nils-herrmann/clean_scirepeval huggingface dataset:
La adquisicion y desarrollo de la competencia digital en alumnos de educacion secundaria. Estudio de caso

El Decreto 86/2015 del 25 de junio por el que se establece el curriculo de educacion secundaria obligatoria y de bachillerato en la Comunidad Autonoma de Galicia establece las siete competencias clave que, en el futuro, los alumnos en estas etapas necesitarian para su realizacion y desarrollo personal para formar parte de una ciudadania activa asi como para su inclusion social y empleo. En el presente trabajo se muestra parte de los resultados de una investigacion cualitativa realizada en centros de educacion secundaria de la comunidad autonoma gallega pertenecientes a la red del Proyecto Abalar. En dicha investigacion se explora el proceso de integracion de las tecnologias de la informacion y comunicacion en las practicas de ensenanza y aprendizaje en alumnos de educacion secundaria y en que medida contribuyen a la adquisicion de la competencia digital (CD) en esta etapa. Despues del analisis de los datos obtenidos se observan los cambios positivos que, a nivel organizativo, se manifiestan en el centro luego de la llegada de la tecnologia. No obstante, no se produce un cambio metodologico determinante en la practica docente diaria en las materias analizadas. El alumnado aun se encuentra en un estadio inicial en la adquisicion de la CD despues del analisis de las distintas dimensiones siguiendo la propuesta de la UE del proyecto DIGCOMP.
Predictors of Whole Body Insulin Sensitivity Across Ages and Adiposity in Adult Humans.
Venue: The Journal of clinical endocrinology and metabolism

Numerous factors are purported to influence insulin sensitivity including age, adiposity, mitochondrial function, and physical fitness. Univariate associations cannot address the complexity of insulin resistance or the interrelationship among potential determinants.  The objective of the study was to identify significant independent predictors of insulin sensitivity across a range of age and adiposity in humans.  Peripheral and hepatic insulin sensitivity were measured by two stage hyperinsulinemic euglycemic clamps in 116 men and women (aged 19 78 y) Insulin stimulated glucose disposal, the suppression of endogenous glucose production during hyperinsulinemia, and homeostatic model assessment of insulin resistance were tested for associations with 11 potential predictors. Abdominal subcutaneous fat, visceral fat (AFVISC) intrahepatic lipid, and intramyocellular lipid (IMCL) were quantified by magnetic resonance imaging and spectroscopy. Skeletal muscle mitochondrial respiratory capacity (state 3) coupling efficiency, and reactive oxygen species production were evaluated from muscle biopsies. Aerobic fitness was measured from whole body maximum oxygen uptake (VO2 peak) and metabolic flexibility was determined using indirect calorimetry.  Multiple regression analysis revealed that AFVISC (P .0001) and intrahepatic lipid (P .002) were independent negative predictors of peripheral insulin sensitivity, whereas VO2 peak (P .0007) and IMCL (P .023) were positive predictors. Mitochondrial capacity and efficiency were not independent determinants of peripheral insulin sensitivity. The suppression of endogenous glucose production during hyperinsulinemia model of hepatic insulin sensitivity revealed percentage fat (P .0001) and AFVISC (P .001) as significant negative predictors. Modeling homeostatic model assessment of insulin resistance identified AFVISC (P .0001) VO2 peak (P .001) and IMCL (P .01) as independent predictors.  The reduction in insulin sensitivity observed with aging is driven primarily by age related changes in the content and distribution of adipose tissue and is independent of muscle mitochondrial function or chronological age.
Probabilistic rectangular shape estimation for extended object tracking
Venue: 2016 IEEE Intelligent Vehicles Symposium (IV)

This paper presents new methods for the representation of a vehicle's contour by an oriented rectangle, also known as the bounding box. The parameters of this bounding box are originally modeled probabilistically by a single multivariate Gaussian distribution. This approach incorporates the sensor uncertainties, where the problem of estimating the parameters of this distribution from range measurements is addressed. Additionally, a transformation of the parameters into the measurement space is introduced. This representation is used to perform probabilistic updates by new measurements. The proposed method can handle strong parameter changes which might be affected by object occlusion. Experiments on real world data demonstrate the robustness and accuracy of the probabilistic approach integrated in a tracking framework incorporating the Doppler measurements of automotive radars and laser measurements.
Novel fusion computing method for bio medical image of WSN based on spherical coordinate

In bio medical field, embedded numerous sensing nodes can be used to monitor and interact with physical world based on signal analysis and processing. Data from many different sources can be collected into massive data sets via localized sensor networks. Understanding the environment requires collecting and analyzing data from thousands of sensors monitoring, this is big data environment. The application of bio medical image fusion for big data computing has strong development momentum, big data bio medical image fusion is one of key problems, so the fusion method study is a hot topic in the field of signal analysis and processing. The existing methods have many limitations, such as large delay, data redundancy, more energy cost, low quality, so novel fusion computing method based on spherical coordinate for big data bio medical image of WSN is proposed in this paper. In this method, the three high frequency coefficients in wavelet domain of bio medical image are pre processed. This pre processing strategy can reduce the redundant ratio of big data bio medical image. Firstly, the high frequency coefficients are transformed to the spherical coordinate domain to reduce the correlation in the same scale. Then, a multi scale model product (MSMP) is used to control the shrinkage function so as to make the small wavelet coefficients and some noise removed. The high frequency parts in spherical coordinate domain are coded by improved SPIHT algorithm. Finally, based on multi scale edge of bio medical image, it can be fused and reconstructed. Experimental results indicate the novel method is effective and very useful for transmission of big data bio medical image, which can solve the problem of data redundancy, more energy cost and low quality.
Zero Knowledge Arguments for Lattice Based Accumulators: Logarithmic Size Ring Signatures and Group Signatures Without Trapdoors
Venue: EUROCRYPT

An accumulator is a function that hashes a set of inputs into a short, constant size string while preserving the ability to efficiently prove the inclusion of a specific input element in the hashed set. It has proved useful in the design of numerous privacy enhancing protocols, in order to handle revocation or simply prove set membership. In the lattice setting, currently known instantiations of the primitive are based on Merkle trees, which do not interact well with zero knowledge proofs. In order to efficiently prove the membership of some element in a zero knowledge manner, the prover has to demonstrate knowledge of a hash chain without revealing it, which is not known to be efficiently possible under well studied hardness assumptions. In this paper, we provide an efficient method of proving such statements using involved extensions of Stern's protocol. Under the Small Integer Solution assumption, we provide zero knowledge arguments showing possession of a hash chain. As an application, we describe new lattice based group and ring signatures in the random oracle model. In particular, we obtain: i The first lattice based ring signatures with logarithmic size in the cardinality of the ring; ii The first lattice based group signature that does not require any GPV trapdoor and thus allows for a much more efficient choice of parameters.
A Lattice based Signcryption Scheme Without Trapdoors

The existing lattice based signcryption schemes are based on trapdoor generation algorithm and preimage sample algorithm. However, both algorithms are complex, require a lot of time to run, and affect the efficiency of latticed based signcryption schemes deeply. To solve this problem, the first lattice based signcryption scheme without trapdoor generation algorithm and preimage sample algorithm is proposed, with the help of the technique of lattice signatures without trapdoors and the associated signature compression technique, as well as the encryption method based on the learning with errors assumption. The scheme achieves indistinguishability against adaptive chosen ciphertext attacks under the learning with errors assumption. It also achieves existential unforgeability against adaptive chosen message attacks under the small integer solution assumption. The proposed scheme is not only quantum resistant, but also efficient.
CF technique with CPM mappers in OFDM systems for reduction of PAPR
Venue: 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

An OFDM system with CPM mappers is considered. Such a system in general is known to enhance bit error rate performance by virtue of the memory introduced by the CPM mapper compared to OFDM systems with conventional memoryless mappers such as BPSK and QPSK. In this paper, the ability of CPM mappers in an OFDM system to reduce Peak to Average Power Ratio (PAPR) is examined as a function of modulation parameters of mappers used in the system. Various subclasses of CPM mapper such as single h CPFSK, multi h CPFSK, and asymmetric multi h CPFSK are also considered. Next, these mappers in conjunction with Clipping and Filtering (CF) technique are considered and it is shown that repeated clipping and frequency domain filtering in OFDM systems can significantly reduce the PAPR of the transmitted signal. The technique causes no increase in out of band power and significant PAPR reduction can be achieved with only moderate levels of clipping. A comparison of PAPR reduction capability of CPM mappers relative to memoryless BPSK mappers in an OFDM system is presented. It is noted that, in general, CPM mappers offer superior PAPR performance compared to memoryless mappers in an OFDM system.
City planning and population health: a global challenge
Venue: The Lancet

Significant global health challenges are being confronted in the 21st century, prompting calls to rethink approaches to disease prevention. A key part of the solution is city planning that reduces non communicable diseases and road trauma while also managing rapid urbanisation. This Series of papers considers the health impacts of city planning through transport mode choices. In this, the first paper, we identify eight integrated regional and local interventions that, when combined, encourage walking, cycling, and public transport use, while reducing private motor vehicle use. These interventions are destination accessibility, equitable distribution of employment across cities, managing demand by reducing the availability and increasing the cost of parking, designing pedestrian friendly and cycling friendly movement networks, achieving optimum levels of residential density, reducing distance to public transport, and enhancing the desirability of active travel modes (eg, creating safe attractive neighbourhoods and safe, affordable, and convenient public transport) Together, these interventions will create healthier and more sustainable compact cities that reduce the environmental, social, and behavioural risk factors that affect lifestyle choices, levels of traffic, environmental pollution, noise, and crime. The health sector, including health ministers, must lead in advocating for integrated multisector city planning that prioritises health, sustainability, and liveability outcomes, particularly in rapidly changing low income and middle income countries. We recommend establishing a set of indicators to benchmark and monitor progress towards achievement of more compact cities that promote health and reduce health inequities.
Label free detection of C reactive protein using an electrochemical DNA immunoassay

A label free electrochemical immunoassay that combines DNA directed immobilization (DDI) with electrochemical impedance spectroscopy (EIS) on microwire sensors is reported for the detection of C reactive protein (CRP) CRP is an acute phase protein that is strongly correlated with systemic inflammation. Since inflammation plays a role in pathogenesis of cardiovascular diseases, CRP can be used to predict the likelihood of coronary events. To demonstrate the new chemistry, 25 mm Au electrodes were modified with single strand DNA (ssDNA) and exposed to a solution containing complementary ssDNA conjugated to monoclonal anti CRP. The charge transfer resistance of the [Fe(CN)6]3 /4 redox couple was used to determine the CRP concentration after binding. A stepwise increase in the charge transfer resistance was observed using EIS for each modification step, ssDNA, ssDNA anti CRP hybridization and the final CRP capture. Cyclic voltammetry (CV) was used to verify the EIS results, and showed an increase in peak potential splitting in a similar stepwise manner for each modification step. Finally, fluorescence microscopy was used to confirm the DNA hybridization and CRP binding. Standard addition of CRP revealed that EIS could be used to detect CRP at clinically relevant levels in serum samples. This new form of electrochemical DNA immunoassay (eDI) has significant potential as a simple, label free sensor for proteins in microfluidic devices.
Exploring Human Technology Interaction in Layered Security Military Applications
Venue: HCI

System of systems modeling has traditionally focused on physical systems rather than humans, but recent events have proved the necessity of considering the human in the loop. As technology becomes more complex and layered security continues to increase in importance, capturing humans and their interactions with technologies within the system of systems will be increasingly necessary. After an extensive job task analysis, a novel type of system of systems simulation model has been created to capture the human technology interactions on an extra small forward operating base to better understand performance, key security drivers, and the robustness of the base. In addition to the model, an innovative framework for using detection theory to calculate d' for individual elements of the layered security system, and for the entire security system as a whole, is under development.
Forest Products Consumption and Trade Deficit in Greece during the Financial Crisis: A Quantitative Statistical Analysis

During the economic crisis in Greece, the national forest sector wounded by a serious decline in demand. The national forest sector faced an important decline in forest products consumption. The paper examines the quantitative changes in the above consumption regarding several wood products categories. The results indicated that a very important decline in consumption per capita took place after 2008. Moreover, an unwanted positive relationship between GDP and the forest products trade deficit is confirmed. During the crisis, the continuant reduction of GDP brings reduction on deficit in trade of forest products while an important increase of exports occurs. However, the results also show that policy makers must act immediately in order to stop the total collapse of the sector.
Consumption Smoothing During the Financial Crisis: The Effect of Unemployment on Household Spending

Because of data limitations, the quantification of consumption smoothing in response to economic shocks has been challenging to investigate empirically. We used monthly data on total household spending, income, and labor force participation to estimate the effects of unemployment on household spending. The data come from the RAND American Life Panel, a standing survey sample that is representative of the United States adult population. We compare monthly spending and income of households prior to unemployment with spending and income following unemployment for up to 40 months. We compare spending and income following re employment with spending and income while unemployed. We find that by month two of unemployment total household spending per month declined to about 83 percent of pre unemployment spending. At about 14 months of unemployment, spending began to decline further, reaching 70 percent of pre unemployment spending by month 30. Income declined much more sharply to 37 percent of its pre unemployment level by month two of unemployment, with little change after that as the duration of unemployment increased. Thus, consumption does not decline as much as income, so that it is somewhat smoothed relative to income; yet, particularly over long duration unemployment the decline is substantial. On re employment, income increased rapidly, spending much less rapidly. As of the third month, high frequency spending was about 9 percent above its value in the last month of unemployment. It continued to increase until it was about 20 percent higher. Just as with an income drop, spending is somewhat smoothed when income increases.
Type Ambiguous Names

The orthodox view of proper names, Millianism, provides a very simple and elegant explanation of the semantic contribution (and semantic properties) of referential uses of names, namely names that occur as bare singulars and as the argument of a predicate. However, one problem for Millianism is that it cannot explain the semantic contribution of predicative uses of names (as in e.g. 'there are two Alberts in my class' In recent years, an alternative view, socalled The Predicativism, has become increasingly popular. According to The Predicativists, names are uniformly count nouns. This straightforwardly explains why names can be used predicatively, but is prima facie less congenial to an analysis of referential uses. To address this issue, The Predicativists argue that referential names are in fact complex determiner phrases consisting of a covert definite determiner and a count noun and so, a referential name is a (covert) definite description. In this paper, I will argue that despite the appearance of increased theoretical complexity, the view that names are ambiguous between predicative and referential types is in fact superior to the unitary The Predicativist view. However, I will also argue that to see why this (type) ambiguity view is better, we need to give up the standard Millian analysis. Consequently, I will first propose an alternative analysis of referential names that (a) retains the virtues of Millianism, but (b) provides an important explanatory connection to the predicative uses. Once this analysis of names is adopted, the explanation for why names are systematically ambiguous between referential and predicative types is both simple and elegant. Second, I will argue that The Predicativism has the appearance of being simpler than an ambiguity view, but is in fact unable to account for certain key properties of referential names without making ad hoc stipulations.
Does Gender Matter in the Export Performance of International New Ventures? Mediation Effects of Firm specific and Country specific Advantages

Based on a sample of 4240 Korean firms, this study explores the relationship between owner gender and export performance of international new ventures (INVs) by investigating four mediating mechanisms: venture capital financing, upstream firm specific advantages, downstream firm specific advantages, and country specific advantages. Our empirical findings are threefold: (1) female owned ventures are disadvantageous in obtaining access to venture capital, but venture capital financing is not positively associated with their export performance; (2) male owned ventures achieve better export performance through superior innovation and marketing capabilities (i.e. mediation effects) than their female owned counterparts; (3) while gender is not associated with the home region destination of exports, the country destination of exports within the Asia Pacific region positively affects INVs' export performance.
Design of a Microscale Selective Laser Sintering System

Micro and nanoscale additive manufacturing methods employing metals and ceramics have many promising applications in the aerospace, medical device, and electronics industries. However, the present state of art metal additive manufacturing tools have feature size resolutions of greater than 100 mm, which is too large to precisely control the geometrical and dimensional aspects of the parts they produce. The weakness is particularly profound in application of additive manufacturing to the fabrication of fine pitch interconnects in the packaging and assembly of integrated circuits. A new microscale selective laser sintering (m SLS) is being developed in this research to improve the minimum feature size resolution of metal additively manufactured parts by up to two orders of magnitude, while still maintaining the throughput of traditional additive manufacturing processes. This paper presents a detailed design of the sub assemblies of the m SLS system including innovative design features such as the use of (1) ultra fast lasers, (2) a micro mirror based optical system, (3) nanoscale particles, (4) a precision spreader mechanism, (5) long range precision XY nano positioner and (6) a global positioner.
Semantic Document Clustering Using a Similarity Graph
Venue: 2016 IEEE Tenth International Conference on Semantic Computing (ICSC)

Document clustering addresses the problem of identifying groups of similar documents without human supervision. Unlike most existing solutions that perform document clustering based on keywords matching, we propose an algorithm that considers the meaning of the terms in the documents. For example, a document that contains the words "dog" and "cat" multiple times may be placed in the same category as a document that contains the word "pet" even if the two documents share only noise words in common. Our semantic clustering algorithm is based on a similarity graph that stores the degree of semantic relationship between terms (extracted from WordNet) where a term can be a word or a phrase. We experimentally validate our algorithm on the Reuters 21578 benchmark, which contains 11,362 newswire stories that are grouped in 82 categories using human judgment. We apply the k means clustering algorithm to group the documents using a similarity metric that is based on keywords matching and one that uses the similarity graph. We show that the second approach produces higher precision and recall, which means that this approach matches closer the results of the human study.
Text Clustering Algorithm Based on the Graph Structures of Semantic Word Co occurrence
Venue: 2016 International Conference on Information System and Artificial Intelligence (ISAI)

Text theme is the key of text clustering, while the co occurrence words can be very stronger to express text theme in document. This paper proposes a text clustering algorithm based on the text semantic representation and the graph structure of word co occurrence on the basis of in depth studying text theme mining and word co occurrence. First, the algorithm constructs the text graph structure according to the co occurrence of feature words. In other words, it uses the graph structure to represent all texts. Then, it adopts the maximum common sub graph between two texts to calculate their similarity and combines with K means clustering algorithm to realize the document clustering. The compared experimental results with hierarchical clustering algorithm show the K means clustering algorithm based on the graph structures of word co occurrence greatly reduce the high dimension of text vector and the algorithm complexity, significantly improves the efficiency and accuracy of text clustering, and it can also produce the clustering effect of good quality.