nils-herrmann/clean_scirepeval huggingface dataset:
Social media meets hotel revenue management : Opportunities , issues and unanswered questions

Hotel companies are struggling to keep up with the rapid consumer adoption of social media. Although many companies have begun to develop social media programs, the industry has yet to fully explore the potential of this emerging data and communication resource. The revenue management department, as it evolves from tactical inventory management to a more expansive role across the organization, is poised to be an early adopter of the opportunities afforded by social media. We propose a framework for evaluating social media-related revenue management opportunities, discuss the issues associated with leveraging these opportunities and propose a roadmap for future research in this area. Journal of Revenue and Pricing Management (2011) 10,
Real-time SVM-based emotion recognition algorithm

The rise in ageing population is a global trend and this increasing number of elderly requires the development of new techniques, especially in the healthcare. Nowadays, already a lot of research is conducted with respect to the development of healthcare robots. However, these robots often focus on practical tasks and lack on a social interaction level. To enhance these social skills it is necessary to analyze both verbal and non-verbal communication. This paper focuses on the latter form of communication, more specific on emotion detection. To accomplish this the developed algorithm extracts specific facial cues, in the form of displacement ratios, and interprets these cues with a cascade of SVMs. In total there are 4 different steps to achieve the emotion detection. First, the countenance is detected with an adapted Histogram of Oriented Gradients (HoG) algorithm. Subsequently, 19 feature points are derived from the facial region. The next step comprises the calculation of 12 displacement ratios based on the distance between those feature points in successive frames. Finally, the displacement ratios are used as feature vectors for a multi-class SVM in cascade with a binary SVM. The developed algorithm is evaluated on the Extended Cohn-Kanade (CK+) dataset and has an overall accuracy of 89.78% with a detection speed of less than 30ms, which makes it suitable for real-time applications.
Trajectory generation and modulation using dynamic neural networks

Generation of desired trajectory behavior using neural networks involves a particularly challenging spatio-temporal learning problem. This paper introduces a novel solution, i.e., designing a dynamic system whose terminal behavior emulates a prespecified spatio-temporal pattern independently of its initial conditions. The proposed solution uses a dynamic neural network (DNN), a hybrid architecture that employs a recurrent neural network (RNN) in cascade with a nonrecurrent neural network (NRNN). The RNN generates a simple limit cycle, which the NRNN reshapes into the desired trajectory. This architecture is simple to train. A systematic synthesis procedure based on the design of relay control systems is developed for configuring an RNN that can produce a limit cycle of elementary complexity. It is further shown that a cascade arrangement of this RNN and an appropriately trained NRNN can emulate any desired trajectory behavior irrespective of its complexity. An interesting solution to the trajectory modulation problem, i.e., online modulation of the generated trajectories using external inputs, is also presented. Results of several experiments are included to demonstrate the capabilities and performance of the DNN in handling trajectory generation and modulation problems.
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network

SemEval 2018 Task 7 tasked participants to build a system to classify two entities within a sentence into one of the 6 possible relation types. We tested 3 classes of models: Linear classifiers, Long Short-Term Memory (LSTM) models, and Convolutional Neural Network (CNN) models. Ultimately, the CNN model class proved most performant, so we specialized to this model for our final submissions. We improved performance beyond a vanilla CNN by including a variant of negative sampling, using custom word embeddings learned over a corpus of ACL articles, training over corpora of both tasks 1.1 and 1.2, using reversed feature, using part of context words beyond the entity pairs and using ensemble methods to improve our final predictions. We also tested attention based pooling, upsampling, and data augmentation, but none improved performance. Our model achieved rank 6 out of 28 (macro-averaged F1-score: 72.7) in subtask 1.1, and rank 4 out of 20 (macro F1: 80.6) in subtask 1.2.
Mediterranean diet, traditional risk factors, and the rate of cardiovascular complications after myocardial infarction: final report of the Lyon Diet Heart Study.

The Lyon Diet Heart Study is a randomized secondary prevention trial aimed at testing whether a Mediterranean-type diet may reduce the rate of recurrence after a first myocardial infarction. An intermediate analysis showed a striking protective effect after 27 months of follow-up. This report presents results of an extended follow-up (with a mean of 46 months per patient) and deals with the relationships of dietary patterns and traditional risk factors with recurrence.



Three composite outcomes (COs) combining either cardiac death and nonfatal myocardial infarction (CO 1), or the preceding plus major secondary end points (unstable angina, stroke, heart failure, pulmonary or peripheral embolism) (CO 2), or the preceding plus minor events requiring hospital admission (CO 3) were studied. In the Mediterranean diet group, CO 1 was reduced (14 events versus 44 in the prudent Western-type diet group, P=0.0001), as were CO 2 (27 events versus 90, P=0.0001) and CO 3 (95 events versus 180, P=0. 0002). Adjusted risk ratios ranged from 0.28 to 0.53. Among the traditional risk factors, total cholesterol (1 mmol/L being associated with an increased risk of 18% to 28%), systolic blood pressure (1 mm Hg being associated with an increased risk of 1% to 2%), leukocyte count (adjusted risk ratios ranging from 1.64 to 2.86 with count >9x10(9)/L), female sex (adjusted risk ratios, 0.27 to 0. 46), and aspirin use (adjusted risk ratios, 0.59 to 0.82) were each significantly and independently associated with recurrence.



The protective effect of the Mediterranean dietary pattern was maintained up to 4 years after the first infarction, confirming previous intermediate analyses. Major traditional risk factors, such as high blood cholesterol and blood pressure, were shown to be independent and joint predictors of recurrence, indicating that the Mediterranean dietary pattern did not alter, at least qualitatively, the usual relationships between major risk factors and recurrence. Thus, a comprehensive strategy to decrease cardiovascular morbidity and mortality should include primarily a cardioprotective diet. It should be associated with other (pharmacological?) means aimed at reducing modifiable risk factors. Further trials combining the 2 approaches are warranted.
Tuning decentralized PID controllers for mimo systems with decouplers

In this paper, a method for controlling multivariable processes is presented. The controller design is divided into two parts: firstly, a decoupling matrix is designed in order to minimize the interaction effects. Then, the controller design is obtained for the process + decoupler block. For this purpose, an iterative numeric algorithm, proposed by same authors, is used. The aim is to meet the design specifications for each loop independently. This sequential design method for multivariable decoupling and multiloop PID controller is applied to several examples from literature. Decentralized PID controller design, specifications analysis and time response simulations has been made using the TITO tool, a set of m functions written in Matlab. It can be obtained in web page http://www.uco.es/~in2vasef. Copyrigth  2002 IFAC.
Exploiting Noise Correlation for Channel Decoding with Convolutional Neural Networks

Inspired by the recent advances in deep learning, we propose a novel iterative belief propagation-convolutional neural network (BP-CNN) architecture to exploit noise correlation for channel decoding under correlated noise. The standard BP decoder is used to estimate the coded bits, followed by a CNN to remove the estimation errors of the BP decoder and obtain a more accurate estimation of the channel noise. Iterating between BP and CNN will gradually improve the decoding SNR and hence result in better decoding performance. To train a well-behaved CNN model, we define a new loss function which involves not only the accuracy of the noise estimation but also the normality test for the estimation errors, i.e., to measure how likely the estimation errors follow a Gaussian distribution. The introduction of the normality test to the CNN training shapes the residual noise distribution and further reduces the BER of the iterative decoding, compared to using the standard quadratic loss function. We carry out extensive experiments to analyze and verify the proposed framework.
Modeling Two-Dimensional Guillotine Cutting Problems via Integer Programming

We propose a framework to model general guillotine restrictions in two-dimensional cutting problems formulated as Mixed Integer Linear Programs (MIP). The modeling framework requires a pseudo-polynomial number of variables and constraints, which can be effectively enumerated for medium-size instances. Our modeling of general guillotine cuts is the first one that, once it is implemented within a state-of-the-art MIP solver, can tackle instances of challenging size. We mainly concentrate our analysis on the Guillotine Two Dimensional Knapsack Problem (G2KP), for which a model, and an exact procedure able to significantly improve the computational performance, are given. We also show how the modeling of general guillotine cuts can be extended to other relevant problems such as the Guillotine Two Dimensional Cutting Stock Problem (G2CSP) and the Guillotine Strip Packing Problem (GSPP). Finally, we conclude the paper discussing an extensive set of computational experiments on G2KP and GSPP benchmark instances from the literature.
Job burnout.

Burnout is a prolonged response to chronic emotional and interpersonal stressors on the job, and is defined by the three dimensions of exhaustion, cynicism, and inefficacy. The past 25 years of research has established the complexity of the construct, and places the individual stress experience within a larger organizational context of people's relation to their work. Recently, the work on burnout has expanded internationally and has led to new conceptual models. The focus on engagement, the positive antithesis of burnout, promises to yield new perspectives on interventions to alleviate burnout. The social focus of burnout, the solid research basis concerning the syndrome, and its specific ties to the work domain make a distinct and valuable contribution to people's health and well-being.
Core Stability Training for Injury Prevention

Enhancing core stability through exercise is common to musculoskeletal injury prevention programs. Definitive evidence demonstrating an association between core instability and injury is lacking; however, multifaceted prevention programs including core stabilization exercises appear to be effective at reducing lower extremity injury rates.



PUBMED WAS SEARCHED FOR EPIDEMIOLOGIC, BIOMECHANIC, AND CLINICAL STUDIES OF CORE STABILITY FOR INJURY PREVENTION  "core OR trunk" AND "training OR prevention OR exercise OR rehabilitation" AND "risk OR prevalence") published between January 1980 and October 2012. Articles with relevance to core stability risk factors, assessment, and training were reviewed. Relevant sources from articles were also retrieved and reviewed.



Stabilizer, mobilizer, and load transfer core muscles assist in understanding injury risk, assessing core muscle function, and developing injury prevention programs. Moderate evidence of alterations in core muscle recruitment and injury risk exists. Assessment tools to identify deficits in volitional muscle contraction, isometric muscle endurance, stabilization, and movement patterns are available. Exercise programs to improve core stability should focus on muscle activation, neuromuscular control, static stabilization, and dynamic stability.



Core stabilization relies on instantaneous integration among passive, active, and neural control subsystems. Core muscles are often categorized functionally on the basis of stabilizing or mobilizing roles. Neuromuscular control is critical in coordinating this complex system for dynamic stabilization. Comprehensive assessment and training require a multifaceted approach to address core muscle strength, endurance, and recruitment requirements for functional demands associated with daily activities, exercise, and sport.
Detecting Adversarial Samples from Artifacts

Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversarial perturbations—small input changes crafted explicitly to fool the model. In this paper, we ask whether a DNN can distinguish adversarial samples from their normal and noisy counterparts. We investigate model confidence on adversarial samples by looking at Bayesian uncertainty estimates, available in dropout neural networks, and by performing density estimation in the subspace of deep features learned by the model. The result is a method for implicit adversarial detection that is oblivious to the attack algorithm. We evaluate this method on a variety of standard datasets including MNIST and CIFAR-10 and show that it generalizes well across different architectures and attacks. Our findings report that 85-93% ROC-AUC can be achieved on a number of standard classification tasks with a negative class that consists of both normal and noisy samples.
Virtual assistants and self-driving cars

Self-driving cars are technologically a reality and in the next decade they are expected to reach the highest level of automation. While there is general agreement that an advanced human-autonomous vehicle (HAV) interaction is key to achieve the benefits of self-driving cars, it is less clear what role artificial intelligence (AI) should play in this context. While the scientific community is debating on the role and intersections of AI, autonomous vehicles and related issues, above all ethics, the automotive industry is already presenting AI-based products and services that may influence, in a direction or in another, our technological and societal futures. This paper focuses on virtual assistants, the personification of the car intelligence incorporating, among others, an algorithmic “brain”, a synthetic human “voice” and powerful sensor-based “senses”. Should virtual assistants just assist humans or replace them whenever necessary? Should their scope of action be limited to safety-related driving tasks or to any activity performed in the car or controlled from the car? Although at a very early stage of commercial development, the paper will review the state-of-the-art of in-car virtual assistants underlining their role and functions in the connected and automated driving ecosystem. By drawing from earlier reflections on automation, robots and intelligent agents, it will then identify a series of issues to be addressed by the scientific community, policy-makers and the automotive industry stakeholders.
A mixed-method empirical study of Function-as-a-Service software development in industrial practice

Function-as-a-Service (FaaS) describes cloud computing services that make infrastructure components transparent to application developers, thus falling in the larger group of “serverless” computing models. When using FaaS offerings, such as AWS Lambda, developers provide atomic and short-running code for their functions, and FaaS providers execute and horizontally scale them on-demand. Currently, there is no systematic research on how developers use serverless, what types of applications lend themselves to this model, or what architectural styles and practices FaaS-based applications are based on. We present results from a mixed-method study, combining interviews with advanced practitioners, a systematic analysis of grey literature, and a Web-based survey. We find that successfully adopting FaaS requires a different mental model, where systems are primarily constructed by composing pre-existing services, with FaaS often acting as the “glue” that brings these services together. Tooling availability and maturity, especially related to testing and deployment, remains a major difficulty. Further, we find that current FaaS systems lack systematic support for function reuse, and abstractions and programming models for building non-trivial FaaS applications are limited. We conclude with a discussion of implications for FaaS providers, software developers, and researchers.
Corporate Social Responsibility Initiatives Addressing Social Exclusion in Bangladesh

The private sector is often seen as a driver of exclusionary processes rather than a partner in improving the health and welfare of socially-excluded populations. However, private-sector initiatives and partnerships- collectively labelled corporate social responsibility (CSR) initiatives-may be able to positively impact social status, earning potential, and access to services and resources for socially-excluded populations. This paper presents case studies of CSR projects in Bangladesh that are designed to reduce social exclusion among marginalized populations and explores whether CSR initiatives can increase economic and social capabilities to reduce exclusion. The examples provide snapshots of projects that (a) increase job-skills and employment opportunities for women, disabled women, and rehabilitated drug-users and (b) provide healthcare services to female workers and their communities. The CSR case studies cover a limited number of people but characteristics and practices replicable and scaleable across different industries, countries, and populations are identified. Common success factors from the case studies form the basis for recommendations to design and implement more CSR initiatives targeting socially-excluded groups. The analysis found that CSR has potential for positive and lasting impact on developing countries, especifically on socially-excluded populations. However, there is a need for additional monitoring and critical evaluation.
Coh-metrix: analysis of text on cohesion and language.

Advances in computational linguistics and discourse processing have made it possible to automate many language- and text-processing mechanisms. We have developed a computer tool called Coh-Metrix, which analyzes texts on over 200 measures of cohesion, language, and readability. Its modules use lexicons, part-of-speech classifiers, syntactic parsers, templates, corpora, latent semantic analysis, and other components that are widely used in computational linguistics. After the user enters an English text, CohMetrix returns measures requested by the user. In addition, a facility allows the user to store the results of these analyses in data files (such as Text, Excel, and SPSS). Standard text readability formulas scale texts on difficulty by relying on word length and sentence length, whereas Coh-Metrix is sensitive to cohesion relations, world knowledge, and language and discourse characteristics.
BLOCKBENCH: A Framework for Analyzing Private Blockchains

Blockchain technologies are taking the world by storm. Public blockchains, such as Bitcoin and Ethereum, enable secure peer-to-peer applications like crypto-currency or smart contracts. Their security and performance are well studied. This paper concerns recent private blockchain systems designed with stronger security (trust) assumption and performance requirement. These systems target and aim to disrupt applications which have so far been implemented on top of database systems, for example banking, finance and trading applications. Multiple platforms for private blockchains are being actively developed and fine tuned. However, there is a clear lack of a systematic framework with which different systems can be analyzed and compared against each other. Such a framework can be used to assess blockchains' viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms.
 In this paper, we first describe BLOCKBENCH, the first evaluation framework for analyzing private blockchains. It serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices. Any private blockchain can be integrated to BLOCKBENCH via simple APIs and benchmarked against workloads that are based on real and synthetic smart contracts. BLOCKBENCH measures overall and component-wise performance in terms of throughput, latency, scalability and fault-tolerance. Next, we use BLOCKBENCH to conduct comprehensive evaluation of three major private blockchains: Ethereum, Parity and Hyperledger Fabric. The results demonstrate that these systems are still far from displacing current database systems in traditional data processing workloads. Furthermore, there are gaps in performance among the three systems which are attributed to the design choices at different layers of the blockchain's software stack. We have released BLOCKBENCH for public use.
Greedy Search with Probabilistic N-gram Matching for Neural Machine Translation

Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also suffers from exposure bias. Sequence-level training under the reinforcement framework can mitigate the problems of the word-level loss, but its performance is unstable due to the high variance of the gradient estimation. On these grounds, we present a method with a differentiable sequence-level training objective based on probabilistic n-gram matching which can avoid the reinforcement framework. In addition, this method performs greedy search in the training which uses the predicted words as context just as at inference to alleviate the problem of exposure bias. Experiment results on the NIST Chinese-to-English translation tasks show that our method significantly outperforms the reinforcement-based algorithms and achieves an improvement of 1.5 BLEU points on average over a strong baseline system.
3D spatial interaction: applications for art, design, and science

3D interfaces use motion sensing, physical input, and spatial interaction techniques to effectively control highly dynamic virtual content. Now, with the advent of the Nintendo Wii, Sony Move, and Microsoft Kinect, game developers and researchers must create compelling interface techniques and game-play mechanics that make use of these technologies. At the same time, it is becoming increasingly clear that emerging game technologies are not just going to change the way we play games, they are also going to change the way we make and view art, design new products, analyze scientific datasets, and more.
 This introduction to 3D spatial interfaces demystifies the workings of modern videogame motion controllers and provides an overview of how it is used to create 3D interfaces for tasks such as 2D and 3D navigation, object selection and manipulation, and gesture-based application control. Topics include the strengths and limitations of various motion-controller sensing technologies in today's peripherals, useful techniques for working with these devices, and current and future applications of these technologies to areas beyond games. The course presents valuable information on how to utilize existing 3D user-interface techniques with emerging technologies, how to develop interface techniques, and how to learn from the successes and failures of spatial interfaces created for a variety of application domains.