What is Twitter

https://hal.archives-ouvertes.fr/hal-01436218/document

Foliar heavy metal uptake, toxicity and detoxification in plants: A comparison of foliar and root metal uptake.

Blockchain is the underlying technology of a number of digital cryptocurrencies. Blockchain is a chain of blocks that store information with digital signatures in a decentralized and distributed network. The features of blockchain, including decentralization, immutability, transparency and auditability, make transactions more secure and tamper proof. Apart from cryptocurrency, blockchain technology can be used in financial and social services, risk management, healthcare facilities, and so on. A number of research studies focus on the opportunity that blockchain provides in various application domains. This paper presents a comparative study of the tradeoffs of blockchain and also explains the taxonomy and architecture of blockchain, provides a comparison among different consensus mechanisms and discusses challenges, including scalability, privacy, interoperability, energy consumption and regulatory issues. In addition, this paper also notes the future scope of blockchain technology.

/pdf/a-survey-of-blockchain-from-the-perspectives-of-applications-30q3yr5dbc.pdf

A Survey of Blockchain From the Perspectives of Applications, Challenges, and Opportunities

In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

Link prediction in social networks: the state-of-the-art

An attack graph is a succinct representation of all paths through a system that end in a state where an intruder has successfully achieved his goal. Today Red Teams determine the vulnerability of networked systems by drawing gigantic attack graphs by hand. Constructing attack graphs by hand is tedious, error-prone, and impractical for large systems. By viewing an attack as a violation of a safety property, we can use off-the-shelf model checking technology to produce attack graphs automatically: a successful path from the intruder's viewpoint is a counterexample produced by the model checker In this paper we present an algorithm for generating attack graphs using model checking as a subroutine. Security analysts use attack graphs for detection, defense and forensics. In this paper we present a minimization analysis technique that allows analysts to decide which minimal set of security measures would guarantee the safety of the system. We provide a formal characterization of this problem: we prove that it is polynomially equivalent to the minimum hitting set problem and we present a greedy algorithm with provable bounds. We also present a reliability analysis technique that allows analysts to perform a simple cost-benefit trade-off depending on the likelihoods of attacks. By interpreting attack graphs as Markov Decision Processes we can use the value iteration algorithm to compute the probabilities of intruder success for each attack the graph.

Two Formal Analyses of Attack Graphs

Despite the availability of the sensor and smart-phone devices to fulfill the ubiquitous computing vision, the-state-of-the-art falls short of this vision. We argue that the reason for this gap is the lack of an infrastructure to task/utilize these devices for collaboration. We propose that microblogging services like Twitter can provide an "open" publish-subscribe infrastructure for sensors and smartphones, and pave the way for ubiquitous crowd-sourced sensing and collaboration applications. We design and implement a crowd-sourced sensing and collaboration system over Twitter, and showcase our system in the context of two applications: a crowd-sourced weather radar, and a participatory noise-mapping application. Our results from real-world Twitter experiments give insights into the feasibility of this approach and outline the research challenges in sensor/smartphone integration to Twitter.

/pdf/crowd-sourced-sensing-and-collaboration-using-twitter-4qyttuwsdl.pdf

Crowd-sourced sensing and collaboration using twitter

A key problem in online social networks is the identification of user characteristics and the analysis of how these are reflected in the graph structure evolution. The basis to tackle this issue is user similarity measures. In this paper, we propose a novel user similarity measure for online social networks, which combines both network and profile similarity. Since user profile data could be missing proposed measure is complemented by a technique to infer missing items from profile of the user’s contacts. The second main contribution of this paper is an extensive performance evaluation of the proposed measures with respect to some of the most relevant measures already proposed in the literature. The performance evaluation study has been conducted on a variety of data sets (i.e., Facebook, Youtube, Epinions and DBLP data sets) to see how different scenarios and graph characteristics affect the measures’ performance.

User similarities on social networks

Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed a flood of attention. In contrast to fiat currencies used worldwide, the Bitcoin distributed ledger is publicly available by design. This facilitates observing all financial interactions on the network, and analyzing how the network evolves in time. We introduce a novel concept of chainlets, or Bitcoin subgraphs, which allows us to evaluate the local topological structure of the Bitcoin graph over time. Furthermore, we assess the role of chainlets on Bitcoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on Bitcoin price and investment risk.

Forecasting Bitcoin Price with Graph Chainlets

While polls are traditionally used for observing public opinion, they provide a point snapshot, not a continuum. We consider the problem of identifying breakpoints in public opinion, and propose using micro-blogging sites to capture trends in public opinion. We develop methods to detect changes in public opinion, and find events that cause these changes.Our experiments show that the proposed methods are able to determine changes in public opinion and extract the major news about the events effectively. We also deploy an application where users can view the important news stories for a continuing event and find the related articles on web.

/pdf/identifying-breakpoints-in-public-opinion-4347y9x0pn.pdf

Identifying breakpoints in public opinion

An interesting problem in online social networks is the identification of user characteristics and the analysis of how these are reflected in the graph structure evolution. The basis of these studies are user similarity measures. In this paper, we approach user similarity from two angles. First, we propose a network similarity measure that considers only the graph structure and that, differently from existing techniques, takes into consideration also how two users are indirectly connected. Secondly, we propose a similarity measure based on user profile information, such to find semantic similarities between users. Moreover, since user profile data could be missing, we present a technique to infer them from profile items of the user contacts. We evaluate our similarity measures on Facebook and DBLP data.

Cuneyt Gurcan Akcora

Papers

Crowd-sourced sensing and collaboration using twitter

User similarities on social networks

Forecasting Bitcoin Price with Graph Chainlets

Identifying breakpoints in public opinion

Network and profile based measures for user similarities on social networks