Trust and Credibility in Online Social Networks

Increasing portions of people's social and communicative activities now take place in the digital world. The growth and popularity of online social networks (OSNs) have tremendously facilitated online interaction and information exchange. As OSNs enable people to communicate more effectively, a large volume of user-generated content (UGC) is produced daily. As UGC contains valuable information, more people now turn to OSNs for news, opinions, and social networking. Besides users, companies and business owners also benefit from UGC as they utilize OSNs as the platforms for communicating with customers and marketing activities. Hence, UGC has a powerful impact on users' opinions and decisions. However, the openness of OSNs also brings concerns about trust and credibility online. The freedom and ease of publishing information online could lead to UGC with problematic quality. It has been observed that professional spammers are hired to insert deceptive content and promote harmful information in OSNs. It is known as the spamming problem, which jeopardizes the ecosystems of OSNs. The severity of the spamming problem has attracted the attention of researchers and many detection approaches have been proposed. However, most existing approaches are based on behavioral patterns. As spammers evolve to evade being detected by faking normal behaviors, these detection approaches may fail. In this dissertation, we present our work of detecting spammers by extracting behavioral patterns that are difficult to be manipulated in OSNs. We focus on two scenarios, review spamming and social bots. We first identify that the rating deviations and opinion deviations are invariant patterns in review spamming activities since the goal of review spamming is to insert deceptive reviews. We utilize the two kinds of deviations as clues for trust propagation and propose our detection mechanisms. For social bots detection, we identify the behavioral patterns among users in a neighborhood is difficult to be manipulated for a social bot and propose a neighborhood-based detection scheme. Our work shows that the trustworthiness of a user can be reflected in social relations and opinions expressed in the review content. Besides, our proposed features extracted from the neighborhood are useful for social bot detection

Fuzzy Natural Logic in IFSA-EUSFLAT 2021

The present book contains five papers accepted and published in the Special Issue, “Fuzzy Natural Logic in IFSA-EUSFLAT 2021”, of the journal Mathematics (MDPI). These papers are extended versions of the contributions presented in the conference “The 19th World Congress of the International Fuzzy Systems Association and the 12th Conference of the European Society for Fuzzy Logic and Technology jointly with the AGOP, IJCRS, and FQAS conferences”, which took place in Bratislava (Slovakia) from September 19 to September 24, 2021. Fuzzy Natural Logic (FNL) is a system of mathematical fuzzy logic theories that enables us to model natural language terms and rules while accounting for their inherent vagueness and allows us to reason and argue using the tools developed in them. FNL includes, among others, the theory of evaluative linguistic expressions (e.g., small, very large, etc.), the theory of fuzzy and intermediate quantifiers (e.g., most, few, many, etc.), and the theory of fuzzy/linguistic IF–THEN rules and logical inference. The papers in this Special Issue use the various aspects and concepts of FNL mentioned above and apply them to a wide range of problems both theoretically and practically oriented. This book will be of interest for researchers working in the areas of fuzzy logic, applied linguistics, generalized quantifiers, and their applications

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

Supporting Complex Queries in P2P Networks

Ph.DDOCTOR OF PHILOSOPH

Essays on the economics of green innovation and climate policy

This thesis consists of four chapters spanning climate policy and green innovation. The first chapter identifies the impacts of China’s regional emission trading scheme pilots on firm carbon emissions and other economic outcomes using a unique dataset of Chinese firm tax records. The results demonstrate evidence that China’s ETS reduces carbon emissions despite low carbon prices and infrequent trading. This chapter also identifies the channels through which firms respond to ETS by adjusting energy consumption and sources, employment, capital, and productivity. The second chapter assesses the impacts of ETS on low-carbon innovation of unregulated subsidiary firms affiliated with regulated firms. The findings demonstrate that ETS induces low-carbon innovation of unregulated subsidiaries and suggest policy spillovers of ETS through corporate ownership networks. Such policy spillovers are contingent on technological proximity between parent firms and their subsidiaries, top managers with R&D experience, and parent firms’ financial constraints. The third chapter investigates the relationship between firms’ green revenues and clean innovation. Using a global firm dataset disaggregating commercial activities based on a new green taxonomy, this chapter finds that firms’ green revenues are enhanced by their own clean innovation and clean innovation of other firms close in technological and product market spaces. Such results suggest both private and social economic benefits of clean innovation. The last chapter explores the role of foreign direct investment in green knowledge spillovers to Chinese domestic firms. Through text-mining business description and tracking patents of foreign-invested firms in China, this chapter develops new definitions of green FDI and identifies the impacts of knowledge stocks resulting from green FDI firms on green innovation of domestic firms. The findings show that knowledge stocks of green FDI firms in downstream industries drive domestic firms’ green patents and suggest knowledge spillovers from downstream green FDI

Прикладна фізика : українсько-російсько-англійський тлумачний словник. У 4 т. Т. 2. З – Н

Словник охоплює близько 30 тис. термінів з прикладної фізики і дотичних до неї галузей знань та їх тлумачення трьома мовами (українською, російською та англійською). Багато термінів і визначень, наведених у словнику, якими послуговуються у відповідній галузі знань, досі не входили до жодного зі спеціалізованих словників. Словник призначений для викладачів, науковців, інженерів, аспірантів, студентів вищих навчальних закладів, перекладачів з природничих і технічних дисциплін