nils-herrmann/clean_scirepeval huggingface dataset:
How Medicine Has Changed the End of Life for Patients With Cardiovascular Disease.

Advances in medicine have changed how patients experience the end of life. With longer life spans, there has also been an increase in years lived with disability. The clustering of illnesses in the last years of life is particularly pronounced in patients with cardiovascular disease. At the end of life, patients with cardiovascular disease are more symptomatic, less likely to die at home, and less likely to receive high-quality palliative care. Social determinants have created widening disparities in end-of-life care. The increasing complexity and duration of care have resulted in an epidemic of caregiver burden. Modern medical care has also resulted in new ethical challenges, for example, those related to deactivation of cardiac devices, such as pacemakers, defibrillators, and mechanical circulatory support. Recommendations to improve end-of-life care for patients with cardiovascular disease include optimizing metrics to assess quality, ameliorating disparities, enhancing education and research in palliative care, overcoming disparities, and innovating palliative care delivery and reimbursement.
Convolutional neural networks applied to high-frequency market microstructure forecasting

Highly sophisticated artificial neural networks have achieved unprecedented performance across a variety of complex real-world problems over the past years, driven by the ability to detect significant patterns autonomously. Modern electronic stock markets produce large volumes of data, which are very suitable for use with these algorithms. This research explores new scientific ground by designing and evaluating a convolutional neural network in predicting future financial outcomes. A visually inspired transformation process translates high-frequency market microstructure data from the London Stock Exchange into four market-event based input channels, which are used to train six deep networks. Primary results indicate that con-volutional networks behave reasonably well on this task and extract interesting microstructure patterns, which are in line with previous theoretical findings. Furthermore, it demonstrates a new approach using modern deep-learning techniques for exploiting and analysing market microstructure behaviour.
What Can Behavioral Economics Teach Us About Privacy ?

Privacy decision making can be surprising or even appear contradictory: we feel entitled to protection of information about ourselves that we do not control, yet willingly trade away the same information for small rewards; we worry about privacy invasions of little significance, yet overlook those that may cause significant damages. Dichotomies between attitudes and behaviors, inconsistencies in discounting future costs or rewards, and other systematic behavioral biases have long been studied in the psychology and behavioral economics literatures. In this paper we draw from those literatures to discuss the role of uncertainty, ambiguity, and behavioral biases in privacy decision making.
MAdFraud: investigating ad fraud in android applications

Many Android applications are distributed for free but are supported by advertisements. Ad libraries embedded in the app fetch content from the ad provider and display it on the app's user interface. The ad provider pays the developer for the ads displayed to the user and ads clicked by the user. A major threat to this ecosystem is ad fraud, where a miscreant's code fetches ads without displaying them to the user or "clicks" on ads automatically. Ad fraud has been extensively studied in the context of web advertising but has gone largely unstudied in the context of mobile advertising.
 We take the first step to study mobile ad fraud perpetrated by Android apps. We identify two fraudulent ad behaviors in apps: 1) requesting ads while the app is in the background, and 2) clicking on ads without user interaction. Based on these observations, we developed an analysis tool, MAdFraud, which automatically runs many apps simultaneously in emulators to trigger and expose ad fraud. Since the formats of ad impressions and clicks vary widely between different ad providers, we develop a novel approach for automatically identifying ad impressions and clicks in three steps: building HTTP request trees, identifying ad request pages using machine learning, and detecting clicks in HTTP request trees using heuristics. We apply our methodology and tool to two datasets: 1) 130,339 apps crawled from 19 Android markets including Play and many third-party markets, and 2) 35,087 apps that likely contain malware provided by a security company. From analyzing these datasets, we find that about 30% of apps with ads make ad requests while in running in the background. In addition, we find 27 apps which generate clicks without user interaction. We find that the click fraud apps attempt to remain stealthy when fabricating ad traffic by only periodically sending clicks and changing which ad provider is being targeted between installations.
World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions

Humans interpret texts with respect to some background information, or world knowledge, and we would like to develop automatic reading comprehension systems that can do the same. In this paper, we introduce a task and several models to drive progress towards this goal. In particular, we propose the task of rare entity prediction: given a web document with several entities removed, models are tasked with predicting the correct missing entities conditioned on the document context and the lexical resources. This task is challenging due to the diversity of language styles and the extremely large number of rare entities. We propose two recurrent neural network architectures which make use of external knowledge in the form of entity descriptions. Our experiments show that our hierarchical LSTM model performs significantly better at the rare entity prediction task than those that do not make use of external resources.
Software Maintenance Maturity Model (SMmm): the software maintenance process model

We address the assessment and improvement of the software maintenance function by proposing improvements to the software maintenance standards and introducing a proposed maturity model for daily software maintenance activities: Software Maintenance Maturity Model (SM). The software maintenance function suffers from a scarcity of management models to facilitate its evaluation, management, and continuous improvement. The SM addresses the unique activities of software maintenance while preserving a structure similar to that of the CMMi4 maturity model. It is designed to be used as a complement to this model. The SM is based on practitioners experience, international standards, and the seminal literature on software maintenance. We present the models purpose, scope, foundation, and architecture, followed by its initial validation.
Data Driven Analytics for Personalized Healthcare

The concept of Learning Health Systems (LHS) is gaining momentum as more and more electronic healthcare data becomes increasingly accessible. The core idea is to enable learning from the collective experience of a care delivery network as recorded in the observational data, to iteratively improve care quality as care is being provided in a real world setting. In line with this vision, much recent research effort has been devoted to exploring machine learning, data mining and data visualization methodologies that can be used to derive real world evidence from diverse sources of healthcare data to provide personalized decision support for care delivery and care management. In this chapter, we will give an overview of a wide range of analytics and visualization components we have developed, examples of clinical insights reached from these components, and some new directions we are taking.
Fat deposition, fatty acid composition and meat quality: A review.

This paper reviews the factors affecting the fatty acid composition of adipose tissue and muscle in pigs, sheep and cattle and shows that a major factor is the total amount of fat. The effects of fatty acid composition on meat quality are also reviewed. Pigs have high levels of polyunsaturated fatty acids (PUFA), including the long chain (C20-22) PUFA in adipose tissue and muscle. The full range of PUFA are also found in sheep adipose tissue and muscle whereas cattle 'conserve' long chain PUFA in muscle phospholipid. Linoleic acid (18:2n-6) is a major ingredient of feeds for all species. Its incorporation into adipose tissue and muscle in relation to the amount in the diet is greater than for other fatty acids. It is deposited in muscle phospholipid at a high level where it and its long chain products eg aracidonic acid (20:4n-6) compete well for insertion into phospholipid molecules. Its proportion in pig adipose tissue declines as fat deposition proceeds and is an index of fatness. The same inverse relationships are not seen in ruminant adipose tissue but in all species the proportion of 18:2n-6 declines in muscle as fat deposition increases. The main reason is that phospholipid, where 18:2n-6 is located, declines as a proportion of muscle lipid and the proportion of neutral lipid, with its higher content of saturated and monounsaturated fatty acids, increases. Oleic acid (18:1cis-9), formed from stearic acid (18:0) by the enzyme stearoyl Co-A desaturase, is a major component of neutral lipid and in ruminants the same enzyme forms conjugated linoleic acid (CLA), an important nutrient in human nutrition. Like 18:2n-6, α-linolenic acid (18:3n-3) is an essential fatty acid and is important to ruminants since it is the major fatty acid in grass. However it does not compete well for insertion into phospholipid compared with 18:2n-6 and its incorporation into adipose tissue and muscle is less efficient. Greater biohydrogenation of 18:3n-3 and a long rumen transit time for forage diets also limits the amount available for tissue uptake compared with 18:2n-6 from concentrate diets. A positive feature of grass feeding is that levels of the nutritionally important long chain n-3 PUFA are increased ie EPA (20:5n-3) and DHA (22:6n-3). Future research should focus on increasing n-3 PUFA proportions in lean carcasses and the use of biodiverse pastures and conservation processes which retain the benefits of fresh leafy grass offer opportunities to achieve this. The varying fatty acid compositions of adipose tissue and muscle have profound effects on meat quality. Fatty acid composition determines the firmness/oiliness of adipose tissue and the oxidative stability of muscle, which in turn affects flavour and muscle colour. Vitamin E is an essential nutrient, which stabilises PUFA and has a central role in meat quality, particularly in ruminants.
The gcd-sum Function

The gcd-sum is an arithmetic function defined as the sum of the gcd’s of the first n integers with n : g(n) = ∑n i=1(i, n). The function arises in deriving asymptotic estimates for a lattice point counting problem. The function is multiplicative, and has polynomial growth. Its Dirichlet series has a compact representation in terms of the Riemann zeta function. Asymptotic forms for values of partial sums of the Dirichlet series at real values are derived, including estimates for error terms.
Why is Software Late? An Empirical Study of Reasons For Delay in Software Development

This paper describes a study of the reasons for delay in software development that was carried out in 1988 and 1989 in a Software Engineering Department. The aim of the study was to gain an insight into the reasons for differences between plans and reality in development activities in order to be able to take actions for improvement. A classification was used to determine the reasons. One hundred and sixty activities, comprising over 15 000 hours of work, have been analyzed. Actions have been taken in the Department as a result of the study. These actions should enable future projects to follow the plan more closely. The actions for improvement include the introduction of maintenance weeks. Similar studies in other software development departments have shown that the reasons varied widely from one department to another. It is recommended that every department should gain an insight into its reasons for delay in software development so as to be able to take appropriate actions for improvement.
Survey Paper on Cloud Computing: Issues, Challenges and Outcomes

Cloud Computing is a term which came into existence in 2007. It refers to applications and services that run on a distributed network using virtual resources and the Internet. It became popular because of the services which are provided by it. Cloud computing uses the technology, services, hardware etc. and turns them into a service and provides them to a user on basis of demand and pay. In this paper we are analyzing the issues and challenges that are likely to be faced when we implement cloud computing platform. As cloud computing is the new technology the paper describes about it. The paper defines clouds and outlines it characteristics with its architecture and also provides information about various models. The paper majorly tells us about the issues, challenges and outcomes of cloud computing.
EMOTEX : Detecting Emotions in Twitter Messages

Social media and microblog tools are increasingly used by individuals to express their feelings and opinions in the form of short text messages. Detecting emotions in text has a wide range of applications including identifying anxiety or depression of individuals and measuring well-being or public mood of a community. In this paper, we propose a new approach for automatically classifying text messages of individuals to infer their emotional states. To model emotional states, we utilize the well-established Circumplex model that characterizes affective experience along two dimensions: valence and arousal. We select Twitter messages as input data set, as they provide a very large, diverse and freely available ensemble of emotions. Using hash-tags as labels, our methodology trains supervised classifiers to detect multiple classes of emotion on potentially huge data sets with no manual effort. We investigate the utility of several features for emotion detection, including unigrams, emoticons, negations and punctuations. To tackle the problem of sparse and high dimensional feature vectors of messages, we utilize a lexicon of emotions. We have compared the accuracy of several machine learning algorithms, including SVM, KNN, Decision Tree, and Naive Bayes for classifying Twitter messages. Our technique has an accuracy of over 90%, while demonstrating robustness across learning algorithms.
A General Survey of Privacy-Preserving Data Mining Models and Algorithms

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.
Question Answering on the SQuAD Dataset

This project explores approaches to learning the SQuAD dataset. It introduces a simple baseline model using an encoder-decoder architecture and shows how pointer networks and coattention techniques result in significant improvements over the baseline. The best model, combining coattention encoding with a pointer network decoder, reaches an F1 score of 65.036% and EM score of 53.205% on the SQuAD test set.
Model-driven Development of Complex Software: A Research Roadmap

The term model-driven engineering (MDE) is typically used to describe software development approaches in which abstract models of software systems are created and systematically transformed to concrete implementations. In this paper we give an overview of current research in MDE and discuss some of the major challenges that must be tackled in order to realize the MDE vision of software development. We argue that full realizations of the MDE vision may not be possible in the near to medium-term primarily because of the wicked problems involved. On the other hand, attempting to realize the vision will provide insights that can be used to significantly reduce the gap between evolving software complexity and the technologies used to manage complexity.
Identifying informative messages in disaster events using Convolutional Neural Networks

Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples’ ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the “bag of words” and n-grams as features on several datasets of messages from flooding events.
Query type classification for web document retrieval

The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insufficiencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need different strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the difference of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply different algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.
Zero-shot Learning with Semantic Output Codes

We consider the problem of zero-shot learning, where the goal is to learn a classifier f : X → Y that must predict novel values of Y that were omitted from the training set. To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of Y to extrapolate to novel classes. We provide a formalism for this type of classifier and study its theoretical properties in a PAC framework, showing conditions under which the classifier can accurately predict novel classes. As a case study, we build a SOC classifier for a neural decoding task and show that it can often predict words that people are thinking about from functional magnetic resonance images (fMRI) of their neural activity, even without training examples for those words.
Microbial Growth and Quorum Sensing Antagonist Activities of Herbal Plants Extracts

Antimicrobial and antiquorum sensing (AQS) activities of fourteen ethanolic extracts of different parts of eight plants were screened against four Gram-positive, five Gram-negative bacteria and four fungi. Depending on the plant part extract used and the test microorganism, variable activities were recorded at 3 mg per disc. Among the Grampositive bacteria tested, for example, activities of Laurus nobilis bark extract ranged between a 9.5 mm inhibition zone against Bacillus subtilis up to a 25 mm one against methicillin resistant Staphylococcus aureus. Staphylococcus aureus and Aspergillus fumigatus were the most susceptible among bacteria and fungi tested towards other plant parts. Of interest is the tangible antifungal activity of a Tecoma capensis flower extract, which is reported for the first time. However, minimum inhibitory concentrations (MIC's) for both bacteria and fungi were relatively high (0.5-3.0 mg). As for antiquorum sensing activity against Chromobacterium violaceum, superior activity (>17 mm QS inhibition) was associated with Sonchus oleraceus and Laurus nobilis extracts and weak to good activity (8-17 mm) was recorded for other plants. In conclusion, results indicate the potential of these plant extracts in treating microbial infections through cell growth inhibition or quorum sensing antagonism, which is reported for the first time, thus validating their medicinal use.
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system

User Generated Content (UGC) is re-shaping the way people watch video and TV, with millions of video producers and consumers. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and developing new business opportunities. To better understand the impact of UGC systems, we have analyzed YouTube, the world's largest UGC VoD system. Based on a large amount of data collected, we provide an in-depth study of YouTube and other similar UGC systems. In particular, we study the popularity life-cycle of videos, the intrinsic statistical properties of requests and their relationship with video age, and the level of content aliasing or of illegal content in the system. We also provide insights on the potential for more efficient UGC VoD systems (e.g. utilizing P2P techniques or making better use of caching). Finally, we discuss the opportunities to leverage the latent demand for niche videos that are not reached today due to information filtering effects or other system scarcity distortions. Overall, we believe that the results presented in this paper are crucial in understanding UGC systems and can provide valuable information to ISPs, site administrators, and content owners with major commercial and technical implications.
Improving Entity Linking by Modeling Latent Relations between Mentions

In-domain: 91% F1 on the dev set, 5 we reduced the learning rate from 10−4 to 10−5. We then stopped the training when F1 was not improved after 20 epochs. We did the same for ment-norm except that the learning rate was changed at 91.5% F1. Note that all the hyper-parameters except K and the turning point for early stopping were set to the values used by Ganea and Hofmann (2017). Systematic tuning is expensive though may have further ncreased the result of our models.