We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

With the purpose of identifying cyber threats and possible incidents, intrusion detection systems (IDSs) are widely deployed in various computer networks. In order to enhance the detection capability of a single IDS, collaborative intrusion detection networks (or collaborative IDSs) have been developed, which allow IDS nodes to exchange data with each other. However, data and trust management still remain two challenges for current detection architectures, which may degrade the effectiveness of such detection systems. In recent years, blockchain technology has shown its adaptability in many fields, such as supply chain management, international payment, interbanking, and so on. As blockchain can protect the integrity of data storage and ensure process transparency, it has a potential to be applied to intrusion detection domain. Motivated by this, this paper provides a review regarding the intersection of IDSs and blockchains. In particular, we introduce the background of intrusion detection and blockchain, discuss the applicability of blockchain to intrusion detection, and identify open challenges in this direction.

/pdf/when-intrusion-detection-meets-blockchain-technology-a-2n64zps0yc.pdf

When Intrusion Detection Meets Blockchain Technology: A Review

This paper provides an assessment of the early contributions of machine learning to economics, as well as predictions about its future contributions. It begins by briefly overviewing some themes from the literature on machine learning, and then draws some contrasts with traditional approaches to estimating the impact of counterfactual policies in economics. Next, we review some of the initial “off-the-shelf” applications of machine learning to economics, including applications in analyzing text and images. We then describe new types of questions that have been posed surrounding the application of machine learning to policy problems, including “prediction policy problems,” as well as considerations of fairness and manipulability. We present some highlights from the emerging econometric literature combining machine learning and causal inference. Finally, we overview a set of broader predictions about the future impact of machine learning on economics, including its impacts on the nature of collaboration, funding, research tools, and research questions.

The Impact of Machine Learning on Economics

https://cronfa.swan.ac.uk/Record/cronfa49214/Download/0049214-17032019223053.pdf

Measuring social media influencer index- insights from facebook, Twitter and Instagram

InterPlaNetary internet: state-of-the-art and research challenges

The problem of anomaly detection has been studied for a long time. In short, anomalies are abnormal or unlikely things. In nancial networks, thieves and illegal activities are often anomalous in nature. Members of a network want to detect anomalies as soon as possible to prevent them from harming the network’s community and integrity. Many Machine Learning techniques have been proposed to deal with this problem; some results appear to be quite promising but there is no obvious superior method. In this paper, we consider anomaly detection particular to the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use three unsupervised learning methods including k-means clustering, Mahalanobis distance, and Unsupervised Support Vector Machine (SVM) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes.

Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods

There is a large literature on semiparametric estimation of average treatment effects under unconfounded treatment assignment in settings with a fixed number of covariates. More recently attention has focused on settings with a large number of covariates. In this paper we extend lessons from the earlier literature to this new setting. We propose that in addition to reporting point estimates and standard errors, researchers report results from a number of supplementary analyses to assist in assessing the credibility of their estimates.

Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

The problem of anomaly detection has been studied for a long time, and many Network Analysis techniques have been proposed as solutions. Although some results appear to be quite promising, no method is clearly to be superior to the rest. In this paper, we particularly consider anomaly detection in the Bitcoin transaction network. Our goal is to detect which users and transactions are the most suspicious; in this case, anomalous behavior is a proxy for suspicious behavior. To this end, we use the laws of power degree and densification and local outlier factor (LOF) method (which is proceeded by k-means clustering method) on two graphs generated by the Bitcoin transaction network: one graph has users as nodes, and the other has transactions as nodes. 
We remark that the methods used here can be applied to any type of setting with an inherent graph structure, including, but not limited to, computer networks, telecommunications networks, auction networks, security networks, social networks, Web networks, or any financial networks. We use the Bitcoin transaction network in this paper due to the availability, size, and attractiveness of the data set.

Anomaly Detection in the Bitcoin System - A Network Perspective

Recently, JPL has begun an assessment of the long-term capability of the antennas for the Deep Space Network (DSN). Various alternative plans for upgrading or replacing the present 70-meter antennas have been considered. Several options have been studied which include modifying the present antennas for extended life and reliability, new 70-meter single aperture antennas with offset or symmetric feeds, 100-meter spherical antennas, an array of a few smaller 34-meter antennas, a much larger array (hundreds) of much smaller (5-10 meter) reflector antennas, and finally active planar phased arrays with millions of elements. In this paper we briefly discuss various options but focus on the feasibility of the phased arrays as a viable option for this application. Of particular concern and consideration will be the cost, reliability, and performance compared to the present 70-meter antenna system. Many alternative phased arrays including planar horizontal arrays, hybrid mechanically/electronically steered arrays, phased array of mechanically steered reflectors, multi-faceted planar arrays, phased array-fed lens antennas, and planar reflect-arrays are compared and their viability is assessed.

Thai Pham

Papers

Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methods

Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges

Anomaly Detection in the Bitcoin System - A Network Perspective

Array antennas for JPL/NASA Deep Space Network