LLukas22/scidocs huggingface dataset:
Software Process Support over the Internet

The MILOS system supports software development processes over the Internet. It integrates process modeling with project planning and enactment. Our flexible workflow engine allows refining and changing process models during project execution, The built-in traceability component supports change notifications and helps the project participants to ensure that the project plan as well as the state of the enactment engine reflect the “real world development process. Tool integration is accomplished by using the built-in capabilities of Web browsers.
No . 6100 A Theory of Crowdfunding – A Mechanism Design Approach with Demand Uncertainty and Moral Hazard

Crowdfunding provides innovation in enabling entrepreneurs to contract with consumers before investment. Under aggregate demand uncertainty, this improves screening for valuable projects. Entrepreneurial moral hazard and private cost information threatens this benefit. Despite these threats, popular crowdfunding schemes are able to implement optimal screening mechanisms. Crowdfunding’s after-markets enable consumers to actively implement deferred payments and thereby optimally manage the entrepreneur’s incentives. Efficiency is sustainable only if expected returns exceed an agency cost associated with the entrepreneurial incentive problems. By reducing demand uncertainty, crowdfunding promotes welfare and complements traditional entrepreneurial financing which focuses on controlling moral hazard. JEL-Codes: D820, G320, L110, M310.
IoT security: Review, blockchain solutions, and open challenges

With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth, with Cisco Inc. predicting to have 50 billion connected devices by 2020. However, most of these IoT devices are easy to hack and compromise. Typically, these IoT devices are limited in compute, storage, and network capacity, and therefore they are more vulnerable to attacks than other endpoint devices such as smartphones, tablets, or computers. In this paper, we present and survey major security issues for IoT. We review and categorize popular security issues with regard to the IoT layered architecture, in addition to protocols used for networking, communication, and management. We outline security requirements for IoT along with the existing attacks, threats, and state-of-the-art solutions. Furthermore, we tabulate and map IoT security problems against existing solutions found in the literature. More importantly, we discuss, how blockchain, which is the underlying technology for bitcoin, can be a key enabler to solve many IoT security problems. The paper also identifies open research problems and challenges for IoT security. © 2017 Elsevier B.V. All rights reserved.
Exercise and type 2 diabetes: molecular mechanisms regulating glucose uptake in skeletal muscle.

Exercise is a well-established tool to prevent and combat type 2 diabetes. Exercise improves whole body metabolic health in people with type 2 diabetes, and adaptations to skeletal muscle are essential for this improvement. An acute bout of exercise increases skeletal muscle glucose uptake, while chronic exercise training improves mitochondrial function, increases mitochondrial biogenesis, and increases the expression of glucose transporter proteins and numerous metabolic genes. This review focuses on the molecular mechanisms that mediate the effects of exercise to increase glucose uptake in skeletal muscle.
Developing the Incongruity-Resolution Theory

The idea of incongruity-resolution has frequently been suggested as an account of many types of joke. However, there is no precise statement either of this “theory” nor of its main concepts (incongruity and resolution), and different authors may disagree on details. We concentrate on two particular variants and attempt to clarify what would be needed to make these into computational models.
Osteo-cise: Strong Bones for Life: Protocol for a community-based randomised controlled trial of a multi-modal exercise and osteoporosis education program for older adults at risk of falls and fractures

BACKGROUND
Osteoporosis affects over 220 million people worldwide, and currently there is no 'cure' for the disease. Thus, there is a need to develop evidence-based, safe and acceptable prevention strategies at the population level that target multiple risk factors for fragility fractures to reduce the health and economic burden of the condition.


METHODS/DESIGN
The Osteo-cise: Strong Bones for Life study will investigate the effectiveness and feasibility of a multi-component targeted exercise, osteoporosis education/awareness and behavioural change program for improving bone health and muscle function and reducing falls risk in community-dwelling older adults at an increased risk of fracture. Men and women aged ≥60 years will participate in an 18-month randomised controlled trial comprising a 12-month structured and supervised community-based program and a 6-month 'research to practise' translational phase. Participants will be randomly assigned to either the Osteo-cise intervention or a self-management control group. The intervention will comprise a multi-modal exercise program incorporating high velocity progressive resistance training, moderate impact weight-bearing exercise and high challenging balance exercises performed three times weekly at local community-based fitness centres. A behavioural change program will be used to enhance exercise adoption and adherence to the program. Community-based osteoporosis education seminars will be conducted to improve participant knowledge and understanding of the risk factors and preventative measures for osteoporosis, falls and fractures. The primary outcomes measures, to be collected at baseline, 6, 12, and 18 months, will include DXA-derived hip and spine bone mineral density measurements and functional muscle power (timed stair-climb test). Secondary outcomes measures include: MRI-assessed distal femur and proximal tibia trabecular bone micro-architecture, lower limb and back maximal muscle strength, balance and function (four square step test, functional reach test, timed up-and-go test and 30-second sit-to-stand), falls incidence and health-related quality of life. Cost-effectiveness will also be assessed.


DISCUSSION
The findings from the Osteo-cise: Strong Bones for Life study will provide new information on the efficacy of a targeted multi-modal community-based exercise program incorporating high velocity resistance training, together with an osteoporosis education and behavioural change program for improving multiple risk factors for falls and fracture in older adults at risk of fragility fracture.


TRIAL REGISTRATION
Australian New Zealand Clinical Trials Registry reference ACTRN12609000100291.
Dirt Cheap Web-Scale Parallel Text from the Common Crawl

Parallel text is the fuel that drives modern machine translation systems. The Web is a comprehensive source of preexisting parallel text, but crawling the entire web is impossible for all but the largest companies. We bring web-scale parallel text to the masses by mining the Common Crawl, a public Web crawl hosted on Amazon’s Elastic Cloud. Starting from nothing more than a set of common two-letter language codes, our open-source extension of the STRAND algorithm mined 32 terabytes of the crawl in just under a day, at a cost of about $500. Our large-scale experiment uncovers large amounts of parallel text in dozens of language pairs across a variety of domains and genres, some previously unavailable in curated datasets. Even with minimal cleaning and filtering, the resulting data boosts translation performance across the board for five different language pairs in the news domain, and on open domain test sets we see improvements of up to 5 BLEU. We make our code and data available for other researchers seeking to mine this rich new data resource.1
Clinically identified maternal depression before, during, and after pregnancies ending in live births.

OBJECTIVE
This study estimated the prevalence of diagnosed depression and treatment among women before, during, and after pregnancies ending in live births.


METHOD
A previously validated algorithm identified health plan members with at least one pregnancy between Jan. 1, 1998, and Dec. 31, 2001. Women with a pregnancy ending in one or more live births and continuously enrolled from 39 weeks before pregnancy through 39 weeks after pregnancy were eligible. Maternal depression was identified from the medical records. Depression treatment included antidepressant medication and/or mental health visits. The authors examined the prevalence of depression and treatments received.


RESULTS
Among 4,398 continuously enrolled women with eligible pregnancies ending in live births, 678 (15.4%) had depression identified during at least one pregnancy phase; 8.7%, 6.9%, and 10.4% had depression identified before, during, and/or after pregnancy, respectively. Among women with identified depression during the 39 weeks before pregnancy, 56.4% also had a depression diagnosis during pregnancy. Of women identified with depression during the 39 weeks following pregnancy, 54.2% had depression diagnoses either during or preceding pregnancy. Most women diagnosed with depression received antidepressant medications and/or had at least one mental health visit. Having at least one mental health visit did not vary before, during, or after pregnancy; however, antidepressant use was lower during pregnancy than before or after pregnancy.


CONCLUSIONS
Approximately one in seven women was identified with and treated for depression during 39 weeks before through 39 weeks after pregnancy, and more than half of these women had recurring indicators for depression.
Face2Face: real-time face capture and reenactment of RGB videos

Face2Face is an approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. Our goal is to animate the facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. To this end, we first address the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling. At run time, we track facial expressions of both source and target video using a dense photometric consistency measure. Reenactment is then achieved by fast and efficient deformation transfer between source and target. The mouth interior that best matches the re-targeted expression is retrieved from the target sequence and warped to produce an accurate fit. Finally, we convincingly re-render the synthesized target face on top of the corresponding video stream such that it seamlessly blends with the real-world illumination. We demonstrate our method in a live setup, where Youtube videos are reenacted in real time. This live setup has also been shown at SIGGRAPH Emerging Technologies 2016, by Thies et al. where it won the Best in Show Award.
Cardiac Signatures of Personality

BACKGROUND
There are well-established relations between personality and the heart, as evidenced by associations between negative emotions on the one hand, and coronary heart disease or chronic heart failure on the other. However, there are substantial gaps in our knowledge about relations between the heart and personality in healthy individuals. Here, we investigated whether amplitude patterns of the electrocardiogram (ECG) correlate with neurotisicm, extraversion, agreeableness, warmth, positive emotion, and tender-mindedness as measured with the Neuroticism-Extraversion-Openness (NEO) personality inventory. Specifically, we investigated (a) whether a cardiac amplitude measure that was previously reported to be related to flattened affectivity (referred to as Eκ values) would explain variance of NEO scores, and (b) whether correlations can be found between NEO scores and amplitudes of the ECG.


METHODOLOGY/PRINCIPAL FINDINGS
NEO scores and rest ECGs were obtained from 425 healthy individuals. Neuroticism and positive emotion significantly differed between individuals with high and low Eκ values. In addition, stepwise cross-validated regressions indicated correlations between ECG amplitudes and (a) agreeableness, as well as (b) positive emotion.


CONCLUSIONS/SIGNIFICANCE
These results are the first to demonstrate that ECG amplitude patterns provide information about the personality of an individual as measured with NEO personality scales and facets. These findings open new perspectives for a more efficient personality assessment using cardiac measures, as well as for more efficient risk-stratification and pre-clinical diagnosis of individuals at risk for cardiac, affective and psychosomatic disorders.
Nature Inspired Metaheuristic Algorithms for Multilevel Thresholding Image Segmentation - A Survey

Segmentation is one of the essential tasks in image processing. Thresholding is one of the simplest techniques for performing image segmentation. Multilevel thresholding is a simple and effective technique. The primary objective of bi-level or multilevel thresholding for image segmentation is to determine a best thresholding value. To achieve multilevel thresholding various techniques has been proposed. A study of some nature inspired metaheuristic algorithms for multilevel thresholding for image segmentation is conducted. Here, we study about Particle swarm optimization (PSO) algorithm, artificial bee colony optimization (ABC), Ant colony optimization (ACO) algorithm and Cuckoo search (CS) algorithm. Keywords—Ant colony optimization, Artificial bee colony optimization, Cuckoo search algorithm, Image segmentation, Multilevel thresholding, Particle swarm optimization.
ReliefF for Multi-label Feature Selection

The feature selection process aims to select a subset of relevant features to be used in model construction, reducing data dimensionality by removing irrelevant and redundant features. Although effective feature selection methods to support single-label learning are abound, this is not the case for multi-label learning. Furthermore, most of the multi-label feature selection methods proposed initially transform the multi-label data to single-label in which a traditional feature selection method is then applied. However, the application of single-label feature selection methods after transforming the data can hinder exploring label dependence, an important issue in multi-label learning. This work proposes a new multi-label feature selection algorithm, RF-ML, by extending the single-label feature selection ReliefF algorithm. RF-ML, unlike strictly univariate measures for feature ranking, takes into account the effect of interacting attributes to directly deal with multi-label data without any data transformation. Using synthetic datasets, the proposed algorithm is experimentally compared to the ReliefF algorithm in which the multi-label data has been previously transformed to single-label data using two well-known data transformation approaches. Results show that the proposed algorithm stands out by ranking the relevant features as the best ones more often.
Predicting chronic left ventricular dysfunction 90 days after ST-segment elevation myocardial infarction: An Assessment of Pexelizumab in Acute Myocardial Infarction (APEX-AMI) Substudy.

OBJECTIVES
The purpose of this study was to determine predictors of 90-day left ventricular function following acute ST-segment elevation myocardial infarction (STEMI) using variables from clinical presentation, biomarker testing, and cardiovascular magnetic resonance imaging (CMR).


BACKGROUND
Identifying patients with acute STEMI who experience adverse remodeling and develop left ventricular dysfunction 3 months post-MI is a priority for guiding subsequent therapy.


METHODS
The Assessment of Pexelizumab in Acute Myocardial Infarction trial tested pexelizumab treatment in STEMI patients presenting within 6 hours of symptom onset who were to undergo primary percutaneous coronary intervention. We studied 64 patients within this trial according to a prespecified substudy that included paired core laboratory delayed-enhancement CMR at days 3 and 90 as well as plasma N-terminal pro-B-type natriuretic peptide (NT-proBNP; in picograms per milliliter) measured at randomization and 24 hours. A multivariable model predicting day 90 left ventricular ejection fraction (LVEF) was developed from clinical, biomarker, and imaging findings.


RESULTS
Patients had a median age of 60 years (52-68), 89% were male, and 60% had anterior STEMI. Time from symptom onset to percutaneous coronary intervention was 3 hours. The median baseline LVEF was 48% (38%-56%) and was 50% (40%-54%) at 90 days: 7 patients (11%) had an LVEF <35% at 90 days. Patients with a lower 90-day LVEF (as a continuous variable) had a higher 24-hour NT-proBNP (P = .02) and a larger baseline infarct size by CMR (median 15% LV [8%-20% LV]) (P < .01). Microvascular obstruction (no reflow) was greater as measured by CMR (median 2.8% [1.4%-6.1%]) in patients with a lower 90-day LVEF (P < .01). Median baseline and 24-hour NT-proBNP levels were 94 pg/mL (54-292 pg/mL) and 1,448 pg/mL (958-2,599 pg/mL), respectively. In a multivariable model with clinical, biomarker, and imaging variables, only 3 variables independently predicted 90-day LVEF: 24-hour NT-proBNP, baseline CMR infarct size, and microvascular obstruction.


CONCLUSIONS
Three key pathophysiologic variables of the post-STEMI myocardium measuring baseline infarct size and the extent of microvascular obstruction on CMR and wall tension (24-hour NT-proBNP) independently predicted 90-day LVEF. Further studies linking these measures with earlier use of clinical therapies may be warranted.
Disposable Screen Printed Electrochemical Sensors: Tools for Environmental Monitoring

Screen printing technology is a widely used technique for the fabrication of electrochemical sensors. This methodology is likely to underpin the progressive drive towards miniaturized, sensitive and portable devices, and has already established its route from "lab-to-market" for a plethora of sensors. The application of these sensors for analysis of environmental samples has been the major focus of research in this field. As a consequence, this work will focus on recent important advances in the design and fabrication of disposable screen printed sensors for the electrochemical detection of environmental contaminants. Special emphasis is given on sensor fabrication methodology, operating details and performance characteristics for environmental applications.
Defining and recognising locally advanced basal cell carcinoma.

BACKGROUND
Rarely, basal cell carcinomas (BCCs) have the potential to become extensively invasive and destructive, a phenomenon that has led to the term "locally advanced BCC" (laBCC). We identified and described the diverse settings that could be considered "locally advanced".


METHODS
The panel of experts included oncodermatologists, dermatological and maxillofacial surgeons, pathologists, radiotherapists and geriatricians. During a 1-day workshop session, an interactive flow/sequence of questions and inputs was debated.


RESULTS
Discussion of nine cases permitted us to approach consensus concerning what constitutes laBCC. The expert panel retained three major components for the complete assessment of laBCC cases: factors of complexity related to the tumour itself, factors related to the operability and the technical procedure, and factors related to the patient. Competing risks of death should be precisely identified. To ensure homogeneous multidisciplinary team (MDT) decisions in different clinical settings, the panel aimed to develop a practical tool based on the three components.


CONCLUSION
The grid presented is not a definitive tool, but rather, it is a method for analysing the complexity of laBCC.
AdaBoost-based artificial neural network learning

A boosting-based method of learning a feed-forward artificial neural network (ANN) with a single layer of hidden neurons and a single output neuron is presented. Initially, an algorithm called Boostron is described that learns a single-layer perceptron using AdaBoost and decision stumps. It is then extended to learn weights of a neural network with a single hidden layer of linear neurons. Finally, a novel method is introduced to incorporate non-linear activation functions in artificial neural network learning. The proposed method uses series representation to approximate non-linearity of activation functions, learns the coefficients of nonlinear terms by AdaBoost. It adapts the network parameters by a layer-wise iterative traversal of neurons and an appropriate reduction of the problem. A detailed performances comparison of various neural network models learned the proposed methods and those learned using the Least Mean Squared learning (LMS) and the resilient back-propagation (RPROP) is provided in this paper. Several favorable results are reported for 17 synthetic and real-world datasets with different degrees of difficulties for both binary and multi-class problems. Email addresses: mubasher.baig@nu.edu.pk, awais@lums.edu.pk (Mirza M. Baig, Mian. M. Awais), alfy@kfupm.edu.sa (El-Sayed M. El-Alfy) Preprint submitted to Neurocomputing March 9, 2017
Towards analysing the rationale of information security non-compliance: Devising a Value-Based Compliance analysis method

Employees’ poor compliance with information security policies is a perennial problem. Current information security analysis methods do not allow information security managers to capture the rationalities behind employees’ compliance and non-compliance. To address this shortcoming, this design science research paper suggests: (a) a Value-Based Compliance analysis method and (b) a set of design principles for methods that analyse different rationalities for information security. Our empirical demonstration shows that the method supports a systematic analysis of why employees comply/do not comply with policies. Thus we provide managers with a tool to make them more knowledgeable about employees’ information security behaviours. 2016 Published by Elsevier B.V.