nils-herrmann/clean_scirepeval huggingface dataset:
Ranking Sentences for Extractive Summarization with Reinforcement Learning

Single document summarization is the task of producing a shorter version of a document while preserving its principal information content. In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. We use our algorithm to train a neural summarization model on the CNN and DailyMail datasets and demonstrate experimentally that it outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by
Neonatal intensive care decision support systems using artificial intelligence techniques: a systematic review

A neonatal intensive care unit (NICU) provides critical services to preterm and high-risk infants. Over the years, many tools and techniques have been introduced to support the clinical decisions made by specialists in the NICU. This study systematically reviewed the different technologies used in neonatal decision support systems (DSS), including cognitive analysis, artificial neural networks, data mining techniques, multi-agent systems, and highlighted their role in patient diagnosis, prognosis, monitoring, and healthcare management. Articles on NICU DSS were surveyed, Searches were based on the PubMed, Science Direct, and IEEE databases and only English articles published after 1990 were included. The overall search strategy was to retrieve articles that included terms that were related to “NICU Decision Support Systems” or “Artificial Intelligence” and “Neonatal”. Different methods and artificial intelligence techniques used in NICU decision support systems were assessed and related outcomes, variables, methods and performance measures was reported and discussed. Because of the dynamic, heterogeneous, and real-time environment of the NICU, the processes and medical rules that are followed within a NICU are complicated, and the data records that are produced are complex and frequent. Therefore, a single tool or technology could not cover all the needs of a NICU. However, it is important to examine and deploy new temporal data mining approaches and system architectures, such as multi-agent systems, services, and sensors, to provide integrated real-time solutions for NICU.
Deep Learning for Generic Object Detection: A Survey

Generic object detection, aiming at locating object instances from a large number of predefined categories in natural images, is one of the most fundamental and challenging problems in computer vision. Deep learning techniques have emerged in recent years as powerful methods for learning feature representations directly from data, and have led to remarkable breakthroughs in the field of generic object detection. Given this time of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought by deep learning techniques. More than 250 key contributions are included in this survey, covering many aspects of generic object detection research: leading detection frameworks and fundamental subproblems including object feature representation, object proposal generation, context information modeling and training strategies; evaluation issues, specifically benchmark datasets, evaluation metrics, and state of the art performance. We finish by identifying promising directions for future research.
Plastics and health risks.

By 2010, the worldwide annual production of plastics will surpass 300 million tons. Plastics are indispensable materials in modern society, and many products manufactured from plastics are a boon to public health (e.g., disposable syringes, intravenous bags). However, plastics also pose health risks. Of principal concern are endocrine-disrupting properties, as triggered for example by bisphenol A and di-(2-ethylhexyl) phthalate (DEHP). Opinions on the safety of plastics vary widely, and despite more than five decades of research, scientific consensus on product safety is still elusive. This literature review summarizes information from more than 120 peer-reviewed publications on health effects of plastics and plasticizers in lab animals and humans. It examines problematic exposures of susceptible populations and also briefly summarizes adverse environmental impacts from plastic pollution. Ongoing efforts to steer human society toward resource conservation and sustainable consumption are discussed, including the concept of the 5 Rs--i.e., reduce, reuse, recycle, rethink, restrain--for minimizing pre- and postnatal exposures to potentially harmful components of plastics.
Toward Unsupervised Text Content Manipulation

Controlled generation of text is of high practical use. Recent efforts have made impressive progress in generating or editing sentences with given textual attributes (e.g., sentiment). This work studies a new practical setting of text content manipulation. Given a structured record, such as (PLAYER: Lebron,  20, ASSISTS: 10), and a reference sentence, such as Kobe easily dropped 30 points, we aim to generate a sentence that accurately describes the full content in the record, with the same writing style (e.g., wording, transitions) of the reference. The problem is unsupervised due to lack of parallel data in practice, and is challenging to minimally yet effectively manipulate the text (by rewriting/adding/deleting text portions) to ensure fidelity to the structured content. We derive a dataset from a basketball game report corpus as our testbed, and develop a neural method with unsupervised competing objectives and explicit content coverage constraints. Automatic and human evaluations show superiority of our approach over competitive methods including a strong rule-based baseline and prior approaches designed for style transfer.
Learning to Disentangle Factors of Variation with Manifold Interaction

Many latent factors of variation interact to generate sensory data; for example, pose, morphology and expression in face images. In this work, we propose to learn manifold coordinates for the relevant factors of variation and to model their joint interaction. Many existing feature learning algorithms focus on a single task and extract features that are sensitive to the task-relevant factors and invariant to all others. However, models that just extract a single set of invariant features do not exploit the relationships among the latent factors. To address this, we propose a higher-order Boltzmann machine that incorporates multiplicative interactions among groups of hidden units that each learn to encode a distinct factor of variation. Furthermore, we propose correspondencebased training strategies that allow effective disentangling. Our model achieves state-of-the-art emotion recognition and face verification performance on the Toronto Face Database. We also demonstrate disentangled features learned on the CMU Multi-PIE dataset.
Automatic Scene Text Recognition using a Convolutional Neural Network

This paper presents an automatic recognition method for color text characters extracted from scene images, which is robust to strong distortions, complex background, low resolution and non uniform lightning. Based on a specific architecture of convolutional neural networks, the proposed system automatically learns how to recognize characters without making any assumptions, without applying any preprocessing or post-processing and without using tunable parameters. For this purpose, we use a training set of scene text images extracted from the ICDAR 2003 public training database. The proposed method is compared to recent character recognition techniques for scene images based on the ICDAR 2003 public samples dataset in order to contribute to the state-of-the-art method comparison efforts initiated in ICDAR 2003. Experimental results show an encouraging average recognition rate of 84.53%, ranging from 93.47% for clear images to 67.86% for seriously distorted images.
Sexual Orientation, Controversy, and Science.

SummaryOngoing political controversies around the world exemplify a long-standing and widespread preoccupation with the acceptability of homosexuality. Nonheterosexual people have seen dramatic surges both in their rights and in positive public opinion in many Western countries. In contrast, in much of Africa, the Middle East, the Caribbean, Oceania, and parts of Asia, homosexual behavior remains illegal and severely punishable, with some countries retaining the death penalty for it. Political controversies about sexual orientation have often overlapped with scientific controversies. That is, participants on both sides of the sociopolitical debates have tended to believe that scientific findings-and scientific truths-about sexual orientation matter a great deal in making political decisions. The most contentious scientific issues have concerned the causes of sexual orientation-that is, why are some people heterosexual, others bisexual, and others homosexual? The actual relevance of these issues to social, political, and ethical decisions is often poorly justified, however.
A Survey on Deep Learning in Big Data

Big Data means extremely huge large data sets that can be analyzed to find patterns, trends. One technique that can be used for data analysis so that able to help us find abstract patterns in Big Data is Deep Learning. If we apply Deep Learning to Big Data, we can find unknown and useful patterns that were impossible so far. With the help of Deep Learning, AI is getting smart. There is a hypothesis in this regard, the more data, the more abstract knowledge. So a handy survey of Big Data, Deep Learning and its application in Big Data is necessary. In this paper, we provide a comprehensive survey on what is Big Data, comparing methods, its research problems, and trends. Then a survey of Deep Learning, its methods, comparison of frameworks, and algorithms is presented. And at last, application of Deep Learning in Big Data, its challenges, open research problems and future trends are presented.
Multi-Objective Particle Swarm Optimizers : A Survey of the State-ofthe-Art

The successof the Particle Swarm Optimization (PSO) algorithm as a single-objective optimizer (mainly when dealing with continuous search spaces) hasmotivated researchers to extend the useof this bioinspired techniqueto other areas.One of them is multiobjective optimization. Despite the fact that the first proposalof a Multi-Objecti veParticle SwarmOptimizer (MOPSO) is over six years old, a considerable number of other algorithms have beenproposedsincethen. This paper presentsa comprehensi ve review of the various MOPSOsreported in the specializedliteratur e. As part of this review, we include a classificationof the approaches,and weidentify the main featuresof eachproposal. In the last part of the paper, we list someof the topicswithin this field that weconsideraspromisingareasof futur e research.
Cosero, Find My Keys! Object Localization and Retrieval Using Bluetooth Low Energy Tags

Personal robots will contribute mobile manipulation capabilities to our future smart homes. In this paper, we propose a low-cost object localization system that uses static devices with Bluetooth capabilities, which are distributed in an environment, to detect and localize active Bluetooth beacons and mobile devices. This system can be used by a robot to coarsely localize objects in retrieval tasks. We attach small Bluetooth low energy tags to objects and require at least four static Bluetooth receivers. While commodity Bluetooth devices could be used, we have built low-cost receivers from Raspberry Pi computers. The location of a tag is estimated by lateration of its received signal strengths. In experiments, we evaluate accuracy and timing of our approach, and report on the successful demonstration at the RoboCup German Open 2014 competition in Magdeburg.
Fake News Detection Enhancement with Data Imputation

Raw datasets collected for fake news detection usually contain some noise such as missing values. In order to improve the performance of machine learning based fake news detection, a novel data preprocessing method is proposed in this paper to process the missing values. Specifically, we have successfully handled the missing values problem by using data imputation for both categorical and numerical features. For categorical features, we imputed missing values with the most frequent value in the columns. For numerical features, the mean value of the column is used to impute numerical missing values. In addition, TF-IDF vectorization is applied in feature extraction to filter out irrelevant features. Experimental results show that Multi-Layer Perceptron (MLP) classifier with the proposed data preprocessing method outperforms baselines and improves the prediction accuracy by more than 15%.
Bottleneck Conditional Density Estimation

We introduce a new framework for training deep generative models for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input x and target y, where both are high-dimensional. Crucially, we propose a new hybrid training method that blends the conditional generative model with a joint generative model. Hybrid blending is the key to effective training of the BCDE, which avoids overfitting and provides a novel mechanism for leveraging unlabeled data. We show that our hybrid training procedure enables models to achieve competitive results in the MNIST quadrant prediction task in the fullysupervised setting, and sets new benchmarks in the semi-supervised regime for MNIST, SVHN, and CelebA.
Diversification as a Corporate Strategy and Its Effect on Firm Performance : A Study of Zimbabwean Listed Conglomerates in the Food and Beverages Sector

Portfolio diversification in capital markets is an accepted investment strategy. On the other hand corporate diversification has drawn many opponents especially the agency theorists who argue that executives must not diversify on behalf of share holders. Diversification is a strategic option used by many managers to improve their firm’s performance. While extensive literature investigates the diversification performance linkage, little agreements exist concerning the nature of this relationship. Both theoretical and empirical disagreements abound as the extensive research has neither reached a consensus nor any interpretable and acceptable findings. This paper looked at diversification as a corporate strategy and its effect on firm performance using Conglomerates in the Food and Beverages Sector listed on the ZSE. The study used a combination of primary and secondary data. Primary data was collected through interviews while secondary data were gathered from financial statements and management accounts. Data was analyzed using SPSS computer package. Three competing models were derived from literature (the linear model, Inverted U model and Intermediate model) and these were empirically assessed and tested.
Unprocessing Images for Learned Raw Denoising

Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical constraints, are often trained on synthetic image data. Though it is understood that generalizing from synthetic to real images requires careful consideration of the noise properties of camera sensors, the other aspects of an image processing pipeline (such as gain, color correction, and tone mapping) are often overlooked, despite their significant effect on how raw measurements are transformed into finished images. To address this, we present a technique to “unprocess” images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available Internet photos. We additionally model the relevant components of an image processing pipeline when evaluating our loss function, which allows training to be aware of all relevant photometric processing that will occur after denoising. By unprocessing and processing training data and model outputs in this way, we are able to train a simple convolutional neural network that has 14%-38% lower error rates and is 9×-18× faster than the previous state of the art on the Darmstadt Noise Dataset [30], and generalizes to sensors outside of that dataset as well.
Text Mining in Biomedical Domain with Emphasis on Document Clustering

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents.



This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain.



Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail.



Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.
Says Who...? Identification of Expert versus Layman Critics' Reviews of Documentary Films

We extend classic review mining work by building a binary classifier that predicts whether a review of a documentary film was written by an expert or a layman with 90.70% accuracy (F1 score), and compare the characteristics of the predicted classes. A variety of standard lexical and syntactic features was used for this supervised learning task. Our results suggest that experts write comparatively lengthier and more detailed reviews that feature more complex grammar and a higher diversity in their vocabulary. Layman reviews are more subjective and contextualized in peoples’ everyday lives. Our error analysis shows that laymen are about twice as likely to be mistaken as experts than vice versa. We argue that the type of author might be a useful new feature for improving the accuracy of predicting the rating, helpfulness and authenticity of reviews. Finally, the outcomes of this work might help researchers and practitioners in the field of impact assessment to gain a more fine-grained understanding of the perception of different types of media consumers and reviewers of a topic, genre or information product.
Life Versus Dark Energy : How An Advanced Civilization Could Resist the Accelerating Expansion of the Universe

The presence of dark energy in our universe is causing space to expand at an accelerating rate. As a result, over the next approximately 100 billion years, all stars residing beyond the Local Group will fall beyond the cosmic horizon and become not only unobservable, but entirely inaccessible, thus limiting how much energy could one day be extracted from them. Here, we consider the likely response of a highly advanced civilization to this situation. In particular, we argue that in order to maximize its access to useable energy, a sufficiently advanced civilization would chose to expand rapidly outward, build Dyson Spheres or similar structures around encountered stars, and use the energy that is harnessed to accelerate those stars away from the approaching horizon and toward the center of the civilization. We find that such efforts will be most effective for stars with masses in the range of M ∼ (0.2 − 1)M , and could lead to the harvesting of stars within a region extending out to several tens of Mpc in radius, potentially increasing the total amount of energy that is available to a future civilization by a factor of several thousand. We also discuss the observable signatures of a civilization elsewhere in the universe that is currently in this state of stellar harvesting.  1 3 Ju n 20 18
Transfer Learning from Alternative Mechanisms to Optimize Differentially Private GAN Release

In order to gain the maximum benefits from the large amounts of data generated by modern software, it is important that the data be shared with researchers and those best able to gain and use the insights it can generate. But, this runs into the problems of privacy. Even with names removed, it is quite possible, and increasingly likely, that the data can be matched with other public data sets and de-anonymized. When the data set contains sensitive data, such as medical, this can be problematic. Differential privacy is the most common technique used now to prevent this de-anonymization and help people feel secure in sharing their information. And, GANs represent an incredibly powerful differentially private mechanism that can produce an infinitely sized synthetic dataset while having one of the greatest privacy protections. However, GANs can be difficult to train and don’t achieve good privacy bounds without additional public data to train on. We present a new mechanism to leverage the power of alternative differential privacy mechanisms in accordance with the GAN training to relieve the necessity for public data. It can be used to create a jumpstarting data set for initial GAN training and then can be refined using the original data set. Additionally, the jumpstart dataset can be used to further optimize the training process. Using this, it is possible to better utilize the privacy budget and produce a better quality output GAN.
Benefit assessment in Germany: implications for price discounts

The AMNOG regulation, introduced in 2011 in Germany, changed the game for new drugs. Now, the industry is required to submit a dossier to the GBA (the central decision body in the German sickness fund system) to show additional benefit. After granting the magnitude of the additional benefit by the GBA, the manufacturer is entitled to negotiate the reimbursement price with the GKV-SV (National Association of Statutory Health Insurance Funds). The reimbursement price is defined as a discount on the drug price at launch. As the price or discount negotiations between the manufacturers and the GKV-SV takes place behind closed doors, the factors influencing the results of the negotiation are not known.



The aim of this evaluation is to identify factors influencing the results of the AMNOG price negotiation process.



The analysis was based on a dataset containing detailed information on all assessments until the end of 2015. A descriptive analysis was followed by an econometric analysis of various potential factors (benefit rating, size of target population, deviating from appropriate comparative therapy and incorporation of HRQoL-data).



Until December 2015, manufacturers and the GKV-SV finalized 96 negotiations in 193 therapeutic areas, based on assessment conducted by the GBA. The GBA has granted an additional benefit to 100/193 drug innovations. Negotiated discount was significantly higher for those drugs without additional benefit (p = 0.030) and non-orphan drugs (p = 0.015). Smaller population size, no deviation from recommended appropriate comparative therapy and the incorporation of HRQoL-data were associated with a lower discount on the price at launch. However, neither a uni- nor the multivariate linear regression showed enough power to predict the final discount.



Although the AMNOG regulation implemented binding and strict rules for the benefit assessment itself, the outcome of the discount negotiations are still unpredictable. Obviously, negotiation tactics, the current political situation and soft factors seem to play a more influential role for the outcome of the negotiations than the five hard and known factors analyzed in this study. Further research is needed to evaluate additional factors.