nils-herrmann/clean_scirepeval huggingface dataset:
Ontologies and languages for representing mathematical knowledge on the Semantic Web

Mathematics is a ubiquitous foundation of science, technology, and engineering. Specific areas of mathematics, such as numeric and symbolic computation or logics, enjoy considerable software support. Working mathematicians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine support for knowledge organization and reuse, and they are disconnected from tools such as computer algebra systems or interactive proof assistants. We argue that such scenarios will benefit from Semantic Web technology. Conversely, mathematics is still underrepresented on the Web of [Linked] Data. There are mathematics-related Linked Data, for example statistical government data or scientific publication databases, but their mathematical semantics has not yet been modeled. We argue that the services for the Web of Data will benefit from a deeper representation of mathematical knowledge. Mathematical knowledge comprises structures given in a logical language – formulae, statements (e.g. axioms), and theories –, a mixture of rigorous natural language and symbolic notation in documents, application-specific metadata, and discussions about conceptualizations, formalizations, proofs, and (counter-)examples. Our review of vocabularies for representing these structures covers ontologies for mathematical problems, proofs, interlinked scientific publications, scientific discourse, as well as mathematical metadata vocabularies and domain knowledge from pure and applied mathematics. Many fields of mathematics have not yet been implemented as proper Semantic Web ontologies; however, we show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to the Web of Data. We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to interlink them, and how to take advantage of these new connections.
Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds

The aggregation of many independent estimates can outperform the most accurate individual judgement 1–3 . This centenarian finding 1,2 , popularly known as the 'wisdom of crowds' 3 , has been applied to problems ranging from the diagnosis of cancer 4 to financial forecasting 5 . It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd’s collective accuracy. We asked a live crowd (N = 5,180) to respond to general-knowledge questions (for example, "What is the height of the Eiffel Tower?"). Participants first answered individually, then deliberated and made consensus decisions in groups of five, and finally provided revised individual estimates. We found that averaging consensus decisions was substantially more accurate than aggregating the initial independent opinions. Remarkably, combining as few as four consensus choices outperformed the wisdom of thousands of individuals. The collective wisdom of crowds often provides better answers to problems than individual judgements. Here, a large experiment that split a crowd into many small deliberative groups produced better estimates than the average of all answers in the crowd.
Assistance-On-Demand: a Speech-Based Assistance System for Urban Intersections

We evaluated a system to support the driver in urban intersections (called "Assistance on Demand" AoD system). The system is controlled via speech and supports the driver in monitoring and decision making by providing recommendations for suitable time gaps to enter the intersection. This speech-based control of the system allows the implementation of an "on-demand"-concept where the driver can activate the assistance only if he desires support. 24 drivers took part in the study, performing three drives each. A comparison with a manual condition without system support showed that the AoD system was highly accepted, decreased workload and facilitated monitoring of traffic. In addition, subjective acceptance highly correlated with objective acceptance measured by the actual usage of the system. This clearly justifies the "on-demand"-concept.
Translation Quality Assessment in Health Research: A Functionalist Alternative to Back-Translation.

As international research studies become more commonplace, the importance of developing multilingual research instruments continues to increase and with it that of translated materials. It is therefore not unexpected that assessing the quality of translated materials (e.g., research instruments, questionnaires, etc.) has become essential to cross-cultural research, given that the reliability and validity of the research findings crucially depend on the translated instruments. In some fields (e.g., public health and medicine), the quality of translated instruments can also impact the effectiveness and success of interventions and public campaigns. Back-translation (BT) is a commonly used quality assessment tool in cross-cultural research. This quality assurance technique consists of (a) translation (target text [TT1]) of the source text (ST), (b) translation (TT2) of TT1 back into the source language, and (c) comparison of TT2 with ST to make sure there are no discrepancies. The accuracy of the BT with respect to the source is supposed to reflect equivalence/accuracy of the TT. This article shows how the use of BT as a translation quality assessment method can have a detrimental effect on a research study and proposes alternatives to BT. One alternative is illustrated on the basis of the translation and quality assessment methods used in a research study on hearing loss carried out in a border community in the southwest of the United States.
Approximate Newton Methods for Policy Search in Markov Decision Processes

Approximate Newton methods are standard optimization tools which aim to maintain the benefits of Newton’s method, such as a fast rate of convergence, while alleviating its drawbacks, such as computationally expensive calculation or estimation of the inverse Hessian. In this work we investigate approximate Newton methods for policy optimization in Markov decision processes (MDPs). We first analyse the structure of the Hessian of the total expected reward, which is a standard objective function for MDPs. We show that, like the gradient, the Hessian exhibits useful structure in the context of MDPs and we use this analysis to motivate two Gauss-Newton methods for MDPs. Like the Gauss-Newton method for non-linear least squares, these methods drop certain terms in the Hessian. The approximate Hessians possess desirable properties, such as negative definiteness, and we demonstrate several important performance guarantees including guaranteed ascent directions, invariance to affine transformation of the parameter space and convergence guarantees. We finally provide a unifying perspective of key policy search algorithms, demonstrating that our second Gauss-Newton algorithm is closely related to both the EMalgorithm and natural gradient ascent applied to MDPs, but performs significantly better in practice on a range of challenging domains.
Solid waste management practices and review of recovery and recycling operations in Turkey.

This paper provides a general overview of solid waste data and management practices employed in Turkey during the last decade. Municipal solid waste statistics and management practices including waste recovery and recycling initiatives have been evaluated. Detailed data on solid waste management practices including collection, recovery and disposal, together with the results of cost analyses, have been presented. Based on these evaluations basic cost estimations on collection and sorting of recyclable solid waste in Turkey have been provided. The results indicate that the household solid waste generation in Turkey, per capita, is around 0.6 kg/year, whereas municipal solid waste generation is close to 1 kg/year. The major constituents of municipal solid waste are organic in nature and approximately 1/4 of municipal solid waste is recyclable. Separate collection programmes for recyclable household waste by more than 60 municipalities, continuing in excess of 3 years, demonstrate solid evidence for public acceptance and continuing support from the citizens. Opinion polls indicate that more than 80% of the population in the project regions is ready and willing to participate in separate collection programmes. The analysis of output data of the Material Recovery Facilities shows that, although paper, including cardboard, is the main constituent, the composition of recyclable waste varies strongly by the source or the type of collection point.
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games

As mobile devices become more and more popular, mobile gaming has emerged as a promising market with billion-dollar revenues. A variety of mobile game platforms and services have been developed around the world. A critical challenge for these platforms and services is to understand the churn behavior in mobile games, which usually involves churn at micro level (between an app and a specific user) and macro level (between an app and all its users). Accurate micro-level churn prediction and macro-level churn ranking will benefit many stakeholders such as game developers, advertisers, and platform operators. In this paper, we present the first large-scale churn analysis for mobile games that supports both micro-level churn prediction and macrolevel churn ranking. For micro-level churn prediction, in view of the common limitations of the state-of-the-art methods built upon traditional machine learning models, we devise a novel semi-supervised and inductive embedding model that jointly learns the prediction function and the embedding function for user-app relationships. We model these two functions by deep neural networks with a unique edge embedding technique that is able to capture both contextual information and relationship dynamics. We also design a novel attributed random walk technique that takes into consideration both topological adjacency and attribute similarities. To address macro-level churn ranking, we propose to construct a relationship graph with estimated micro-level churn probabilities as edge weights and adapt link analysis algorithms on the graph. We devise a simple algorithm SimSum and adapt two more advanced algorithms PageRank and HITS. The performance of our solutions for the two-level churn analysis problems is evaluated on real-world data collected from the Samsung Game Launcher platform. The data includes tens of thousands of mobile games and hundreds of millions of user∗ This work was done during the authors’ internships at Samsung Research America Received xxx Revised xxx Accepted xxx ar X iv :1 90 1. 06 24 7v 1 [ cs .L G ] 1 4 Ja n 20 19
Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds

The space of graphs is often characterised by a non-trivial geometry, which complicates performing inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs. Among these, constantcurvature Riemannian manifolds (CCMs) offer embedding spaces suitable for studying the statistical properties of a graph distribution, as they provide ways to easily compute metric geodesic distances. In this paper, we focus on the problem of detecting changes in a stream of attributed graphs. To this end, we introduce a novel change detection framework based on neural networks and CCMs that takes into account the non-Euclidean nature of graphs. Our contributions in this work are twofold. First, via a novel approach based on adversarial learning, we compute graph embeddings by training an autoencoder to represent graphs on CCMs. Second, we introduce two novel change detection tests operating on CCMs. We perform experiments on synthetic graph streams, and on sequences of functional networks extracted from intracranial EEG data with the aim of predicting the onset of epileptic seizures. Results show that the proposed methods are able to detect even small changes in the graphgenerating process, consistently outperforming approaches based on Euclidean embeddings.
Class vs. Student in a Bayesian Network Student Model

For decades, intelligent tutoring systems researchers have been developing various methods of student modeling. Most of the models, including two of the most popular approaches: Knowledge Tracing model and Performance Factor Analysis, all have similar assumption: the information needed to model the student is the student’s performance. However, there are other sources of information that are not utilized, such as the performance on other students in same class. This paper extends the Student-Skill extension of Knowledge Tracing, to take into account the class information, and learns four parameters: prior knowledge, learn, guess and slip for each class of students enrolled in the system. The paper then compares the accuracy using the four parameters for each class versus the four parameters for each student to find out which parameter set works better in predicting student performance. The result shows that modeling at coarser grain sizes can actually result in higher predictive accuracy, and data about classmates’ performance is results in a higher predictive accuracy on unseen test data.
On then Use of Windows for Harmonic Analysis with the Discrete Fourier Transform

HERE IS MUCH signal processing devoted to detection and estimation. Detection is the task of detetmitdng if a specific signal set is pteaettt in an obs&tion, whflc estimation is the task of obtaining the va.iues of the parameters derriblng the signal. Often the s@tal is complicated or is corrupted by interfeting signals or noise To facilitate the detection and estimation of signal sets. the obsenation is decomposed by a basis set which spans the signal space [ 1) For many problems of engineering interest, the class of aigttlls being sought are periodic which leads quite natuallv to a decomposition by a basis consistittg of simple petiodic fun=tions, the sines and cosines. The classic Fourier tran.,fot,,, h the mechanism by which we M able to perform this decomposttmn. BY necessity, every observed signal we pmmust be of finite extent. The extent may be adjustable and Axtable. but it must be fire. Proces%ng a fiite-duration observation ~POSCS mteresting and interacting considentior,s on the hamomc analysic rhese consldentions include detectability of tones in the Presence of nearby strong tones, rcoohability of similarstrength nearby tones, tesolvability of Gxifting tona, and biases in estimating the parameten of my of the alonmenhoned signals. For practicality, the data we pare N unifomdy spaced samples of the obsetvcd signal. For convenience. N is highJy composite, and we will zwtme N is evett. The harmottic estm~afes we obtain UtmugJt the discrae Fowie~ tmnsfotm (DFT) arc N mifcwmly spaced samples of the asaciated periodic spectra. This approach in elegant and attnctive when the proce~ scheme is cast as a spectral decomposition in an N-dimensional orthogonal vector space 121. Unfottunately, in mmY practical situations, to obtain meaningful results this elegance must be compmmised. One such t=O,l;..,Nl.N.N+l.
Distributed REScala: an update algorithm for distributed reactive programming

Reactive programming improves the design of reactive applications by relocating the logic for managing dependencies between dependent values away from the application logic to the language implementation. Many distributed applications are reactive. Yet, existing change propagation algorithms are not suitable in a distributed setting.
 We propose Distributed REScala, a reactive language with a change propagation algorithm that works without centralized knowledge about the topology of the dependency structure among reactive values and avoids unnecessary propagation of changes, while retaining safety guarantees (glitch freedom). Distributed REScala enables distributed reactive programming, bringing the benefits of reactive programming to distributed applications. We demonstrate the enabled design improvements by a case study. We also empirically evaluate the performance of our algorithm in comparison to other algorithms in a simulated distributed setting.
Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing.
Tagsplanations: explaining recommendations using tags

While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations.
Refund Attacks on Bitcoin's Payment Protocol

BIP70 is a community-accepted Payment Protocol standard that governs how merchants and customers perform payments in Bitcoin. This standard is supported by most major wallets and the two dominant Payment Processors: Coinbase and BitPay, who collectively provide the infrastructure for accepting Bitcoin as a form of payment to more than 100,000 merchants. In this paper, we present new attacks on the Payment Protocol, which affect all BIP70 merchants. The Silkroad Trader attack highlights an authentication vulnerability in the Payment Protocol while the Marketplace Trader attack exploits the refund policies of existing Payment Processors. Both attacks have been experimentally verified on real-life merchants using a modified Bitcoin wallet. The attacks have been acknowledged by both Coinbase and Bitpay with temporary mitigation measures put in place. However, to fully address the identified issues will require revising the BIP70 standard. We present a concrete proposal to revise BIP70 by providing the merchant with publicly verifiable evidence to prevent both attacks.
Quality of life after traumatic brain injury: the clinical use of the QOLIBRI, a novel disease-specific instrument.

To report the clinical use of the QOLIBRI, a disease-specific measure of health-related quality-of-life (HRQoL) after traumatic brain injury (TBI).



The QOLIBRI, with 37 items in six scales (cognition, self, daily life and autonomy, social relationships, emotions and physical problems) was completed by 795 patients in six languages (Finnish, German, Italian, French, English and Dutch). QOLIBRI scores were examined by variables likely to be influenced by rehabilitation interventions and included socio-demographic, functional outcome, health status and mental health variables.



The QOLIBRI was self-completed by 73% of participants and 27% completed it in interview. It was sensitive to areas of life amenable to intervention, such as accommodation, work participation, health status (including mental health) and functional outcome.



The QOLIBRI provides information about patient's subjective perception of his/her HRQoL which supplements clinical measures and measures of functional outcome. It can be applied across different populations and cultures. It allows the identification of personal needs, the prioritization of therapeutic goals and the evaluation of individual progress. It may also be useful in clinical trials and in longitudinal studies of TBI recovery.
Implementing industry 4.0 — A technological readiness perspective

This paper identifies the relative importance of key enabling factors for implementing industry 4.0 from a technological readiness perspective. The research involves the identification of enabling factors, their categorization into technological readiness dimensions, followed by the determination of the relative importance of both technological readiness dimensions and key objective measures. The results show a strong relationship between technological readiness and design principles of Industry 4.0. The findings suggest that process-related objectives are more important than economic-related and environmental-related objectives when implementing industry 4.0. The results also show that “the knowledge of humans in technology and how to leverage it” and “improving the ability of machines and devices in connecting to the internet” are the most important factors for achieving all objective measures. Practitioners can use the apparent relationship between process related objectives and key technological dimensions for setting appropriate strategies and policies when moving towards Industry 4.0.