bluuebunny/crossref_metadata_2025_split huggingface dataset:
Caimanes del mismo pozo. Populismo y representaciones de los medios y la prensa en la estabilización hegemónica del chavismo
Authors: Kitzberger Philip 
Published: July 2018

<p>Los populismos contemporáneos, de izquierda y de derecha, se han caracterizado por confrontarse con instituciones mediático-periodísticas establecidas. En la comunicación política de los liderazgos populistas, esto se refleja en cuestionamientos públicos sostenidos, a través de dispositivos de comunicación directa, a medios y/o periodismo, como parte o expresión de una elite opuesta al pueblo.</p><p>Los objetivos de este trabajo son: primero, avanzar en la caracterización sistemática de las representaciones sobre la naturaleza, papel (rol) y funciones de medios y periodismo contenidas en los discursos públicos de los gobernantes que optan por antagonizar con estas instituciones; segundo, establecer factores explicativos de las inflexiones particulares y de las variaciones particulares de cada caso; tercero, explorar las conexiones entre discurso y estrategia políticas frente las instituciones mediático-periodísticas.</p><p>A través de un análisis del contenido de discursos e intervenciones públicas de Hugo Chávez enunciados entre 1999 y 2004, el artículo reconstruye la variación en presencia e intensidad de encuadres populistas sobre los medios en relación con las fases del conflicto político. Muestra, además, cómo factores, tales como los ideológicos, los patrones históricos de la relación Estado-medios en el petroestado venezolano y la dinámica particular del conflicto político, explican algunas de las representaciones sobre los medios y las orientaciones prácticas frente a ellos. </p>
Impact of the divorce experience on the marital and family mindsets
Authors: Лактюхина Елена , Laktyukhina Elena , Антонов Георгий , Antonov Georgy 
Published: July 2018

The article presents a comparative analysis of marital and family mindsets of two categories of the demographically active population of modern Russia: (1) individuals that have no experience of a divorce and (2) those who have already experienced one or more official termination of a marriage. The empirical base of the analysis is the data of the author’s questionnaire survey conducted by representative sampling in Volgograd and Volgograd Region in 2015–2016. The analysis was made on the following basic empiric indicators: optimal (from the viewpoint of the respondents) age for the first marriage, frequency of mentioning marital and family statuses as the respondents describe their own social and demographic “portrait”, legitimate causes of a divorce and a number of others.
It is found that, in the case of sufficiently strong traditional marital and family mindsets, perception of marital norms is adjusted, if an “abnormal” event (such as a divorce) occurs in the individual’s life course. At the same time, perception of the marriage stability is less variable and does not depend on the social and demographic characteristics of the respondents, including the presence/absence of a marriage termination experience. The “strongest” factor that affects the change of the marital and family mindsets is age. With age (and, consequently, experience accumulation), importance of the majority of main factors capable of preventing the individual from a divorce decreases and, therefore, the risk of such event increases.
Manejo endoscópico de quiste de duplicación esofágico: descripción de un caso
Authors: Ricardo Ramírez Andrés Mauricio , Ferreira Bohórquez Edgar Julián , Arango Molano Lázaro Antonio , Sánchez Gil Andrés 
Published: July 2018

Los quistes de duplicación esofágica son una anomalía congénita poco frecuente. La mayoría de los casos son asintomáticos y su diagnóstico frecuentemente es un hallazgo incidental. La posibilidad de que se desarrollen complicaciones (hemorragia, infección, etc.) y el temor a la posibilidad de transformación maligna, hace que el tratamiento de de estas lesiones haya sido considerado quirúrgico en el pasado. Sin embargo, la extremadamente baja taza de malignizacion, y la posibilidad de un desenlace inadecuado asociado a la cirugía, hace precisa la consideración de otras alternativas, teniendo en cuenta el riesgo vs. el beneficio del procedimiento quirúrgico. Reportamos el caso de una paciente de 56 años con diagnostico endosonográfico de quiste de duplicación esofágica, que fue sometida a marsupialización endoscópica del quiste con una resolución completa de sus síntomas, sin complicaciones y con una reintegración inmediata a la vida diaria.La endosonografia (EUS) ha sido ampliamente utilizada como una herramienta para la evaluación y el diagnóstico de los quistes de duplicación. Es el método diagnóstico de elección para investigar esta patología ya que puede distinguir entre lesiones sólidas y quísticas. La cuestión de si se debe realizar o no la aspiración con aguja EUS-FNA en una lesión sospechosa, en el caso de un quiste de duplicación, es controvertida ya que estas lesiones pueden infectarse con consecuencias significativas, aunque a menudo se requiere que la punción obtenga un diagnóstico definitivo que descarte lesiones ominosas.
T1-REDEEM: A Randomized Controlled Trial to Reduce Diabetes Distress Among Adults With Type 1 Diabetes
Authors: Fisher Lawrence , Hessler Danielle , Polonsky William H. , Masharani Umesh , Guzman Susan , Bowyer Vicky , Strycker Lisa , Ahmann Andrew , Basina Marina , Blumer Ian , Chloe Charles , Kim Sarah , Peters Anne L. , Shumway Martha , Weihs Karen , Wu Patricia 
Published: July 2018

OBJECTIVE
To compare the effectiveness of two interventions to reduce diabetes distress (DD) and improve glycemic control among adults with type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS
Individuals with T1D (n = 301) with elevated DD and HbA1c were recruited from multiple settings and randomly assigned to OnTrack, an emotion-focused intervention, or to KnowIt, an educational/behavioral intervention. Each group attended a full-day workshop plus four online meetings over 3 months. Assessments occurred at baseline and 3 and 9 months. Primary and secondary outcomes were change in DD and change in HbA1c, respectively.

RESULTS
With 12% attrition, both groups demonstrated dramatic reductions in DD (effect size d = 1.06; 78.4% demonstrated a reduction of at least one minimal clinically important difference). There were, however, no significant differences in DD reduction between OnTrack and KnowIt. Moderator analyses indicated that OnTrack provided greater DD reduction to those with initially poorer cognitive or emotion regulation skills, higher baseline DD, or greater initial diabetes knowledge than those in KnowIt. Significant but modest reductions in HbA1c occurred with no between-group differences. Change in DD was modestly associated with change in HbA1c (r = 0.14, P = 0.01), with no significant between-group differences.

CONCLUSIONS
DD can be successfully reduced among distressed individuals with T1D with elevated HbA1c using both education/behavioral and emotion-focused approaches. Reductions in DD are only modestly associated with reductions in HbA1c. These findings point to the importance of tailoring interventions to address affective, knowledge, and cognitive skills when intervening to reduce DD and improve glycemic control.
Copyright Protection for Digital Certificate using Blind Watermarking Technique
Authors: Hasan Harith Raad 
Published: July 2018

This paper proposes a copyright protection system for digital certificate image using blind watermarking technique by applying discrete wavelet transform algorithm (DWT). The proposed technique utilizes three interrelated watermarks namely, Red watermark (RW), Green-watermark (GW) and Blue-watermark (BW). Firstly, RGB cover and RGB watermark image are divided for three color space R,G,B. Then the R component is chosen for embedding (RW), the B component is selected for embedding the (BW) and the G component is selected for embedding the (GW), R,G and B of the cover image are converted into the transform domain using DWT, and is subsequently decomposed into three levels viz. LH1, LL2 and LL3 sub-bands. RW, GW and BW are then embedded onto LL3. Experimental results show that the performance of the proposed technique is very encouraging with average PSNR of 44 db, and NCC value of more than 0.99 for extracted watermarks after performing several types of attacks.
Fair Division Under Cardinality Constraints
Authors: Biswas Arpita , Barman Siddharth 
Published: July 2018

We consider the problem of fairly allocating indivisible goods, among agents, under cardinality constraints and additive valuations. In this setting, we are given a partition of the entire set of goods---i.e., the goods are categorized---and a limit is specified on the number of goods that can be allocated from each category to any agent. The objective here is to find a fair allocation in which the subset of goods assigned to any agent satisfies the given cardinality constraints. This problem naturally captures a number of resource-allocation applications, and is a generalization of the well-studied unconstrained fair division problem. The two central notions of fairness, in the context of fair division of indivisible goods, are envy freeness up to one good (EF1) and the (approximate) maximin share guarantee (MMS). We show that the existence and algorithmic guarantees established for these solution concepts in the unconstrained setting can essentially be achieved under cardinality constraints. Furthermore, focusing on the case wherein all the agents have the same additive valuation, we establish that EF1 allocations exist even under matroid constraints.
An FPTAS for Computing Nash Equilibrium in Resource Graph Games
Authors: Chan Hau , Jiang Albert Xin 
Published: July 2018

We consider the problem of computing a mixed-strategy Nash equilibrium (MSNE) in resource graph games (RGGs), a compact representation for games with an exponential number of strategies. In an RGG, each player's pure strategy is a subset of resources, represented by a binary vector, and her pure strategy set is represented compactly using a set of linear inequality constraints. Given the pure strategies of the players, each player's utility depends on the resource graph and the numbers of times the neighboring resources are used. RGGs are general enough to capture a wide variety of games studied in literature, including congestion games and security games.In this paper, we provide the first Fully Polytnomial Time Approximation Scheme (FPTAS) for computing an MSNE in any symmetric multilinear RGG where its constraint moralized resource graph (a graph formed between the moralized resource graph and the constraints defining the strategy polytope) has bounded treewidth. Our FPTAS can be generalized to compute optimal MSNE, and to games with a constant number of player types. As a consequence, our FPTAS provides new approximation results for security games, network congestion games, and bilinear games.
CSNN: An Augmented Spiking based Framework with Perceptron-Inception
Authors: Xu Qi , Qi Yu , Yu Hang , Shen Jiangrong , Tang Huajin , Pan Gang 
Published: July 2018

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is limited by shallow structures. This paper focuses on improving the feature extraction ability of SNNs in virtue of powerful feature extraction ability of Convolutional Neural Networks (CNNs). CNNs can extract abstract features resorting to the structure of the convolutional feature maps. We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs. The CSNN model learns the encoded spatial temporal representations of images in an event-driven way. We evaluate the CSNN model on the handwritten digits images dataset MNIST and its variational databases. In the presented experimental results, the proposed CSNN model is evaluated regarding learning capabilities, encoding mechanisms, robustness to noisy stimuli and its classification performance. The results show that CSNN behaves well compared to other cognitive models with significantly fewer neurons and training samples. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this high-level vision task.
Embracing Change by Abstraction Materialization Maintenance for Large ABoxes
Authors: Brenner Markus , Glimm Birte 
Published: July 2018

Abstraction Refinement is a recently introduced technique which allows for reducing materialization of an ontology with a large ABox to materialization of a smaller (compressed) `abstraction' of this ontology. In this paper, we show how Abstraction Refinement can be adopted for incremental ABox materialization by combining it with the well-known DRed algorithm for materialization maintenance. Such a combination is non-trivial and to preserve soundness and completeness, already Horn ALCHI requires more complex abstractions. Nevertheless, we show that significant benefits can be obtained for synthetic and real-world ontologies.
Fast Cross-Validation
Authors: Liu Yong , Lin Hailun , Ding Lizhong , Wang Weiping , Liao Shizhong 
Published: July 2018

Cross-validation (CV) is the most widely adopted approach for selecting the optimal model. However, the computation of CV has high complexity due to multiple times of learner training, making it disabled for large scale model selection. In this paper, we present an approximate approach to CV based on the theoretical notion of Bouligand influence function (BIF) and the Nystr\"{o}m method for kernel methods. We first establish the relationship between the theoretical notion of BIF and CV, and propose a method to approximate the CV via the Taylor expansion of BIF. Then, we provide a novel computing method to calculate the BIF for general distribution, and evaluate BIF for sample distribution. Finally, we use the Nystr\"{o}m method to accelerate the computation of the BIF matrix for giving the finally approximate CV criterion. The proposed approximate CV requires training only once and is suitable for a wide variety of kernel methods. Experimental results on lots of datasets how that our approximate CV has no statistical discrepancy with the original CV, but can significantly improve the efficiency.
An Axiomatic View of the Parimutuel Consensus Mechanism
Authors: Freeman Rupert , Pennock David M. 
Published: July 2018

We consider an axiomatic view of the Parimutuel Consensus Mechanism defined by Eisenberg and Gale (1959). The parimutuel consensus mechanism can be interpreted as a parimutuel market for wagering with a proxy that bets optimally on behalf of the agents, depending on the bets of the other agents. We show that the parimutuel consensus mechanism uniquely satisfies the desirable properties of Pareto optimality, individual rationality, budget balance, anonymity, sybilproofness and envy-freeness. While the parimutuel consensus mechanism does violate the key property of incentive compatibility, it is incentive compatible in the limit as the number of agents becomes large. Via simulations on real contest data, we show that violations of incentive compatibility are both rare and only minimally beneficial for the participants. This suggests that the parimutuel consensus mechanism is a reasonable mechanism for eliciting information in practice.
Adversarially Regularized Graph Autoencoder for Graph Embedding
Authors: Pan Shirui , Hu Ruiqi , Long Guodong , Jiang Jing , Yao Lina , Zhang Chengqi 
Published: July 2018

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.
Label Enhancement for Label Distribution Learning
Authors: Xu Ning , Tao An , Geng Xin 
Published: July 2018

Label distribution is more general than both single-label annotation and multi-label annotation. It covers a certain number of labels, representing the degree to which each label describes the instance. The learning process on the instances labeled by label distributions is called label distribution learning (LDL). Unfortunately, many training sets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. To solve the problem, one way is to recover the label distributions from the logical labels in the training set via leveraging the topological information of the feature space and the correlation among the labels. Such process of recovering label distributions from logical labels is defined as label enhancement (LE), which reinforces the supervision information in the training sets. This paper proposes a novel LE algorithm called Graph Laplacian Label Enhancement (GLLE). Experimental results on one artificial dataset and fourteen real-world datasets show clear advantages of GLLE over several existing LE algorithms.
Where to Prune: Using LSTM to Guide End-to-end Pruning
Authors: Zhong Jing , Ding Guiguang , Guo Yuchen , Han Jungong , Wang Bin 
Published: July 2018

Recent years have witnessed the great success of convolutional neural networks (CNNs) in many related fields. However, its huge model size and computation complexity bring in difficulty when deploying CNNs in some scenarios, like embedded system with low computation power. To address this issue, many works have been proposed to prune filters in CNNs to reduce computation. However, they mainly focus on seeking which filters are unimportant in a layer and then prune filters layer by layer or globally. In this paper, we argue that the pruning order is also very significant for model pruning. We propose a novel approach to figure out which layers should be pruned in each step. First, we utilize a long short-term memory (LSTM) to learn the hierarchical characteristics of a network and generate a pruning decision for each layer, which is the main difference from previous works. Next, a channel-based method is adopted to evaluate the importance of filters in a to-be-pruned layer, followed by an accelerated recovery step. Experimental results demonstrate that our approach is capable of reducing 70.1% FLOPs for VGG and 47.5% for Resnet-56 with comparable accuracy. Also, the learning results seem to reveal the sensitivity of each network layer.
Beyond Similar and Dissimilar Relations : A Kernel Regression Formulation for Metric Learning
Authors: Zhu Pengfei , Qi Ren , Hu Qinghua , Wang Qilong , Zhang Changqing , Yang Liu 
Published: July 2018

Most existing metric learning methods focus on learning a similarity or distance measure relying on similar and dissimilar relations between sample pairs. However, pairs of samples cannot be simply identified as similar or dissimilar in many real-world applications, e.g., multi-label learning, label distribution learning or tasks with continuous decision values. To this end, in this paper we propose a novel relation alignment metric learning (RAML) formulation to handle the metric learning problem in those scenarios. Since the relation of two samples can be measured by the difference degree of the decision values, motivated by the consistency of the sample relations in the feature space and decision space, our proposed RAML utilizes the sample relations in the decision space to guide the metric learning in the feature space. Specifically, our RAML method formulates metric learning as a kernel regression problem, which can be efficiently optimized by the standard regression solvers. We carry out several experiments on the single-label classification, multi-label classification, and label distribution learning tasks, to demonstrate that our method achieves favorable performance against the state-of-the-art methods.
Sampling for Approximate Bipartite Network Projection
Authors: Ahmed Nesreen , Duffield Nick , Xia Liangzhen 
Published: July 2018

Bipartite graphs manifest as a stream of edges that represent transactions, e.g., purchases by retail customers. Recommender systems employ neighborhood-based measures of node similarity, such as the pairwise number of common neighbors (CN) and related metrics. While the number of node pairs that share neighbors is potentially enormous, only a relatively small proportion of them have many common neighbors. This motivates finding a weighted sampling approach to preferentially sample these node pairs. This paper presents a new sampling algorithm that provides a fixed size unbiased estimate of the similarity matrix resulting from a bipartite edge stream projection. The algorithm has two components. First, it maintains a reservoir of sampled bipartite edges with sampling weights that favor selection of high similarity nodes. Second, arriving edges generate a stream of similarity updates, based on their adjacency with the current sample. These updates are aggregated in a second reservoir sample-based stream aggregator to yield the final unbiased estimate. Experiments on real world graphs show that a 10% sample at each stage yields estimates of high similarity edges with weighted relative errors of about 1%.
A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data
Authors: Akagi Yasunori , Nishimura Takuya , Kurashima Takeshi , Toda Hiroyuki 
Published: July 2018

Real-time spatiotemporal population data is attracting a great deal of attention for understanding crowd movements in cities.The data is the aggregation of personal location information and consists of just areas and the number of people in each area at certain time instants. Accordingly, it does not explicitly represent crowd movement. This paper proposes a probabilistic model based on collective graphical models that can estimate crowd movement from spatiotemporal population data. There are two technical challenges: (i) poor estimation accuracy as the traditional approach means the model would have too many degrees of freedom, (ii) excessive computation cost. Our key idea for overcoming these two difficulties is to model the transition probability between grid cells (cells hereafter) in a geospatial grid space by using three factors: departure probability of cells, gathering score of cells, and geographical distance between cells. These advances enable us to reduce the degrees of freedom of the model appropriately and derive an efficient estimation algorithm. To evaluate the performance of our method, we conduct experiments using real-world spatiotemporal population data. The results confirm the effectiveness of our method, both in estimation accuracy and computation cost.