financial market and the recourse to dual class model and shareholder agreements by Italian listed companies in the period 2009-2020. The analysis shows that the control market did not show signs in the period that would lead to presume an increase in the contestability of our companies. The attenuation in ownership concentration, highlighted by the reduction in the value of the Shapley-Shubik index, and the increase in the average market participation did not produce an increase in the contestability of Italian listed companies since the high concentration and limited contestability of control continue to characterize their ownership structures. Findings also show less recourse by the Italian companies to the instruments of separation between ownership and control in the considered period. The reduction in the number of companies that resort to the issue of shares without voting rights and the shareholders' agreements is also reflected in the lower incidence of the capitalization of these companies compared to the market capitalization.

Ontology-Based Transformation and Verification of UML/OCL Constraints

In Software Engineering (SE), the graphical models specify the system's architecture, connection, and characteristics. New SE methods such as MDA utilize graphical models as a nucleus of all development activities. This paper presents the transformation and verification of class diagram and Object Constraint Language (OCL) and transformation algorithm from Class model to ontology in the continuity of our research on UML and ontology integration. The class diagram is transformed into ontology, and constraints specified through OCL are transformed into SPARQL.

One-Class Model for Fabric Defect Detection

An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representations from the outputs of the Gabor filter bank. Lastly, we develop a nearest neighbor density estimator to locate potential defects and draw them on the fabric images. We demonstrate the effectiveness and robustness of the proposed model by testing it on various types of fabrics such as plain, patterned, and rotated fabrics. Our model also achieves a true positive rate (a.k.a recall) value of 0.895 with no false alarms on our dataset based upon the Standard Fabric Defect Glossary.

Automated object-oriented technology for software module development

It is shown that most technologies for creating information systems are based on an object-oriented approach and provide for the presentation of functional requirements in the form of use cases. However, there is no general agreement on the format of the use cases and the rules for describing script items. The work has improved the classification of items of use cases basing on the analysis of a great number of existing descriptions from different subject areas. New rules have been introduced and the existing rules have been clarified for describing use cases, which made it possible to further formalize and automate the process of describing use cases. It is also proposed to automate the process of forming a model of program classes by introducing additional information linking the class with use cases. Thus, the programming class model contains significantly more information for coding than the existing models in UML diagrams. A method for constructing a model of program classes has been developed. Methods for the automated description of use cases and the construction of a model of program classes are linked into a single process. The level of information richness of the class model also makes it possible to automate the debugging process associated with changing requirements. Since the decisions made cover most of the steps in the software module creation process, they collectively represent a new technology. The proposed model, methods and technology were implemented in the ModelEditor and UseCaseEditor software products. Approbation of the method for automating the description of use cases demonstrated a decrease in the number of errors compared to the traditional method of describing more than two times, and shortening the time  more than one and a half times. Testing the method for constructing a model of program classes showed its advantage over the existing technology: errors and time reduction  almost one and a half times. The proposed technology can be used in the development of any information systems.

What Makes Consumers Purchase Fresh Eggs in Supermarkets: The Effect of Unrealistic Choice Set Matters

Eggs are the crucial component of daily meals for almost everyone in Taiwan, while the multi-attributes of fresh egg products generate the challenges of marketing and promotions in supermarkets. This study analyzes the market segmentation and consumer willingness-to-pay (WTP) for fresh egg attributes (i.e., color, traceability, animal welfare, brand, and price). In particular, the effect of the unrealistic choice set is considered in this study. The data collection was distributed near markets, schools, and train stations across Taiwan from July to September in 2020. A total of 1115 valid responses were collected, and the Latent Class Model was used. Results show that fresh egg products in supermarkets reveal a strong preference for animal welfare label with the highest WTP, which is about 64.2 NT$ (≈US$ 2.29). Furthermore, traceability label, farm brand, and brown-color egg still exhibit positive WTP of about 33.4 NT$ (≈US$ 1.19), 32.6 NT$ (≈US$ 1.16), and 32.5 NT$ (≈US$ 1.16) in supermarkets, respectively. However, including the unrealistic choice set can potentially alter the final outcomes, and it provides a good example for researchers who may have the same situation. This research helps to know more about the complexity of attributes for fresh egg products in supermarkets, so marketers would be able to adopt the effective marketing strategies for fresh egg products in supermarkets.

The Effects Of Latent Classes Of Stress On Health Outcomes In Korean Middle-aged Adults: A Focus On Gender Differences

This study indicated the effect of the latent classes of stress on the physical and psychological health outcomes in Korea. Using the 2010 Korea Health Panel Study, 1,689 middle-aged adults (women: n=793, men: n=896) were analyzed to identify the latent classes of stress by gender using Latent Profile Analysis (LPA). After the determination of the number of latent classes, health outcomes (anxiety/depression and health status) were also regressed on the latent classes including covariates (age, marital status, and education level). The perceived stresses (financial diversity, disease of self or family, children’s education, and family conflicts) are classified as the 2-class model for women and the 3-class model for men. The classes of women are named ‘high stress and ‘low stress; however, the classes of men are named ‘family-related stress’, ‘disease stress’, and ‘low stress.’ The different combinations of stress are associated with anxiety/depression and health status respectively. This study will discuss the difference of latent stress classes by gender and extend the understanding of stress groups and health outcomes.