ARTICONF decentralized social media platform for democratic crowd journalism

Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Mutual effects of land distribution and economic development : evidence from Asia, Africa, and Latin America

Land plays an important role in the economies of developing countries, and many theories connecting land inequality with different dimensions of economic development already exist. Even though efficacious land distribution allows societies to transition from poverty to a human capital-based developed economy, ongoing issues related to property rights, inequality, and the political economy of land distribution are unavoidable. The general objective of this paper is to explore the nexus between land distribution and economic development. The specific objectives are to: (i) identify which land distribution programs/activities contribute to economic development; (ii) investigate the role of stakeholders in land distribution programs that affect the growth of productivity; and (iii) assess the deficiencies of current land distribution policies in Asia, Africa, and Latin America to explore how economic development theories contribute to decreasing income inequality. This paper provides an overview of land distribution history and the main economic development theories. It also highlights the links between land distribution and the main elements of economic development. Finally, it provides a comparative review of the most recent empirical works regarding the characteristics, limitations, and potential (mutual) effects of land distribution and economic development settings on developing countries worldwide

AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective

In recent years, blockchain technology has introduced decentralized finance (DeFi) as an alternative to traditional financial systems. DeFi aims to create a transparent and efficient financial ecosystem using smart contracts and emerging decentralized applications. However, the growing popularity of DeFi has made it a target for fraudulent activities, resulting in losses of billions of dollars due to various types of frauds. To address these issues, researchers have explored the potential of artificial intelligence (AI) approaches to detect such fraudulent activities. Yet, there is a lack of a systematic survey to organize and summarize those existing works and to identify the future research opportunities. In this survey, we provide a systematic taxonomy of various frauds in the DeFi ecosystem, categorized by the different stages of a DeFi project's life cycle: project development, introduction, growth, maturity, and decline. This taxonomy is based on our finding: many frauds have strong correlations in the stage of the DeFi project. According to the taxonomy, we review existing AI-powered detection methods, including statistical modeling, natural language processing and other machine learning techniques, etc. We find that fraud detection in different stages employs distinct types of methods and observe the commendable performance of tree-based and graph-related models in tackling fraud detection tasks. By analyzing the challenges and trends, we present the findings to provide proactive suggestion and guide future research in DeFi fraud detection. We believe that this survey is able to support researchers, practitioners, and regulators in establishing a secure and trustworthy DeFi ecosystem.Comment: 38 pages, update reference

Who is Gambling? Finding Cryptocurrency Gamblers Using Multi-modal Retrieval Methods

With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling have transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a large-scale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights

A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings

Buildings are one of the main consumers of energy in cities, which is why a lot of research has been generated around this problem. Especially, the buildings energy management systems must improve in the next years. Artificial intelligence techniques are playing and will play a fundamental role in these improvements. This work presents a systematic review of the literature on researches that have been done in recent years to improve energy management systems for smart building using artificial intelligence techniques. An originality of the work is that they are grouped according to the concept of "Autonomous Cycles of Data Analysis Tasks", which defines that an autonomous management system requires specialized tasks, such as monitoring, analysis, and decision-making tasks for reaching objectives in the environment, like improve the energy efficiency. This organization of the work allows us to establish not only the positioning of the researches, but also, the visualization of the current challenges and opportunities in each domain. We have identified that many types of researches are in the domain of decision-making (a large majority on optimization and control tasks), and defined potential projects related to the development of autonomous cycles of data analysis tasks, feature engineering, or multi-agent systems, among others.European Commissio

Future Smart Grid Systems

This book focuses on the analysis, design and implementation of future smart grid systems. This book contains eleven chapters, which were originally published after rigorous peer-review as a Special Issue in the International Journal of Energies (Basel). The chapters cover a range of work from authors across the globe and present both the state-of-the-art and emerging paradigms across a range of topics including sustainability planning, regulations and policy, estimation and situational awareness, energy forecasting, control and optimization and decentralisation. This book will be of interest to researchers, practitioners and scholars working in areas related to future smart grid systems