nils-herrmann/clean_scirepeval huggingface dataset:
An Introduction to Data Mining Applied to Health Oriented Databases

The application of data mining (DM) in healthcare is increasing. Healthcare organizations generate and collect large voluminous and heterogeneous information daily and DM helps to uncover some interesting patterns, which leads to the manual tasks elimination, easy data extraction directly from records, to save lives, to reduce the cost of medical services and to enable early detection of diseases. These patterns can help healthcare specialists to make forecasts, put diagnoses, and set treatments for patients in health facilities. This work overviews DM methods and main issues. Three case studies illustrate DM in healthcare applications: (i) In Vitro Fertilization; (ii) Content Based Image Retrieval (CBIR) and (iii) Organ transplantation.
Procesos de preparacion, almacenamiento y distribucion del material quirurgico para garantizar la esterilidad en el servicio de central de esterilizacion del Hospital del Instituto Ecuatoriano de Seguridad Social Santo Domingo de Los Tsachilas

Los procesos de esterilizacion son aquellos que permiten la limpieza del material reutilizable en los centros asistenciales de salud, los cuales son fundamentales ya que las etapas iniciales de este proceso reducen la carga microbiana inicial en un 99,99% y un porcentaje mayor en el proceso completo (Rutala WA y cols, 2001; Alfa MJ, 2000; Eduardo Crema y cols, 2010) Tal es su importancia, que la limpieza aparece como el primer elemento en el reglamento del Centro para el Control de Enfermedades (CDC) que regula la reutilizacion de instrumental medico. Esto es debido a que un adecuado procedimiento de esterilizacion y desinfeccion garantizara la reduccion de infecciones nosocomiales y con ello una mejoria en las politicas de salud (Eduardo Crema y cols, 2010; Denser CPAC y cols, 2006; Goveia VR y cols, 2007; Heeg P y cols, 2001) Ademas, este proceso permite la disminucion de costes en salud debido a la produccion de instrumentos reutilizables posterior al metodo de esterilizacion, donde la calidad del dispositivo reprocesado debe ser comparable a la ofrecida por un nuevo elemento de un solo uso, especialmente con respecto a su funcionalidad, falta de toxicidad y la esterilidad; para ello se debe garantizar un adecuado proceso de esterilizacion (Graziano KU y cols, 2006)
XNOR Net: ImageNet Classification Using Binary Convolutional Neural Networks
Venue: ECCV

We propose two efficient approximations to standard convolutional neural networks: Binary Weight Networks and XNOR Networks. In Binary Weight Networks, the filters are approximated with binary values resulting in 32\\times memory saving. In XNOR Networks, both the filters and the input to convolutional layers are binary. XNOR Networks approximate convolutions using primarily binary operations. This results in 58\\times faster convolutional operations (in terms of number of the high precision operations) and 32\\times memory savings. XNOR Nets offer the possibility of running state of the art networks on CPUs (rather than GPUs) in real time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary Weight Network version of AlexNet is the same as the full precision AlexNet. We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than (16\ in top 1 accuracy. Our code is available at: http:/allenai.org/plato/xnornet.
Free, Fair and Credible Election and Democratic Governance in Bangladesh: How Far the Dream of Success?

The paper has evaluated the relation between the free, fair and credible election and democratic governance in Bangladesh. The paper is a descriptive and qualitative in nature and mainly based on secondary literature. For doing this study, conceptual clarification has been done first and identified few elements of free, fair and credible elections. Then, how far these elements have been ensured in Bangladeshi elections has been evaluated by analyzing all the national elections held since independence. Apart from these, major factors and challenges of holding a free, fair and credible election in Bangladesh have been examined Major findings of this study are: Since the independence of Bangladesh, 10 national elections held in various regimes. 4 out of 10 national elections have been found free, fair and credible which have been conducted by the non party caretaker government. Rests of the elections are not out of controversy and full of manipulation held under elected government. The present AL government is elected by the 10th parliamentary election under incumbent (AL) government, but a major opposition allies (20 parties) lead by BNP boycotted this election and 154 of the total 300 seats being uncontested. As a result, AL again came to the power without a competitive election and most of the national and International election observers including media world consider this election as unfair and the government is suffering from lack of legitimacy which creates political pressure for another interim election and need a permanent solution.
Synthesis and verification of self aware systems

Self aware computing systems are envisaged to exploit the knowledge of their own software architecture, hardware infrastructure and environment in order to follow high level goals through proactively adapting as their environment evolves. This chapter describes two classes of key enabling techniques for self adaptive systems: automated synthesis and formal verification. The ability to dynamically synthesise component connectors and compositions underpins the proactive adaptation of the architecture of self aware systems. Deciding when adaptation is needed and selecting valid new architectures or parameters for self aware systems often requires formal verification. We present the state of the art in the use of the two techniques for the development of self aware computing systems, and summarise the main research challenges associated with their adoption in practice. Radu Calinescu University of York, UK, e mail: radu.calinescu@york.ac.uk Marco Autili University of L'Aquila, Italy, e mail: marco.autili@univaq.it Javier Camara Carnegie Mellon University, US,  Simos Gerasimou University of York, UK,  University of L'Aquila, Italy, e mail: paola.inverardi@univaq.it Alexander Perucci University of L'Aquila, Italy, e mail: alexander.perucci@graduate.univaq.it Nils Jansen University of Texas at Austin, US, e mail: njansen@utexas.edu Joost Pieter Katoen RWTH Aachen University, Germany, e mail: katoen@cs.rwth aachen.de Marta Kwiatkowska University of Oxford, UK,  marta.kwiatkowska@cs.ox.ac.uk Ole J Mengshoel Carnegie Mellon University, US e mail: ole.mengshoel@sv.cmu.edu Romina Spalazzese Malmo University, Sweden, e mail: romina.spalazzese@mah.se Massimo Tivoli University of L'Aquila, Italy, e mail: massimo.tivoli@univaq.it
Le robot Blue Bot et le renouveau de la robotique pedagogique

Resume de mettre a la disposition de l'ecole, par l'intermediaire de son distributeur Easytice, une solution de robotique pedagogique appropriee pour des eleves ordinaires ou en situation de handicap. La complementarite d'un veritable robot de plancher, d'un clavier de commande et d'un programme informatique qui communiquent entre eux constitue une reelle nouveaute, essentielle a une utilisation judicieuse de la robotique pedagogique en classe et ouvre des voies inexplorees pour des eleves en situation de handicap.
Structured microblog sentiment classification via social context regularization
Venue: Neurocomputing

Microblog sentiment analysis is a fundamental problem for many interesting applications. Existing microblog sentiment classification methods judge the sentiment polarity mainly according to textual content. However, since microblog messages are very short and noisy, and their sentiment polarities are often ambiguous and context dependent, the accuracy of microblog sentiment classification is usually unsatisfactory. Fortunately, microblog messages lie in social media and contain rich social contexts. The social context information often implies sentiment connections between microblog messages. For example, a microblogging user usually expresses the same sentiment when posting multiple messages towards the same topic. Motivated by these observations, in this paper we propose a structured microblog sentiment classification (SMSC) framework. Our framework can combine social context information with textual content information to improve microblog sentiment classification accuracy. Two kinds of social contexts are used in our framework, i.e. social connections between microblog messages brought by the same author and social connections brought by social relations between users. In our framework, social context information is formulated as the graph structure over the sentiments of microblog messages. The objective function of our framework is a tradeoff between the agreement with content based sentiment predictions and the consistency with social contexts. An efficient optimization algorithm is introduced to solve our framework. Experimental results on two Twitter sentiment analysis benchmark datasets indicate that our method can outperform baseline methods consistently and significantly.
Characterizing Twitter Data using Sentiment Analysis and Topic Modeling

As the global community becomes increasingly connected, it gets more and more common to express thoughts and opinions through social networking websites. Twitter, currently the largest microblog website in the world, is heavily used for this purpose. Well known politicians, comedians and trending persons use this medium to express their minds through 140 character messages. This makes Twitter one of the platforms being most influential on the global web communities' way of thinking. This thesis combines topic modeling and sentiment analysis in order to obtain information from tweets. While sentiment analysis seeks to find out what opinions people have, topic modeling tries to find out what they talk about. Convential topic modeling schemes, such as Latent Dirichlet Allocation, are known to perform inadequately when applied to tweets, due to the sparsity of short documents. To alleviate these disadvantages, we apply several pooling techniques, aggregating similar tweets into individual documents. We specifically study the aggregation of tweets sharing authors or hashtags. Our Twitter Sentiment Analysis system is comprised of seven di erent machine learning classifiers. These aim to predict whether a message's polarity is of neutral, negative or positive sentiment. Four machine learning algorithms, Maximum Entropy, Naive Bayes, Support Vector Machines and Stochastic Gradient Descent, have been proposed for performing sentiment classification in this thesis. The classifiers were trained through experiments of extensive grid searches on a parameter space and preprocessing methods in order to achieve optimal classification scores. To combine topic modeling with sentiment analysis, a visualization application called TweetMoods was built. TweetMoods simultaneously examines the topics contained in a Twitter corpus retrieved by a search query, and the sentiments expressed in these tweets. Our topic modeling results show that aggregating similar tweets into individual documents increases the topic coherence significantly. On performing message polarity classification on tweets, the Maximum Entropy classifier yielded results outperforming most earlier submitted work to the International Workshop on Semantic Evaluation of 2015. This proves the importance of our extensive grid searches on optimizing the parameter space of the classifiers.
Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks
Venue: IEEE Geoscience and Remote Sensing Letters

Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two stage framework, we successfully deal with the limited data problem in an end to end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4% Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.
Structural Basis for the Altered PAM Specificities of Engineered CRISPR Cas9.
Venue: Molecular cell

The RNA guided endonuclease Cas9 cleaves double stranded DNA targets bearing a PAM (protospacer adjacent motif) and complementarity to the guide RNA. A recent study showed that, whereas wild type Streptococcus pyogenes Cas9 (SpCas9) recognizes the 5' NGG 3' PAM, the engineered VQR, EQR, and VRER SpCas9 variants recognize the 5' NGA 3' 5' NGAG 3' and 5' NGCG 3' PAMs, respectively, thus expanding the targetable sequences in Cas9 mediated genome editing applications. Here, we present the high resolution crystal structures of the three SpCas9 variants in complexes with a single guide RNA and its altered PAM containing, partially double stranded DNA targets. A structural comparison of the three SpCas9 variants with wild type SpCas9 revealed that the multiple mutations synergistically induce an unexpected displacement in the phosphodiester backbone of the PAM duplex, thereby allowing the SpCas9 variants to directly recognize the altered PAM nucleotides. Our findings explain the altered PAM specificities of the SpCas9 variants and establish a framework for further rational engineering of CRISPR Cas9.
Unsupervised Domain Adaptation Using Approximate Label Matching

Domain adaptation addresses the problem created when training data is generated by a so called source distribution, but test data is generated by a significantly different target distribution. In this work, we present approximate label matching (ALM) a new unsupervised domain adaptation technique that creates and leverages a rough labeling on the test samples, then uses these noisy labels to learn a transformation that aligns the source and target samples. We show that the transformation estimated by ALM has favorable properties compared to transformations estimated by other methods, which do not use any kind of target labeling. Our model is regularized by requiring that a classifier trained to discriminate source from transformed target samples cannot distinguish between the two. We experiment with ALM on simulated and real data, and show that it outperforms techniques commonly used in the field.
Direct Evidence of the Leaky Emission in Oxide Confined Vertical Cavity Lasers
Venue: IEEE Journal of Quantum Electronics

We report the observation of narrow tilted lobes in the far field emission pattern of leaky oxide confined vertical cavity surface emitting lasers (VCSELs) The VCSEL cavity is surrounded by two selectively oxidized aperture layers, which are intentionally designed to produce a high lateral leakage of the high order transverse modes of the vertical cavity. The device operates in the fundamental transverse mode at oxide aperture diameters below $5~\mu \text{m} and currents up to 4 and 5 mA. At higher currents or larger aperture diameters, an additional high order transverse optical mode evolves. This mode is revealed by the appearance of a shorter wavelength emission line in the electroluminescence spectra of the device, resulting in multi mode lasing and in the related changes in the near and far field patterns. In the far field pattern, the evolution of the high order mode is revealed by the evolution of the two overlapping emission lobes at moderate ~5deg) tilt angles with respect to the normal to the surface and by the appearance of a multi spot or a ring pattern in the CCD camera images. Most importantly, the appearance of this high order transverse mode is accompanied by an observation of much narrower lobes in the far field pattern observed at significantly larger tilt angles ~35deg) The tilt angle and the narrow angular width of these emission lobes in the far field spectrum are in agreement with those calculated in the 3 D cold cavity modeling of the optical modes of the device. These narrow tilted lobes revealed in this paper are the fingerprints of the leakage effect in specially designed oxide confined VCSELs with their intensity being proportional to the optical power leaving the aperture region in the direction parallel to the surface and propagating into the oxidized region. The effect can be applied for engineering of single mode VCSELs, coherently coupled 2 D VCSEL arrays, laterally integrated VCSEL photodetector chips, and VCSELs integrated to slow light waveguides, for coupled optical gates for optical computers and other types of photonic integrated circuits.
Optical particle detection in liquid suspensions with a hybrid integrated microsystem

In this work, we present an optical microsystem for particle flow detection, based on hybrid integration and silicon micromachining. Sensing principle is light obscuration. We demonstrate the feasibility of integrating commercial components such as light sources (850 nm near infrared Vertical Cavity Surface Emitting Lasers VCSELs) and optics (an array of microlenses) into a silicon fabricated structure to create an optoelectronic package of reduced dimensions (14 mm x 12 mm) and low power consumption (7.2 mW) that satisfies optical requirements and provides four collimated laser beams with reduced diameter spots (minimum radius of 124 mm measured as 1/e 2 standard) Besides this microoptical system, the detection system is completed with a double linear array imaging sensor of 256 pixels, designed and fabricated in 0.35 mm CMOS technology, and a microfluidic platform. The designed set up allows the simultaneous optical detection of singly aligned microbeads flowing in liquid suspensions through several parallel microfluidic channels, which share the pitch of the laser emitting sources, i.e. 250 mm. While the throughput of the system is increased up to 5000 particles/s thanks to the possibility of performing parallel measurements, the double array of the imaging sensor improves the detection process discarding false detected events. Experimental results prove that the system is able to detect and distinguish microparticles of several sizes in a mixed solution, with detection limit set at 10 mm diameter. In conclusion, the fabricated microsystem is presented as a miniaturized and robust alternative to current optical microflow cytometers based on the use of optical fibre, and the complete system, including fluidics and imaging sensor stages, represents a portable solution packaged in less than 5 cm 3 These features make it an interesting choice to consider in the field of point of care biosensing applications.
Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling
Venue: ArXiv

Research on interference has provided evidence that the formation of dependencies between non adjacent words relies on a cue based retrieval mechanism. Two different models can account for one of the main predictions of interference, i.e. a slowdown at a retrieval site, when several items share a feature associated with a retrieval cue: Lewis and Vasishth's (2005) activation based model and McElree's (2000) direct access model. Even though these two models have been used almost interchangeably, they are based on different assumptions and predict differences in the relationship between reading times and response accuracy. The activation based model follows the assumptions of ACT R, and its retrieval process behaves as a lognormal race between accumulators of evidence with a single variance. Under this model, accuracy of the retrieval is determined by the winner of the race and retrieval time by its rate of accumulation. In contrast, the direct access model assumes a model of memory where only the probability of retrieval varies between items; in this model, differences in latencies are a by product of the possibility and repairing incorrect retrievals. We implemented both models in a Bayesian hierarchical framework in order to evaluate them and compare them. We show that some aspects of the data are better fit under the direct access model than under the activation based model. We suggest that this finding does not rule out the possibility that retrieval may be behaving as a race model with assumptions that follow less closely the ones from the ACT R framework. We show that by introducing a modification of the activation model, i.e, by assuming that the accumulation of evidence for retrieval of incorrect items is not only slower but noisier (i.e. different variances for the correct and incorrect items) the model can provide a fit as good as the one of the direct access model.
Methods in Bilingual Reading Comprehension Research

Marcel A. Just Patricia A. Carpenter Chapter 1: Reading paradigms and bilingual language processing: Empiricism and theory (Jeanette Altarriba, SUNY Albany Anna B. Cieslicka, Texas A&M International University [TAMIU] Roberto R. Heredia, TAMIU) Chapter 2 Context effects in bilingual sentence processing: Task specificity (Debra Jared, University of Western Ontario, Canada) Chapter 3: Off line versus on line psycholinguistic techniques: Which is which? (Tracy Love, San Diego State University) Chapter 4: Rapid Serial Visual Presentation (RSVP) What is so rapid about RSVP? (Jeanette Altarriba, SUNY Albany) Chapter 5: The reading visual moving window.
Enhancing students interest in English language via multimedia presentation

It is often said that interest is the best teacher. In learning English language, sometimes students have certain barriers of understanding and they often reject to learn. As a result, it is very hard for many students to improve their English language learning level. To make the students learn more autonomously and effectively, it is necessary to make them be interested in learning. With new technological tools continuing to emerge, multimedia plays very important role in English language teaching. The use of Multimedia presentation offers a potentially venue for improving student understanding about language. This paper discusses changing trends in English language teachinglearning, highlights the concept of multimedia, explains theoretical basis of multimedia English teaching and also states the significant role of multimedia in motivating learner's interest in English