nils-herrmann/clean_scirepeval huggingface dataset:
AdAttester: Secure Online Mobile Advertisement Attestation Using TrustZone

Mobile advertisement (ad for short) is a major financial pillar for developers to provide free mobile apps. However, it is frequently thwarted by ad fraud, where rogue code tricks ad providers by forging ad display or user clicks, or both. With the mobile ad market growing drastically (e.g., from $8.76 billion in 2012 to $17.96 billion in 2013), it is vitally important to provide a verifiable mobile ad framework to detect and prevent ad frauds. Unfortunately, this is notoriously hard as mobile ads usually run in an execution environment with a huge TCB.
 This paper proposes a verifiable mobile ad framework called AdAttester, based on ARM?s TrustZone technology. AdAttester provides two novel security primitives, namely unforgeable clicks and verifiable display. The two primitives attest that ad-related operations (e.g., user clicks) are initiated by the end user (instead of a bot) and that the ad is displayed intact and timely. AdAttester leverages the secure world of TrustZone to implement these two primitives to collect proofs, which are piggybacked on ad requests to ad providers for attestation. AdAttester is non-intrusive to mobile users and can be incrementally deployed in existing ad ecosystem. A prototype of AdAttester is implemented for Android running on a Samsung Exynos 4412 board. Evaluation using 182 typical mobile apps with ad frauds shows that AdAttester can accurately distinguish ad fraud from legitimate ad operations, yet incurs small performance overhead and little impact on user experience.
How product owner teams scale agile methods to large distributed enterprises

Software development teams in large scale offshore enterprise development programmes are often under intense pressure to deliver high quality software within challenging time contraints. Project failures can attract adverse publicity and damage corporate reputations. Agile methods have been advocated to reduce project risks, improving both productivity and product quality. This article uses practitioner descriptions of agile method tailoring to explore large scale offshore enterprise development programmes with a focus on product owner role tailoring, where the product owner identifies and prioritises customer requirements. In globalised projects, the product owner must reconcile competing business interests, whilst generating and then prioritising large numbers of requirements for numerous development teams. The study comprises eight international companies, based in London, Bangalore and Delhi. Interviews with 46 practitioners were conducted between February 2010 and May 2012. Grounded theory was used to identify that product owners form into teams. The main contribution of this research is to describe the nine product owner team functions identified: groom, prioritiser, release master, technical architect, governor, communicator, traveller, intermediary and risk assessor. These product owner functions arbitrate between conflicting customer requirements, approve release schedules, disseminate architectural design decisions, provide technical governance and propogate information across teams. The functions identified in this research are mapped to a scrum of scrums process, and a taxonomy of the functions shows how focusing on either decision-making or information dissemination in each helps to tailor agile methods to large scale offshore enterprise development programmes.
Impact of Dietary Polyphenols on Carbohydrate Metabolism

Polyphenols, including flavonoids, phenolic acids, proanthocyanidins and resveratrol, are a large and heterogeneous group of phytochemicals in plant-based foods, such as tea, coffee, wine, cocoa, cereal grains, soy, fruits and berries. Growing evidence indicates that various dietary polyphenols may influence carbohydrate metabolism at many levels. In animal models and a limited number of human studies carried out so far, polyphenols and foods or beverages rich in polyphenols have attenuated postprandial glycemic responses and fasting hyperglycemia, and improved acute insulin secretion and insulin sensitivity. The possible mechanisms include inhibition of carbohydrate digestion and glucose absorption in the intestine, stimulation of insulin secretion from the pancreatic beta-cells, modulation of glucose release from the liver, activation of insulin receptors and glucose uptake in the insulin-sensitive tissues, and modulation of intracellular signalling pathways and gene expression. The positive effects of polyphenols on glucose homeostasis observed in a large number of in vitro and animal models are supported by epidemiological evidence on polyphenol-rich diets. To confirm the implications of polyphenol consumption for prevention of insulin resistance, metabolic syndrome and eventually type 2 diabetes, human trials with well-defined diets, controlled study designs and clinically relevant end-points together with holistic approaches e.g., systems biology profiling technologies are needed.
Detection and Identification of Brain Tumor in Brain MR Images Using Fuzzy C-Means Segmentation

Brain tumor is an uncontrolled growth of tissues in human brain. This tumor, when turns in to cancer become life-threatening. So medical imaging, it is necessary to detect the exact location of tumor and its type. For locating tumor in magnetic resonance image (MRI) segmentation of MRI plays an important role. This paper includes survey on different segmentation techniques applied to MR Images for locating tumor. It also includes a proposed method for the same using Fuzzy C-Means algorithm and an algorithm to find area of tumor which is usefull to decide type of brain tumor.
Millimeter-wave system-on-package complex waveguide to microstrip line transition integrated with antenna array

A complex waveguide to microstrip line transition is proposed for system-on-package (SOP) on low temperature cofired ceramic (LTCC). Transition is designed to operate around 60 GHz and is used to feed the 16 elements microstrip antenna array. Transition includes waveguide to stripline transition, stripline to embedded microstrip line transition, and finally embedded microstrip line to microstrip line transition. Return loss characteristics for single transitions are presented. For the assembled complex transition 10-dB return loss bandwidth is from 52 GHz up to 75 GHz. System with antenna array and feed line has gain more then 17 dB. Analysis has been performed using full-wave simulation software.
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architecture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet’s application of local convolutional filters to structural target information successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark.
Comparison of lumbar spine stabilization exercise versus general exercise in young male patients with lumbar disc herniation after 1 year of follow-up.

The safest and most effective conservative treatment for patients with lumbar disc herniation (LDH) has not been established. The purpose of this study was to evaluate the effect of lumbar spine stabilization exercise (LSSE) and general exercise (GE) on pain intensity and functional capacity in young male patients with LDH.



Sixty-three young male adults aged 20 to 29 years with the diagnosis of LDH were enrolled and divided into an LSSE group (n=30) and a GE group (n=33). Patients in both groups received low-power laser (LPL) therapy during the first week of the onset of LDH. Patients in the GE group underwent a GE program. Patients in the LSSE group followed an LSSE program for 3 months. All of the patients were subjected to pain intensity and functional capacity evaluations four times: at pre-and post-LPL therapy, and at 3 months and 1 year post-exercise. Pain intensity of the lower back and legs was evaluated with the visual analogue scale (VAS), and functional capacity was evaluated with the Oswestry Disability Index (ODI).



Both groups showed a significant reduction in VAS and ODI scores at 3 and 12 months post-exercise compared with before treatment (P<0.001). The LSSE group showed a significant reduction in the average score of the VAS for low back pain (P=0.012) and the ODI (P=0.003) at 12 months post-exercise compared with the GE group.



LSSE and GE are considered as effective interventions for young male patients with LDH. Moreover, LSSE is more effective than GE, and physical therapy, such as LPL, is required during acute LDH.
Using data mining to predict secondary school student performance

Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of Business Intelligence (BI)/Data Mining (DM), which aim at extracting high-level knowledge from raw data, offer interesting automated tools that can aid the education domain. The present work intends to approach student achievement in secondary education using BI/DM techniques. Recent real-world data (e.g. student grades, demographic, social and school related features) was collected by using school reports and questionnaires. The two core classes (i.e. Mathematics and Portuguese) were modeled under binary/five-level classification and regression tasks. Also, four DM models (i.e. Decision Trees, Random Forest, Neural Networks and Support Vector Machines) and three input selections (e.g. with and without previous grades) were tested. The results show that a good predictive accuracy can be achieved, provided that the first and/or second school period grades are available. Although student achievement is highly influenced by past evaluations, an explanatory analysis has shown that there are also other relevant features (e.g. number of absences, parent’s job and education, alcohol consumption). As a direct outcome of this research, more efficient student prediction tools can be be developed, improving the quality of education and enhancing school resource management.
Understanding knowledge sharing in virtual communities: An integration of social capital and social cognitive theories

The biggest challenge in fostering a virtual community is the supply of knowledge, namely the willingness to share knowledge with other members. This paper integrates the Social Cognitive Theory and the Social Capital Theory to construct a model for investigating the motivations behind people's knowledge sharing in virtual communities. The study holds that the facets of social capital — social interaction ties, trust, norm of reciprocity, identification, shared vision and shared language — will influence individuals' knowledge sharing in virtual communities. We also argue that outcome expectations — community-related outcome expectations and personal outcome expectations — can engender knowledge sharing in virtual communities. Data collected from 310 members of one professional virtual community provide support for the proposed model. The results help in identifying the motivation underlying individuals' knowledge sharing behavior in professional virtual communities. The implications for theory and practice and future research directions are discussed.
Civility 2 . 0 : A comparative analysis of incivility in online political discussion

Online political discussion amongst citizens has often been labelled uncivil. Indeed, as online discussion allows participants to remain relatively anonymous, and by extension, unaccountable for their behaviour, citizens often engage in angry, hostile, and derogatory discussion, taking the opportunity to attack the beliefs and values of others without fear of retribution. Some commentators believe that this type of incivility, however, could soon be a thing of the past as citizens increasingly turn to online social network sites such as Facebook.com to discuss politics. Facebook requires users, when registering for an account, to do so using their real name, and encourages them to attach a photograph and other personal details to their profile. As a result, users are both identified with and accountable for the comments they make, presumably making them less likely to engage in uncivil discussion. This paper aims to test this assumption by analysing the occurrence of incivility in reader comments left in response to political news articles by the Washington Post. Specifically, it will quantitatively content analyse the comments, comparing the occurrence of incivility evident in comments left on the Washington Post website with comments left on the Washington Post’s Facebook page. Analysis suggests that, in line with the hypothesis, these online platforms differ significantly when it comes to incivility. Paper to be presented at the Elections, Public Opinion, and Parties (EPOP) Conference, September 13-15, 2013, University of Lancaster, UK.  This work was supported by the Economic and Social Research Council [grant number ES/I902767/1].
Evaluation of Medical Concept Annotation Systems on Clinical Records

Large volumes of electronic health records, including free-text documents, are extensively generated within various sectors of healthcare. Medical concept annotation systems are designed to enrich these documents with key concepts in the domain using reference terminologies. Although there is a wide range of annotation systems, there is a lack of comparative analysis that enables thorough understanding of the effectiveness of both the concept extraction and concept recognition components of these systems, especially within the clinical domain. This paper analyses and evaluates four annotation systems (i.e., MetaMap, NCBO annotator, Ontoserver, and QuickUMLS) for the task of extracting medical concepts from clinical free-text documents. Empirical findings have shown that each annotator exhibits various levels of strengths in terms of overall precision or recall. The concept recognition component of each system, however, was found to be highly sensitive to the quality of the text spans output by the concept extraction component of the annotation system. The effects of these components on each other are quantified in such way as to provide evidence for an informed choice of an annotation system as well as avenues for future research.
Defining gamification: a service marketing perspective

During recent years "gamification" has gained significant attention among practitioners and game scholars. However, the current understanding of gamification has been solely based on the act of adding systemic game elements into services. In this paper, we propose a new definition for gamification, which emphases the experiential nature of games and gamification, instead of the systemic understanding. Furthermore, we tie this definition to theory from service marketing because majority of gamification implementations aim towards goals of marketing, which brings to the discussion the notion of how customer / user is always ultimately the creator of value. Since now, the main venue for academic discussion on gamification has mainly been the HCI community. We find it relevant both for industry practitioners as well as for academics to study how gamification can fit in the body of knowledge of existing service literature because the goals and the means of gamification and marketing have a significant overlap.
Overcoming Procrastination through Planning

Have you ever purposefully delayed or postponed a goal-directed action (for example, writing an essay or filling out tax forms) despite strong intentions to achieve the goal and suffident opportunities to pursue it7 If so, then you are in good company: so has a large percentage of the general (15 to 20 percent) 1 and the academic (80 to 85 percent of American college students) population. 2 In addition to academic exampies, procrastination has been studied in the areas of personal health (dieting, exerdsing), sodal relationships (contacting friends), work (j obseeking behavior), and financial management (retirement savings).3 Procrastination 1S a widespread phenomenon with potentially severe consequences, such as dropping out of scho01, compromised health, divorce, and job 10ss. Intuitive1y, it would seem relatively easy to dassify certain behaviors as procrastination. For example, the fact that a student put off reading a book chapter might at first glance be sufficient to label his or her behavior as procrastination. But after a doser look at the phenomenon, additional criteria that cannot be objectively judged from an outside perspective emerge. Building on the numerous definitions of the commonly used term pTOcrastination that can be found in the scientific literature,4 we suggest four criteria that must be fulfilled in order to dassify a behavior or a lack of behavior as procrastination. A person has to (1) cOlnmit to the goal in question, (2) have the opportunity to act on the goal, (3) expect to be worse off later in the case of a delay, and (4) voluntarily decide to put off the intended action Of in action until a later point. For example, not
Hierarchical Attention: What Really Counts in Various NLP Tasks

Attention mechanisms in sequence to sequence models have shown great ability and wonderful performance in various natural language processing (NLP) tasks, such as sentence embedding, text generation, machine translation, machine reading comprehension, etc. Unfortunately, existing attention mechanisms only learn either high-level or low-level features. In this paper, we think that the lack of hierarchical mechanisms is a bottleneck in improving the performance of the attention mechanisms, and propose a novel Hierarchical Attention Mechanism (Ham) based on the weighted sum of different layers of a multi-level attention. Ham achieves a state-of-the-art BLEU score of 0.26 on Chinese poem generation task and a nearly 6.5% averaged improvement compared with the existing machine reading comprehension models such as BIDAF and Match-LSTM. Furthermore, our experiments and theorems reveal that Ham has greater generalization and representation ability than existing attention mechanisms.
Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches

Developing state-of-the-art approaches for specific tasks is a major driving force in our research community. Depending on the prestige of the task, publishing it can come along with a lot of visibility. The question arises how reliable are our evaluation methodologies to compare approaches? One common methodology to identify the stateof-the-art is to partition data into a train, a development and a test set. Researchers can train and tune their approach on some part of the dataset and then select the model that worked best on the development set for a final evaluation on unseen test data. Test scores from different approaches are compared, and performance differences are tested for statistical significance. In this publication, we show that there is a high risk that a statistical significance in this type of evaluation is not due to a superior learning approach. Instead, there is a high risk that the difference is due to chance. For example for the CoNLL 2003 NER dataset we observed in up to 26% of the cases type I errors (false positives) with a threshold of p < 0.05, i.e., falsely concluding a statistically significant difference between two identical approaches. We prove that this evaluation setup is unsuitable to compare learning approaches. We formalize alternative evaluation setups based on score distributions.
Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach

Current developments in ICTs such as in Internet-of-Things (IoT) and Cyber–Physical Systems (CPS) allow us to develop healthcare solutions with more intelligent and prediction capabilities both for daily life (home/office) and in-hospitals. In most of IoT-based healthcare systems, especially at smart homes or hospitals, a bridging point (i.e., gateway) is needed between sensor infrastructure network and the Internet. The gateway at the edge of the network often just performs basic functions such as translating between the protocols used in the Internet and sensor networks. These gateways have beneficial knowledge and constructive control over both the sensor network and the data to be transmitted through the Internet. In this paper, we exploit the strategic position of such gateways at the edge of the network to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., presenting thus a Smart e-Health Gateway. We then propose to exploit the concept of Fog Computing in Healthcare IoT systems by forming a Geo-distributed intermediary layer of intelligence between sensor nodes and Cloud. By taking responsibility for handling some burdens of the sensor network and a remote healthcare center, our Fog-assisted system architecture can cope with many challenges in ubiquitous healthcare systems such asmobility, energy efficiency, scalability, and reliability issues. A successful implementation of Smart e-Health Gateways can enable massive deployment of ubiquitous health monitoring systems especially in clinical environments. We also present a prototype of a Smart e-Health Gateway called UT-GATE where some of the discussed higher-level features have been implemented. We also implement an IoT-based Early Warning Score (EWS) health monitoring to practically show the efficiency and relevance of our systemon addressing amedical case study. Our proofof-concept design demonstrates an IoT-based health monitoring system with enhanced overall system intelligence, energy efficiency, mobility, performance, interoperability, security, and reliability.
Annotating Sentiment and Irony in the Online Italian Political Debate on #labuonascuola

In this paper we present the TWitterBuonaScuola corpus (TW-BS), a novel Italian linguistic resource for Sentiment Analysis, developed with the main aim of analyzing the online debate on the controversial Italian political reform “Buona Scuola” (Good school), aimed at reorganizing the national educational and training systems. We describe the methodologies applied in the collection and annotation of data. The collection has been driven by the detection of the hashtags mainly used by the participants to the debate, while the annotation has been focused on sentiment polarity and irony, but also extended to mark the aspects of the reform that were mainly discussed in the debate. An in-depth study of the disagreement among annotators is included. We describe the collection and annotation stages, and the in-depth analysis of disagreement made with Crowdflower, a crowdsourcing annotation platform.