Breeding unicorns:Developing trustworthy and scalable randomness beacons

Randomness beacons are services that periodically emit a random number, allowing users to base decisions on the same random value without trusting anyone: ideally, the randomness beacon does not only produce unpredictable values, but is also of low computational complexity for the users, bias-resistant and publicly verifiable. Such randomness beacons can serve as an important primitive for smart contracts in a variety of contexts. This paper first presents a structured security analysis, based on which we then design, implement, and evaluate a trustworthy and efficient randomness beacon. Our approach does not require users to register or run any computationally intensive operations. We then compare different implementation and deployment options on distributed ledgers, and report on an Ethereum smart contract-based lottery using our beacon

How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time series

Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we present a new anomaly concept called "unicorn" or unique event and present a new, model-independent, unsupervised detection algorithm to detect unicorns. The Temporal Outlier Factor (TOF) is introduced to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily outlier in either pointwise or collective sense; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the standard Local Outlier Factor (LOF). TOF had superior performance compared to LOF even in recognizing traditional outliers and it also recognized unique events that LOF did not. Benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully recognized unique events in those cases where they were already known such as the gravitational waves of a black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years

Unicorn, hare, or tortoise? Using machine learning to predict working memory training performance

People differ considerably in the extent to which they benefit from working memory (WM) training. Although there is increasing research focusing on individual differences associated with WM training outcomes, we still lack an understanding of which specific individual differences, and in what combination, contribute to inter-individual variations in training trajectories. In the current study, 568 undergraduates completed one of several N-back intervention variants over the course of two weeks. Participants\u27 training trajectories were clustered into three distinct training patterns (high performers, intermediate performers, and low performers). We applied machine-learning algorithms to train a binary tree model to predict individuals\u27 training patterns relying on several individual difference variables that have been identified as relevant in previous literature. These individual difference variables included pre-existing cognitive abilities, personality characteristics, motivational factors, video game experience, health status, bilingualism, and socioeconomic status. We found that our classification model showed good predictive power in distinguishing between high performers and relatively lower performers. Furthermore, we found that openness and pre-existing WM capacity to be the two most important factors in distinguishing between high and low performers. However, among low performers, openness and video game background were the most significant predictors of their learning persistence. In conclusion, it is possible to predict individual training performance using participant characteristics before training, which could inform the development of personalized interventions

Creating UNICORNS: Teaching IEP Literacy and Accommodation Self-Advocacy Through Asynchronous Interactive Video Modules

Data indicate that individuals who disclose their disability status to self-advocate for accommodations at the postsecondary level may be as rare as the mythical unicorn. During the 2019–20 school year in the United States, 7.3 million public education students aged 3–21 years received some form of special education services. These students account for 14% of the nation’s public school enrollment (Irwin et al., 2021). In one study, only 20% of high school students reported having received any instruction on reading and understanding their own Individualized Education Program (IEP; Agran & Hughes, 2008). In another study, only 19% of postsecondary students reported receiving services or accommodations, while 87% of the same sample reported receiving services or accommodations at the secondary level (Raue et al., 2011). The current study explored the effects of a program designed to fill a research and instructional gap by teaching college-bound secondary students with hidden disabilities how to self-advocate for accommodations. The UNICORNS program delivered instruction via asynchronous interactive video modules (IVMs). The IVMs taught students about self-advocacy, and IEP literacy. The program used a mnemonic to teach eight target behaviors for self-advocating and requesting accommodations. The UNICORNS program included instruction on the four subskills within Test et al.’s (2005) conceptual model of self-advocacy. The study\u27s findings suggest that asynchronous IVMs positively impacted all participants. Implications for practice and future research are provided

In Search of Unicorns: An Analysis of Japan\u27s Startup Ecosystem

While Japan previously dominated global consumer markets in innovative electronic products, the economy has failed to live up to this performance since the burst of the asset price bubble in 1992 and the subsequent lost decades. In the modern economy, startup companies have become synonymous with innovation. Most of these companies have emerged from Silicon Valley, as well as other startup hubs around the globe. However, critics have criticized Japan for the limited number of notable startup companies from the country. My thesis seeks to answer the two following questions: First, what is the current state of Japan’s startup ecosystem? Second, what factors contribute to the current startup scene? I seek to answer the two questions by analyzing the six main factors that make up Japan’s startup ecosystem: financial markets, state capital allocation, culture, global expansion, and human capital. In order to avoid any generalizations, my thesis attempts to use data, statistics, and surveys in order to back up most of the claims made throughout the thesis