nils-herrmann/clean_scirepeval huggingface dataset:
CATERPILLAR: A Business Process Execution Engine on the Ethereum Blockchain

Correspondence Institute of Computer Science, University of Tartu, Juhan Liivi 2, 50409 Tartu, Estonia  Blockchain platforms, such as Ethereum, allow a set of actors to maintain a ledger of transactions without relying on a central authority and to deploy scripts, called smart contracts, that are executedwhenever certain transactions occur. These features can be used as basic building blocks for executing collaborative business processes between mutually untrusting parties. However, implementing business processes using the low-level primitives provided by blockchain platforms is cumbersome and error-prone. In contrast, established business process management systems, such as those based on the standard Business Process Model and Notation (BPMN), provide convenient abstractions for rapid development of process-oriented applications. This article demonstrates how to combine the advantages of a business process management system with those of a blockchain platform. The article introduces a blockchain-based BPMN execution engine, namely Caterpillar. Like any BPMN execution engine, Caterpillar supports the creation of instances of a process model and allows users to monitor the state of process instances and to execute tasks thereof. The specificity of Caterpillar is that the state of each process instance is maintained on the (Ethereum) blockchain and the workflow routing is performed by smart contracts generated by a BPMN-to-Solidity compiler. The Caterpillar compiler supports a large array of BPMN constructs, including subprocesses, multi-instances activities and event handlers. The paper describes the architecture of Caterpillar, and the interfaces it provides to support the monitoring of process instances, the allocation and execution of work items, and the execution of service tasks.
Hunting Malicious TLS Certificates with Deep Neural Networks

Encryption is widely used across the internet to secure communications and ensure that information cannot be intercepted and read by a third party. However, encryption also allows cybercriminals to hide their messages and carry out successful malware attacks while avoiding detection. Further aiding criminals is the fact that web browsers display a green lock symbol in the URL bar when a connection to a website is encrypted. This symbol gives a false sense of security to users, who are in turn more likely to fall victim to phishing attacks. The risk of encrypted traffic means that information security researchers must explore new techniques to detect, classify, and take countermeasures against malicious traffic. So far there exists no approach for TLS detection in the wild. In this paper, we propose a method for identifying malicious use of web certificates using deep neural networks. Our system uses the content of TLS certificates to successfully identify legitimate certificates as well as malicious patterns used by attackers. The results show that our system is capable of identifying malware certificates with an accuracy of 94.87% and phishing certificates with an accuracy of 88.64%.
Nurses discuss bedside handover and using written handover sheets.

The analysis of nursing errors in clinical management highlighted that clinical handover plays a pivotal role in patient safety. Changes to handover including conducting handover at the bedside and the use of written handover summary sheets were subsequently implemented.


AIM
  The aim of the study was to explore nurses' perspectives on the introduction of bedside handover and the use of written handover sheets.


METHOD
  Using a qualitative approach, data were obtained from six focus groups containing 30 registered and enrolled (licensed practical) nurses. Thematic analysis revealed several major themes.


FINDINGS
  Themes identified included: bedside handover and the strengths and weaknesses; patient involvement in handover, and good communication is about good communicators. Finally, three sources of patient information and other issues were also identified as key aspects.


How bedside handover is delivered should be considered in relation to specific patient caseloads (patients with cognitive impairments), the shift (day, evening or night shift) and the model of service delivery (team versus patient allocation).


Flexible handover methods are implicit within clinical setting issues especially in consideration to nursing teamwork. Good communication processes continue to be fundamental for successful handover processes.
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience

We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulations, we adapt the simulation parameter distribution using a few real world roll-outs interleaved with policy training. In doing so, we are able to change the distribution of simulations to improve the policy transfer by matching the policy behavior in simulation and the real world. We show that policies trained with our method are able to reliably transfer to different robots in two real world tasks: swing-peg-in-hole and opening a cabinet drawer. The video of our experiments can be found at https: //sites.google.com/view/simopt.
How to read a paper. Statistics for the non-statistician. I: Different types of data need different statistical tests.

As medicine leans increasingly on mathematics no clinician can afford to leave the statistical aspects of a paper to the "experts." If you are numerate, try the "Basic Statistics for Clinicians" series in the Canadian Medical Association Journal,1 2 3 4 or a more mainstream statistical textbook.5 If, on the other hand, you find statistics impossibly difficult, this article and the next in this series give a checklist of preliminary questions to help you appraise the statistical validity of a paper.
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites

This is an innovative work for the field of web usage mining. The main feature of our work a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the difficult aspects of real-life Web usage mining, including developing user profiles and external data describing an ontology of the Web content. We are presenting a method for discovering and tracking evolving user profiles. Profiles are also enriched with other domain-specific information facets that give a panoramic view of the discovered mass usage modes. An objective validation plan is also used to assess the quality of the mined profiles, in particular their adaptability in the face of evolving user behaviour.  Web mining, Cookies, Session.
Model-Based Interoperability Engineering in Systems-of-Systems and Civil Aviation

Interoperability is the capability of multiple parties and systems to collaborate and exchange information and matter to obtain their objectives. Interoperability challenges call for a model-based systems engineering approach. This paper describes a conceptual modeling framework for model-based interoperability engineering (MoBIE) for systems of systems, which integrates multilayered interoperability specification, modeling, architecting, design, and testing. Treating interoperability infrastructure as a system in its own right, MoBIE facilitates interoperability among agents, processes, systems, services, and interfaces. MoBIE is founded on ISO 19450 standard—object-process methodology, a holistic paradigm for modeling and architecting complex, dynamic, and multidisciplinary systems—and allows for synergistic integration of the interoperability model with system-centric models. We also discuss the implementation of MoBIE with the unified modeling language. We discuss the importance of interoperability in the civil aviation domain, and apply MoBIE to analyze the passenger departure process in an airport terminal as a case-in-point. The resulting model enables architectural and operational decision making and analysis at the system-of-systems level and adds significant value at the interoperability engineering program level.
Correlated Time Series Forecasting using Multi-Task Deep Neural Networks

Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on a large real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings.
Freedom of Choice, Ease of Use, and the Formation of Interface Preferences

How does users’ freedom of choice, or the lack thereof, affect interface preferences? The research reported in this article approaches this question from two theoretical perspectives. The first of these argues that an interface with a dominant market share benefits from the absence of competition because users acquire skills that are specific to that particular interface, which in turn reduces the probability that they will switch to a new competitor interface in the future. By contrast, the second perspective proposes that the advantage that a market leader has in being able to install a set of non-transferable skills in its user base is offset by a psychological force that causes humans to react against perceived constraints on their freedom of choice. We test a research model that incorporates the key predictions of these two theoretical perspectives in an experiment involving consequential interface choices. We find strong support for the second perspective, which builds upon the theory of psychological reactance.
Effects of reuse on quality, productivity, and economics

This article presents metrics from two Hewlett-Packard (HP) reuse programs that document the improved quality, increased productivity, shortened time-to-market, and enhanced economics resulting from reuse. Work products are the products or by-products of the software-development process: for example, code, design, and test plans. Reuse is the use of these work products without modification in the development of other software. Leveraged reuse is modifying existing work products to meet specific system requirements. A producer is a creator of reusable work products, and the consumer is someone who uses them to create other software. Time-to-market is the time it takes to deliver a product from the time it is conceived. Experience with reuse has been largely positive. Because work products are used multiple times, the accumulated defect fixes result in a higher quality work product. Because the work products have already been created, tested, and documented, productivity increases because consumers of reusable work products need to do less work. However, increased productivity from reuse does not necessarily shorten time-to-market. To reduce time-to-market, reuse must be used effectively on the critical path of a development project. Finally, we have found that reuse allows an organization to use personnel more effectively because it leverages expertise. However, software reuse is not free. It requires resources to create and maintain reusable work products, a reuse library, and reuse tools. To help evaluate the costs and benefits of reuse, we have developed an economic analysis method, which we have applied to multiple reuse programs at HP.<<ETX>>
An Investigation of Gamification Typologies for Enhancing Learner Motivation

In this paper we present a new gamified learning system called Reflex which builds on our previous research, placing greater emphasis on variation in learner motivation and associated behaviour, having a particular focus on gamification typologies. Reflex comprises a browser based 3D virtual world that embeds both learning content and learner feedback. In this way the topography of the virtual world plays an important part in the presentation and access to learning material and learner feedback. Reflex presents information to learners based on their curriculum learning objectives and tracks their movement and interactions within the world. A core aspect of Reflex is its gamification design, with our engagement elements and processes based on Marczewski's eight gamification types . We describe his model and its relationship to Bartle's player types  as well as the RAMP intrinsic motivation model . We go on to present an analysis of experiments using Reflex with students on two 2nd year Computing modules. Our data mining and cluster analysis on the results of a gamification typology questionnaire expose variation in learner motivation. The results from a comprehensive tracking of the interactions of learners within Reflex are discussed and the acquired tracking data is discussed in context of gamification typologies and metacognitive tendencies of the learners. We discuss correlations in actual learner behaviour to that predicted by gamified learner profile. Our results illustrate the importance of taking variation in learner motivation into account when designing gamified learning systems.
Attempted auto-enucleation in two incarcerated young men with psychosis.

Auto-enucleation is a sign of untreated psychosis. We describe two patients who presented with attempted auto-enucleation while being incarcerated. This is an observation two-case series of two young men who suffered untreated psychosis while being incarcerated. These young men showed severe self-inflicted ocular trauma during episodes of untreated psychosis. Injuries included orbital bone fracture and dehiscence of the lateral rectus in one patient and severe retinal hemorrhage and partial optic nerve avulsion in the second patient. Auto-enucleation is a severe symptom of untreated psychosis. This urgent finding can occur in a jail setting in which psychiatric care may be minimal.
Ultrasonic Wind Velocity Measurement Based on Phase Discrimination Technique

According to the shortcomings of the existing ultrasonic wind velocity measurement device, for instance, complexity of circuit and difficulty of signal processing, a new ultrasonic wind velocity measurement is put forward based on phase discrimination and a new sensor configuration as well. Thus, a system model is built. Firstly, an equilateral triangle should be constituted by an ultrasonic emission sensor and two ultrasonic receiving sensors. Then by high-precision phase discrimination circuit, the lag between the ultrasonic which is received by two ultrasonic receiving sensors during the traveling time in the upwind and downwind is converted to the phase difference. After that, the wind velocity is measured. Besides, a mathematical model is established among the wind velocity, the ultrasonic velocity, and the structural parameters with ambient temperature. The factors which influence the precision of the wind velocity measurement are analyzed and the solutions are given as well. The experimental results show that in view of the phase discrimination technology, the system has a good numerical stability and the resolution is one order magnitude better than that of the cup anemometer.
Comparison of Si, GaAs, SiC AND GaN FET-type switches for pulsed power applications

Among the present limitations on the peak voltage of traditional Si-MOSFET switches are fundamental materials properties that are related both to intrinsic properties (such as bandgap), and to defects. Switches fabricated from semiconductors such as GaAs, SiC and GaN hold promise if hold-off voltages of several kilovolts and fast rise rates are needed. High power and short pulse (< 1/spl mu/s) applications require both fast switching speed and great current handling capability. The question arises whether any single material has all of these desired properties or whether there are intrinsic limitations. In order to investigate this, we have performed simulations of the electrical properties of FET-type switches fabricated from each of these materials. Both perfect material properties and the effects of defects have been included. The simulation results show that deep level defects degrade the device performance. Based on our simulations and on the available data, in the near term, 4H-SiC is the most attractive of the four materials for pulsed power applications.
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.
Conceptual domain of the matrix in fragmented landscapes.

In extensively modified landscapes, how the matrix is managed determines many conservation outcomes. Recent publications revise popular conceptions of a homogeneous and static matrix, yet we still lack an adequate conceptual model of the matrix. Here, we identify three core effects that influence patch-dependent species, through impacts associated with movement and dispersal, resource availability, and the abiotic environment. These core effects are modified by five 'dimensions': spatial and temporal variation in matrix quality; spatial scale; temporal scale of matrix variation; and adaptation. The conceptual domain of the matrix, defined as three core effects and their interaction with these five dimensions, provides a much-needed framework to underpin management of fragmented landscapes and highlights new research priorities.
Predicting Rich Drug-Drug Interactions via Biomedical Knowledge Graphs and Text Jointly Embedding

Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs and ignore other interactions. In this paper, we propose a novel framework, called PRD, to predict drug-drug interactions. The framework uses the graph embedding that can overcome data incompleteness and sparsity issues to achieve multiple DDI label prediction. First, a large-scale drug knowledge graph is generated from different sources. Then, the knowledge graph is embedded with comprehensive biomedical text into a common low dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world datasets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.
Burn-In Demonstrations for Multi-Modal Imitation Learning

Recent work on imitation learning has generated policies that reproduce expert behavior from multi-modal data. However, past approaches have focused only on recreating a small number of distinct, expert maneuvers, or have relied on supervised learning techniques that produce unstable policies. This work extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce behavior over an extended period of time. Our approach involves reformulating the typical imitation learning setting to include “burn-in demonstrations” upon which policies are conditioned at test time. We demonstrate that our approach outperforms standard InfoGAIL in maximizing the mutual information between predicted and unseen style labels in road scene simulations, and we show that our method leads to policies that imitate expert autonomous driving systems over long time horizons.
Re: CAPTCHAs-Understanding CAPTCHA-Solving Services in an Economic Context

Turing tests, or CAPTCHAs, have become an ubiquitous defense used to protect open Web resources from being exploited at scale. An effective CAPTCHA resists existing mechanistic software solving, yet can be solved with high probability by a human being. In response, a robust solving ecosystem has emerged, reselling both automated solving technology and realtime human labor to bypass these protections. Thus, CAPTCHAs can increasingly be understood and evaluated in purely economic terms; the market price of a solution vs the monetizable value of the asset being protected. We examine the market-side of this question in depth, analyzing the behavior and dynamics of CAPTCHA-solving service providers, their price performance, and the underlying labor markets driving this economy.