nils-herrmann/clean_scirepeval huggingface dataset:
From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles

We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information. We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 89.59.
New Transfer Learning Techniques for Disparate Label Sets

In natural language understanding (NLU), a user utterance can be labeled differently depending on the domain or application (e.g., weather vs. calendar). Standard domain adaptation techniques are not directly applicable to take advantage of the existing annotations because they assume that the label set is invariant. We propose a solution based on label embeddings induced from canonical correlation analysis (CCA) that reduces the problem to a standard domain adaptation task and allows use of a number of transfer learning techniques. We also introduce a new transfer learning technique based on pretraining of hidden-unit CRFs (HUCRFs). We perform extensive experiments on slot tagging on eight personal digital assistant domains and demonstrate that the proposed methods are superior to strong baselines.
Sparse Overcomplete Word Vector Representations

Current distributed representations of words show little resemblance to theories of lexical semantics. The former are dense and uninterpretable, the latter largely based on familiar, discrete classes (e.g., supersenses) and relations (e.g., synonymy and hypernymy). We propose methods that transform word vectors into sparse (and optionally binary) vectors. The resulting representations are more similar to the interpretable features typically used in NLP, though they are discovered automatically from raw corpora. Because the vectors are highly sparse, they are computationally easy to work with. Most importantly, we find that they outperform the original vectors on benchmark tasks.
Spreadsheet data manipulation using examples

Millions of computer end users need to perform tasks over large spreadsheet data, yet lack the programming knowledge to do such tasks automatically. We present a programming by example methodology that allows end users to automate such repetitive tasks. Our methodology involves designing a domain-specific language and developing a synthesis algorithm that can learn programs in that language from user-provided examples. We present instantiations of this methodology for particular domains of tasks: (a) syntactic transformations of strings using restricted forms of regular expressions, conditionals, and loops, (b) semantic transformations of strings involving lookup in relational tables, and (c) layout transformations on spreadsheet tables. We have implemented this technology as an add-in for the Microsoft Excel Spreadsheet system and have evaluated it successfully over several benchmarks picked from various Excel help forums.
PROCRASTINATION, CONSCIENTIOUSNESS, ANXIETY, AND GOALS: EXPLORING THE MEASUREMENT AND CORRELATES OF PROCRASTINATION AMONG SCHOOL-AGED CHILDREN

We explore the reliability and validity of a self-report measure of procrastination and conscientiousness designed for use with thirdto fifth-grade students. The responses of 120 students are compared with teacher and parent ratings of the student. Confirmatory and exploratory factor analyses were also used to examine the structure of the scale. Procrastination and conscientiousness are highly correlated (inversely); evidence suggests that procrastination and conscientiousness are aspects of the same construct. Procrastination and conscientiousness are correlated with the Physiological Anxiety subscale of the Revised Children’s Manifest Anxiety Scale, and with the Task (Mastery) and Avoidance (Task Aversiveness) subscales of Skaalvik’s (1997) Goal Orientation Scales. Both theoretical implications and implications for interventions are discussed.
Dehumanizing the lowest of the low: neuroimaging responses to extreme out-groups.

Traditionally, prejudice has been conceptualized as simple animosity. The stereotype content model (SCM) shows that some prejudice is worse. The SCM previously demonstrated separate stereotype dimensions of warmth (low-high) and competence (low-high), identifying four distinct out-group clusters. The SCM predicts that only extreme out-groups, groups that are both stereotypically hostile and stereotypically incompetent (low warmth, low competence), such as addicts and the homeless, will be dehumanized. Prior studies show that the medial prefrontal cortex (mPFC) is necessary for social cognition. Functional magnetic resonance imaging provided data for examining brain activations in 10 participants viewing 48 photographs of social groups and 12 participants viewing objects; each picture dependably represented one SCM quadrant. Analyses revealed mPFC activation to all social groups except extreme (low-low) out-groups, who especially activated insula and amygdala, a pattern consistent with disgust, the emotion predicted by the SCM. No objects, though rated with the same emotions, activated the mPFC. This neural evidence supports the prediction that extreme out-groups may be perceived as less than human, or dehumanized.
Concepts in a Probabilistic Language of Thought

Knowledge organizes our understanding of the world, determining what we expect given what we have already seen. Our predictive representations have two key properties: they are productive, and they are graded. Productive generalization is possible because our knowledge decomposes into concepts—elements of knowledge that are combined and recombined to describe particular situations. Gradedness is the observable effect of accounting for uncertainty—our knowledge encodes degrees of belief that lead to graded probabilistic predictions. To put this a different way, concepts form a combinatorial system that enables description of many different situations; each such situation specifies a distribution over what we expect to see in the world, given what we have seen. We may think of this system as a probabilistic language of thought (PLoT) in which representations are built from language-like composition of concepts and the content of those representations is a probability distribution on world states. The purpose of this chapter is to formalize these ideas in computational terms, to illustrate key properties of the PLoT approach with a concrete example, and to draw connections with other views of conceptual structure. People are remarkably flexible at understanding new situations, guessing at unobserved properties or events, and making predictions on the basis of sparse evidence combined with general background knowledge. Consider the game of tug-of-war: two teams matching their strength by pulling on either side of a rope. If a team containing the first author (NG) loses to a team containing the third author (TG), that might provide weak evidence that TG is the stronger of the two. If these teams contain only two members each, we might believe more in TG’s greater strength than if the teams contain eight members each. If TG beats NG in a one-on-one tug-of-war, and NG goes on to beat three other individuals in similar one-on-one contests, we might believe that TG is not only stronger than NG but strong in an absolute sense, relative to the general population, even though we have only directly observed TG participating in a single match. However, if we later found out that NG did not try very hard in his match against TG, but did try hard in his later matches, our convictions about TG’s strength
Deep Diffeomorphic Normalizing Flows

The Normalizing Flow (NF) models a general probability density by estimating an invertible transformation applied on samples drawn from a known distribution. We introduce a new type of NF, called Deep Diffeomorphic Normalizing Flow (DDNF). A diffeomorphic flow is an invertible function where both the function and its inverse are smooth. We construct the flow using an ordinary differential equation (ODE) governed by a time-varying smooth vector field. We use a neural network to parametrize the smooth vector field and a recursive neural network (RNN) for approximating the solution of the ODE. Each cell in the RNN is a residual network implementing one Euler integration step. The architecture of our flow enables efficient likelihood evaluation, straightforward flow inversion, and results in highly flexible density estimation. An end-to-end trained DDNF achieves competitive results with state-of-the-art methods on a suite of density estimation and variational inference tasks. Finally, our method brings concepts from Riemannian geometry that, we believe, can open a new research direction for neural density estimation.
Modelling Experimental Game Design

This paper uses two models of design, Stolterman’s and Löwgren’s three abstraction levels and Lawson’s model of designing, from the general design research to describe the game design process of an experimental pervasive mobile phone game. The game was designed to be deployed at a big science fiction convention for two days and was part of a research through design project where the focus was to understand which core mechanics could work for pervasive mobile phone games. The design process was, as is usual for experimental designs, very iterative. Data were gathered during the design process as entries in a design diary, notes from playtesting and bodystorming sessions, user interface sketches, and a series of software prototypes. The two complementary models of design were used to analyse the design process and the result is that the models give a good overview to an experimental game design process and reveal activities, design situations, and design choices which could have otherwise been lost in the analysis.
How Transferable are Neural Networks in NLP Applications?

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.1
Anticipating Human Activities Using Object Affordances for Reactive Robotic Response

An important aspect of human perception is anticipation, which we use extensively in our day-to-day activities when interacting with other humans as well as with our surroundings. Anticipating which activities will a human do next (and how) can enable an assistive robot to plan ahead for reactive responses. Furthermore, anticipation can even improve the detection accuracy of past activities. The challenge, however, is two-fold: We need to capture the rich context for modeling the activities and object affordances, and we need to anticipate the distribution over a large space of future human activities. In this work, we represent each possible future using an anticipatory temporal conditional random field (ATCRF) that models the rich spatial-temporal relations through object affordances. We then consider each ATCRF as a particle and represent the distribution over the potential futures using a set of particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, we first show that anticipation improves the state-of-the-art detection results. We then show that for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 84.1, 74.4 and 62.2 percent for an anticipation time of 1, 3 and 10 seconds respectively. Finally, we also show a robot using our algorithm for performing a few reactive responses.
OPENDPI: A Toolkit for Developing Document-Centered Environments

Documents are ubiquitous in modern desktop environments, yet these environments are based on the notion of application rather than document. As a result, editing a document often requires juggling with several applications to edit its different parts. This paper presents OpenDPI, an experimental user-interface toolkit designed to create document-centered environments, therefore getting rid of the concept of application. OpenDPI relies on the DPI (Document, Presentation, Instrument) model: documents are visualized through one or more presentations, and manipulated with interaction instruments. The implementation is based on a component model that cleanly separates documents from their presentations and from the instruments that edit them. OpenDPI supports advanced visualization and interaction techniques such as magic lenses and bimanual interaction. Document sharing is also supported with single display groupware as well as remote shared editing. The paper describes the component model and illustrates the use of the toolkit through concrete examples, including multiple views and concurrent interaction.
Fully Convolutional Speech Recognition

Current state-of-the-art speech recognition systems build on recurrent neural networks for acoustic and/or language modeling, and rely on feature extraction pipelines to extract mel-filterbanks or cepstral coefficients. In this paper we present an alternative approach based solely on convolutional neural networks, leveraging recent advances in acoustic models from the raw waveform and language modeling. This fully convolutional approach is trained end-to-end to predict characters from the raw waveform, removing the feature extraction step altogether. An external convolutional language model is used to decode words. On Wall Street Journal, our model matches the current state-of-the-art. On Librispeech, we report state-of-the-art performance among end-to-end models, including Deep Speech 2 trained with 12 times more acoustic data and significantly more linguistic data.
What Data Is Necessary to Data Mine for Knowledge?

databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.
Service-dominant logic : continuing the evolution

Since the introductory article for what has become known as the “service-dominant (S-D) logic of marketing,” “Evolving to a New Dominant Logic for Marketing,” was published in the Journal of Marketing (Vargo, S. L., & Lusch, R. F. (2004a)), there has been considerable discussion and elaboration of its specifics. This article highlights and clarifies the salient issues associated with S-D logic and updates the original foundational premises (FPs) and adds an FP. Directions for future work are also discussed.
Expanding Holographic Embeddings for Knowledge Completion

Neural models operating over structured spaces such as knowledge graphs require a continuous embedding of the discrete elements of this space (such as entities) as well as the relationships between them. Relational embeddings with high expressivity, however, have high model complexity, making them computationally difficult to train. We propose a new family of embeddings for knowledge graphs that interpolate between a method with high model complexity and one, namely Holographic embeddings (HOLE), with low dimensionality and high training efficiency. This interpolation, termed HOLEX, is achieved by concatenating several linearly perturbed copies of original HOLE. We formally characterize the number of perturbed copies needed to provably recover the full entity-entity or entity-relation interaction matrix, leveraging ideas from Haar wavelets and compressed sensing. In practice, using just a handful of Haar-based or random perturbation vectors results in a much stronger knowledge completion system. On the Freebase FB15K dataset, HOLEX outperforms originally reported HOLE by 14.7% on the HITS@10 metric, and the current path-based state-of-the-art method, PTransE, by 4% (absolute).
FLNG compared to LNG carriers – requirements and recommendations for LNG production facilities and regas units

An increasing price and demand for natural gas has made it possible to explore remote gas fields. Traditional offshore production platforms for natural gas have been exporting the partially processed natural gas to shore, where it is further processed to permit consumption by end-users. Such an approach is possible where the gas field is located within a reasonable distance from shore or from an existing gas pipeline network. However, much of the world’s gas reserves are found in remote offshore fields where transport via a pipeline is not feasible or is uneconomic to install and therefore, to date, has not been possible to explore. The development of floating production platforms and, on the receiving end, regasification platforms, have increased the possibilities to explore these fields and transport the liquefied gas in a more efficient form, i.e. liquefied natural gas (LNG), to the end user who in turn can readily import the gas. Floating production platforms and regasification platforms, collectively referred to as FLNG, imply a blend of technology from land-based LNG industry, offshore oil and gas industry and marine transport technology. Regulations and rules based on experience from these applications could become too conservative or not conservative enough when applied to a FLNG unit. Alignment with rules for conventional LNG carriers would be an advantage since this would increase the transparency and possibility for standardization in the building of floating LNG production vessels. The objective of this study is to identify the risks relevant to FLNG. The risks are compared to conventional LNG carriers and whether or not regulatory alignment possibilities exist. To identify the risks, a risk analysis was performed based on the principles of formal safety assessment methodology. To propose regulatory alignment possibilities, the risks found were also evaluated against the existing rules and regulations of Det Norske Veritas. The conclusion of the study is that the largest risk-contributing factor on an FLNG is the presence of processing, liquefaction or regasification equipment and for an LNG carrier it is collision, grounding and contact accidents. Experience from oil FPSOs could be used in the design of LNG FPSOs, and attention needs to be drawn to the additional requirements due to processing and storage of cryogenic liquid on board. FSRUs may follow either an approach for offshore rules or, if intended to follow a regular docking scheme, follow an approach for ship rules with additional issues addressed in classification notes.
Polarized training has greater impact on key endurance variables than threshold, high intensity, or high volume training

ENDURANCE ATHLETES INTEGRATE FOUR CONDITIONING CONCEPTS IN THEIR TRAINING PROGRAMS: high-volume training (HVT), "threshold-training" (THR), high-intensity interval training (HIIT) and a combination of these aforementioned concepts known as polarized training (POL). The purpose of this study was to explore which of these four training concepts provides the greatest response on key components of endurance performance in well-trained endurance athletes.



Forty eight runners, cyclists, triathletes, and cross-country skiers (peak oxygen uptake: (VO2peak): 62.6 ± 7.1 mL·min(-1)·kg(-1)) were randomly assigned to one of four groups performing over 9 weeks. An incremental test, work economy and a VO2peak tests were performed. Training intensity was heart rate controlled.



POL demonstrated the greatest increase in VO2peak (+6.8 ml·min·kg(-1) or 11.7%, P < 0.001), time to exhaustion during the ramp protocol (+17.4%, P < 0.001) and peak velocity/power (+5.1%, P < 0.01). Velocity/power at 4 mmol·L(-1) increased after POL (+8.1%, P < 0.01) and HIIT (+5.6%, P < 0.05). No differences in pre- to post-changes of work economy were found between the groups. Body mass was reduced by 3.7% (P < 0.001) following HIIT, with no changes in the other groups. With the exception of slight improvements in work economy in THR, both HVT and THR had no further effects on measured variables of endurance performance (P > 0.05).



POL resulted in the greatest improvements in most key variables of endurance performance in well-trained endurance athletes. THR or HVT did not lead to further improvements in performance related variables.
CPL: a core language for cloud computing

Running distributed applications in the cloud involves deployment. That is, distribution and configuration of application services and middleware infrastructure. The considerable complexity of these tasks resulted in the emergence of declarative JSON-based domain-specific deployment languages to develop deployment programs. However, existing deployment programs unsafely compose artifacts written in different languages, leading to bugs that are hard to detect before run time. Furthermore, deployment languages do not provide extension points for custom implementations of existing cloud services such as application-specific load balancing policies. To address these shortcomings, we propose CPL (Cloud Platform Language), a statically-typed core language for programming both distributed applications as well as their deployment on a cloud platform. In CPL, application services and deployment programs interact through statically typed, extensible interfaces, and an application can trigger further deployment at run time. We provide a formal semantics of CPL and demonstrate that it enables type-safe, composable and extensible libraries of service combinators, such as load balancing and fault tolerance.
Statistical Themes and Lessons for Data Mining

Data mining is on the interface of Computer Science andStatistics, utilizing advances in both disciplines to make progressin extracting information from large databases. It is an emergingfield that has attracted much attention in a very short period oftime. This article highlights some statistical themes and lessonsthat are directly relevant to data mining and attempts to identifyopportunities where close cooperation between the statistical andcomputational communities might reasonably provide synergy forfurther progress in data analysis.
Challenges in Data-to-Document Generation

Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate humangenerated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copyand reconstructionbased extensions lead to noticeable improvements.