nils-herrmann/clean_scirepeval huggingface dataset:
Mangos and their bioactive components: adding variety to the fruit plate for health.

The diet is an essential factor affecting the risk for development and progression of modern day chronic diseases, particularly those with pathophysiological roots in inflammation and oxidative stress-induced damage. The potential impact of certain foods and their bioactive compounds to reverse or prevent destructive dysregulated processes leading to disease has attracted intense research attention. The mango (Mangifera indica Linn.) is a tropical fruit with distinctive nutritional and phytochemical composition. Notably, the mango contains several essential water- and lipid-soluble micronutrients along with the distinguishing phytochemicals gallotannins and mangiferin. In vitro and in vivo studies reveal various mechanisms through which mangos or their associated compounds reduce risk or reverse metabolic- and inflammation-associated diseases. Health benefits of isolated individual mango compounds and extracts from mango by-products are well described in the literature with less attention devoted to the whole fruit. Here, we review and summarize the available literature assessing the health promoting potential of mango flesh, the edible portion contributing to dietary fruit intake, focusing specifically on modern day health issues of obesity and the risk factors and diseases it precipitates, including diabetes and cardiovascular disease. Additionally, this review explores new insights on the benefits of mango for brain, skin and intestinal health. Overall, the foundation of research supporting the potential role of mangos in reducing risk for inflammation- and metabolically-based chronic diseases is growing.
ENTREPRENEURSHIP IN THE LARGE CORPORATION : A LONGITUDINAL STUDY OF HOW ESTABLISHED FIRMS CREATE BREAKTHROUGH INVENTIONS

We present a model that explains how established firms create breakthrough inventions. We identify three organizational pathologies that inhibit breakthrough inventions: the familiarity trap – favoring the familiar; the maturity trap – favoring the mature; and the propinquity trap – favoring search for solutions near to existing solutions. We argue that by experimenting with novel (i.e., technologies in which the firm lacks prior experience), emerging (technologies that are recent or newly developed in the industry), and pioneering (technologies that do not build on any existing technologies) technologies firms can overcome these traps and create breakthrough inventions. Empirical evidence from the chemicals industry supports our model.
Empirical assessment of machine learning models for agile software development effort estimation using story points

In the present day developing houses, the procedures adopted during the development of software using agile methodologies are acknowledged as a better option than the procedures followed during conventional software development due to its innate characteristics such as iterative development, rapid delivery and reduced risk. Hence, it is desirable that the software development industries should have proper planning for estimating the effort required in agile software development. The existing techniques such as expert opinion, analogy and disaggregation are mostly observed to be ad hoc and in this manner inclined to be mistaken in a number of cases. One of the various approaches for calculating effort of agile projects in an empirical way is the story point approach (SPA). This paper presents a study on analysis of prediction accuracy of estimation process executed in order to improve it using SPA. Different machine learning techniques such as decision tree, stochastic gradient boosting and random forest are considered in order to assess prediction more qualitatively. A comparative analysis of these techniques with existing techniques is also presented and analyzed in order to critically examine their performance.
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization

In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, {actor, director}, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit taskspecific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21% relative improvement over a recently proposed state of the art method and 10% relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available. 1 ∗* The first three authors have contributed equally to this work. https://github.com/PrekshaNema25/ StructuredData_To_Descriptions
Design and realization of two-wheel micro-mouse diagonal dashing

In order to reduce micromouse dashing time in complex maze, and improve micromouse’s stability in high speed dashing, diagonal dashing method was proposed. Considering the actual dashing trajectory of micromouse in diagonal path, the path was decomposed into three different trajectories; Fully consider turning in and turning out of micromouse dashing action in diagonal, leading and passing of the every turning was used to realize micromouse posture adjustment, with the help of accelerometer sensor ADXL202, rotation angle error compensation was done and the micromouse realized its precise position correction; For the diagonal dashing, front sensor S1,S6 and accelerometer sensor ADXL202 were used to ensure micromouse dashing posture. Principle of new diagonal dashing method is verified by micromouse based on STM32F103. Experiments of micromouse dashing show that diagonal dashing method can greatly improve its stability, and also can reduce its dashing time in complex maze.
Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs

With the increased use of high degree-of-freedom robots that must perform tasks in real-time, there is a need for fast algorithms for motion planning. In this work, we view motion planning from a probabilistic perspective. We consider smooth continuous-time trajectories as samples from a Gaussian process (GP) and formulate the planning problem as probabilistic inference. We use factor graphs and numerical optimization to perform inference quickly, and we show how GP interpolation can further increase the speed of the algorithm. Our framework also allows us to incrementally update the solution of the planning problem to contend with changing conditions. We benchmark our algorithm against several recent trajectory optimization algorithms on planning problems in multiple environments. Our evaluation reveals that our approach is several times faster than previous algorithms while retaining robustness. Finally, we demonstrate the incremental version of our algorithm on replanning problems, and show that it often can find successful solutions in a fraction of the time required to replan from scratch.
A generalized accurate modelling method for automotive bulk current injection (BCI) test setups up to 1 GHz

Development of accurate system models of immunity test setups might be extremely time consuming or even impossible. Here a new generalized approach to develop accurate component-based models of different system-level EMC test setups is proposed on the example of a BCI test setup. An equivalent circuit modelling of the components in LF range is combined with measurement-based macromodelling in HF range. The developed models show high accuracy up to 1 GHz. The issues of floating PCB configurations and incorporation of low frequency behaviour could be solved. Both frequency and time-domain simulations are possible. Arbitrary system configurations can be assembled quickly using the proposed component models. Any kind of system simulation like parametric variation and worst-case analysis can be performed with high accuracy.
Assessing the effectiveness of land resource management practices on erosion and vegetative cover using GIS and remote sensing techniques in Melaka watershed , Ethiopia

Land resource management measures, such as soil bund, trench, check dams and plantation had been practiced in Melaka watershed, Ethiopia since 2010. The objective of this study is to assess the impact of above measures on soil loss rate, vegetative cover and livelihood of the population.  The land cover spatial data sets were created from Landsat satellite images of 2010 and 2015 using ERDAS IMAGINE 2014®. Soil loss rate was calculated using Revised Universal Soil Loss Equation (RUSLE) and its input data were generated from field investigation, satellite imageries and rainfall analysis. Data on land resource of the study area and its impact on livelihood were collected through face-to-face interview and key informants. The results revealed that cropland decreased by 9% whereas vegetative cover and grassland increased by 96 and 136%, respectively. The soil loss rate was 19.2 Mg ha−1 year−1 in 2010 and 12.4 Mg ha−1 year−1 in 2015, accounting to 34% decrease over 5 years. It may be attributed to construction of soil bund and the biological measures practiced by the stakeholders. Consequently, land productivity and availability of forage was improved which substantially contributed to the betterment of people’s livelihood.  The land resource management measures practiced in the study area were highly effective for reducing soil loss, improving vegetation cover and livelihood of the population.
Deep learning is a good steganalysis tool when embedding key is reused for different images, even if there is a cover sourcemismatch

Since the BOSS competition, in 2010, most steganalysis approaches use a learning methodology involving two steps: feature extraction, such as the Rich Models (RM), for the image representation, and use of the Ensemble Classifier (EC) for the learning step. In 2015, Qian et al. have shown that the use of a deep learning approach that jointly learns and computes the features, was very promising for the steganalysis. In this paper, we follow-up the study of Qian et al., and show that in the scenario where the steganograph always uses the same embedding key for embedding with the simulator in the different images, due to intrinsic joint minimization and the preservation of spatial information, the results obtained from a Convolutional Neural Network (CNN) or a Fully Connected Neural Network (FNN), if well parameterized, surpass the conventional use of a RM with an EC. First, numerous experiments were conducted in order to find the best ”shape” of the CNN. Second, experiments were carried out in the clairvoyant scenario in order to compare the CNN and FNN to an RM with an EC. The results show more than 16% reduction in the classification error with our CNN or FNN. Third, experiments were also performed in a cover-source mismatch setting. The results show that the CNN and FNN are naturally robust to the mismatch problem. In Addition to the experiments, we provide discussions on the internal mechanisms of a CNN, and weave links with some previously stated ideas, in order to understand the results we obtained. We also have a discussion on the scenario ”same embedding key”.
Suicide, Suicide Attempts, and Suicidal Ideation.

Suicidal behavior is a leading cause of death and disability worldwide. Fortunately, recent developments in suicide theory and research promise to meaningfully advance knowledge and prevention. One key development is the ideation-to-action framework, which stipulates that (a) the development of suicidal ideation and (b) the progression from ideation to suicide attempts are distinct phenomena with distinct explanations and predictors. A second key development is a growing body of research distinguishing factors that predict ideation from those that predict suicide attempts. For example, it is becoming clear that depression, hopelessness, most mental disorders, and even impulsivity predict ideation, but these factors struggle to distinguish those who have attempted suicide from those who have only considered suicide. Means restriction is also emerging as a highly effective way to block progression from ideation to attempt. A third key development is the proliferation of theories of suicide that are positioned within the ideation-to-action framework. These include the interpersonal theory, the integrated motivational-volitional model, and the three-step theory. These perspectives can and should inform the next generation of suicide research and prevention.
Multi-class Generative Adversarial Networks with the L2 Loss Function

Generative adversarial networks (GANs) have achieved huge success in unsupervised learning. Most of GANs treat the discriminator as a classifier with the binary sigmoid cross entropy loss function. However, we find that the sigmoid cross entropy loss function will sometimes lead to the saturation problem in GANs learning. In this work, we propose to adopt the L2 loss function for the discriminator. The properties of the L2 loss function can improve the stabilization of GANs learning. With the usage of the L2 loss function, we propose the multi-class generative adversarial networks for the purpose of image generation with multiple classes. We evaluate the multi-class GANs on a handwritten Chinese characters dataset with 3740 classes. The experiments demonstrate that the multi-class GANs can generate elegant images on datasets with a large number of classes. Comparison experiments between the L2 loss function and the sigmoid cross entropy loss function are also conducted and the results demonstrate the stabilization of the L2 loss function.
Planning in Narrative Generation : A Review of Plan-Based Approaches to the Generation of Story , Discourse and Interactivity in Narratives

The last ten years have seen a significant increase in computationally relevant research seeking to build models of narrative and its use. These efforts have focused in and/or drawn from a range of disciplines, including narrative theory Many of these research efforts have been informed by a focus on the development of an explicit model of narrative and its function. Computational approaches from artificial intelligence (AI) are particularly well-suited to such modeling tasks, as they typically involve precise definitions of aspects of some domain of discourse and well-defined algorithms for reasoning over those definitions. In the case of narrative modeling, there is a natural fit with AI techniques. AI approaches often concern themselves with representing and reasoning about some real world domain of discourse – a microworld where inferences must be made in order to draw conclusions about some higher order property of the world or to explain, predict, control or communicate about the microworld's dynamic state. In this regard, the fictional worlds created by storytellers and the ways that we communicate about them suggest promising and immediate analogs for application of existing AI methods. One of the most immediate analogs between AI research and narrative models lies in the area of reasoning about actions and plans. The goals and plans that characters form and act upon within a story are the primary elements of the story's plot. At first glance, story plans have many of the same features as knowledge representations developed by AI researchers to characterize the plans formed by industrial robots operating to assemble automobile parts on a factory floor or by autonomous vehicles traversing unknown physical landscapes. As we will discuss below, planning representations have offered significant promise in modeling plot structure. Equally as significantly, however, is their ability to be used by intelligent algorithms in the automatic creation of plot lines. Just as AI planning systems can produce new plans to achieve an agent's goals in the face of a unanticipated execution context, so too may planning systems work to produce the plans of a collection of characters as they scheme to obtain, thwart, overcome or succeed.
The Role and Security of Firewalls in IaaS Cloud Computing

Cloud computing is playing an ever larger role in the IT infrastructure. The migration into the cloud means that we must rethink and adapt our security measures. Ultimately, both the cloud provider and the customer have to accept responsibilities to ensure security best practices are followed. Firewalls are one of the most critical security features. Most IaaS providers make firewalls available to their customers. In most cases, the customer assumes a best-case working scenario which is often not assured. In this paper, we studied the filtering behavior of firewalls provided by five different cloud providers. We found that three providers have firewalls available within their infrastructure. Based on our findings, we developed an open-ended firewall monitoring tool which can be used by cloud customers to understand the firewall's filtering behavior. This information can then be efficiently used for risk management and further security considerations. Measuring today's firewalls has shown that they perform well for the basics, although may not be fully featured considering fragmentation or stateful behavior.
Mapping library and information science in China: a coauthorship network analysis

This paper aims to identify the collaboration pattern and network structure of the coauthorship network of library and information science (LIS) in China. Using data from 18 core source LIS journals in China covering 6 years, we construct the LIS coauthorship network. We analyze the network from both macro and micro perspectives and identify some key features of this network: this network is a small-world network, and follows the scale-free character. In the micro-level, we calculate each author’s centrality values and compare them with citation counts. We find that centrality rankings are highly correlated with citation rankings. We also discuss the limitation of current centrality measures for coauthorship network analysis.
AENEID: A generic lithography-friendly detailed router based on post-RET data learning and hotspot detection

In the era of deep sub-wavelength lithography for nanometer VLSI designs, manufacturability and yield issues are critical and need to be addressed during the key physical design implementation stage, in particular detailed routing. However, most existing studies for lithography-friendly routing suffer from either huge run-time due to the intensive lithographic computations involved, or severe loss of quality of results because of the inaccurate predictive models. In this paper, we propose AENEID - a fast, generic and high performance lithography-friendly detailed router for enhanced manufacturability. AENEID combines novel hotspot detection and routing path prediction techniques through modern data learning methods and applies them at the detailed routing stage to drive high fidelity lithography-friendly routing. Compared with existing litho-friendly routing works, AENEID demonstrates 26% to 66% (avg. 50%) of lithography hotspot reduction at the cost of only 18%-38% (avg. 30%) of run-time overhead.
Relation Extraction with Weak Supervision and Distributional Semantics

Extraction aims at detecting and categorizing semantic relations between pairs of entities in unstructured text. It benefits an enormous number of applications such as Web search and Question Answering. Traditional approaches for relation extraction either rely on learning from a large number of accurate human-labeled examples or pattern matching with hand-crafted rules. These resources are very laborious to obtain and can only be applied to a narrow set of target types of interest. This dissertation focuses on learning relations with little or no human supervision. First, we examine the approach that treats relation extraction as a supervised learning problem. We develop an algorithm that is able to train a model with approximately 1/3 of the human-annotation cost and that matches the performance of models trained with high-quality annotation. Second, we investigate distant supervision, a weakly supervised algorithm that automatically generates its own labeled training data. We develop a latent Bayesian framework for this purpose. By using a model which provides a better approximation of the weak source of supervision, it outperforms the state-of-the-art methods. Finally, we investigate the possibility of building all relational tables beforehand with an unsupervised relation extraction algorithm. We develope an effective yet efficient algorithm that combines the power of various semantic resources that are automatically mined from a corpus based on distributional semantics. The algorithm is able to extract a very large set of relations from the web at high precision.
Sensemaking in Organizations : Taking Stock and Moving Forward

Sensemaking is the process through which people work to understand issues or events that are novel, ambiguous, confusing, or in some other way violate expectations. As an activity central to organizing, sensemaking has been the subject of considerable research which has intensified over the last decade. We begin this review with a historical overview of the field, and develop a definition of sensemaking rooted in recurrent themes from the literature. We then review and integrate existing theory and research, focusing on two key bodies of work. The first explores how sensemaking is accomplished, unpacking the sensemaking process by examining how events become triggers for sensemaking, how intersubjective meaning is created, and the role of action in sensemaking. The second body considers how sensemaking enables the accomplishment of other key organizational processes, such organizational change, learning, and creativity and innovation. The final part of the chapter draws on areas