nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge and Practices Related to Screening for Breast Cancer among Women in Delhi, India

Breast cancer is a major public health problem globally. The ongoing epidemiological, socio-cultural
and demographic transition by accentuating the associated risk factors has disproportionately increased the incidence
of breast cancer cases and resulting mortality in developing countries like India. Early diagnosis with rapid initiation
of treatment reduces breast cancer mortality. Therefore awareness of breast cancer risk and a willingness to undergo
screening are essential. The objective of the present study was to assess the knowledge and practices relating to screening
for breast cancer among women in Delhi.  Data were obtained from 222 adult women using a pretested selfadministered
questionnaire.  Rates for knowledge of known risk factors of breast cancer were: family history
of breast cancer, 59.5%; smoking, 57.7%; old age, 56.3%; lack of physical exercise, 51.9%; lack of breastfeeding,
48.2%; late menopause, 37.4%; and early menarche, 34.7%. Women who were aged < 30 and those who were unmarried
registered significantly higher knowledge scores (p ≤ 0.01). Breast self-examination (BSE) was regularly practiced
at-least once a month by 41.4% of the participants. Some 48% knew mammography has a role in the early detection
of breast cancer. Since almost three-fourths of the participants believed BSE could help in early diagnosis of breast
cancer, which is not supported by evidence, future studies should explore the consequences of promoting BSE at the
potential expense of screening mammography.  Our findings highlight the need for awareness generation
among adult women regarding risk factors and methods for early detection of breast cancer.
Distilling Policy Distillation

The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning. This process, referred to as distillation, has been used to great success, for example, by enhancing the optimisation of agents, leading to stronger performance faster, on harder domains [26, 32, 5, 8]. Despite the widespread use and conceptual simplicity of distillation, many different formulations are used in practice, and the subtle variations between them can often drastically change the performance and the resulting objective that is being optimised. In this work, we rigorously explore the entire landscape of policy distillation, comparing the motivations and strengths of each variant through theoretical and empirical analysis. Our results point to three distillation techniques, that are preferred depending on specifics of the task. Specifically a newly proposed expected entropy regularised distillation allows for quicker learning in a wide range of situations, while still guaranteeing convergence.
Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for archiving patient information and performing administrative healthcare tasks like billing, many researchers have found secondary use of these records for various clinical informatics applications. Over the same period, the machine learning community has seen widespread advances in the field of deep learning. In this review, we survey the current research on applying deep learning to clinical tasks based on EHR data, where we find a variety of deep learning techniques and frameworks being applied to several types of clinical applications including information extraction, representation learning, outcome prediction, phenotyping, and deidentification. We identify several limitations of current research involving topics such as model interpretability, data heterogeneity, and lack of universal benchmarks. We conclude by summarizing the state of the field and identifying avenues of future deep EHR research.
Framework for Academic Advice through Mobile Applications

The increasing rate of high (secondary) school leavers choosing academic majors to study at the university without proper guidance has most times left students with unfavorable consequences including low grades, extra year(s), the need to switch programs and ultimately having to withdraw from the university. In a bid to proffer a solution to the issue, this research aims to build an expert system that recommends university or academic majors to high school students in developing countries where there is a dearth of human career counselors. This is to reduce the adverse effects caused as a result of wrong choices made by students. A mobile rule-based expert system supported with ontology was developed for easy accessibility by the students.
The LAMBADA dataset: Word prediction requiring a broad discourse context

We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-ofthe-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural
Follow the Sun through the Clouds: Application Migration for Geographically Shifting Workloads

Global cloud services have to respond to workloads that shift geographically as a function of time-of-day or in response to special events. While many such services have support for adding nodes in one region and removing nodes in another, we demonstrate that such mechanisms can lead to significant performance degradation. Yet other services do not support application-level migration at all. Live VM migration between availability zones or even across cloud providers would be ideal, but cloud providers do not support this flexible mechanism.
 This paper presents the Supercloud, a uniform cloud service that supports live VM migration between data centers of all major public cloud providers. The Supercloud also provides a scheduler that automatically determines when and where to move VMs for optimal performance. We demonstrate that live VM migration can support shifting workloads effectively, with low downtimes and transparently to both services and their clients. The Supercloud also addresses challenges for supporting cross-cloud storage and networking.
Convolutional Neural Network with Word Embeddings for Chinese Word Segmentation

Character-based sequence labeling framework is flexible and efficient for Chinese word segmentation (CWS). Recently, many character-based neural models have been applied to CWS. While they obtain good performance, they have two obvious weaknesses. The first is that they heavily rely on manually designed bigram feature, i.e. they are not good at capturing n-gram features automatically. The second is that they make no use of full word information. For the first weakness, we propose a convolutional neural model, which is able to capture rich n-gram features without any feature engineering. For the second one, we propose an effective approach to integrate the proposed model with word embeddings. We evaluate the model on two benchmark datasets: PKU and MSR. Without any feature engineering, the model obtains competitive performance — 95.7% on PKU and 97.3% on MSR. Armed with word embeddings, the model achieves state-of-the-art performance on both datasets — 96.5% on PKU and 98.0% on MSR, without using any external labeled resource.
Analyzing the Past to Prepare for the Future: Writing a Literature Review

The lack of theoretical progress in the IS field may be surprising. From an empirical viewpoint, the IS field resembles other management fields. Specifically, as fields of inquiry develop, their theories are often placed on a hierarchy from ad hoc classification systems (in which categories are used to summarize empirical observations), to taxonomies (in which the relationships between the categories can be described), to conceptual frameworks (in which propositions summarize explanations and predictions), to theoretical systems (in which laws are contained within axiomatic or formal theories) (Parsons and Shils 1962). In its short history, IS research has developed from classification systems to conceptual frameworks. In the 1970s, it was considered pre-paradigmatic. Today, it is approaching the level of development in empirical research of other management fields, like organizational behavior (Webster 2001). However, unlike other fields that have journals devoted to review articles (e.g., the Academy of Management Review), we see few review articles in ISand hence the creation of MISQ Review as a device for accelerating development of the discipline.
Social Marketing : Its Definition and Domain

I t is clear that the temi social marketing is now a wellestablished part of the marketing vocabulary in universities, govemmetit agencies, private nonprofit organizations, and private for-profit firms. There are now social marketing textbooks (Kotler and Roberto 1989; Manoff 1975), readings books (Fine 1990), chapters within mainstream texts (Kotler and Andreasen 1991) and a Harvard teaching note (Rangun and Karim 1991). There have been reviews ofthe accomplishments of social marketing (Fox and Kotler 1980; Malafarina and Loken 1993) and calls to researchers to become more deeply involved in studies of social marketing to advance the science of marketing (Andreasen 1993). Major international and domestic behavior change programs now routinely have social marketing components (Debus 1987; Ramah 1992; Smith 1989). People with titles like Manager of Social Marketing now can be found in private consulting organizations.
Contextual understanding of microservice architecture: current and future directions

Current industry trends in enterprise architectures indicate movement from Service-Oriented Architecture (SOA) to Microservices. By understanding the key differences between these two approaches and their features, we can design a more effective Microservice architecture by avoiding SOA pitfalls. To do this, we must know why this shift is happening and how key SOA functionality is addressed by key features of the Microservice-based system. Unfortunately, Microservices do not address all SOA shortcomings. In addition, Microservices introduce new challenges. This work provides a detailed analysis of the differences between these two architectures and their features. Next, we describe both research and industry perspectives on the strengths and weaknesses of both architectural directions. Finally, we perform a systematic mapping study related to Microservice research, identifying interest and challenges in multiple categories from a range of recent research.
Scenarios for Development, Test and Validation of Automated Vehicles

The latest version of the ISO 26262 standard from 2016 represents the state of the art for a safety-guided development of safety-critical electric/electronic vehicle systems. These vehicle systems include advanced driver assistance systems and vehicle guidance systems. The development process proposed in the ISO 26262 standard is based upon multiple V-models, and defines activities and work products for each process step. In many of these process steps, scenario based approaches can be applied to achieve the defined work products for the development of automated driving functions. To accomplish the work products of different process steps, scenarios have to focus on various aspects like a human understandable notation or a description via state variables. This leads to contradictory requirements regarding the level of detail and way of notation for the representation of scenarios. In this paper, the authors discuss requirements for the representation of scenarios in different process steps defined by the ISO 26262 standard, propose a consistent terminology based on prior publications for the identified levels of abstraction, and demonstrate how scenarios can be systematically evolved along the phases of the development process outlined in the ISO 26262 standard.
Counseling for Wellness With Older Adults

A longevity revolution (Butler, 2008) is occurring across the globe. Because of factors ranging from the reduction of early-age mortality to an increase in life expectancy at later ages, most of the world’s population is now living longer than preceding generations (Bengtson, 2014). There are currently more than 44 million older adults—typically defined as persons 65 years and older—living in the United States, and this number is expected to increase to 98 million by 2060 (Administration on Aging, 2016). Although most older adults report higher levels of life satisfaction than do younger or middle-aged adults (George, 2010), between 5.6 and 8 million older Americans have a diagnosable mental health or substance use disorder (Bartels & Naslund, 2013). Furthermore, because of the rapid growth of the older adult population, this figure is expected to nearly double by 2030 (Bartels & Naslund, 2013). Mental health care is effective for older adults, and evidence-based treatments exist to address a broad range of issues, including anxiety disorders, depression, sleep disturbances, substance abuse, and some symptoms of dementia (Myers & Harper, 2004). Counseling interventions may also be beneficial for nonclinical life transitions, such as coping with loss, adjusting to retirement and a reduced income, and becoming a grandparent (Myers & Harper, 2004). Yet, older adults are underserved when it comes to mental
Punctuation Prediction for Unsegmented Transcript Based on Word Vector

In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only.
The Qualitative Research Interview : Participants ’ Responsive Participation in Knowledge Making

Discussions of qualitative research interviews have centered on promoting an ideal interactional style and articulating the researcher behaviors by which this might be realized. Although examining what researchers do in an interview continues to be valuable, this focus obscures the reflexive engagement of all participants in the exchange and the potential for a variety of possible styles of interacting. The author presents her analyses of participants’ accounts of past research interviews and explores the implications of this for researchers’ orientation to qualitative research inter-
Pharmacological Foundations of Cannabis Chemovars.

An advanced Mendelian Cannabis breeding program has been developed utilizing chemical markers to maximize the yield of phytocannabinoids and terpenoids with the aim to improve therapeutic efficacy and safety. Cannabis is often divided into several categories based on cannabinoid content. Type I, Δ9-tetrahydrocannabinol-predominant, is the prevalent offering in both medical and recreational marketplaces. In recent years, the therapeutic benefits of cannabidiol have been better recognized, leading to the promotion of additional chemovars: Type II, Cannabis that contains both Δ9-tetrahydrocannabinol and cannabidiol, and cannabidiol-predominant Type III Cannabis. While high-Δ9-tetrahydrocannabinol and high-myrcene chemovars dominate markets, these may not be optimal for patients who require distinct chemical profiles to achieve symptomatic relief. Type II Cannabis chemovars that display cannabidiol- and terpenoid-rich profiles have the potential to improve both efficacy and minimize adverse events associated with Δ9-tetrahydrocannabinol exposure. Cannabis samples were analyzed for cannabinoid and terpenoid content, and analytical results are presented via PhytoFacts, a patent-pending method of graphically displaying phytocannabinoid and terpenoid content, as well as scent, taste, and subjective therapeutic effect data. Examples from the breeding program are highlighted and include Type I, II, and III Cannabis chemovars, those highly potent in terpenoids in general, or single components, for example, limonene, pinene, terpinolene, and linalool. Additionally, it is demonstrated how Type I - III chemovars have been developed with conserved terpenoid proportions. Specific chemovars may produce enhanced analgesia, anti-inflammatory, anticonvulsant, antidepressant, and anti-anxiety effects, while simultaneously reducing sequelae of Δ9-tetrahydrocannabinol such as panic, toxic psychosis, and short-term memory impairment.
A production planning model for an unreliable production facility: Case of finite horizon and single demand

We study a two-level inventory system that is subject to failures and repairs. The objective is to minimize the expected total cost so as to determine the production plan for a single quantity demand. The expected total cost consists of the inventory carrying costs for finished and unfinished items, the backlog cost for not meeting the demand due-date, and the planning costs associated with the ordering schedule of unfinished items. The production plan consists of the optimal number of lot sizes, the optimal size for each lot, the optimal ordering schedule for unfinished items, and the optimal due-date to be assigned to the demand. To gain insight, we solve special cases and use their results to device an efficient solution approach for the main model. The models are solved to optimality and the solution is either obtained in closed form or through very efficient algorithms.
Beyond Fine Tuning: A Modular Approach to Learning on Small Data

In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically rely on strategies such as fine-tuning a pre-trained neural network or the use of domain-specific hand-engineered features. Here we take the approach of treating network layers, or entire networks, as modules and combine pre-trained modules with untrained modules, to learn the shift in distributions between data sets. The central impact of using a modular approach comes from adding new representations to a network, as opposed to replacing representations via fine-tuning. Using this technique, we are able surpass results using standard fine-tuning transfer learning approaches, and we are also able to significantly increase performance over such approaches when using smaller amounts of data.
Estimating Consumers’ Knowledge and Attitudes Towards Over-The-Counter Analgesic Medication in Greece in the Years of Financial Crisis: The Case of Paracetamol

Non-prescription over-the-counter (OTC) drugs are widely used by patients to control aches, pain, and fever. One of the most frequently used OTC medications worldwide is paracetamol (acetaminophen). The aim of the present study was to fill the current knowledge gap regarding the beliefs and attitudes of people in Greece associated with the use of paracetamol during the years of financial crisis.



The present study employed a sample of individuals visiting community pharmacies in the second largest city of Greece, Thessaloniki. All participants anonymously answered a questionnaire regarding their beliefs and characteristics of paracetamol consumption. Their answers were then statistically analyzed.



The generic paracetamol compound was shown to be more well known than the original. A significant percentage of participants, ranging between 9.9% and 33.7%, falsely believed that certain medications [mainly non-steroidal anti-inflammatory drugs (NSAIDs)] contained paracetamol. Participants' age, level of education, and gender were shown to be predictive of this false belief. Additionally, 11.1% of participants believed that the maximum allowed daily dose of paracetamol was higher than the correct one. Better educated individuals were less likely to consume alcohol in parallel with paracetamol (odd ratio 0.230, 95% confidence interval 0.058-0.916, P = 0.037).



Paracetamol is commonly used, both in its original and generic forms. However, a significant number of individuals confuse it with NSAIDs. Age, level of education, and gender are important determinants of the characteristics of paracetamol consumption. It seems that patients prefer to take paracetamol on their own decision during the financial crisis.
Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis

Financial time-series forecasting has long been a challenging problem because of the inherently noisy and stochastic nature of the market. In the high-frequency trading, forecasting for trading purposes is even a more challenging task, since an automated inference system is required to be both accurate and fast. In this paper, we propose a neural network layer architecture that incorporates the idea of bilinear projection as well as an attention mechanism that enables the layer to detect and focus on crucial temporal information. The resulting network is highly interpretable, given its ability to highlight the importance and contribution of each temporal instance, thus allowing further analysis on the time instances of interest. Our experiments in a large-scale limit order book data set show that a two-hidden-layer network utilizing our proposed layer outperforms by a large margin all existing state-of-the-art results coming from much deeper architectures while requiring far fewer computations.
Defining control strategies for MicroGrids islanded operation

This paper describes and evaluates the feasibility of control strategies to be adopted for the operation of a microgrid when it becomes isolated. Normally, the microgrid operates in interconnected mode with the medium voltage network; however, scheduled or forced isolation can take place. In such conditions, the microgrid must have the ability to operate stably and autonomously. An evaluation of the need of storage devices and load shedding strategies is included in this paper.
Bitcoin price prediction using machine learning

In this paper, we attempt to predict the Bitcoin price accurately taking into consideration various parameters that affect the Bitcoin value. For the first phase of our investigation, we aim to understand and identify daily trends in the Bitcoin market while gaining insight into optimal features surrounding Bitcoin price. Our data set consists of various features relating to the Bitcoin price and payment network over the course of five years, recorded daily. For the second phase of our investigation, using the available information, we will predict the sign of the daily price change with highest possible accuracy.