nils-herrmann/clean_scirepeval huggingface dataset:
Caracterizacion del polivinalcohol (PVOH) en medios de disolucion complejos
Year: 2009

El polivinalcohol es un polimero con muchas aplicaciones en la industria cosmetica, farmaceutica y alimentaria generalmente en forma de gel. El comportamiento del PVOH en agua y su caracterizacion han sido estudiados y se ha determinado que la presencia de otros compuestos organicos e inorganicos en el agua cambian sus propiedades asi como su metodo de cuantificacion (1) Por sus multiples aplicaciones en medios de disolucion complejos, es importante disponer de un metodo rapido para su caracterizacion, que no sea afectado por la presencia de otros componentes en la solucion acuosa. Las enzimas producidas por el hongo Phanerochaete chrysosporium es un medio de cultivo que causa cambios en la concentracion de muestras de PVOH disueltas en el. La concentracion del PVOH en este medio se determino usando el metodo descrito en la norma japonesa JIS K6726 (2) y los resultados no muestran cambios en la concentracion del POVH con el tiempo, lo cual indica la inoperancia de este metodo en estos casos. Para cuantificar el PVOH es necesario separarlo del medio congelando la solucion y luego se analiza por el mismo metodo. La concentracion del PVOH presento una disminucion del 50% en 15 dias. Los cambios en la estructura del PVOH con el tiempo de degradacion fueron estudiador por IRTF. Los resultados confirman la biodegradacion del polimero.
Transcriptomes from German shepherd dogs reveal differences in immune activity between atopic dermatitis affected and control skin
Venue: Immunogenetics
Year: 2020

Canine atopic dermatitis (CAD) is an inflammatory and pruritic allergic skin disease with both genetic and environmental risk factors described. We performed mRNA sequencing of non lesional axillary skin biopsies from nine German shepherd dogs. Obtained RNA sequences were mapped to the dog genome (CanFam3.1) and a high quality skin transcriptome was generated with 23,510 expressed gene transcripts. Differentially expressed genes (DEGs) were defined by comparing three controls to five treated CAD cases. Using a leave one out analysis, we identified seven DEGs: five known to encode proteins with functions related to an activated immune system CD209 CLEC4G LOC102156842 lipopolysaccharide binding protein like) LOC480601 regakine 1 like) LOC479668 haptoglobin like) one OBP encoding an odorant binding protein potentially connected to rhinitis, and the last LOC607095 encoding a novel long non coding RNA. Furthermore, high mRNA expression of inflammatory genes was found in axillary skin from an untreated mild CAD case compared with healthy skin. In conclusion, we define genes with different expression patterns in CAD case skin helping us understand post treatment atopic skin. Further studies in larger sample sets are warranted to confirm and to transfer these results into clinical practice.
Mining customer requirements from online reviews: A product improvement perspective
Venue: Inf. Manag.
Year: 2016

We propose a filtering model to predict helpfulness of reviews for product design.We provide a way to use the KANO model based on online reviews.We explore how to obtain insights from Big Data through knowledge based view. Big data commerce has become an e commerce trend. Learning how to extract valuable and real time insights from big data to drive smarter and more profitable business decisions is a main task of big data commerce. Using online reviews as an example, manufacturers have come to value how to select helpful online reviews and what can be learned from online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online reviews from the perspective of the product designer. The KANO method, which is based on the classical conjoint analysis model, is then innovatively applied to analyze online reviews to develop appropriate product improvement strategies. Moreover, an empirical case study using the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and robustness of the proposed approach. Our research suggests that the combination of big data and classical management models can bring success for big data commerce.
A Study of Deterministic Pilot Allocation for Sparse Channel Estimation in OFDM Systems
Venue: IEEE Communications Letters
Year: 2012

In this letter, we investigate the deterministic pilot allocation for sparse channel estimation in OFDM systems. Based on the rule of minimizing the coherence of the DFT submatrix, we derive that the pilot design according to the cyclic different set (CDS) is optimal. However, the CDS only exists for some specific number of OFDM subcarriers. For those cases where the CDS is unavailable, we propose a scheme using discrete stochastic approximation to obtain a near optimal pilot pattern. Simulation results demonstrate that our scheme is much faster convergent and more efficient than the exhaustive search; and it has been shown that substantial improvement for channel estimation can be achieved.
Art as Redemption
Year: 2010

Mexico in your Senses By Willy Souza Mexico City March 4 April 23, 2010 Mexico has recently suffered from negative media attention due to its social and criminal problems. With a major new touring exhibition of his work, "Mexico in your Sense" Mexican filmmaker and photographer Willy Souza aims to counteract, these stereotypes and encourage Mexicans to regain pride in the myriad strengths and strong cultural heritage of their country. This extensive exhibition, sponsored by the federal government of Mexico and offering free admission, was recently on display in the Zocalo (the main plaza) of Mexico City. It incorporates nearly one thousand photographs in the form of prints and video (culled from the 1.5 million photographs Souza has taken across the country in the last eight years) housed in a specially built traveling venue intended through form and hue to look like a Mayan temple. Designed by noted architect Javier Sordo Madaleno, the 13,000 square foot exhibition space includes a snaking main gallery and an exhibition and video room, representing the traditional pyramids of the sun and the moon found at such archeological sites as Teotihuacan. The first room in the exhibition is floor to (20 foot) ceiling close up color photographs (20 x 24 inches) of eyes of Mexican citizens, interspersed with mirrors of the same size. This opening is a statement suggesting that visitors immediately engage in a process of .self reflection. Souza is asking us to consider what we "have stopped seeing," to "stop accepting preconceived notions and interpreting without seeing." (1) The next four rooms of the first building house images of "things representing the human condition," as Souza notes. The vast halls and ceilings of the main rooms are covered with images: still, color, glossy photographs, unmated and unframed, printed large; a few are backlit or printed on scrims hanging from the ceiling. Photographs from across the country are arranged thematically in such categories as masks, landscapes, markets, and tradespeople. Interpretive panels at the beginning of each theme offer background on the subject and smaller keys at the end offer a few identifying details and in all cases in which Mexican state each image was created. Plasma screens throughout the exhibition hall show series of still images, often offering brief visual narratives. In "Fiestas" there are colorful costumes, gold jewelry and a stunning ground view, close up of a pair of aged, brown, cracked feet. "Architecture" displays structures ranging from Mayan ruins to colonial buildings. "Religious iconography" focuses on details of lit candles, crucifixes, figurines of the Virgin of Guadalupe, and bleeding Jesus statues. "Children" work and play across the country. The "Cycle of Life" begins with a Madonna figure of a young mother bathing a baby in a river as if in baptism and concludes with celebrations of the Day of the Dead, in a series of images teeming with the vibrant orange of the cempasuchil flower, which is traditional for this celebration.
Environmental claims in online video advertising: effects for fast fashion and luxury brands
Venue: International Journal of Advertising
Year: 2019

Online video advertisements are an increasingly important marketing tool for rapidly growing video sharing sites such as YouTube. The authors conduct two studies to examine how consumers respond to fast fashion and luxury brand online video environmental claims and find agreement with hypotheses that consumers who are highly involved in environmental issues will respond more favourably to substantive rather than associative claims and that luxury brands benefit more than fashion brands. The findings have important theoretical and managerial implications for luxury and fast fashion practitioners.
Selectivity Estimation for Range Predicates using Lightweight Models
Venue: Proc. VLDB Endow.
Year: 2019

Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan. When a query contains multiple predicates, today's optimizers use a variety of assumptions, such as independence between predicates, to estimate selectivity. While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. In this work, we reconsider selectivity estimation as a regression problem. We explore application of neural networks and tree based ensembles to the important problem of selectivity estimation of multi dimensional range predicates. While their straightforward application does not outperform even simple baselines, we propose two simple yet effective design choices, i.e. regression label transformation and feature engineering, motivated by the selectivity estimation context. Through extensive empirical evaluation across a variety of datasets, we show that the proposed models deliver both highly accurate estimates as well as fast estimation.
Male sex identified by global COVID 19 meta analysis as a risk factor for death and ITU admission
Venue: Nature communications
Year: 2020

Anecdotal evidence suggests that Coronavirus disease 2019 (COVID 19) caused by the coronavirus SARS CoV 2, exhibits differences in morbidity and mortality between sexes. Here, we present a meta analysis of 3,111,714 reported global cases to demonstrate that, whilst there is no difference in the proportion of males and females with confirmed COVID 19, male patients have almost three times the odds of requiring intensive treatment unit (ITU) admission (OR 2.84; 95% CI 2.06, 3.92) and higher odds of death (OR 1.39; 95% CI 1.31, 1.47) compared to females. With few exceptions, the sex bias observed in COVID 19 is a worldwide phenomenon. An appreciation of how sex is influencing COVID 19 outcomes will have important implications for clinical management and mitigation strategies for this disease.
Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28 30 May 2018, Universitat d'Alacant, Alacant, Spain
Year: 2018

Automatic machine translation evaluation was crucial for the rapid development of machine translation systems over the last two decades. So far, most attention has been paid to the evaluation metrics that work with text on the sentence level and so did the translation systems. Acrosssentence translation quality depends on discourse phenomena that may not manifest at all when staying within sentence boundaries (e.g. coreference, discourse connectives, verb tense sequence etc. To tackle this, we propose several documentlevel MT evaluation metrics: generalizations of sentence level metrics, language(pair) independent versions of lexical cohesion scores and coreference and morphology preservation in the target texts. We measure their agreement with human judgment on a newly created dataset of pairwise paragraph comparisons for four language pairs.
Realistic Transformation of Facial and Vocal Smiles in Real Time Audiovisual Streams
Venue: IEEE Transactions on Affective Computing
Year: 2020

Research in affective computing and cognitive science has shown the importance of emotional facial and vocal expressions during human computer and human human interactions. But, while models exist to control the display and interactive dynamics of emotional expressions, such as smiles, in embodied agents, these techniques can not be applied to video interactions between humans. In this work, we propose an audiovisual smile transformation algorithm able to manipulate an incoming video stream in real time to parametrically control the amount of smile seen on the user's face and heard in their voice, while preserving other characteristics such as the user's identity or the timing and content of the interaction. The transformation is composed of separate audio and visual pipelines, both based on a warping technique informed by real time detection of audio and visual landmarks. Taken together, these two parts constitute a unique audiovisual algorithm which, in addition to providing simultaneous real time transformations of a real person's face and voice, allows to investigate the integration of both modalities of smiles in real world social interactions.
Hierarchical Reinforcement Learning for Quadruped Locomotion
Venue: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019

Legged locomotion is a challenging task for learning algorithms, especially when the task requires a diverse set of primitive behaviors. To solve these problems, we introduce a hierarchical framework that can automatically learn to decompose complex locomotion tasks. A high level policy issues commands in the form of a latent vector and also selects for how long the low level policy will execute the latent command. Concurrently, the low level policy uses the latent command and only the robot's on board sensors to control the robot's actuators. Our approach allows the high level policy to run at a lower frequency than the low level one. We test our framework on a path following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low level skills are needed for this task. We then show efficient adaptation of the trained policy to new tasks by transfer of the trained low level policy. Finally, we validate the policies on a real quadruped robot. To the best of our knowledge, this is the first application of end to end hierarchical learning to a real robotic locomotion task.
Transferable Cost Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning.
Year: 2020

Malware detection is an ever present challenge for all organizational gatekeepers, who must maintain high detection rates while minimizing interruptions to the organization's workflow. To improve detection rates, organizations often deploy an ensemble of detectors. While effective, this approach is computationally expensive, since every file even clear cut cases needs to be analyzed by all detectors. Moreover, with an ever increasing number of files to process, the use of ensembles may incur unacceptable processing times and costs (e.g. cloud resources) In this study, we propose SPIREL, a reinforcement learning based method for cost effective malware detection. Our method enables organizations to directly associate costs to correct/incorrect classification, computing resources and run time, and then dynamically establishes a security policy. This security policy is then implemented, and for each inspected file, a different set of detectors is assigned and a different detection threshold is set. Our evaluation on two malware domains Portable Executable (PE) and Android Application Package (APK)files shows that SPIREL is both accurate and extremely resource efficient: the proposed method either outperforms the best performing baselines while achieving a modest improvement in efficiency, or reduces the required running time by ~80% while decreasing the accuracy and F1 score by only 0.5% We also show that our approach is both highly transferable across different datasets and adaptable to changes in individual detector performance.
The Blackstone Group: Merlin Entertainment
Year: 2009

The Blackstone Group had conducted a roll up of theme parks and attractions business in Europe. It was considering how to generate liquidity for its investors. Blackstone entered the theme parks and attractions business in Europe by acquiring a majority stake in U.K. based Merlin Entertainment in 2005. In 2005 and 2006, Merlin Entertainment acquired two other similar businesses, LEGOLAND based in Denmark and Gardaland based in Italy. At the end of 2006, Blackstone's team was weighing its options for generating liquidity for its investors. The options were to conduct a dividend recapitalization of Merlin Entertainment or to acquire The Tussauds Group. The acquisition, if successful, would result in the second largest theme parks and attractions business in the world after Disney. The Tussauds Group was owned by another private equity firm, Dubai International Capital (DIC) Blackstone's goal was to make a minimum of 3x on its initial Merlin investment through the dividend recapitalization and at least 5x through the Tussauds acquisition. A third option arose while Blackstone was in negotiation with DIC. This was the opportunity to perform a sale leaseback of the underlying real estate assets owned by Merlin and Tussauds. Based on the facts and financials provided, it is clear there were tradeoffs between the size of the potential returns for each option, timing, and the risks that have to be managed. What should the Blackstone team do?Learning  Calculation of financial returns on private equity investments and clearly laying out the tradeoffs in liquidity decision.
RGB D Salient Object Detection via Minimum Barrier Distance Transform and Saliency Fusion
Venue: IEEE Signal Processing Letters
Year: 2017

Automatic detection of salient objects in images has gained its popularity in computer vision field for its usage in numerous vision tasks in recent years. Depth information plays an important role in the human vision system while it is underutilized in most existing two dimensional (2 D) saliency detection methods. In this letter, a multistage salient object detection framework via minimum barrier distance transform and saliency fusion based on multilayer cellular automata (MCA) is proposed. First, we independently generate the 3 D spatial prior, depth bias, and RGB produced and depth induced saliency maps. Next, the two saliency maps are weighted by depth bias to obtain two initial maps. Then, we adopt a saliency optimization step to generate more precise depth induced saliency map. Moreover, the initial RGB produced and the optimized depth induced maps are further fused with 3 D spatial prior. Finally, we utilize MCA to fuse all saliency maps generated previously and obtain the final saliency result with complete salient object. The proposed method is evaluated on the publicly available benchmark dataset, RGBD1000. Compared to several state of the art 2 D and depth aware approaches, the experimental results demonstrate the effectiveness and superiority of our method, which can accurately detect the salient objects from RGB D images, and has the most satisfactory overall performance.
COMPARISION OF FUZZY/ANN TECHNIQUES FOR COMPENSATION OF UNBALANCED VOLTAGES IN GRID CONNECTED PMSG BASED WIND TURBINE
Year: 2018

This paper proposes an effective controller for grid connected wind turbine based permanent magnet synchronous machine for improving unbalanced voltages. In this paper, we proposed a comparative analysis under different controllers like PI, Fuzzy and Artificial Neural Network Controller to PMSG system. A control structure is designed based on the positive sequence reference signals. With the help of these controllers, the double frequency oscillations in DC link voltages and variations in active power can be eliminated. More ever, the proposed system can be implemented in Matlab/Simulink and the performance of the proposed Grid based PMSG wind system under grid fault conditions is verified.  Wind Turbine, PI, Fuzzy, ANN, Grid system.
A new model for assessing the role of customer behavior history, product classification, and prices on the success of the recommender systems in e commerce
Venue: Kybernetes
Year: 2020

This paper aims to propose a new method for evaluating the success of the recommender systems based on customer history, product classification and prices criteria in the electronic commerce. To evaluate the validity of the model, the structural equation modeling technique is employed.  A method has been suggested to evaluate the impact of customer history, product classification and prices on the success of the recommender systems in electronic commerce. After that, the authors investigated the relationship between these factors. To achieve this goal, the structural equation modeling technique was used for statistical conclusion validity. The results of gathered data from employees of a company in Iran is indicated the impact of the customer history on the success of recommender systems in e commerce which is related with the user profile, expert opinion, neighbors, loyalty and clickstream. These factors positively influence the success of recommender systems in ecommerce.  The obtained results demonstrated the efficiency and effectiveness of the proposed model in term of the success of the recommender systems in the electronic commerce.  In this paper, the effective factors of success of recommender systems in electronic commerce are pointed out and the approach to increase the efficiency of this system is applied into a practical example.
Transfer Learning with Joint Distribution Adaptation and Maximum Margin Criterion
Venue: Journal of Physics: Conference Series
Year: 2019

Transfer learning aims to transfer knowledge from the labelled source domain data to build a good classifier for the target domain data which has few labels or none. Existing feature based transfer learning methods seek to transform the data to a new feature space under which the distribution discrepancy is reduced. However, data in different classes may not be easy to be separated in the new feature representation. Therefore, a modified transfer learning algorithm with Joint Distribution Adaptation (JDA) and Maximum Margin Criterion (MMC) is put forward in this paper, which we call MMC JDA for short. MMC JDA is aimed at minimizing the distribution discrepancy between two domains and maximizing the separability of each class at the same time. Comparative experiments on 16 cross domain public image datasets show that MMC JDA is effective and performs better than several common transfer learning methods.