nils-herrmann/clean_scirepeval huggingface dataset:
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture

We focus on predicting sleep stages from radio measurements without any attached sensors on subjects. We introduce a new predictive model that combines convolutional and recurrent neural networks to extract sleep-specific subjectinvariant features from RF signals and capture the temporal progression of sleep. A key innovation underlying our approach is a modified adversarial training regime that discards extraneous information specific to individuals or measurement conditions, while retaining all information relevant to the predictive task. We analyze our game theoretic setup and empirically demonstrate that our model achieves significant improvements over state-of-the-art solutions.
Learning a time-dependent master saliency map from eye-tracking data in videos

To predict the most salient regions of complex natural scenes, saliency models commonly compute several feature maps (contrast, orientation, motion...) and linearly combine them into a master saliency map. Since feature maps have different spatial distribution and amplitude dynamic ranges, determining their contributions to overall saliency remains an open problem. Most state-of-the-art models do not take time into account and give feature maps constant weights across the stimulus duration. However, visual exploration is a highly dynamic process shaped by many time-dependent factors. For instance, some systematic viewing patterns such as the center bias are known to dramatically vary across the time course of the exploration. In this paper, we use maximum likelihood and shrinkage methods to dynamically and jointly learn feature map and systematic viewing pattern weights directly from eye-tracking data recorded on videos. We show that these weights systematically vary as a function of time, and heavily depend upon the semantic visual category of the videos being processed. Our fusion method allows taking these variations into account, and outperforms other stateof-the-art fusion schemes using constant weights over time. The code, videos and eye-tracking data we used for this study are available online.
Understanding Abuse: A Typology of Abusive Language Detection Subtasks

As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and we discuss its implications for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.
Generative Adversarial Residual Pairwise Networks for One Shot Learning

Deep neural networks achieve unprecedented performance levels over many tasks and scale well with large quantities of data, but performance in the low-data regime and tasks like one shot learning still lags behind. While recent work suggests many hypotheses from better optimization to more complicated network structures, in this work we hypothesize that having a learnable and more expressive similarity objective is an essential missing component. Towards overcoming that, we propose a network design inspired by deep residual networks that allows the efficient computation of this more expressive pairwise similarity objective. Further, we argue that regularization is key in learning with small amounts of data, and propose an additional generator network based on the Generative Adversarial Networks where the discriminator is our residual pairwise network. This provides a strong regularizer by leveraging the generated data samples. The proposed model can generate plausible variations of exemplars over unseen classes and outperforms strong discriminative baselines for few shot classification tasks. Notably, our residual pairwise network design outperforms previous state-of-theart on the challenging mini-Imagenet dataset for one shot learning by getting over 55% accuracy for the 5-way classification task over unseen classes.
Closed-chest cardiac massage.

or as ventricular fibrillation, the circulation must be restored promptly; otherwise anoxia will result in irreversible damage. There are two techniques that may be used to meet the emergency: one is to open the chest and massage the heart directly and the other is to accomplish the same end by a new method of closed-chest cardiac massage. The latter method is described in this communication. The closed-chest alternating current defibrillator ' that
Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression

Compressing convolutional neural networks (CNNs) has received ever-increasing research focus. However, most existing CNN compression methods do not interpret their inherent structures to distinguish the implicit redundancy. In this paper, we investigate the problem of CNN compression from a novel interpretable perspective. The relationship between the input feature maps and 2D kernels is revealed in a theoretical framework, based on which a kernel sparsity and entropy (KSE) indicator is proposed to quantitate the feature map importance in a feature-agnostic manner to guide model compression. Kernel clustering is further conducted based on the KSE indicator to accomplish highprecision CNN compression. KSE is capable of simultaneously compressing each layer in an efficient way, which is significantly faster compared to previous data-driven feature map pruning methods. We comprehensively evaluate the compression and speedup of the proposed method on CIFAR-10, SVHN and ImageNet 2012. Our method demonstrates superior performance gains over previous ones. In particular, it achieves 4.7× FLOPs reduction and 2.9× compression on ResNet-50 with only a Top-5 accuracy drop of 0.35% on ImageNet 2012, which significantly outperforms state-of-the-art methods.
Document Similarity for Texts of Varying Lengths via Hidden Topics

Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of incorporating domain knowledge to text matching.
Problem and Prospects of E-Commerce

E-commerce as anything that involves an online transaction. E-commerce provides multiple benefits to the consumers in form of availability of goods at lower cost, wider choice and saves time. The general category of ecommerce can be broken down into two parts: Emerchandise: E-finance. E commerce involves conducting business using modern communication instruments: telephone, fax, e-payment, money transfer systems, e-data interchange and the Internet. Online businesses like financial services, travel, entertainment, and groceries are all likely to grow. Forces influencing the distribution of global e-commerce and its forms include economic factors, political factors, cultural factors and supranational
First feeding of freshwater fish larvae with live feed versus compound diets: a meta-analysis

A meta-analysis of published results was used to quantify differences in mortality and growth of freshwater fish larvae when live feed was replaced by compound diets at first feeding. A mean relative risk of 2.4560 (95% confidence interval = 2.0879–2.8891), calculated with 75 observations from 47 studies conducted with 27 freshwater fish species according to a random effects model, indicated that larvae fed on compound diets have a 2.5 times higher chance to die than those fed on live feed. Compared to Artemia nauplii as sole live feed, compound diets were more effective (causing a lower mortality) when replacing zooplankton other than Artemia nauplii. A mean effect size (Hedges’ d) of −3.1813 (95% confidence interval = −3.8099 to −2.5527), calculated (random effects model) from 51 values determined in 33 studies with 21 fish species, represents the size of the negative effect that compound diets would have on growth of larvae. Numerical differences obtained in this study could be use to monitor future development of larval diets.
Designing Organizational Models for Service Integration in a Multi-sourcing Context

With the persistent shift towards multi-sourcing, the complexity of service delivery is continuously increasing. This presents new challenges for clients who now have to integrate interdependent services from multiple providers. As other functions, service integration is subject to make-or-buy decisions: clients can either build the required capabilities themselves or delegate service integration to external functions. To define detailed organizational models, one requires understanding of specific tasks and how to allocate them. Based on a qualitative and quantitative expert study, we analyze generic organizational models, and identify key service integration tasks. The allocation of these tasks to clients or their providers generates a set of granular organizational structures. We analyze drivers for delegating these tasks, and develop typical allocations in practice. Our work contributes to expanding the theoretical foundations of service integration. Moreover, our findings will assist clients to design their service integration organization, and to build more effective multi-sourcing solutions.
Natural Language Processing for EHR-Based Computational Phenotyping

This article reviews recent advances in applying natural language processing NLP to Electronic Health Records EHRs for computational phenotyping. NLP-based computational phenotyping has numerous applications including diagnosis categorization, novel phenotype discovery, clinical trial screening, pharmacogenomics, drug-drug interaction DDI, and adverse drug event ADE detection, as well as genome-wide and phenome-wide association studies. Significant progress has been made in algorithm development and resource construction for computational phenotyping. Among the surveyed methods, well-designed keyword search and rule-based systems often achieve good performance. However, the construction of keyword and rule lists requires significant manual effort, which is difficult to scale. Supervised machine learning models have been favored because they are capable of acquiring both classification patterns and structures from data. Recently, deep learning and unsupervised learning have received growing attention, with the former favored for its performance and the latter for its ability to find novel phenotypes. Integrating heterogeneous data sources have become increasingly important and have shown promise in improving model performance. Often, better performance is achieved by combining multiple modalities of information. Despite these many advances, challenges and opportunities remain for NLP-based computational phenotyping, including better model interpretability and generalizability, and proper characterization of feature relations in clinical narratives.
Factors Affecting the Adoption of Formal Accounting Systems by SMEs

Small to medium-sized enterprises’ (SMEs) poor performance is often ascribed to external factors such as burdensome character of the legal framework, limited scope to penetrate the export market and access to finance. However, internal factors such as marketing, operation and in particular accounting services may be equally responsible to such state of affairs. This study therefore attempts to analyse the importance attached to formal accounting systems among the small to medium-sized Mauritian manufacturing firms operating in six main industry groups. The research findings are based on a comprehensive survey of the financial and working capital management practices of 141 SMEs. Additional rigour to the research findings was possible through the use of 12 mini case studies.
Determinants of Customer Satisfaction in Fast Food Industry A Study of Fast Food Restaurants Peshawar Pakistan

This research is focused to find what are the key success factors for fast food industry in region of Peshawar Pakistan. Fast food concepts developed very rapidly in last few years in Peshawar region. The failure or success of a fast food industry based on some factors like Promotion, Service quality, Customer expectations, Brand, Physical Environment, Price, and Taste of the product. To find which of these factors has greater influence on consumer satisfaction, four fast food restaurants customers were targeted randomly. These four restaurants were KFC, CHIEF, ARBAIN CHICK, and PIZZA HUT. The data collected from the customers of these restaurants. The data collected from customers of these restaurants when the customers were in restaurants for refreshment. Total number of customers who were targeted was 120. From each restaurant 30 customers were targeted on availability basis. On the basis of their responses multiple regression and correlation test was applied. Findings of the study shows that service quality and brand are the key factors for satisfaction in fast food industry in Peshawar Pakistan.
RESTful Web service frameworks in Java

In order to make the Web services, Web applications in Java more powerful, flexible and user friendly, building unified Web applications is very significant. By introducing a new style-Representational State Transfer, this paper studied the goals and design principles of REST, the idea of REST and RESTful Web service design principles, RESTful style Web service, RESTful Web service frameworks in Java and the ways to develop RESTful Web service. The RESTful Web Service frameworks in Java can effectively simplify the Web development in many aspects.
Hormone therapy for inmates: a metonym for transgender rights.

The issue of hormone therapy for transgender inmates, while seemingly limited in importance, is one that involves issues of greater importance for the transgender community. The greatest issue at the heart of the matter is the legal argument that is traditionally used to gain access to hormone therapy: the Eighth Amendment. The Eighth Amendment prohibits deliberate indifference to the medical needs of inmates. Traditionally, transgender inmates have gained access to hormone therapy by appealing to the DSM-IV's classification of Gender Identity Disorder (GID) as a mental illness, and by establishing that prison officials' failure to provide hormone therapy constitutes deliberate indifference to a serious medical need. However, appeal to GID is a double-edged sword: while it allows access to hormone therapy, it does so by describing transgender individuals as somehow sick or infirm. This description is at odds with the transgender community's conceptualization of itself. This Note seeks to square the legal arguments for provision of hormone therapy to transgender inmates with the philosophical backdrop that shapes the transgender rights movement by using Plyler v. Doe as a model. This Note argues that access to hormone therapy by transgender inmates involves the intersection of a quasi-fundamental right with a quasi-suspect class. By utilizing such an argument, the transgender community is not bound by the negative expressive effect that the law may have in describing it as infirm or deficient.
Active music therapy in Parkinson's disease: an integrative method for motor and emotional rehabilitation.

Modern management of Parkinson's disease (PD) aims to obtain symptom control, to reduce clinical disability, and to improve quality of life. Music acts as a specific stimulus to obtain motor and emotional responses by combining movement and stimulation of different sensory pathways. We explored the efficacy of active music therapy (MT) on motor and emotional functions in patients with PD.



This prospective, randomized, controlled, single-blinded study lasted 3 months. It consisted of weekly sessions of MT and physical therapy (PT). Thirty-two patients with PD, all stable responders to levodopa and in Hoehn and Yahr stage 2 or 3, were randomly assigned to two groups of 16 patients each. We assessed severity of PD with the Unified Parkinson's Disease Rating Scale, emotional functions with the Happiness Measure, and quality of life using the Parkinson's Disease Quality of Life Questionnaire. MT sessions consisted of choral singing, voice exercise, rhythmic and free body movements, and active music involving collective invention. PT sessions included a series of passive stretching exercises, specific motor tasks, and strategies to improve balance and gait.



MT had a significant overall effect on bradykinesia as measured by the Unified Parkinson's Disease Rating Scale (p < .034). Post-MT session findings were consistent with motor improvement, especially in bradykinesia items (p < .0001). Over time, changes on the Happiness Measure confirmed a beneficial effect of MT on emotional functions (p < .0001). Improvements in activities of daily living and in quality of life were also documented in the MT group (p < .0001). PT improved rigidity (p < .0001).



MT is effective on motor, affective, and behavioral functions. We propose active MT as a new method for inclusion in PD rehabilitation programs.
THE ROLE OF PERFECTIONISM AND EMOTIONAL REGULATION IN EXPLAINING DECISION-- MAKING STYLES

Individuals differ in the manner they approach decision making, namely their decision-making styles. While some people typically make all decisions fast and without hesitation, others invest more effort into deciding even about small things and evaluate their decisions with much more scrutiny. The goal of the present study was to explore the relationship between decision-making styles, perfectionism and emotional processing in more detail. Specifically, 300 college students majoring in social studies and humanities completed instruments designed for assessing maximizing, decision commitment, perfectionism, as well as emotional regulation and control. The obtained results indicate that maximizing is primarily related to one dimension of perfectionism, namely the concern over mistakes and doubts, as well as emotional regulation and control. Furthermore, together with the concern over mistakes and doubts, maximizing was revealed as a significant predictor of individuals' decision commitment. The obtained findings extend previous reports regarding the association between maximizing and perfectionism and provide relevant insights into their relationship with emotional regulation and control. They also suggest a need to further explore these constructs that are, despite their complex interdependence, typically investigated in separate contexts and domains.
Knowledge base completion via search-based question answering

Over the past few years, massive amounts of world knowledge have been accumulated in publicly available knowledge bases, such as Freebase, NELL, and YAGO. Yet despite their seemingly huge size, these knowledge bases are greatly incomplete. For example, over 70% of people included in Freebase have no known place of birth, and 99% have no known ethnicity. In this paper, we propose a way to leverage existing Web-search-based question-answering technology to fill in the gaps in knowledge bases in a targeted way. In particular, for each entity attribute, we learn the best set of queries to ask, such that the answer snippets returned by the search engine are most likely to contain the correct value for that attribute. For example, if we want to find Frank Zappa's mother, we could ask the query `who is the mother of Frank Zappa'. However, this is likely to return `The Mothers of Invention', which was the name of his band. Our system learns that it should (in this case) add disambiguating terms, such as Zappa's place of birth, in order to make it more likely that the search results contain snippets mentioning his mother. Our system also learns how many different queries to ask for each attribute, since in some cases, asking too many can hurt accuracy (by introducing false positives). We discuss how to aggregate candidate answers across multiple queries, ultimately returning probabilistic predictions for possible values for each attribute. Finally, we evaluate our system and show that it is able to extract a large number of facts with high confidence.
Moringa oleifera: A Review of the Medical Evidence for Its Nutritional, Therapeutic, and Prophylactic Properties. Part 1.

Moringa oleifera, or the horseradish tree, is a pan-tropical species that is known by such regional names as benzolive, drumstick tree, kelor, marango, mlonge, mulangay, nébéday, saijhan, and sajna. Over the past two decades, many reports have appeared in mainstream scientific journals describing its nutritional and medicinal properties. Its utility as a non-food product has also been extensively described, but will not be discussed herein, (e.g. lumber, charcoal, fencing, water clarification, lubricating oil). As with many reports of the nutritional or medicinal value of a natural product, there are an alarming number of purveyors of “healthful” food who are now promoting M. oleifera as a panacea. While much of this recent enthusiasm indeed appears to be justified, it is critical to separate rigorous scientific evidence from anecdote. Those who charge a premium for products containing Moringa spp. must be held to a high standard. Those who promote the cultivation and use of Moringa spp. in regions where hope is in short supply must be provided with the best available evidence, so as not to raise false hopes and to encourage the most fruitful use of scarce research capital. It is the purpose of this series of brief reviews to: (a) critically evaluate the published scientific evidence on M. oleifera, (b) highlight claims from the traditional and tribal medicinal lore and from non-peer reviewed sources that would benefit from further, rigorous scientific evaluation, and (c) suggest directions for future clinical research that could be carried out by local investigators in developing regions. This is the first of four planned papers on the nutritional, therapeutic, and prophylactic properties of Moringa oleifera. In this introductory paper, the scientific evidence for health effects are summarized in tabular format, and the strength of evidence is discussed in very general terms. A second paper will address a select few uses of Moringa in greater detail than they can be dealt with in the context of this paper. A third paper will probe the phytochemical components of Moringa in more depth. A fourth paper will lay out a number of suggested research projects that can be initiated at a very small scale and with very limited resources, in geographic regions which are suitable for Moringa cultivation and utilization. In advance of this fourth paper in the series, the author solicits suggestions and will gladly acknowledge contributions that are incorporated into the final manuscript. It is the intent and hope of the journal’s editors that such a network of small-scale, locally executed investigations might be successfully woven into a greater fabric which will have enhanced scientific power over similar small studies conducted and reported in isolation. Such an approach will have the added benefit that statistically sound planning, peer review, and multi-center coordination brings to a scientific investigation.