nils-herrmann/clean_scirepeval huggingface dataset:
The effect of knowledge management, organizational culture and organizational learning on innovation in automotive industry

The main objective of this study was to examine the direct and indirect effects of organizational culture, knowledge management and organizational learning on innovation. The study combined knowledge based view theory (KBV) competitive value framework to develop a new original theoretical framework for investigation of factors that affect innovation. Data was gathered from a survey of 279 companies supplying automobile parts to Iran Khodro Company, an Iranian leading automobile manufacturer. Study discovered that organizational culture and knowledge management influenced organizational innovation. Besides that, organizational learning played a significant role as a mediator in that relationship. However, knowledge management was not considered as a mediator in the relationship between organizational culture and organizational innovation. As a practical contribution, the findings of the study serve as a guideline for policy makers and managers in the formulation of policies and strategies for sustainable innovation. Knowing the effectiveness of the innovation can help the government to make decisions about the continuation of this policy. Moreover, study contributes to firm management in formulation of policies and strategies for sustainability in innovation context. Innovation assists organizations supplying the product or service in the automotive sector to operate innovatively, competitively and profitably.
Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression
Venue: Neurocomputing

Electric load forecasting is an important issue for power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non linear learning capability of support vector regression (SVR) this paper presents a SVR model hybridized with the differential empirical mode decomposition (DEMD) method and auto regression (AR) for electric load forecasting. The differential EMD method is used to decompose the electric load into several detail parts associated with high frequencies (intrinsic mode function (IMF) and an approximate part associated with low frequencies. The electric load data from the New South Wales (NSW, Australia) market and the New York Independent System Operator (NYISO, USA) are employed for comparing the forecasting performances of different alternative models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.
A Hybrid Model of EMD and PSO SVR for Short Term Load Forecasting in Residential Quarters

Short term load forecasting plays a vital role in the daily operational management of power utility. To improve the forecasting accuracy, this paper proposes a hybrid EMD PSO SVR forecasting model for short term load forecasting based on empirical mode decomposition (EMD) support vector regression (SVR) and particle swarm optimization (PSO) also considering the effects of temperature, weekends, and holidays. EMD is used to decompose the residential electric load data into a number of intrinsic mode function (IMF) components and one residue; then SVR is constructed to forecast these IMFs and residual value individually. In order to gain optimization parameters of SVR, PSO is implemented to automatically perform the parameter selection in SVR modeling. Then all of these forecasting values are reconstructed to produce the final forecasting result for residential electric load data. Compared with the results from the EMD SVR model, traditional SVR model, and PSO SVR model, the result indicates that the proposed EMD PSO SVR model performs more effectively and more stably in forecasting the residential short term load.
Delaunay based derivative free optimization via global surrogates, part I: linear constraints
Venue: J. Glob. Optim.

A new derivative free optimization algorithm is introduced for nonconvex functions within a feasible domain bounded by linear constraints. Global convergence is guaranteed for twice differentiable functions with bounded Hessian, and is found to be remarkably efficient even for many functions which are not differentiable. Like other Response Surface Methods, at each optimization step, the algorithm minimizes a metric combining an interpolation of existing function evaluations and a model of the uncertainty of this interpolation. By adjusting the respective weighting of these two terms, the algorithm incorporates a tunable balance between global exploration and local refinement; a rule to adjust this balance automatically is also presented. Unlike other methods, any well behaved interpolation strategy may be used. The uncertainty model is built upon the framework of a Delaunay triangulation of existing datapoints in parameter space. A quadratic function which goes to zero at each datapoint is formed within each simplex of this triangulation; the union of each of these quadratics forms the desired uncertainty model. Care is taken to ensure that function evaluations are performed at points that are well situated in parameter space; that is, such that the simplices of the resulting triangulation have circumradii with a known bound. This facilitates well behaved local refinement as additional function evaluations are performed.
Fat Finger Trade and Market Quality: The First Evidence from China

More trading is algorithmic or computer generated, and in markets where it is allowed, high frequency. However, what happens when there is an algorithmic trading error? This study attempts to answer that question by examining the August 16, 2013, fat finger trade in Chinese equity and equity futures markets. We find that both markets were excessively volatile, illiquid, and positively skewed. Moreover, we document that index returns are predictable for a short time, indicating that the fat finger event induced an inefficient market. Our results highlight the importance of market surveillance and regulation to lessen the damage of future fat finger events.
Attitudes, Motivations and Beliefs about L2 Reading in the Filipino Secondary School Classroom: A Mixed methods Study

This study is a two part investigation. The first part focuses on ESL learners' attitudes and motivations for reading in the target language. The second part deals with ESL teachers' beliefs about motivating L2 learners to read. The study involved 100 ESL learners N =100) and 30 teachers N =30) from rural schools in Mindanao, the Philippines. All the participants were recruited through convenience sampling. In other words, participants were selected based on their convenient accessibility and proximity. The current study is a mixed methods project. Both quantitative and qualitative methods were employed to collect different types of data. The instruments used were: a L2 reading attitude survey, a questionnaire dealing with motivations for L2 reading, a survey on beliefs about motivating L2 learners to read in English, a semi structured interview and a L2 reading lesson observation. The quantitative data were statistically analysed. Whenever appropriate, the data were presented in tables and on graphs. The qualitative data were analysed through thematic coding and used to support the quantitative data. The findings show that students have both positive and negative attitudes towards various aspects of L2 reading. They also have different levels of motivation for reading in English, with female participants having higher scores than male participants. The teachers, on the other hand, hold diverse beliefs about motivating learners to read in English. No significant correlation was found between teacher beliefs and students' motivations for reading in English. After the findings have been described, implications for teacher education and instructional practice are offered.
National, regional, and global levels and trends in maternal mortality between 1990 and 2015 with scenario based projections to 2030: a systematic analysis by the United Nations Maternal Mortality Estimation Inter Agency Group
Venue: The Lancet

Millennium Development Goal (MDG) 5 calls for a reduction of 75% in the maternal mortality ratio (MMR) between 1990 and 2015. We estimated levels and trends in maternal mortality for 183 countries to assess progress made. Based on MMR estimates for 2015, we constructed scenario based projections to highlight the accelerations needed to accomplish the Sustainable Development Goal (SDG) global target of less than 70 maternal deaths per 100,000 live births globally by 2030.  We updated the open access UN Maternal Mortality Estimation Inter agency Group (MMEIG) database. Based upon nationally representative data for 171 countries, we generated estimates of maternal mortality and related indicators with uncertainty intervals using a Bayesian model, which extends and refines the previous UN MMEIG estimation approach. The model combines the rate of change implied by a multilevel regression model with a time series model to capture data driven changes in country specific MMRs, and includes a data model to adjust for systematic and random errors associated with different data sources.  The global MMR declined from 385 deaths per 100,000 live births (80% uncertainty interval ranges from 359 to 427) in 1990 to 216 (207 to 249) in 2015, corresponding to a relative decline of 43.9% (34.0 to 48.7) during the 25 year period, with 303,000 (291,000 to 349,000) maternal deaths globally in 2015. Regional progress in reducing the MMR since 1990 ranged from an annual rate of reduction of 1.8% (0 to 3.1) in the Caribbean to 5.0% (4.0 to 6.0) for Eastern Asia. Regional MMRs for 2015 range from 12 (11 to 14) for developed regions to 546 (511 to 652) for sub Saharan Africa. Accelerated progress will be needed to achieve the SDG goal; countries will need to reduce their MMRs at an annual rate of reduction of at least 7.5%  Despite global progress in reducing maternal mortality, immediate action is required to begin making progress towards the ambitious SDG 2030 target, and ultimately eliminating preventable maternal mortality. While the rates of reduction that are required to achieve country specific SDG targets are ambitious for the great majority of high mortality countries, the experience and rates of change between 2000 and 2010 in selected countries those with concerted efforts to reduce the MMR provide inspiration as well as guidance on how to accomplish the acceleration necessary to substantially reduce preventable maternal deaths. Funding Funding from grant R 155 000 146 112 from the National University of Singapore supported the research by LA and SZ. AG is the recipient of a National Institute of Child Health and Human Development, grant T32 HD007275. Funding also provided by USAID and HRP (the UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction)
The politics of fictionality in documentary form: The Act of Killing and The Ambassador

This essay sets out to do two related things: to investigate how the documentary films The Act of Killing and The Ambassador challenge the political and ideological ground of their audiences through radical experiments with documentarist strategies and fictional discourse. In order to carry out this investigation, the essay, secondly, presents and further elaborates a new way of approaching and making sense of fictional discourse using the concept of 'fictionality' an approach inspired by The Rhetoric of Fictionality by Richard Walsh and recently developed further by Henrik Skov Nielsen, James Phelan and Walsh. One of the benefits of this new paradigm is that it offers productive ways of coming to terms with the aesthetic and ethical ramifications of cultural forms that are heavily invested in current political events and issues while at the same time employing experimental uses of fictional discourse.
Enhanced Estimation of Local Intrinsic Dimensionality Using Auxiliary Distances

Estimating Intrinsic Dimensionality (ID) is of high interest in many machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering. Our proposed estimation strategy, ALID, makes use of a subset of the available intra neighborhood distances to achieve faster convergence with fewer samples, and can thus be used on applications in which the data consists of many natural groups of small size. Moreover, it has a smaller bias and variance than state of the art estimators, especially on nonlinear subspaces. We provide a theoretical analysis of the properties of the ALID estimator, and a thorough experimental framework that shows its faster convergence, smaller bias, and smaller variance compared with state of the art estimators of ID.
Hyperspectral Local Intrinsic Dimensionality
Venue: IEEE Transactions on Geoscience and Remote Sensing

The intrinsic dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the spectral signatures of macroscopic materials) in the image, for dimensionality reduction or for subspace learning, among others. Recently, some approaches to perform spectral unmixing and superresolution locally have been proposed, which require a local estimation of the number of endmembers to use. However, the role of ID in local regions of hyperspectral images has not been properly addressed. Some important issues when dealing with small regions of hyperspectral data can seriously affect the performance of conventional hyperspectral ID estimators. We show that three factors mainly affect local ID estimation: the number of pixels in the local regions, which has to be high enough for the estimations to be relevant, the number of hyperspectral bands which complicates the estimations if the ambient space has a high dimensionality, and the noise, which can be misinterpreted as a signal when its power is important. Here, we review the hyperspectral ID estimators on the literature for local ID estimation, we show how they behave in a local setting on synthetic and real data sets, and we provide some guidelines to make proper use of these estimators in local approaches.
Learning Opaque and Transparent Interactions in Harmonic Serialism

This paper presents initial modeling results comparing the relative learnability of four basic types of process interactions: bleeding, feeding, counterfeeding and counterbleeding. The principle finding is that the learning model does not predict an absolute hierarchy of learning difficulty based on the type of process interaction involved. Rather, relative learning difficulty depends on numerous interacting factors, including how evidence for the target language is quantitatively distributed in the learning data, how distant the target language is from the initial grammar, and what alternative hypotheses the learner must rule out during learning. The model predicts that under some circumstances transparent orderings (Kiparsky 1971) are easier to learn but that under different conditions, orderings that maximize process utilization (Kiparsky 1968) are learned more easily. The model also predicts that different aspects of interactions can be difficult to learn under different circumstances: in some cases it is the orderings themselves that are difficult to learn, while in others, the model is slow to learn one of the processes involved in an interaction. The discussion relates these learning difficulties to Kiparsky's (1968, 1971) discussion of diachronic rule reordering and rule loss and highlights directions for further research.
Harmonic serialism with lexical selection: Evidence from Jerriais allomorphy

Harmonic Serialism (HS) is a constraint based theory of phonology that has gained interest in the last ten years. As a theory of phonology, HS must be able to account for phonologically conditioned allomorphy. Currently in HS, phonologically conditioned allomorphy is analyzed in one of two ways either using a single underlying representation in HS or through the realizational framework of Optimal Interleaving (known as Harmonic Serialism with Optimal Interleaving (HS/OI) Yet, using data from Jersey Norman French (Jerriais) I show that neither approach can account for certain cases of phonologically conditioned allomorphy. To remedy this deficiency, I propose the inclusion of Lexical Selection (LS) in HS in a framework termed Harmonic Serialism with Lexical Selection (HS/LS) LS provides for the lexical listing of allomorphs and, when needed, the ordering of allomorphs in the input to reflect a grammar's preference to use certain allomorphs regardless of surface markedness. Using data from Jerriais, Dyirbal, Moroccan Arabic, Polish, and Catalan I develop a full theory of HS/LS. I explore how GEN is conceived of in HS, how allomorph selection functions within the theory, and how HS/LS can handle certain cases of opacity. I propose a revision of the constraint PRIORITY, which is the LS constraint responsible for ensuring respect of allomorph ordering, from a gradient faithfulness constraint to a categorical markedness constraint. The incorporation of
Kids Design Glass

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Efficient electrochromic smart windows of one dimensional pure brookite TiO2 nanoneedles

We report on the electrochromic behaviors of pure 1D brookite TiO2 nanoneedles grown on a conducting indium tin oxide thin film coated a glass substrate. The pure 1D brookite TiO2 nanoneedles of ~650 nm long and ~30 nm wide were synthesized via hot filament metal oxide vapor deposition. X ray diffractometry analysis revealed the pure 1D brookite TiO2 nanoneedles to be in orthorhombic crystalline form within space group Pbca. The excellent electrochromic characteristics, including the stable and reversible coloration bleaching cycles, and fair diffusion coefficient ~1.56x10 11 cm2/s) high reversibility ~99% great coloration efficiency (226 cm2/C) high optical transmittance difference ~67% large optical density (0.85) and fast coloration and bleaching times (13.10 and 5.14 s) at a wavelength of 600 nm, make the pure 1D brookite TiO2 nanoneedles very good materials for use in smart windows.
The future of mental health care: peer to peer support and social media
Venue: Epidemiology and Psychiatric Sciences

People with serious mental illness are increasingly turning to popular social media, including Facebook, Twitter or YouTube, to share their illness experiences or seek advice from others with similar health conditions. This emerging form of unsolicited communication among self forming online communities of patients and individuals with diverse health concerns is referred to as peer to peer support. We offer a perspective on how online peer to peer connections among people with serious mental illness could advance efforts to promote mental and physical wellbeing in this group.  In this commentary, we take the perspective that when an individual with serious mental illness decides to connect with similar others online it represents a critical point in their illness experience. We propose a conceptual model to illustrate how online peer to peer connections may afford opportunities for individuals with serious mental illness to challenge stigma, increase consumer activation and access online interventions for mental and physical wellbeing.  People with serious mental illness report benefits from interacting with peers online from greater social connectedness, feelings of group belonging and by sharing personal stories and strategies for coping with day to day challenges of living with a mental illness. Within online communities, individuals with serious mental illness could challenge stigma through personal empowerment and providing hope. By learning from peers online, these individuals may gain insight about important health care decisions, which could promote mental health care seeking behaviours. These individuals could also access interventions for mental and physical wellbeing delivered through social media that could incorporate mutual support between peers, help promote treatment engagement and reach a wider demographic. Unforeseen risks may include exposure to misleading information, facing hostile or derogatory comments from others, or feeling more uncertain about one's health condition. However, given the evidence to date, the benefits of online peer to peer support appear to outweigh the potential risks.  Future research must explore these opportunities to support and empower people with serious mental illness through online peer networks while carefully considering potential risks that may arise from online peer to peer interactions. Efforts will also need to address methodological challenges in the form of evaluating interventions delivered through social media and collecting objective mental and physical health outcome measures online. A key challenge will be to determine whether skills learned from peers in online networks translate into tangible and meaningful improvements in recovery, employment, or mental and physical wellbeing in the offline world.
Towards Personal infrastructure to manage long term open learner models
Venue: UMAP

Nowadays, the lifelong learning is a key issue in our life. Learners have their personal learning data scattered on different platforms and websites without any control on them and without any defined access duration. In this paper, we propose to explore the feasibility of Personal information manager systems in the Open Learner Model context that allows the control of personal learning data by learners themselves, the persistence, and the privacy. We propose to focus on a relevant technical infrastructure giving full personal control to users in order to manage Open Learner Models in lifelong and life wide perspectives. This work is dedicated for all lifelong learners without any specific IT competency to manage their own personal learning data in a lifelong perspective.
Sex with Robots for Love Free Encounters
Venue: LSR

This paper considers sex with robots for love free encounters within the context of pornographic experiences. Leisure sex and pornography are briefly outlined, along with the potential of the market. Limited research on both the user experience of pornography and the physical functionality of sex robots is highlighted. The physical embodiment of sex robots is considered, questioning whether we need human like robots or something else entirely. Technological advances for pornography and their relevance for sex robots are explored examining the potential offered through the integration of Virtual Reality, teledildonics, soft and wearable robots. The potential of categorising sex robots as fantasy hardware is considered seeking to provide a palatable terminology. This paper concludes that researchers need to engage with the Porn Sector in creating innovative sexual experiences with robots, aiming to create a new type of sexual experience, rather than replicating humans as seen in most science fiction.