nils-herrmann/clean_scirepeval huggingface dataset:
A Satellite Account for Unpaid Activities A First Step Towards Integration in the System of National Accounts

This paper deals with the set up of a satellite account for unpaid household activities. It will first discuss a framework in which the traditional System of National Accounts (SNA) is enlarged with observed data according to time use surveys, to arrive at an integrated approach in physical units (hours) In this framework time spent on paid labour activities, unpaid labour activities, education and training, leisure time, etc. is integrated in one comprehensive framework, providing ample opportunity to monitor and analyse changes and trade offs between time spent on activities which are traditionally recorded in national accounts and time spent on unpaid household activities. In addition, it is proposed to break out certain national accounts transactions that are particularly relevant in the context of monitoring and analysing unpaid activities, such as, for example, consumption expenditure on food and other materials for preparing meals, various consumer durables used in the production of household market services, and market services which may be close substitutes to the relevant non market services.
Tourism Satellite Account Support Using Online Analytical Processing

The 10 tables in the Tourism Satellite Account (TSA) framework store a significant amount of data. These data when broken down are able to support significantly more statistical analysis. However, sophisticated software is needed, not only for the efficient processing of and access to the data but also for the effective extraction of the information needed in a user friendly, consistent, and efficient manner. This enforces researchers and tourism actors in the use of tourism statistics and thus supports future research and decision making. To achieve this, we have developed a Tourism Information System application using Online Analytical Processing (OLAP) the "TSA OLAP Model," based on commercial "off the shelf" software to ensure worldwide access. Additionally, through the OLAP process, we probe deeply in TSA's information and identify extended methodological and/or theoretical issues of TSA. Thus, we discuss the future research and development of the TSA framework driven by the TSA OLAP model.
Valuing Unpaid Child Care in the U.S. A Prototype Satellite Account Using the American Time Use Survey

This paper builds on previous satellite accounts that treat households as production units, but challenges their measurement and valuation of time devoted to child care, making a case for the inclusion of supervisory child care time that does not overlap with other productive activities. We also suggest several other methodological refinements for estimates based on analysis of data from the American Time Use Survey: application of a vector of specialized replacement cost wage estimates for different child care activities rather than a single wage, and adjustments for the ratio of children to adults present and for the educational attainment of caregivers. Our estimates of the value of child care alone in 2004 and 2010 exceed previous estimates of the value of all non market household production in the U.S. The end result is an upward adjustment of Gross Domestic Product by about 43 percent compared to previous adjustments of about 26 percent.
Muscle activity of leg muscles during unipedal stance on therapy devices with different stability properties.
Venue: Physical therapy in sport official journal of the Association of Chartered Physiotherapists in Sports Medicine

To test the hypotheses that less stable therapy devices require greater muscle activity and that lower leg muscles will have greater increases in muscle activity with less stable therapy devices than upper leg muscles.    Twenty five healthy subjects.  Electromyographic activity of four lower (gastrocnemius medialis, soleus, tibialis anterior, peroneus longus) and four upper leg muscles (vastus medialis and lateralis, biceps femoris, semitendinosus) during unipedal quiet barefoot stance on the dominant leg on a flat rigid surface and on five therapy devices with varying stability properties.  Muscle activity during unipedal stance differed significantly between therapy devices (P 0.001) The order from lowest to highest relative muscle activity matched the order from most to least stable therapy device. There was no significant interaction between muscle location (lower versus upper leg) and therapy device (P 0.985) Magnitudes of additional relative muscle activity for the respective therapy devices differed substantially among lower extremity muscles.  The therapy devices offer a progressive increase in training intensity, and thus may be useful for incremental training programs in physiotherapeutic practice and sports training programs.
Memory Bounded Left Corner Unsupervised Grammar Induction on Child Directed Input
Venue: COLING

This paper presents a new memory bounded left corner parsing model for unsupervised raw text syntax induction, using unsupervised hierarchical hidden Markov models (UHHMM) We deploy this algorithm to shed light on the extent to which human language learners can discover hierarchical syntax through distributional statistics alone, by modeling two widely accepted features of human language acquisition and sentence processing that have not been simultaneously modeled by any existing grammar induction algorithm: (1) a left corner parsing strategy and (2) limited working memory capacity. To model realistic input to human language learners, we evaluate our system on a corpus of child directed speech rather than typical newswire corpora. Results beat or closely match those of three competing systems.
Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community
Venue: ICWSM

Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors' use of gender stereotypes is associated with the community's ratings. We find that male over representation and traditional gender stereotypes (e.g. dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.
Children's embodied experience of living with domestic violence: "I'd go into my panic, and shake, really bad"

Children who experience domestic violence are often described in academic and professional literature as passive victims, whose 'exposure' to violence and abuse at home leaves them psychologically damaged, socially impaired, inarticulate, cognitively 'concrete' and emotionally 'incompetent' Whilst we recognise the importance of understanding the hurt, disruption and damage that domestic violence can cause, we also explore alternative possible ways of talking about and thinking about the lives of children who have experienced domestic violence. We report on interviews and drawings with 27 UK children, using interpretive analysis to explore their capacity for agency and resistance. We explore the paradoxical interplay of children's acceptance and resistance to coercive control, paying specific attention to embodied experience and use of space. We consider how children articulate their experiences of pain and coercion, how they position themselves as embodied and affective subjects, and challenge Scarry's (The Body in Pain, OUP, Oxford, 1985) suggestion that embodied pain and violence are inexpressible.
Understanding Fidget Widgets: Exploring the Design Space of Embodied Self Regulation
Venue: NordiCHI

We argue that embodied self regulation managing attention and emotion through physical activity is an underexplored opportunity for HCI researchers and practitioners. We present research exploring the self regulation design space through the use of Fidget Widgets objects manipulated with the hands alongside daily deskwork. Through a novel qualitative study method we collected 132 exemplars of these behaviors and objects. We employed Tumblr, a social media site, as a formative research instrument and used a grounded theory approach to reveal patterns in physical properties and use. Submissions were markedly varied and included evocative, colorful descriptors such as "crinkly" "squishy" and "clickyclackety" revealing a sensory experience and desires far different than those typically associated with computational work. Our analysis of the submissions revealed evidence of self regulation towards achieving calm, focus, and creativity; additionally, design opportunities in somesthetics and social context emerged. Our ongoing research has implications for HCI practitioners working in tangible computing and peripheral interaction as well as for those supporting wellbeing through interactive technology design.
Embodied techno space: An auto ethnography on affective citizenship in the techno electronic dance music scene

This study examines auto ethnographical experience with bodily participation in spaces of techno electronic dance music (EDM) The article engages with how inner and inter corporeal lived experience in techno space constructs affective citizenship on the very personal level of the participant researcher. In this context, the article attends to the underexplored field of how affective citizenship is attained and valued along embodied knowledge of subcultural capital in the EDM scene. It particularly addresses its overlooked gendered/sexual and technologically mediated (e)motional body. Drawing on a feminist scholar artist method, the article renders embodied encounters with techno space through evocative vignettes that include affective writing, a drawing and introspective poetic revelation. This method aims to convey embodied knowledge of techno space as creative transformative experience beyond conventional modes of retrospective narration. The article concludes with two key lived experiences of affective citizenship: first, at times the gendered/sexual and cyborgian body was mobilised into a state of emotionally shared publicness that co produced techno space. Second, (inter)actions in techno space incited subcultural capital as a set of tacit knowledge assets (including affective, empathic and therapeutic qualities) to be accumulated over techno events and to be occasionally transferred to inclusive participation in the everyday life.
e Taxation: the attitude and intention to use technology in Malaysia

The changing trend to electronic device instead of manual filing system could enhance the collection of revenue and expedite the process.In fact, it could help in reducing the burden and time of routine task.However, the acceptability level is still a major concern in certain area in government applications, particularly in Malaysia (Abdul Aziz Idris, 2012).The allocation of a huge amount in upgrading the technology is seems to be wasted as there are certain operations not fully utilized the resources provided by the government.The evidence of unsatisfactory acceptance level specifically in the tax e filing system introduced by the Inland Revenue Board of Malaysia is one of the concerns.The main objective of this study is to gain understanding on the gaps existed in the e filing among the tax agents/preparer.In seeking for the imperative answers, this research is design for the following objectives: (1) to identify the factors affected the behavioral intention to accept tax e filing among tax agents/preparers in Malaysia; and (2) to determine the relationship between attitude and intention to use tax e filing among tax agents/prepares in Malaysia.Via the Structural Equation Modeling (SEM) it is revealed and supported that Performance and Effort Expectancies as well as Social Influence are the significant paths in influencing the behavioral intention to accept tax e filing in Malaysia.
The Transmission of Legal Precedent among State Supreme Courts in the Twenty First Century
Venue: State Politics Policy Quarterly

Theories of legislative policy diffusion are well formed and extensively tested, but scholars know far less about the diffusion of legal policy and reasoning. Three decades ago, Caldeira's "The Transmission of Legal Precedent: A Study of State Supreme Courts" examined this topic, but the intervening decades have been marked by considerable changes in both technology and the institutional structure of state supreme courts. We explore the effect of these changes by explaining modern translegal judicial communication in the United States. Relying on an original dataset encompassing every citation in every legal decision made by all 52 state supreme courts in 2010, we explore the effect of the proximity of two states and the prestige of the cited court on how frequently state high courts use one another's precedents. We find evidence that both proximity and prestige increase cross state citations.
Role of the Renin Angiotensin Aldosterone System beyond Blood Pressure Regulation: Molecular and Cellular Mechanisms Involved in End Organ Damage during Arterial Hypertension
Venue: International journal of molecular sciences

Arterial hypertension is a common condition worldwide and an important predictor of several complicated diseases. Arterial hypertension can be triggered by many factors, including physiological, genetic, and lifestyle causes. Specifically, molecules of the renin angiotensin aldosterone system not only play important roles in the control of blood pressure, but they are also associated with the genesis of arterial hypertension, thus constituting a need for pharmacological interventions. Chronic high pressure generates mechanical damage along the vascular system, heart, and kidneys, which are the principal organs affected in this condition. In addition to mechanical stress, hypertension induced oxidative stress, chronic inflammation, and the activation of reparative mechanisms lead to end organ damage, mainly due to fibrosis. Clinical trials have demonstrated that renin angiotensin aldosterone system intervention in hypertensive patients lowers morbidity/mortality and inflammatory marker levels as compared to placebo patients, evidencing that this system controls more than blood pressure. This review emphasizes the detrimental effects that a renin angiotensin aldosterone system (RAAS) imbalance has on health considerations above and beyond high blood pressure, such as fibrotic end organ damage.
Neural Associative Memory for Dual Sequence Modeling
Venue: Rep4NLP@ACL

Many important NLP problems can be posed as dual sequence or sequence to sequence modeling tasks. Recent advances in building end to end neural architectures have been highly successful in solving such tasks. In this work we propose a new architecture for dual sequence modeling that is based on associative memory. We derive AM RNNs, a recurrent associative memory (AM) which augments generic recurrent neural networks (RNN) This architecture is extended to the Dual AM RNN which operates on two AMs at once. Our models achieve very competitive results on textual entailment. A qualitative analysis demonstrates that long range dependencies between source and target sequence can be bridged effectively using Dual AM RNNs. However, an initial experiment on auto encoding reveals that these benefits are not exploited by the system when learning to solve sequence to sequence tasks which indicates that additional supervision or regularization is needed.
A Manifold Learning Approach for Personalizing HRTFs from Anthropometric Features
Venue: IEEE/ACM Transactions on Audio, Speech, and Language Processing

We present a new anthropometry based method to personalize head related transfer functions (HRTFs) using manifold learning in both azimuth and elevation angles with a single nonlinear regression model. The core element of our approach is a domain specific nonlinear dimensionality reduction technique, denominated Isomap, over the intraconic component of HRTFs resulting from a spectral decomposition. HRTF intraconic components encode the most important cues for HRTF individualization, leaving out subject independent cues. First, we modify the graph construction procedure of Isomap to integrate relevant prior knowledge of spatial audio into a single manifold for all subjects by exploiting the existing correlations among HRTFs across individuals, directions, and ears. Then, with the aim of preserving the multifactor nature of HRTFs (i.e. subject, direction and frequency) we train a single artificial neural network to predict low dimensional HRTFs from anthropometric features. Finally, we reconstruct the HRTF from its estimated low dimensional version using a neighborhood based reconstruction approach. Our findings show that introducing prior knowledge in Isomap's manifold is a powerful way to capture the underlying factors of spatial hearing. Our experiments show, with p values less than 0.05, that our approach outperforms using, either a PCA linear reduction, or the full HTRF, in its intermediate stages.
Learning the Semantics of Structured Data Sources
Venue: J. Web Semant.

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes) However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.
Named Entity Recognition with Bidirectional LSTM CNNs
Venue: TACL

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance. In this paper, we present a novel neural network architecture that automatically detects word and character level features using a hybrid bidirectional LSTM and CNN architecture, eliminating the need for most feature engineering. We also propose a novel method of encoding partial lexicon matches in neural networks and compare it to existing approaches. Extensive evaluation shows that, given only tokenized text and publicly available word embeddings, our system is competitive on the CoNLL 2003 dataset and surpasses the previously reported state of the art performance on the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed from publicly available sources, we establish new state of the art performance with an F1 score of 91.62 on CoNLL 2003 and 86.28 on OntoNotes, surpassing systems that employ heavy feature engineering, proprietary lexicons, and rich entity linking information.
Attention based LSTM for Aspect level Sentiment Classification
Venue: EMNLP

Aspect level sentiment classification is a fine grained task in sentiment analysis. Since it provides more complete and in depth results, aspect level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, "The appetizers are ok, but the service is slow." for aspect taste, the polarity is positive while for service, the polarity is negative. Therefore, it is worthwhile to explore the connection between an aspect and the content of a sentence. To this end, we propose an Attention based Long Short Term Memory Network for aspect level sentiment classification. The attention mechanism can concentrate on different parts of a sentence when different aspects are taken as input. We experiment on the SemEval 2014 dataset and results show that our model achieves state ofthe art performance on aspect level sentiment classification.
Solar molten salt heated membrane reformer for natural gas upgrading and hydrogen generation: A CFD model

Parabolic trough solar collectors can be used to drive endothermic reactions, such as methane reforming, while using molten salts as a heat transfer fluid. However, linear focus concentrators can only provide temperatures under 600 degC, resulting in CH4 conversions well below 50% The equilibrium can be shifted toward much higher conversions if H2 is continuously removed from the reaction mixture via a H2 selective membrane, while simultaneously generating a high purity H2 stream. In this study, a tube and shell, molten salt heated packed bed membrane reformer (Ni/Al2O3 catalyst, 5 mm supported Pd film membrane) is analyzed numerically using computational fluid dynamics and non isothermal formulation. The effects of molten salt supply rate and reforming feed flow rate on the reformer performance, which is evaluated in terms of CH4 conversion, H2 recovery, and selectivity to CO, are investigated. Depending on operating parameters, significant temperature and concentration gradients may form in both axial and radial directions. These gradients can be prevented by adjusting feed rates in the reforming and molten salt compartments. For the optimized case, CH4 conversion of 99% and H2 recovery of 87% are predicted for the molten salt feed temperature of 600 degC and reforming feed space velocity of 5000 h 1, which corresponds to a power density of 1.9 kW/L and a fuel heating value upgrade of 40% A preliminary techno economic evaluation is provided.