snu-aidas/HVMap-dataset huggingface dataset:
2-hop Blockchain : Combining Proof-of-Work and Proof-of-Stake Securely

Cryptocurrencies like Bitcoin have proven to be a phenomenal success. Bitcoin-like systems use proofof-work mechanism which is therefore considered as 1-hop blockchain, and their security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently and Bitcoin-like systems will fail when this assumption is broken. We propose the rst provably secure 2-hop blockchain by combining proof-of-work (rst hop) and proof-of-stake (second hop) mechanisms. On top of Bitcoin’s brilliant ideas of utilizing the power of the honest miners, via their computing resources, to secure the blockchain, we further leverage the power of the honest users/stakeholders, via their coins/stake, to achieve this goal. e security of our blockchain holds if the honest players control majority of the collective resources (which consists of both computing power and stake). at said, even if the adversary controls more than 50% computing power, the honest players still have the chance to defend the blockchain via honest stake. ∗An early version with title “Securing Bitcoin-like Blockchains against a Malicious Majority of Computing Power” appeared in ePrint Archive in July 2016. e current version shares the same motivation. But the construction idea and modeling approach have been completely revised. †Virginia Commonwealth University. Email: duong3@vcu.edu. ‡Shanghai Jiao Tong University. Most work done while visiting Cryptography Lab at Virginia Commonwealth University. Email: fanlei@sjtu.edu.cn. §Virginia Commonwealth University. Email: hszhou@vcu.edu.
Business ecosystem analysis framework

The concept of business ecosystems has gained popularity from researchers and practitioners since first introduced in Moore's article in Harvard Business Review in 1993 “Predators and prey: a new ecology of competition”. Inspired by biology, the concept of business ecosystems provides a metaphor to comprehend the inter-wined nature of industries [1]. The reason behind the popularity of this concept and its differentiation from other business theories like Osterwalder's Business Model Canvas [2] and Porter's Value Chain Approach [3] is its comprehensiveness and inclusion of several other components surrounding any business. In this paper, the necessary methods, components and tools for creating, analyzing and managing a business ecosystem successfully will be introduced. The aim of the paper is to propose a Business Ecosystem Analysis Framework that covers all the steps needed to analyze, manage and ensure the feasibility and sustainability of any ecosystem. This framework can be used to locate strengths and weaknesses of an ecosystem, identify its main actors and define how their maturity, interconnectedness and continuity can be improved. The results of the research can be applied to any industry sector.
Open data kit: tools to build information services for developing regions

This paper presents Open Data Kit (ODK), an extensible, open-source suite of tools designed to build information services for developing regions. ODK currently provides four tools to this end: Collect, Aggregate, Voice, and Build. Collect is a mobile platform that renders application logic and supports the manipulation of data. Aggregate provides a "click-to-deploy" server that supports data storage and transfer in the "cloud" or on local servers. Voice renders application logic using phone prompts that users respond to with keypad presses. Finally, Build is a application designer that generates the logic used by the tools. Designed to be used together or independently, ODK core tools build on existing open standards and are supported by an open-source community that has contributed additional tools. We describe four deployments that demonstrate how the decisions made in the system architecture of ODK enable services that can both push and pull information in developing regions.
Recurrent neural network based recommendation for time heterogeneous feedback

In recommender systems, several kinds of user feedback with time stamps are collected and used for recommendation, which is called the time heterogeneous feedback recommendation problem. The existing recommendation methods can handle only one kind of feedback or ignore the time stamps of the feedback. To solve the time heterogeneous feedback recommendation problem, in this paper, we propose a recurrent neural network model to predict the probability that the user will access an item given the time heterogeneous feedback of this user. To our best knowledge, it is the first time to solve the time heterogeneous feedback recommendation problem by deep neural network model. The proposed model is learned by back propagation algorithm and back propagation through time algorithm. The comparison results on four real-life datasets indicate that the proposed method outperforms the compared state-ofthe-art approaches. © 2016 Elsevier B.V. All rights reserved.
Financial Frictions and the Persistence of History : A Quantitative Exploration

The transitional dynamics of the neoclassical growth model is at odds with the growth experiences of many developing economies. For example, consider the post-communist transition of Eastern Europe: Following liberalization, output grew slowly (and even fell), while interest rates and investment rates remained low, contrary to the predictions of the standard growth theory. We incorporate financial frictions and resource misallocation into an otherwise-standard growth model, and account for the observed growth dynamics. We discipline the model by calibrating its stationary equilibrium to the US data on standard macroeconomic aggregates, wealth distribution, and firms’ internal v. external financing. Our model economy converges slowly to the steady state, with the interest rate and the investment rate starting low and rising over time. We also obtain a new result on heterogeneity and macroeconomic dynamics: Even though strict approximate aggregation—that relies only on unconditional first moments—does not hold in our economy, the effect of heterogeneity on aggregate dynamics is almost completely captured by conditional first moments. Department of Economics, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208, USA (e-mail: f-buera@northwestern.edu). Department of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI 53706, USA (email: yshin@ssc.wisc.edu). The transitional dynamics of the neoclassical growth model is characterized by swift convergence to the steady state, staggeringly high real interest rates in the early stages of development, and decreasing investment-to-output ratio over time. However, most economies’ growth experiences are at odds with these neoclassical predictions. One such example is the “miracle” economies of East Asia. As Figure 1 illustrates, growth and investment rates were relatively low initially and peaked in the latter stages. More dramatic evidence against the neoclassical growth model can be found in the post-communist transition of the Eastern European economies. The liberalization of these economies was followed by disappointingly low—even negative—growth rates and slowly increasing investment rates (Figure 2).1 Even after going through an exhaustive list of modified neoclassical growth models, King and Rebelo (1993) conclude that neoclassical transitional dynamics can only play a minor role in explaining observed growth experiences. This is a fundamental challenge for the neoclassical growth theory. Output Per Capita Relative to the US + + + + + + + + + + + JPN KOR SGP bc bc bc bc bc bc bc bc bc bc TWN rs rs rs rs rs rs rs rs rs rs rs THA 195
Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000

BACKGROUND
Information about the distribution of causes of and time trends for child mortality should be periodically updated. We report the latest estimates of causes of child mortality in 2010 with time trends since 2000.


METHODS
Updated total numbers of deaths in children aged 0-27 days and 1-59 months were applied to the corresponding country-specific distribution of deaths by cause. We did the following to derive the number of deaths in children aged 1-59 months: we used vital registration data for countries with an adequate vital registration system; we applied a multinomial logistic regression model to vital registration data for low-mortality countries without adequate vital registration; we used a similar multinomial logistic regression with verbal autopsy data for high-mortality countries; for India and China, we developed national models. We aggregated country results to generate regional and global estimates.


FINDINGS
Of 7·6 million deaths in children younger than 5 years in 2010, 64·0% (4·879 million) were attributable to infectious causes and 40·3% (3·072 million) occurred in neonates. Preterm birth complications (14·1%; 1·078 million, uncertainty range [UR] 0·916-1·325), intrapartum-related complications (9·4%; 0·717 million, 0·610-0·876), and sepsis or meningitis (5·2%; 0·393 million, 0·252-0·552) were the leading causes of neonatal death. In older children, pneumonia (14·1%; 1·071 million, 0·977-1·176), diarrhoea (9·9%; 0·751 million, 0·538-1·031), and malaria (7·4%; 0·564 million, 0·432-0·709) claimed the most lives. Despite tremendous efforts to identify relevant data, the causes of only 2·7% (0·205 million) of deaths in children younger than 5 years were medically certified in 2010. Between 2000 and 2010, the global burden of deaths in children younger than 5 years decreased by 2 million, of which pneumonia, measles, and diarrhoea contributed the most to the overall reduction (0·451 million [0·339-0·547], 0·363 million [0·283-0·419], and 0·359 million [0·215-0·476], respectively). However, only tetanus, measles, AIDS, and malaria (in Africa) decreased at an annual rate sufficient to attain the Millennium Development Goal 4.


INTERPRETATION
Child survival strategies should direct resources toward the leading causes of child mortality, with attention focusing on infectious and neonatal causes. More rapid decreases from 2010-15 will need accelerated reduction for the most common causes of death, notably pneumonia and preterm birth complications. Continued efforts to gather high-quality data and enhance estimation methods are essential for the improvement of future estimates.


FUNDING
The Bill & Melinda Gates Foundation.
LKIM : The Linux Kernel Integrity

509 he Linux Kernel Integrity Measurer (LKIM) is a next-generation technology for the detection of malicious modifications to a running piece of software. Unlike traditional antivirus systems, LKIM does not rely on a database of known malware signatures; instead, LKIM uses a precise model of expected program behavior to verify the consistency of critical data structures at runtime. APL and the Research Directorate of the National Security Agency (NSA) developed the LKIM prototype and are now working to transition the technology to a variety of critical government applications. LKIM: The Linux Kernel Integrity Measurer
Why healthcare workers don't wash their hands: a behavioral explanation.

OBJECTIVE
To elucidate behavioral determinants of handwashing among nurses.


DESIGN
Statistical modeling using the Theory of Planned Behavior and relevant components to handwashing behavior by nurses that were derived from focus-group discussions and literature review.


SETTING
The community and 3 tertiary care hospitals.


PARTICIPANTS
Children aged 9-10 years, mothers, and nurses.


RESULTS
Responses from 754 nurses were analyzed using backward linear regression for handwashing intention. We reasoned that handwashing results in 2 distinct behavioral practices--inherent handwashing and elective handwashing--with our model explaining 64% and 76%, respectively, of the variance in behavioral intention. Translation of community handwashing behavior to healthcare settings is the predominant driver of all handwashing, both inherent (weighted beta =2.92) and elective (weighted beta =4.1). Intended elective in-hospital handwashing behavior is further significantly predicted by nurses' beliefs in the benefits of the activity (weighted beta =3.12), peer pressure of senior physicians (weighted beta =3.0) and administrators (weighted beta =2.2), and role modeling (weighted beta =3.0) but only to a minimal extent by reduction in effort (weighted beta =1.13). Inherent community behavior (weighted beta =2.92), attitudes (weighted beta =0.84), and peer behavior (weighted beta =1.08) were strongly predictive of inherent handwashing intent.


CONCLUSIONS
A small increase in handwashing adherence may be seen after implementing the use of alcoholic hand rubs, to decrease the effort required to wash hands. However, the facilitation of compliance is not simply related to effort but is highly dependent on altering behavioral perceptions. Thus, introduction of hand rub alone without an associated behavioral modification program is unlikely to induce a sustained increase in hand hygiene compliance.
Platelet-rich plasma vs hyaluronic acid to treat knee degenerative pathology: study design and preliminary results of a randomized controlled trial

BACKGROUND
Platelet rich plasma (PRP), a blood-derived product rich in growth factors, is a promising treatment for cartilage defects but there is still a lack of clinical evidence. The aim of this study is to show, through a randomized double blind prospective trial, the efficacy of this procedure, by comparing PRP to Hyaluronic Acid (HA) injections for the treatment of knee chondropathy or osteoarthritis (OA).


METHODS
109 patients (55 treated with HA and 54 with PRP) were treated and evaluated at 12 months of follow-up. The patients were enrolled according to the following inclusion criteria: age > 18 years, history of chronic (at least 4 months) pain or swelling of the knee and imaging findings of degenerative changes of the joint (Kellgren-Lawrence Score up to 3). A cycle of 3 weekly injections was administered blindly. All patients were prospectively evaluated before and at 2, 6, and 12 months after the treatment by: IKDC, EQ-VAS, TEGNER, and KOOS scores. Range of motion and knee circumference changes were measured over time. Adverse events and patient satisfaction were also recorded.


RESULTS
Only minor adverse events were detected in some patients, such as mild pain and effusion after the injections, in particular in the PRP group, where a significantly higher post-injective pain reaction was observed (p=0.039). At the follow-up evaluations, both groups presented a clinical improvement but the comparison between the two groups showed a not statistically significant difference in all scores evaluated. A trend favorable for the PRP group was only found in patients with low grade articular degeneration (Kellgren-Lawrence score up to 2).


CONCLUSIONS
Results suggest that PRP injections offer a significant clinical improvement up to one year of follow-up. However, conversely to what was shown by the current literature, for middle-aged patients with moderate signs of OA, PRP results were not better than those obtained with HA injections, and thus it should not be considered as first line treatment. More promising results are shown for its use in low grade degeneration, but they still have to be confirmed.
The eyeBook – Using Eye Tracking to Enhance the Reading Experience

Introduction A rapid development of eye tracking technology has been observed in recent years. Today’s eye trackers can determine the current focus point of the eye precisely while being relatively unobtrusive in their application. Also, a variety of research and commercial groups has been working on this technology, and there is a growing interest for such devices on the market. Eye tracking has great potential and it can be assumed that it will advance further and might become a widespread technology used at a large number of personal or office computer workplaces. Approaches using simple webcams for eye tracking already exist, for example webcams integrated into laptop computers by default. Thus, they allow for new kinds of applications using eye gaze data. However, not only eye tracking technology is advancing rapidly to an easily usable state. Additionally, during the past 100 years researchers gathered a considerable amount of knowledge on eye movements, why and how they occur, and what they might mean. So, today, we have the technology and knowledge for tracking and analyzing eye movements, making an excellent starting point for sophisticated interactive gaze-based applications. Naive approaches where gaze data is directly employed for interacting with the system, e. g., pressing buttons on the screen with the “blink of an eye” generally have serious problems. Because the eyes are organs used for perceiving the world and not for manipulating the world, it is hard and against human nature to control eye movements deliberately. However, a highly promising approach is just to observe eye movements of the user during his or her daily work in front of the computer, to infer user intentions based on eye movement behavior, and to provide assistance where helpful. Gaze can be seen as a proxy for the user’s attention, and eye movements are known to be usually tightly coupled with cognitive processes in the brain, so that a great deal about those processes can be observed by eye tracking. For example, by interpreting eye movements, reading behavior of the user can be detected, which most likely entails cognitive processes of understanding with regard to the currently read text. In this paper we are focusing particularly on reading behavior since reading is probably the most common activity of knowledge workers sitting in front of a computer screen. We present an algorithm for online reading detection based on eye tracking data and introduce an application for assisted and augmented reading called the eyeBook. The idea behind the eyeBook is to create an interactive and entertaining reading experience. The system observes, which text parts are currently being read by the user on the screen and generates appropriate effects such as playing sounds, presenting
Prevalence and predictors of internet bullying.

PURPOSE
With the Internet quickly becoming a new arena for social interaction, it has also become a growing venue for bullying among youth. The purpose of the present study was to contrast the prevalence of Internet bullying with physical and verbal bullying among elementary, middle, and high school boys and girls, and to examine whether key predictors of physical and verbal bullying also predicted Internet bullying.


METHODS
As part of an ongoing, statewide bullying prevention initiative in Colorado, 3,339 youth in Grades 5, 8, and 11 completed questionnaires in 78 school sites during the fall of 2005, and another 2,293 youth in that original sample participated in a follow-up survey in 65 school sites in the spring of 2006. Questionnaires included measures of bullying perpetration and victimization, normative beliefs about bullying, perceptions of peer social support, and perceptions of school climate.


RESULTS
The highest prevalence rates were found for verbal, followed by physical, and then by Internet bullying. Physical and Internet bullying peaked in middle school and declined in high school. Verbal bullying peaked in middle school and remained relatively high during high school. Males were more likely to report physical bullying than females, but no gender differences were found for Internet and verbal bullying. All three types of bullying were significantly related to normative beliefs approving of bullying, negative school climate, and negative peer support.


CONCLUSIONS
Preventive interventions that target school bullying by changing norms about bullying and school context may also impact Internet bullying, given the shared predictors.
Do Personal Ethics Influence Corporate Ethics ?

In this paper, we introduce a new measure of personal ethics in the form of marital cheating to examine the relationship between personal ethics and corporate misconduct. Firms with CEOs and CFOs who use a marital infidelity website are more than twice as likely to engage in two forms of corporate misconduct. The relationship is not explained by a wide range of regional, firm, and executive characteristics or by the infidelity website usage of other executives. Additionally, white-collar SEC defendants also have elevated levels of infidelity website usage. Our findings suggest that personal and professional ethics are closely related. (JEL G30, M14)
Advanced analytics: opportunities and challenges

Purpose – Advanced analytics-driven data analyses allow enterprises to have a complete or “360 degrees” view of their operations and customers. The insight that they gain from such analyses is then used to direct, optimize, and automate their decision making to successfully achieve their organizational goals. Data, text, and web mining technologies are some of the key contributors to making advanced analytics possible. This paper aims to investigate these three mining technologies in terms of how they are used and the issues that are related to their effective implementation and management within the broader context of predictive or advanced analytics. Design/methodology/approach – A range of recently published research literature on business intelligence (BI); predictive analytics; and data, text and web mining is reviewed to explore their current state, issues and challenges learned from their practice. Findings – The findings are reported in two parts. The first part discusses a framework for BI using the data, text, and web mining technologies for advanced analytics; and the second part identifies and discusses the opportunities and challenges the business managers dealing with these technologies face for gaining competitive advantages for their businesses. Originality/value – The study findings are intended to assist business managers to effectively understand the issues and emerging technologies behind advanced analytics implementation.
Predicting driver drowsiness using vehicle measures: recent insights and future challenges.

INTRODUCTION
Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.


METHOD
The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.


RESULTS
Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.


CONCLUSIONS
Further research is necessary to examine more complex functions, as well as individual differences between drivers.


IMPACT ON INDUSTRY
A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.
WORDNET: A Lexical Database For English