154 research outputs found
Voting: What Has Changed, What Hasn't, & Why: Research Bibliography
Since the origins of the Caltech/MIT Voting Technology Project in the fall of 2000, there has been an explosion of research and analysis on election administration and voting technology. As we worked throughout 2012 on our most recent study, Voting: What Has Changed, What Hasn’t, & What Needs Improvement, we found many more research studies. In this research bibliography, we present the research literature that we have found; future revisions of this research bibliography will update this list.Carnegie Corporation of New Yor
Applying Machine Learning to enhance payments systems security
Ph. D. Thesis.During the last two decades, the economic losses because fraudulent card payment transactions have tripled. The significant percentage of losses is because of fraud on e-commerce
transactions. Nowadays, there is a clear trend to use more and more mobile devices to make
electronic purchases, and it is estimated that this trend will continue in the coming years.
In the card payment scheme, big financial institutions process millions of transactions every
day; thus, they can model the processed transactions to predict fraud. On the other hand,
merchants process a much lower number of transactions, but they have access to valuable
information that they can collect from the devices that users utilise during the transaction.
In this thesis, we propose a series of measures to enhance the security of these two scenarios
based on past transactional data and information collected from the users’ device. Most of
the approaches proposed so far to model processed transactions were based on supervised
Machine Learning techniques. We propose a fraud detection system for card payments based
on an unsupervised machine learning technique; thus, the system may be able to recognise
new patterns of fraud.
On the other hand, we are looking far ahead, and because of the increment of use of mobile
devices to conduct payments, we propose a series of measures to enhance the security of the
mobile payment system. We have proposed a user identification and verification systems
for smartphones. We base the identification and verification systems on motion data, so the
systems will not require any explicit action from users
Understanding, Analyzing and Predicting Online User Behavior
abstract: Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources.Dissertation/ThesisDoctoral Dissertation Business Administration 201
Blockchain for digital government
In less than ten years from its advent in 2008, the concept of distributed ledgers has entered into mainstream research and policy agendas. Enthusiastic reception, fuelled by the success of Bitcoin and the explosion of potential use cases created high, if not hyped, expectations with respect to the transformative role of blockchain for the industry and the public sector. Growing experimentation with distributed ledgers and the emergence of the first operational implementations provide an opportunity to go beyond hype and speculation based on theoretical use cases.
This report looks at the ongoing exploration of blockchain technology by governments. The analysis of a group of pioneering developments of public services shows that blockchain technology can reduce bureaucracy, increase the efficiency of administrative processes and increase the level of trust in public record keeping. Based on the state-of-art developments, blockchain has not yet demonstrated to be either transformative or even disruptive innovation for governments as it is sometimes portrayed. Ongoing projects bring incremental rather than fundamental changes to the operational capacities of governments. Nevertheless some of them propose clear value for citizens.
Technological and ecosystem maturity of distributed ledgers have to increase in order to unlock the transformative power of blockchain. Policy agenda should focus on non-technological barriers, such as incompatibility between blockchain-based solutions and existing legal and organizational frameworks. This principal policy goal cannot be achieved by adapting technology to legacy systems. It requires using the transformative power of blockchain to be used to create new processes, organizations, structures and standards. Hence, policy support should stimulate more experimentation with both the technology and new administrative processes that can be re-engineered for blockchain.JRC.B.6-Digital Econom
Blockchain Neutrality
Blockchain technology is transforming how markets work.Blockchains eliminate the need for trusted gatekeepers likebanks to execute, verify, and record transactions. In thefinancial markets, their disruptive potential threatens bothWall Street banks and Silicon Valley venture capitalists. Howblockchain technology is regulated will determine whether itencourages or inhibits competition. Some blockchainapplications present serious fraud and systemic risks,complicating regulation. This Article explores the antitrust andcompetition policy challenges blockchain presents and proposesa regulatory strategy, modeled on Internet regulation and netneutrality principles, to unlock blockchain’s competitivepotential. It contends that financial regulators should promoteblockchain competition—and the resulting marketdecentralization—except in cases where specific applicationsare shown to harm consumers or threaten systemic safety.Regulators also should ensure open access and non-discrimination on dominant blockchain networks. Thisapproach will not only serve traditional antitrust goals oflowering prices and promoting innovation, but it also mightachieve broader economic and social reform by reducing thepower and influence of the biggest financial institutions
Activity Recognition with Evolving Data Streams: A Review
Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging
sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an
emerging field in the areas of pervasive and ubiquitous computing. A typical activity recognition technique
processes data streams that evolve from sensing platforms such as mobile sensors, on body sensors, and/or
ambient sensors. This paper surveys the two overlapped areas of research of activity recognition and data
stream mining. The perspective of this paper is to review the adaptation capabilities of activity recognition
techniques in streaming environment. Categories of techniques are identified based on different features
in both data streams and activity recognition. The pros and cons of the algorithms in each category are
analysed and the possible directions of future research are indicated
Analyzing Granger causality in climate data with time series classification methods
Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested
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