TimeWeaver: Opportunistic One Way Delay Measurement via NTP

One-way delay (OWD) between end hosts has important implications for Internet applications, protocols, and measurement-based analyses. We describe a new approach for identifying OWDs via passive measurement of Network Time Protocol (NTP) traffic. NTP traffic offers the opportunity to measure OWDs accurately and continuously from hosts throughout the Internet. Based on detailed examina- tion of NTP implementations and in-situ behavior, we develop an analysis tool that we call TimeWeaver, which enables assessment of precision and accuracy of OWD measurements from NTP. We apply TimeWeaver to a ~1TB corpus of NTP traffic collected from 19 servers located in the US and report on the characteristics of hosts and their associated OWDs, which we classify in a precision/accuracy hierarchy. To demonstrate the utility of these measurements, we apply iterative hard-threshold singular value decomposition to estimate OWDs between arbitrary hosts from the high- est tier in the hierarchy. We show that this approach results in highly accurate estimates of OWDs, with average error rates on the order of less than 2%. Finally, we outline a number of applications---in particular, IP geolocation, network operations and management---for hosts in lower tiers of the precision hierarchy that can benefit from TimeWeaver, offering directions for future work.Comment: 14 page

Privacy-Aware and Secure Decentralized Air Quality Monitoring

Indoor Air Quality monitoring is a major asset to improving quality of life and building management. Today, the evolution of embedded technologies allows the implementation of such monitoring on the edge of the network. However, several concerns need to be addressed related to data security and privacy, routing and sink placement optimization, protection from external monitoring, and distributed data mining. In this paper, we describe an integrated framework that features distributed storage, blockchain-based Role-based Access Control, onion routing, routing and sink placement optimization, and distributed data mining to answer these concerns. We describe the organization of our contribution and show its relevance with simulations and experiments over a set of use cases

On Locus of Control in Empirical Microeconomics

Investigating the psychological black box behind individual economic decision making is, without a doubt, one of the most prevalent concerns in recent empirical microeconomics. This is based on the urge of modern behavioral economics to provide the stochastic idiosyncratic shocks in standard economic models with meaningful content. Especially the growing availability of large microdata sources such as in longitudinal household panel studies has tremendously supported this scientific movement. This data regularly includes important self-reported information on inherent attributes such as personality traits which have a high potential of explaining large parts of the deviations which have previously been labeled as stochastic shocks and idiosyncratic errors. This is the point at which also this doctoral thesis lines up. The present thesis contains four studies that investigate the relationship of inherent personality traits with individual behavior and economic outcomes. Concretely, the studies address the domains female labor force participation, labor market mobility, drinking behavior and unemployment. The unifying element of all four studies is the focus on one specific personality trait within this context: the individual perception of control or locus of control (LOC). LOC is characterized as a ``generalized attitude, belief, or expectancy regarding the nature of the causal relationship between one's own behavior and its consequences'' (Rotter 1966, p.2) and describes whether individuals believe in the causal effects of their own efforts and abilities on their lives' outcomes. Chapter 2 initiates the discussion by analyzing the implications of LOC for female labor force participation. In the empirical analysis, internal women are found to have a significantly higher probability of being available for market production, which also translates into higher employment probabilities at the extensive margin. These effects are additionally found to be highly heterogenous with respect to underlying monetary incentives for participation and home production as well as prevalent social norms for working. In a quite similar manner, Chapter 3 discusses the role of LOC for regional labor market mobility within Germany. The empirical analysis identifies a distinct positive effect of an internal LOC on the general self-reported willingness to move as well as the probability of moving between regions. A prove that the importance of LOC for decision making cross the boarders of labor economics is provided within Chapter 4. The chapter is devoted to the question of whether LOC is also able to explain alcohol consumption as an important domain of risky health behavior. The study identifies a strongly positive effect of an internal LOC on moderate as well as regular drinking which is comparable to effect of traditional preference parameters such as risk aversion and time preferences. Eventually, Chapter 5 importantly contributes to the value all parts of this thesis adds to the body of literature on behavioral consequences of LOC by carefully discussion the stability of the trait and thus potential problems with reverse causality in the three other studies as well as in the existing literature in general. In order to access the stability of LOC, the study investigates the reaction of reported LOC to an exogenous unemployment shock. Reassuringly, the empirical analysis finds no long-lasting effects of job losses due to plant closures on LOC and thus cannot reject the common assumption of its stability during adulthood. Nevertheless, the study identifies an important temporary deviation in the measurement of LOC during periods of unemployment and therefore concludes that the reported LOC is affected by unemployment likely due to a situation-specific state effect

Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach

The UbuntuNet Alliance, a consortium of National Research and Education Networks (NRENs) runs an exclusive data network for education and research in east and southern Africa. Despite a high degree of route redundancy in the Alliance's topology, a large portion of Internet traffic between the NRENs is circuitously routed through Europe. This thesis proposes a performance-based strategy for dynamic ranking of inter-NREN paths to reduce latencies. The thesis makes two contributions: firstly, mapping Africa's inter-NREN topology and quantifying the extent and impact of circuitous routing; and, secondly, a dynamic traffic engineering scheme based on Software Defined Networking (SDN), Locator/Identifier Separation Protocol (LISP) and Reinforcement Learning. To quantify the extent and impact of circuitous routing among Africa's NRENs, active topology discovery was conducted. Traceroute results showed that up to 75% of traffic from African sources to African NRENs went through inter-continental routes and experienced much higher latencies than that of traffic routed within Africa. An efficient mechanism for topology discovery was implemented by incorporating prior knowledge of overlapping paths to minimize redundancy during measurements. Evaluation of the network probing mechanism showed a 47% reduction in packets required to complete measurements. An interactive geospatial topology visualization tool was designed to evaluate how NREN stakeholders could identify routes between NRENs. Usability evaluation showed that users were able to identify routes with an accuracy level of 68%. NRENs are faced with at least three problems to optimize traffic engineering, namely: how to discover alternate end-to-end paths; how to measure and monitor performance of different paths; and how to reconfigure alternate end-to-end paths. This work designed and evaluated a traffic engineering mechanism for dynamic discovery and configuration of alternate inter-NREN paths using SDN, LISP and Reinforcement Learning. A LISP/SDN based traffic engineering mechanism was designed to enable NRENs to dynamically rank alternate gateways. Emulation-based evaluation of the mechanism showed that dynamic path ranking was able to achieve 20% lower latencies compared to the default static path selection. SDN and Reinforcement Learning were used to enable dynamic packet forwarding in a multipath environment, through hop-by-hop ranking of alternate links based on latency and available bandwidth. The solution achieved minimum latencies with significant increases in aggregate throughput compared to static single path packet forwarding. Overall, this thesis provides evidence that integration of LISP, SDN and Reinforcement Learning, as well as ranking and dynamic configuration of paths could help Africa's NRENs to minimise latencies and to achieve better throughputs

Predictive model creation approach using layered subsystems quantified data collection from LTE L2 software system

Abstract. The road-map to a continuous and efficient complex software system’s improvement process has multiple stages and many interrelated on-going transformations, these being direct responses to its always evolving environment. The system’s scalability on this on-going transformations depends, to a great extent, on the prediction of resources consumption, and systematic emergent properties, thus implying, as the systems grow bigger in size and complexity, its predictability decreases in accuracy. A predictive model is used to address the inherent complexity growth and be able to increase the predictability of a complex system’s performance. The model creation processes are driven by the recollection of quantified data from different layers of the Long-term Evolution (LTE) Data-layer (L2) software system. The creation of such a model is possible due to the multiple system analysis tools Nokia has already implemented, allowing a multiple-layers data gathering flow. The process starts by first, stating the system layers differences, second, the use of a layered benchmark approach for the data collection at different levels, third, the design of a process flow organizing the data transformations from recollection, filtering, pre-processing and visualization, and forth, As a proof of concept, different Performance Measurements (PM) predictive models, trained by the collected pre-processed data, are compared. The thesis contains, in parallel to the model creation processes, the exploration, and comparison of various data visualization techniques that addresses the non-trivial graphical representation of the in-between subsystem’s data relations. Finally, the current results of the model process creation process are presented and discussed. The models were able to explain 54% and 67% of the variance in the two test configurations used in the instantiation of the model creation process proposed in this thesis

The Influence of Personality Traits on University Performance: Evidence from Italian Freshmen Students

Despite several attempts to provide a definite pattern regarding the effects of personality traits on performance in higher education, the debate over the nature of the relationship is far from being conclusive. The use of different subject pools and sample sizes, as well as the use of identification strategies that either do not adequately account for selection bias or are unable to establish causality between measures of academic performance and noncognitive skills, are possible sources of heterogeneity. This paper investigates the impact of the Big Five traits, as measured before the beginning of the academic year, on the grade point average achieved in the first year after the enrolment, taking advantage of a unique and large dataset from a cohort of Italian students in all undergraduate programs containing detailed information on student and parental characteristics. Relying on a robust strategy to credibly satisfy the conditional independence assumption, we find that higher levels of conscientiousness and openness to experience positively affect student score

Feature Space Modeling for Accurate and Efficient Learning From Non-Stationary Data

A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and heterogeneity, can pose another challenge for effective computational learning. The problem is to enable accurate and efficient learning from non-stationary data in a continuous fashion over time while facing and managing the critical challenges of time, memory, concept change, and complexity simultaneously. Feature space modeling is one of the most effective solutions to address this problem. For non-stationary data, selecting relevant features is even more critical than stationary data due to the reduction of feature dimension which can ensure the best use a computational resource to produce higher accuracy and efficiency by data mining algorithms. In this dissertation, we investigated a variety of feature space modeling techniques to improve the overall performance of data mining algorithms. In particular, we built Relief based feature sub selection method in combination with data complexity iv analysis to improve the classification performance using ovarian cancer image data collected in a non-stationary batch mode. We also collected time series health sensor data in a streaming environment and deployed feature space transformation using Singular Value Decomposition (SVD). This led to reduced dimensionality of feature space resulting in better accuracy and efficiency produced by Density Ration Estimation Method in identifying potential change points in data over time. We have also built an unsupervised feature space modeling using matrix factorization and Lasso Regression which was successfully deployed in conjugate with Relative Density Ratio Estimation to address the botnet attacks in a non-stationary environment. Relief based feature model improved 16% accuracy of Fuzzy Forest classifier. For change detection framework, we observed 9% improvement in accuracy for PCA feature transformation. Due to the unsupervised feature selection model, for 2% and 5% malicious traffic ratio, the proposed botnet detection framework exhibited average 20% better accuracy than One Class Support Vector Machine (OSVM) and average 25% better accuracy than Autoencoder. All these results successfully demonstrate the effectives of these feature space models. The fundamental theme that repeats itself in this dissertation is about modeling efficient feature space to improve both accuracy and efficiency of selected data mining models. Every contribution in this dissertation has been subsequently and successfully employed to capitalize on those advantages to solve real-world problems. Our work bridges the concepts from multiple disciplines ineffective and surprising ways, leading to new insights, new frameworks, and ultimately to a cross-production of diverse fields like mathematics, statistics, and data mining

Escalating risk and the moderating effect of resistance to peer influence on the P200 and feedback-related negativity

Young people frequently socialize together in contexts that encourage risky decision making, pointing to a need for research into how susceptibility to peer influence is related to individual differences in the neural processing of decisions during sequentially escalating risk. We applied a novel analytic approach to analyze EEG activity from college-going students while they completed the Balloon Analogue Risk Task (BART), a well-established risk-taking propensity assessment. By modeling outcome-processing-related changes in the P200 and feedback-related negativity (FRN) sequentially within each BART trial as a function of pump order as an index of increasing risk, our results suggest that analyzing the BART in a progressive fashion may provide valuable new insights into the temporal neurophysiological dynamics of risk taking. Our results showed that a P200, localized to the left caudate nucleus, and an FRN, localized to the left dACC, were positively correlated with the level of risk taking and reward. Furthermore, consistent with our hypotheses, the rate of change in the FRN was higher among college students with greater self-reported resistance to peer influence

Be the Change: How Living With Virtue Contributes to the Collective Good

The vast majority of humans yearn for a better world. Underlying that desire is a hope that others will be better. We want politicians to act with integrity; social media CEOs to prioritize our mental health; energy executives to care for our planet; romantic partners to understand our needs; children to spend less time online. In short, we want people to live more virtuously. But how do go about achieving this? I believe Gandhi’s teachings provide the answer. He taught that we need not wait for others to change, instead, we can be the change that we are seeking. Gandhi believed humans are interconnected and that when one person changes, the collective also changes. To some, this might sound far-fetched, but scientific research is emerging that demystifies this wisdom. This paper underscores the benefits to the collective when individuals live with virtue. It begins with a review of Gandhi’s life, then highlights research related to sustainable behavior change, and culminates with an amalgamation of research that demonstrates behavior contagion from individuals to the collective. As we strive to create a better world for future generations, we\u27d be smart to be the change that we are seeking