nils-herrmann/clean_scirepeval huggingface dataset:
The Adoption Process of Cryptocurrencies Identifying Factors That Influence the Adoption of Cryptocurrencies from a Multiple Stakeholder Perspective. Identifying Factors That Influence the Adoption of Cryptocurrencies from a Multiple Stakeholder Perspective

II The adoption process of cryptocurrencies Management  Cryptocurrencies 1 are rapidly gaining more and more interest as a technology that is potentially groundbreaking and disruptive for the whole payments industry on a global scale. However the future of cryptocurrencies is very unclear as there are many different usage scenarios and different stakeholders have different needs. In order to be able to give a better future perspective and to determine possibilities for improvement of cryptocurrencies factors that influence adoption will have to be determined. To achieve this the following main research question was formulated: What are factors influencing the adoption of cryptocurrencies in different usage scenarios for different stakeholders? The way current payment systems work throughout the world varies widely. The added value of cryptocurrencies therefore also hugely differs per geographical area. In order to be able to give clear and concise conclusions this research is scoped towards looking at the European market and the Dutch market in particular. Based on the Diffusion of Innovations Theory, which defines characteristics necessary for an innovation in order to be successfully adopted, a conceptual model to describe the adoption of cryptocurrencies was formulated. This model was based on academic literature resulting from a literature review. The model was then validated by means of qualitative semi‐structured interviews with subject experts. To make sure that a balanced view was obtained stakeholders from a wide range of industries were interviewed: employees from the four biggest Dutch banks, the Dutch Central Bank, the three largest Dutch cryptocurrency exchanges, senior payments consultants, Payment Service Providers, a cryptography expert and the largest Dutch company that accepts cryptocurrency, Thuisbezorgd.nl. During the interviews barriers were identified that have to be overcome in order for cryptocurrencies to be adopted on a large scale. The three main pillars which are important for future mass adoption are: 1. Ease of use: at the moment there is a lack of user‐friendliness when using bitcoins. Sending and receiving bitcoin is still cumbersome and holding bitcoins is prone to many risks. Users need to be able to have more confidence in the safety of their funds. 2. Price stability: the current price volatility driven by speculation and a lack of liquidity makes that it is very risky for a user to keep his funds in cryptocurrency as the value varies wildly. This undermines the function of cryptocurrency as a store of value. 3. Governance: the current bitcoin …
Representing Attitude : Euler Angles , Unit Quaternions , and Rotation Vectors

We present the three main mathematical constructs used to represent the attitude of a rigid body in threedimensional space. These are (1) the rotation matrix, (2) a triple of Euler angles, and (3) the unit quaternion. To these we add a fourth, the rotation vector, which has many of the benefits of both Euler angles and quaternions, but neither the singularities of the former, nor the quadratic constraint of the latter. There are several other subsidiary representations, such as Cayley-Klein parameters and the axis-angle representation, whose relations to the three main representations are also described. Our exposition is catered to those who seek a thorough and unified reference on the whole subject; detailed derivations of some results are not presented. Euler angles, quaternion, Euler-Rodrigues parameters, Cayley-Klein parameters, rotation matrix, direction cosine matrix, transformation matrix, Cardan angles, Tait-Bryan angles, nautical angles, rotation vector, orientation, attitude, roll, pitch, yaw, bank, heading, spin, nutation, precession, Slerp
Unsupervised and Transfer Learning Challenge: a Deep Learning Approach

Learning good representations from a large set of unlabeled data is a particularly challenging task. Recent work (see Bengio (2009) for a review) shows that training deep architectures is a good way to extract such representations, by extracting and disentangling gradually higher-level factors of variation characterizing the input distribution. In this paper, we describe different kinds of layers we trained for learning representations in the setting of the Unsupervised and Transfer Learning Challenge. The strategy of our team won the final phase of the challenge. It combined and stacked different one-layer unsupervised learning algorithms, adapted to each of the five datasets of the competition. This paper describes that strategy and the particular one-layer learning algorithms feeding a simple linear classifier with a tiny number of labeled training samples (1 to 64 per class).
Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks

Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings.  The proposed approach used a combination of Convolution Neural Networks (CNNs) and a sequence of Long Short-Term Memory (LSTM) units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and we selected the final prediction for classification. Results were cross-validated on the Physionet Challenge 2017 training dataset, which contains 8,528 single lead ECG recordings lasting from 9s to just over 60s.  Using the proposed structure and no explicit feature selection, 10-fold stratified cross-validation gave an overall F-measure of 0.83.10±0.015 on the held-out test data (mean ± standard deviation over all folds) and 0.80 on the hidden dataset of the Challenge entry server.
A SeqGAN for Polyphonic Music Generation

We propose an application of SeqGAN, generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences. Instead of monophonic melody generation suggested in the original work, we present an efficient representation of polyphony MIDI file that captures chords and melodies with dynamic timings simultaneously. The network can create sequences that are musically coherent. We also report that careful tuning of reinforcement learning signals of the model are crucial for general application.
Generalizing Data to Provide Anonymity when Disclosing Information (Abstract)

The proliferation of information on the Internet and access to fast computers with large storage capacities has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private information is released in data believed to be anonymous. At the same time, increasing demands are made on organizations to release individualized data rather than aggregate statistical information. Even when explicit identi ers, such as name and phone number, are removed or encrypted when releasing individualized data, other characteristic data, which we term quasi-identi ers, can exist which allow the data recipient to re-identify individuals to whom the data refer. In this paper, we provide a computational disclosure technique for releasing information from a private table such that the identity of any individual to whom the released data refer cannot be de nitively recognized. Our approach protects against linking to other data. It is based on the concepts of generalization, by which stored values can be replaced with semantically consistent and truthful but less precise alternatives, and of k-anonymity . A table is said to provide k-anonymity when the contained data do not allow the recipient to associate the released information to a set of individuals smaller than k. We introduce the notions of generalized table and of minimal generalization of a table with respect to a k-anonymity requirement. As an optimization problem, the objective is to minimally distort the data while providing adequate protection. We describe an algorithm that, given a table, e ciently computes a preferred minimal generalization to provide anonymity.
Estimation and Removal of Clock Skew from Network Delay Measurements

Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze netw ork performance. The clocks on the end-systems used to measure the dela ys, however, are not always synchronized, and this lack of synchron ization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. In this paper we introduce a linear programming-based algorithm to estimate the clock skew in network delay measur ements and compare it with three other algorithms. We show that our algorithm has time complexity of O(N), leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew est imate is independent of the magnitude of the skew. We use traces of real Int ernet delay measurements to assess the algorithm, and compare its perfo rmance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimate is better (smaller) than existing algorithms.  clock skew, clock ratio, end-to-end delay, delay measurement.
Handover Performance in 3GPP Long Term Evolution (LTE) Systems

The specification of the long term evolution (LTE) of 3G systems is currently ongoing in 3GPP with a target date of ready specification at the end of 2007. The evolved radio access network (RAN) involves a new radio interface based on OFDM technology and a radically different RAN architecture, where radio functionality is distributed into the base stations. The distributed nature of the RAN architecture calls for new radio control algorithms and procedures that operate in a distributed manner, including a distributed handover scheme as well. The most important aspects of the handover procedure in LTE has been already settled in 3GPP except a few details. In this paper we give an overview of the LTE intra-access handover procedure and evaluate its performance focusing on the user perceived performance aspects of it. We investigate the necessity of packet forwarding from a TCP throughput point of view, we analyse the problem of out of order packet delivery during handover and propose a simple solution for it. Finally, we investigate the impact of HARQ/ARQ state discard at handover on the radio efficiency. The results show that neither the user perceived performance nor the radio efficiency are compromised by the relocation based handover procedure of LTE.
A Generic and Robust System for Automated Patient-Specific Classification of ECG Signals

This paper presents a generic and patient-specific classification system designed for robust and accurate detection of ECG heartbeat patterns. The proposed feature extraction process utilizes morphological wavelet transform features, which are projected onto a lower dimensional feature space using principal component analysis, and temporal features from the ECG data. For the pattern recognition unit, feedforward and fully connected artificial neural networks, which are optimally designed for each patient by the proposed multidimensional particle swarm optimization technique, are employed. By using relatively small common and patient-specific training data, the proposed classification system can adapt to significant interpatient variations in ECG patterns by training the optimal network structure, and thus, achieves higher accuracy over larger datasets. The classification experiments over a benchmark database demonstrate that the proposed system achieves such average accuracies and sensitivities better than most of the current state-of-the-art algorithms for detection of ventricular ectopic beats (VEBs) and supra-VEBs (SVEBs). Over the entire database, the average accuracy-sensitivity performances of the proposed system for VEB and SVEB detections are 98.3%-84.6% and 97.4%-63.5%, respectively. Finally, due to its parameter-invariant nature, the proposed system is highly generic, and thus, applicable to any ECG dataset.
H.264/AVC over IP

H.264 is the ITU-T’s new, nonbackward compatible video compression Recommendation that significantly outperforms all previous video compression standards. It consists of a video coding layer (VCL) which performs all the classic signal processing tasks and generates bit strings containing coded macroblocks, and a network adaptation layer (NAL) which adapts those bit strings in a network friendly way. The paper describes the use of H.264 coded video over best-effort IP networks, using RTP as the real-time transport protocol. After the description of the environment, the error-resilience tools of H.264 and the draft specification of the RTP payload format are introduced. Next the performance of several possible VCLand NAL-based error-resilience tools of H.264 are verified in simulations.
Feature Selection Based on Structured Sparsity: A Comprehensive Study

Feature selection (FS) is an important component of many pattern recognition tasks. In these tasks, one is often confronted with very high-dimensional data. FS algorithms are designed to identify the relevant feature subset from the original features, which can facilitate subsequent analysis, such as clustering and classification. Structured sparsity-inducing feature selection (SSFS) methods have been widely studied in the last few years, and a number of algorithms have been proposed. However, there is no comprehensive study concerning the connections between different SSFS methods, and how they have evolved. In this paper, we attempt to provide a survey on various SSFS methods, including their motivations and mathematical representations. We then explore the relationship among different formulations and propose a taxonomy to elucidate their evolution. We group the existing SSFS methods into two categories, i.e., vector-based feature selection (feature selection based on lasso) and matrix-based feature selection (feature selection based on  ${l_{r,p}}$ -norm). Furthermore, FS has been combined with other machine learning algorithms for specific applications, such as multitask learning, multilabel learning, multiview learning, classification, and clustering. This paper not only compares the differences and commonalities of these methods based on regression and regularization strategies, but also provides useful guidelines to practitioners working in related fields to guide them how to do feature selection.
Story Segmentation and Detection of Commercials in Broadcast News Video

The Informedia Digital Library Project [Wactlar96] allows full content indexing and retrieval of text, audio and video material. Segmentation is an integral process in the Informedia digital video library. The success of the Informedia project hinges on two critical assumptions: that we can extract sufficiently accurate speech recognition transcripts from the broadcast audio and that we can segment the broadcast into video paragraphs, or stories, that are useful for information retrieval. In previous papers [Hauptmann97, Witbrock97, Witbrock98], we have shown that speech recognition is sufficient for information retrieval of pre-segmented video news stories. In this paper we address the issue of segmentation and demonstrate that a fully automatic system can extract story boundaries using available audio, video and closed-captioning cues. The story segmentation step for the Informedia Digital Video Library splits full-length news broadcasts into individual news stories. During this phase the system also labels commercials as separate “stories”. We explain how the Informedia system takes advantage of the closed captioning frequently broadcast with the news, how it extracts timing information by aligning the closed-captions with the result of the speech recognition, and how the system integrates closed-caption cues with the results of image and audio processing.
Visualization of medical data based on EHR standards.

To organize an efficient interaction between a doctor and an EHR the data has to be presented in the most convenient way. Medical data presentation methods and models must be flexible in order to cover the needs of the users with different backgrounds and requirements. Most visualization methods are doctor oriented, however, there are indications that the involvement of patients can optimize healthcare.



The research aims at specifying the state of the art of medical data visualization. The paper analyzes a number of projects and defines requirements for a generic ISO 13606 based data visualization method. In order to do so it starts with a systematic search for studies on EHR user interfaces.



In order to identify best practices visualization methods were evaluated according to the following criteria: limits of application, customizability, re-usability. The visualization methods were compared by using specified criteria.



The review showed that the analyzed projects can contribute knowledge to the development of a generic visualization method. However, none of them proposed a model that meets all the necessary criteria for a re-usable standard based visualization method. The shortcomings were mostly related to the structure of current medical concept specifications.



The analysis showed that medical data visualization methods use hardcoded GUI, which gives little flexibility. So medical data visualization has to turn from a hardcoded user interface to generic methods. This requires a great effort because current standards are not suitable for organizing the management of visualization data. This contradiction between a generic method and a flexible and user-friendly data layout has to be overcome.
Propagating Confidences through CNNs for Sparse Data Regression

In most computer vision applications, convolutional neural networks (CNNs) operate on dense image data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open problem with numerous applications in autonomous driving, robotics, and surveillance. To tackle this challenging problem, we introduce an algebraically-constrained convolution layer for CNNs with sparse input and demonstrate its capabilities for the scene depth completion task. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. Furthermore, we propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. Comprehensive experiments are performed on the KITTI depth benchmark and the results clearly demonstrate that the proposed approach achieves superior performance while requiring three times fewer parameters than the state-of-the-art methods. Moreover, our approach produces a continuous pixel-wise confidence map enabling information fusion, state inference, and decision support.
EyeLinks: A Gaze-Only Click Alternative for Heterogeneous Clickables

In this paper, we introduce a novel gaze-only interaction technique called EyeLinks, which was designed i) to support various types of discrete clickables (e.g. textual links, buttons, images, tabs, etc.); ii) to be easy to learn and use; iii) to mitigate the inaccuracy of affordable eye trackers. Our technique uses a two-step fixation approach: first, we assign numeric identifiers to clickables in the region where users gaze at and second, users select the desired clickable by performing a fixation on the corresponding confirm button, displayed in a sidebar. This two-step selection enables users to freely explore Web pages, avoids the Midas touch problem and improves accuracy.
 We evaluated our approach by comparing it against the mouse and another gaze-only technique (Actigaze). The results showed no statistically significant difference between EyeLinks and Actigaze, but users considered EyeLinks easier to learn and use than Actigaze and it was also the most preferred. Of the three, the mouse was the most accurate and efficient technique.
A Neural Network Degradation Model for Computing and Updating Residual Life Distributions

The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components. Initial predicted failure times are estimated through trained neural networks using real-time sensory signals. These estimates are used to derive a prior failure time distribution for the component that is being monitored. Subsequent failure time estimates are then utilized to update the prior distributions using a Bayesian approach. The proposed methodology is tested using real world vibration-based degradation signals from rolling contact thrust bearings. The proposed methodology performed favorably when compared to other reliability-based and statistical-based benchmarks.
Mimir: a Market-based Real-time Question and Answer Service

Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.
 In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.