Robust Trust Establishment in Decentralized Networks

The advancement in networking technologies creates new opportunities for computer users to communicate and interact with one another. Very often, these interacting parties are strangers. A relevant concern for a user is whether to trust the other party in an interaction, especially if there are risks associated with the interaction. Reputation systems are proposed as a method to establish trust among strangers. In a reputation system, a user who exhibits good behavior continuously can build a good reputation. On the other hand, a user who exhibits malicious behavior will have a poor reputation. Trust can then be established based on the reputation ratings of a user. While many research efforts have demonstrated the effectiveness of reputation systems in various situations, the security of reputation systems is not well understood within the research community. In the context of trust establishment, the goal of an adversary is to gain trust. An adversary can appear to be trustworthy within a reputation system if the adversary has a good reputation. Unfortunately, there are plenty of methods that an adversary can use to achieve a good reputation. To make things worse, there may be ways for an attacker to gain an advantage that may not be known yet. As a result, understanding an adversary is a challenging problem. The difficulty of this problem can be witnessed by how researchers attempt to prove the security of their reputation systems. Most prove security by using simulations to demonstrate that their solutions are resilient to specific attacks. Unfortunately, they do not justify their choices of the attack scenarios, and more importantly, they do not demonstrate that their choices are sufficient to claim that their solutions are secure. In this dissertation, I focus on addressing the security of reputation systems in a decentralized Peer-to-Peer (P2P) network. To understand the problem, I define an abstract model for trust establishment. The model consists of several layers. Each layer corresponds to a component of trust establishment. This model serves as a common point of reference for defining security. The model can also be used as a framework for designing and implementing trust establishment methods. The modular design of the model can also allow existing methods to inter-operate. To address the security issues, I first provide the definition of security for trust establishment. Security is defined as a measure of robustness. Using this definition, I provide analytical techniques for examining the robustness of trust establishment methods. In particular, I show that in general, most reputation systems are not robust. The analytical results lead to a better understanding of the capabilities of the adversaries. Based on this understanding, I design a solution that improves the robustness of reputation systems by using accountability. The purpose of accountability is to encourage peers to behave responsibly as well as to provide disincentive for malicious behavior. The effectiveness of the solution is validated by using simulations. While simulations are commonly used by other research efforts to validate their trust establishment methods, their choices of simulation scenarios seem to be chosen in an ad hoc manner. In fact, many of these works do not justify their choices of simulation scenarios, and neither do they show that their choices are adequate. In this dissertation, the simulation scenarios are chosen based on the capabilities of the adversaries. The simulation results show that under certain conditions, accountability can improve the robustness of reputation systems

TransNFCM: Translation-Based Neural Fashion Compatibility Modeling

Identifying mix-and-match relationships between fashion items is an urgent task in a fashion e-commerce recommender system. It will significantly enhance user experience and satisfaction. However, due to the challenges of inferring the rich yet complicated set of compatibility patterns in a large e-commerce corpus of fashion items, this task is still underexplored. Inspired by the recent advances in multi-relational knowledge representation learning and deep neural networks, this paper proposes a novel Translation-based Neural Fashion Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion item embeddings and category-specific complementary relations in a unified space via an end-to-end learning manner. TransNFCM places items in a unified embedding space where a category-specific relation (category-comp-category) is modeled as a vector translation operating on the embeddings of compatible items from the corresponding categories. By this way, we not only capture the specific notion of compatibility conditioned on a specific pair of complementary categories, but also preserve the global notion of compatibility. We also design a deep fashion item encoder which exploits the complementary characteristic of visual and textual features to represent the fashion products. To the best of our knowledge, this is the first work that uses category-specific complementary relations to model the category-aware compatibility between items in a translation-based embedding space. Extensive experiments demonstrate the effectiveness of TransNFCM over the state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc

Navigating K-12 Education Leadership Not Designed for Us: Perspectives from a Hmong Woman

This impact essay examines the intersection of race, ethnicity, and gender as a first-generation Hmong-American woman in a senior-level K-12 educational leadership role. Dr. Yang Keo shares her story of resistance and resilience as she navigates different educational and workforce systems as the daughter of Hmong refugees

Mechanisms of Cancer Induction by Tobacco-Specific NNK and NNN

Tobacco use is a major public health problem worldwide. Tobacco-related cancers cause millions of deaths annually. Although several tobacco agents play a role in the development of tumors, the potent effects of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N'-nitrosonornicotine (NNN) are unique. Metabolically activated NNK and NNN induce deleterious mutations in oncogenes and tumor suppression genes by forming DNA adducts, which could be considered as tumor initiation. Meanwhile, the binding of NNK and NNN to the nicotinic acetylcholine receptor promotes tumor growth by enhancing and deregulating cell proliferation, survival, migration, and invasion, thereby creating a microenvironment for tumor growth. These two unique aspects of NNK and NNN synergistically induce cancers in tobacco-exposed individuals. This review will discuss various types of tobacco products and tobacco-related cancers, as well as the molecular mechanisms by which nitrosamines, such as NNK and NNN, induce cancer

INFORMATION CONTENT OF FINANCIAL REPORTING AND INFORMATION VALUE OF FINANCIAL NEWS – AN OPINION ANALYTICS APPROACH

This study investigates the impact of the quality of disclosures of financial reports of the listed firms in Taiwan under her first full adoption of International Financial Reporting Standards (IFRS) in 2013. We select the semi-annual reports of firms in the three main industry sectors of high technology, financial service, and biotechnology representing 80% of the capital market in the first year of mandatory adoption. The dictionary of financial reporting is developed according to opinion mining and sentiment analysis. In contrast to prior studies, we explore the gap analytics between financial reporting and financial news to explore whether the disclosure quality is related with the adoption of IFRS. Results are promising and show that the disclosures have relationship with the IFRS first adoption