Miracle-dz/UniFAR-FTUs huggingface dataset:
Countering Double-Spend Attacks on Bitcoin Fast-Pay Transactions

Bitcoin is a Proof-of-Work-based payment system that does not rely on a trusted authority. As its popularity grows, more businesses are starting to accept it as payment, including services like fast food restaurants and vending machines that must deliver goods soon upon payment. Unfortunately, these fast pay scenarios are vulnerable to fraudulent double-spending of Bitcoins. To protect consumers from having to cover the costs of these attacks, there is a growing need to find countermeasures to such attacks that are scalable and realistic to deploy. Several possible countermeasures have been proposed in prior work but not evaluated carefully. In this paper, we analyze these countermeasures and simulate their effects in a scaled Bitcoin P2P network using the Shadow framework. We find that integrating several of these countermeasures into an enhanced observer can be effective in detecting and alerting a vendor to an incoming double-spend attack in less than 28 seconds on average in our model.
Social media as an information source for rapid flood inundation mapping

Abstract. During and shortly after a disaster, data about the hazard and its consequences are scarce and not readily available. Information provided by eyewitnesses via social media is a valuable information source, which should be explored in a~more effective way. This research proposes a methodology that leverages social media content to support rapid inundation mapping, including inundation extent and water depth in the case of floods. The novelty of this approach is the utilization of quantitative data that are derived from photos from eyewitnesses extracted from social media posts and their integration with established data. Due to the rapid availability of these posts compared to traditional data sources such as remote sensing data, areas affected by a flood, for example, can be determined quickly. The challenge is to filter the large number of posts to a manageable amount of potentially useful inundation-related information, as well as to interpret and integrate the posts into mapping procedures in a timely manner. To support rapid inundation mapping we propose a methodology and develop "PostDistiller", a tool to filter geolocated posts from social media services which include links to photos. This spatial distributed contextualized in situ information is further explored manually. In an application case study during the June 2013 flood in central Europe we evaluate the utilization of this approach to infer spatial flood patterns and inundation depths in the city of Dresden.
J-PLOS : Java-based Platform for Learning Order Sets

Order sets are a critical component that are included in hospital information systems to reduce clinician workload. They represent clustered order items, such as medications and treatments prescribed at hospital admission, which are administered to patients during their hospital stay. In this project showcase track, our aim is to demonstrate a platform that allows practitioners to learn order sets from usage data and to compare their current order set with heuristic and optimal order set configuration. Optimal order sets can be determined using two tailored mathematical programming formulations of a clustering problem. Because of the computational complexity of the problem, there is also a quick and heuristic option to generate order sets using machine learning approaches linked with the mathematical programs.
Toxoplasma gondii: A mathematical model of its transfer between cats and the environment

We discuss the transmission of Toxoplasma gondii and its effects in a cat population. Due to the severity of T. gondii spillover infections in pregnant women and monk seals, understanding its dynamics in cats is key to unlocking preventative measures against this parasite. Taking into account susceptible, acutely infectious, and chronically infectious cats, and the surrounding environment, we build a differential equations model of T. gondii transmission in cats. We present our model and the results, identifying conditions under which infection persists. Specifically, we find that higher rates of infection and larger shedding rates facilitate endemic infection. We conclude by discussing how this information can be used to minimize risks to other species.
Beneficial effects of the synbiotic kefir on the neural control of cardiovascularfunction.

The microbiota, a natural community of trillions of microorganisms colonizing the gastrointestinal ecosystem, maintains a mutual interaction with different organs and systems of the host. This complex bidirectional interaction occurs via neural, circulatory, hormonal, immune and inflammatory systems, in addition to biogenic compounds and mediators originated in the gut microbiota. The present review is focused on the effects of prebiotics, probiotics and synbiotics on the mechanisms of neural autonomic control of cardiac rhythm and baroreflex control of blood pressure. Approaches that can be used to evaluate the cardiac vagal tone and sympathetic tone, and to evaluate the baroreflex control of arterial blood pressure are also detailed in this review.
T Organizational Routines : An Agent Based Model Replication

this extended abstract focuses on organizational routines as an emergent phenomenon of individual behavior within collectives. Micro foundations related to dynamics of performing and remembering organizational routines are based on psychological constructs such as procedural, declarative and transactive memory. Replicating the agent-based model “Dynamics of Performing and Remembering Organizational Routines” in a different programming language is used to analyze the model ́s credibility. To enhance the replication transparency and quality the existing model is initially transferred into an ODD protocol before its re-implementation. If the model can be replicated without significant errors, it can be used in further research to examine organizational routines in accounting.
Approximate Bayesian Computation for Estimating Parameters of Data-Consistent Forbush Decrease Model

Realistic modeling of complex physical phenomena is always quite a challenging task. The main problem usually concerns the uncertainties surrounding model input parameters, especially when not all information about a modeled phenomenon is known. In such cases, Approximate Bayesian Computation (ABC) methodology may be helpful. The ABC is based on a comparison of the model output data with the experimental data, to estimate the best set of input parameters of the particular model. In this paper, we present a framework applying the ABC methodology to estimate the parameters of the model of Forbush decrease (Fd) of the galactic cosmic ray intensity. The Fd is modeled by the numerical solution of the Fokker–Planck equation in five-dimensional space (three spatial variables, the time and particles energy). The most problematic in Fd modeling is the lack of detailed knowledge about the spatial and temporal profiles of the parameters responsible for the creation of the Fd. Among these parameters, the diffusion coefficient plays a central role. We employ the ABC Sequential Monte Carlo algorithm, scanning the space of the diffusion coefficient parameters within the region of the heliosphere where the Fd is created. Assessment of the correctness of the proposed parameters is done by comparing the model output data with the experimental data of the galactic cosmic ray intensity. The particular attention is put on the rigidity dependence of the rigidity spectrum exponent. The proposed framework is adopted to create the model of the Fd observed by the neutron monitors and ground muon telescope in November 2004.
The Effect of Fiscal Policy on Output in Times of Crisis and Prosperity: Historical Evidence From Greece

Empirical analysis of a unique and unexplored historical dataset for Greece provides new insight into the state and regime dependence of the government spending multiplier. Greece fought numerous wars between the establishment of the modern Greek state and the outbreak of World War II. Using data for both armament and disarmament, and controlling for states and regimes in the economy, our empirical findings suggest that the exchange rate regime, the presence of exchange controls, and the business cycle all have a significant impact on the size of the government spending multiplier. However, analysing the interaction of these states and regimes turns out to be crucial to removing the bias from our multiplier estimates. In particular, regardless of other states and regimes in the economy, the multiplier is estimated to be zero during good times. In contrast, it is well above unity when spending decreases in a recession.
Comparison of Monte Carlo Tree Search Methods in the Imperfect Information Card Game Cribbage

Non-deterministic imperfect information games pose challenges for Artificial Intelligence (AI) design, as compared to AI for perfect information games. Monte Carlo Tree Search (MCTS), an AI technique that uses random sampling of game playouts to build a search tree rather than domainspecific knowledge about how to play a given game, has been used successfully in some perfect information games. MCTS has also been implemented for imperfect information board and card games, using techniques including sampling over many determinizations of a starting game state, and considering which information set each player belongs to. In this paper, we first describe the imperfect information card game Cribbage and the MCTS algorithm. We then describe our implementation of Cribbage for two players and several MCTS and non-MCTSbased AI players. We compare their performance and find that Single-Observer Information Set MCTS performs well in this domain.
Greedy Selections for Structured Sparse Probabilistic Projections

We introduce a framework for designing structured sparsity inducing priors in 1 Probabilistic models using Information Projection. Our approach is flexible and can 2 be used for any structure that allows an enumeration using a matroid. By leveraging 3 advancements in submodular optimization, our framework makes greedy selections 4 on the said matroid for efficient inference and guarantees provable approximations 5 to the best possible projections. We complement the algorithmic development and 6 theoretical guarantees with strong empirical performance on simulated and real 7 world fMRI datasets compared to established baselines for three special cases of 8 our framework Group Sparse Regression, Group Sparse PCA, and Sparse CCA. 9
Photonic Quantum Information Applications of Patterned Liquid Crystals

In this paper we review recent results we obtained in the field of photonic quantum information that were made possible by the introduction of patterned non-uniform liquid crystal cells known as “q-plates”: (i) the generation of entangled states of polarization and orbital angular momentum of a photon; (ii) the transfer of a qubit of quantum information from the spin to the orbital angular momentum of photons and vice versa; (iii) the Hong-Ou-Mandel coalescence in the same outgoing mode of a beam-splitter of two photons having nonzero orbital angular momentum; (iv) the universal optimal quantum cloning of orbital-angular-momentum-encoded qubits.
ArguminSci: A Tool for Analyzing Argumentation and Rhetorical Aspects in Scientific Writing

Argumentation is arguably one of the central features of scientific language. We present ArguminSci, an easy-to-use tool that analyzes argumentation and other rhetorical aspects of scientific writing, which we collectively dub scitorics. The main aspect we focus on is the fine-grained argumentative analysis of scientific text through identification of argument components. The functionality of ArguminSci is accessible via three interfaces: as a command line tool, via a RESTful application programming interface, and as a web application.
How to Lie with Maps

Originally published to wide acclaim, this illustrated essay on the use and abuse of maps teaches us how to evaluate maps critically and promotes a healthy skepticism about these easy-to-manipulate models of reality. Monmonier shows that, despite their immense value, maps lie. In fact, they must. The second edition is updated with the addition of two new chapters, 10 color plates, and a new foreword by renowned geographer H. J. de Blij. One new chapter examines the role of national interest and cultural values in national mapping organizations, including the United States Geological Survey, while the other explores the new breed of multimedia, computer-based maps. To show how maps distort, Monmonier introduces basic principles of mapmaking, gives entertaining examples of the misuse of maps in situations from zoning disputes to census reports, and covers all the typical kinds of distortions from deliberate oversimplifications to the misleading use of color.
Data Driven Multispectral Image Registration Framework

Multispectral imaging is widely used in remote sensing applications from UAVs and ground-based platforms. Multispectral cameras often use a physically different camera for each wavelength causing misalignment in the images for different imaging bands. This misalignment must be corrected prior to concurrent multi-band image analysis. The traditional approach for multispectral image registration process is to select a target channel and register all other image channels to the target. There is no objective evidence-based method to select a target channel. The possibility of registration to some intermediate channel before registering to the target is not usually considered, but could be beneficial if there is no target channel for which direct registration performs well for every other channel. In this paper, we propose an automatic data-driven multispectral image registration framework that determines a target channel, and possible intermediate registration steps based on the assumptions that 1) some reasonable minimum number of control-points correspondences between two channels is needed to ensure a low-error registration; 2) a greater number of such correspondences generally results in higher registration performance. Our prototype is tested on five multispectral datasets captured with UAV-mounted multispectral cameras. The output of the prototype is a registration scheme in the form of a directed acyclic graph (actually a tree) that represents the target channel and the process to register other image channels. The resulting registration schemes had more control point correspondences on average than the traditional register-all-to-one-targetchannel approach. Data-driven registration scheme consistently showed low back-projection error across all the image channel pairs in most of the experiments. Our data-driven framework has generated registration schemes with the best control point extraction algorithm for each image channel pair and registering images in a data-driven approach. The data-driven image registration framework is dataset independent, and it performs on datasets with any number of image channels. With the growing need of remote sensing and the lack of a proper evidence-based method to register multispectral image channels, a data-driven registration framework is an essential tool in the field of image registration and multispectral imaging.
F5-2 Understanding Human Factor Issues of The Shared Control System: a literature review

The aim of this study is to identify human factor issues related to the shared control system. With the rapid development of machine and robot technology, an enormous number of system introduces the human-machine cooperation to reduce physical and psychological workload. In the shared control system, which is one type of the human-machine cooperation, the machine agent senses the environment and then autonomously takes the initiative to choose its activity responding to the environment. There are many human factor issues in this kind of system. However, most of the research on the shared control system is focused on the performance improvement of the machine, not on human factor issues. Literature review was conducted to collect and understand the academic literature that containing information about the shared control system. The topic varies from defining the shared control system to evaluating the system. Collected problems and guidelines are classified from the perspective of the human agent. Control authority, trust, workload and performance are frequently appeared issues. In addition, it is found that there are relationships between issues. By considering the derived human factor issues in the system design step, it is possible to develop more effective the shared control system. The result of this study can be applied to other types of human-machine cooperation system.
Concert Hall Parameters 2012 – a status report

Since Wallace Sabine introduced the reverberation time as a critical measure of acoustical quality in concert halls some hundred years ago, it soon became evident that two halls having the same reverberation time could still be subjectively very different. During the 20th century, a great number of additional parameters were suggested in order to provide a complete objective characterization of concert halls for symphonic music, but ISO 3382 have reduced these to a set of five listener aspects with their corresponding objective parameters. However, it remains to prove that this set of parameters actually is capable of explaining the subjective preference of concert halls, and even more important – to prove critical enough to be able to predict the success or failure of new concert halls. Given the set of five parameters, an actually highly ranked hall and a bottom ranked hall would have been predicted to become equivalent medium ranked halls.
Who Needs Parent-teacher Meetings in the Technological Era?

In this study we shall examine parent-teacher meetings in the technological era, i.e., whether parent-teacher meetings are still relevant, how they should be conducted, whether it is necessary to change the structure of the parent-teacher meeting or its conception by parents, students, and teachers. For this purpose, identical questionnaires were administered to teachers and parents, with an added question on suggestions for increasing efficacy. The questionnaires show that there is need for change. This study is a pioneer study, and therefore it proposes suggestions for further research. Furthermore, a proposal for increasing the efficacy of parent-teacher meetings was offered by the researchers. Once further research is undertaken in this field, it will be possible to perform a more thorough exploration of the issue, of which the current study provides an initial overview.
Genetic diversity and pathogenic variability among Cercospora beticola Sacc. isolates causing leaf spot of sugar beet in Iran

Cercospora leaf spot caused by Cercospora beticola has a great negative impact on yield and quality of sugar beet. In the present study, pathogenic and genotypic variation of 24 C. beticola isolates collected from different regions of Iran were studied using RFLP of the Internal Transcribed Spacer (ITS-RFLP), and Random Amplified Polymorphic DNA (RAPDPCR). Pathogenic variability and genotype × isolate interaction were evaluated in greenhouse experiments on five sugar beet cultivars (FD0018, HM1836, Puma, Eudora and Monatuna). All of the 24 isolates tested were found to be pathogenic on the cultivars with significant variation in disease severity. Results of RAPD analysis showed wide DNA polymorphism among the Iranian C. beticola isolates. Restriction pattern of the internal transcribed spacer of rDNA (ITS1-5.8-ITS4) was studied using three restriction endonucleases: EcoR1, Taq1, and Busr1. The length of undigested DNA fragment of all isolates was estimated to be 500bp without rDNA polymorphism after digestion with EcoR1 (280, 270 bp), Taq1 (330 bp) and Busr1 (240, 220, 90 bp). RAPD and ITS-RFLP markers showed the highest level of genetic diversity which confirms the variation in C. beticola detection.
A new periodic interdigital structure - three dimensional realization based on two dimensional design

A novel design concept of new periodic interdigital structure for total transmission is proposed from the perspective of electromagnetic (EM) field in this paper. The new structure is constructed in two-dimensional (2D) firstly by using the constructive interference of EM fields, and its transmission characteristic and band structure are calculated. Based on the calculated results, the interaction of the structure with EM fields is clearly demonstrated to provide a better physical understanding for the structure design. Meanwhile, the final three-dimensional (3D) implementation of the presented structure in actual printed circuit board is conducted for its potential applications. The maximum transmission frequency, bandgap and electric field distribution calculated by using the 2D structure and its final implementation all testify the validity of the realization process.
Imputation Strategies for the Estimation of Natural Direct and Indirect Effects

Abstract Mediation analysis is widely adopted to infer causal mechanism by disentangling indirect or mediated effects of an exposure on an outcome through given intermediaries, from the remaining direct effect. Traditional approaches build on standard regression models for the outcome and mediator, but easily result in difficult-to-interpret or difficult-to-report results when some of these models involve non-linearities. In this article, we overcome this via a general class of so-called natural effect models, which directly parameterize the (natural) direct and indirect effects of interest. We propose flexible estimation strategies for the direct and indirect effect parameters indexing these models, that are easy to perform with standard statistical software: one based on regression mean imputation and one based on doubly robust imputation. We give a theoretical discussion of the properties of these estimation strategies. We moreover assess their finite-sample performance through a simulation study, and through the analysis of the WHO-LARES study on the association between residence in a damp and moldy dwelling and the risk of depression.
Prioritization of the biomarkers to be analyzed in the French biomonitoring program

Abstract The aim of this work was to develop a comprehensive prioritization method to select the biomarkers to be monitored in the French national biomonitoring program. The first step consisted in building an exhaustive list of biomarkers. The next step involved prioritizing the initial list of biomarkers according to specific scientific questions about human exposure to chemicals in the environment, and meet logistical, feasibility and budgetary constraints. The Delphi consensus method was used to prioritize biomarkers and was developed in three phases: i) the definition of relevant criteria for selecting biomarkers; ii) the prioritization of the biomarker list based on these criteria and iii) the validation of the list by the stakeholders. Among the eight relevant criteria for selecting biomarkers, hazard identification and social perception were the highest-rated and lowestrated criteria, respectively. After scoring each criterion for each group of biomarkers, and discussing the relative ranking of each group during a round table meeting, the final prioritized list obtained contained both historic (e.g. dioxins or lead) and emerging substances (e.g. phthalates, bisphenol A). Combining rigor and flexibility, our method has clearly helped to build a prioritized list shared and supported by many international actors.