2,133 research outputs found

    Data Analytics for the Cryptocurrencies Behavior

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    The cryptocurrencies are a new paradigm of transferring money be-tween users. Their anonymous and non-centralized is a subject of debate around the globe that paired with the massive spikes and declines in value that are in-herit to an unregistered asset. These facts make difficult for the common daily use of the cryptocurrencies as an exchange currency as instead they are being used as a new way to invest. What we propose in this article is a system for the better understanding of the cryptocurrencies economical behavior against the global market. For that we are using Data Analytics techniques to build a pre-dictor that uses as inputs said external financial variable. These forecasts would help determine if a coin is safe to trade with, if those forecasts can be precise by only using this external data. The results obtained indicates us that there is a certain degree of influence of the global market to the cryptocurrencies, but that is it not enough to correctly predict the fluctuations in price of the coins and that they care more about others factors and that they have their own bubbles, like the crypto collapse in late 2017.Instituto de Investigación en Informátic

    Data Analytics for the Cryptocurrencies Behavior

    Get PDF
    The cryptocurrencies are a new paradigm of transferring money be-tween users. Their anonymous and non-centralized is a subject of debate around the globe that paired with the massive spikes and declines in value that are in-herit to an unregistered asset. These facts make difficult for the common daily use of the cryptocurrencies as an exchange currency as instead they are being used as a new way to invest. What we propose in this article is a system for the better understanding of the cryptocurrencies economical behavior against the global market. For that we are using Data Analytics techniques to build a pre-dictor that uses as inputs said external financial variable. These forecasts would help determine if a coin is safe to trade with, if those forecasts can be precise by only using this external data. The results obtained indicates us that there is a certain degree of influence of the global market to the cryptocurrencies, but that is it not enough to correctly predict the fluctuations in price of the coins and that they care more about others factors and that they have their own bubbles, like the crypto collapse in late 2017.Instituto de Investigación en Informátic

    Master of sheets: A tale of compromised cloud documents

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    As of 2014, a fifth of EU citizens relied on cloud accounts to store their documents according to a Eurostat report. Although useful, there are downsides to the use of cloud documents. They often accumulate sensitive information over time, including financial information. This makes them attractive targets to cybercriminals. To understand what happens to compromised cloud documents that contain financial information, we set up 100 fake payroll sheets comprising 1000 fake records of fictional individuals. We populated the sheets with traditional bank payment information, cryptocurrency details, and payment URLs. To lure cybercriminals and other visitors into visiting the sheets, we leaked links pointing to the sheets via paste sites. We collected data from the sheets for a month, during which we observed 235 accesses across 98 sheets. Two sheets were not opened. We also recorded 38 modifications in 7 sheets. We present detailed measurements and analysis of accesses, modifications, edits, and devices that visited payment URLs in the sheets. Contrary to our expectations, bank payment URLs received many more clicks than cryptocurrency payment URLs despite the popularity of cryptocurrencies and emerging blockchain technologies. On the other hand, sheets that contained cryptocurrency details recorded more modifications than sheets that contained traditional banking information. In summary, we present a comprehensive picture of what happens to compromised cloud spreadsheets.Accepted manuscrip

    South American Expert Roundtable : increasing adaptive governance capacity for coping with unintended side effects of digital transformation

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    This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspectives. The two-day ERT discussed the main drivers and challenges as well as vulnerabilities or unseens and provided suggestions for: (i) the mechanisms underlying major unseens; (ii) understanding possible ways in which rebound effects of digital transformation may become the subject of overarching research in three main categories of impact: development factors, society, and individuals; and (iii) a set of potential action domains for transdisciplinary follow-up processes, including a case study in Brazil. A content analysis of the propositions and related mechanisms provided insights in the genesis of unseens by identifying 15 interrelated causal mechanisms related to critical issues/concerns. Additionally, a cluster analysis (CLA) was applied to structure the challenges and critical developments in South America. The discussion elaborated the genesis, dynamics, and impacts of (groups of) unseens such as the digital divide (that affects most countries that are not included in the development of digital business, management, production, etc. tools) or the challenge of restructuring small- and medium-sized enterprises (whose service is digitally substituted by digital devices). We identify specific issues and effects (for most South American countries) such as lack of governmental structure, challenging geographical structures (e.g., inclusion in high-performance transmission power), or the digital readiness of (wide parts) of society. One scientific contribution of the paper is related to the presented methodology that provides insights into the phenomena, the causal chains underlying “wanted/positive” and “unwanted/negative” effects, and the processes and mechanisms of societal changes caused by digitalization

    Leveraging Twitter data to understand the dynamics of social media interactions on cryptocurrencies

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    Rapid technological change in the last decades has led to the emergence of new platforms and fields such as cryptocurrencies and social media data. Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide. The social media platform has millions of users where tweets are continuously shared every second. Therefore, tweets are useful when a large amount of data is generated to conduct a social media analysis. In addition, Twitter is broadly utilized by investors and financial analysts to gather valuable information. Several studies have shown that the content posted on Twitter can predict the movement of cryptocurrency prices. However, limited research has been conducted on the dynamics of Twitter interactions on cryptocurrencies among users. By leveraging 1724328 tweets, this research aims to understand the dynamics of social media users’ interactions on cryptocurrencies. Essentially by shedding light on larger cryptocurrencies contrary to smaller. The findings reveal that Twitter users are more positive than negative about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The thesis contributes to demonstrating the effectiveness of the Social set analysis framework to analyze and visualize a massive amount of social media data and user-generated data created on social media platforms such as Twitter
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